What Is the Immigrant Achievement Gap?: a Conceptualization and Examination of Immigrant Student Achievement Globally Anabelle Andon

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THE FLORIDA STATE UNIVERSITY

COLLEGE OF EDUCATION

WHAT IS THE IMMIGRANT ACHIEVEMENT GAP?

A CONCEPTUALIZATION AND EXAMINATION

OF IMMIGRANT STUDENT ACHIEVEMENT GLOBALLY

ANABELLE ANDON

A Dissertation submitted to the Department of Educational Leadership and Policy Studies in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2012

Anabelle Andon defended this dissertation on June 1, 2012.

The members of the supervisory committee were:

Laura B. Lang Professor Directing Dissertation

Carol Connor University Representative

Jeffrey Ayala Milligan Committee Member

Peter Easton Committee Member

Thomas F. Luschei Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

Without question, I dedicate this work to the two most wonderful beings in my life. You, for being my rock, my partner in life, and my best friend. Having you in my life is a blessing I cherish every waking moment. Thank you for loving me, for listening, for laughing, and for sharing your life with me. And to you, my little kicking man. How can so much – love, joy, life – be wrapped up in such a tiny package? You have shown me that my greatest accomplishment, this dissertation, could be surpassed by the arrival of a being that cannot even say a word yet.

To you, Stephen and Sebastian, you are my life. Everything I do, I do for you.

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ACKNOWLEDGEMENTS

I would like to acknowledge first and foremost the guidance of Drs. Laura Lang and Tom Luschei. Without them, my graduate education would not have been as fulfilling, and this manuscript would not be complete. Without their patience and continued support, I would have waivered in more ways than one. Thank you. I also extend my gratitude to the other members in my committee, Drs. Peter Easton, Jeff Milligan, and Carol Connor, for their constructive criticism that undoubtedly made this dissertation a quality study. I would further like to acknowledge the assistance of those in my lab, who read multiple renditions of this manuscript in the span of a couple of years. Thank you for your thoughtful advice – Meghan Hauptli, Kristina and Mark LaVenia, and Josh Rew. Next, I would like to acknowledge the assistance and support of Drs. Chris Lonigan and Chris Schatschneider who believed in my potential as an educational researcher and taught me invaluable lessons both professionally and academically. Thank you for your support. I also acknowledge the assistance of the Institute of Education Sciences, as this research was conducted while I received the Florida Center for Reading Research Predoctoral Interdisciplinary Research Training Program (PIRT) Fellowship US DOE (R305B04074)1.

Finally, I would like to acknowledge my family. All of you have always believed in me and have helped me in every way you could. This dissertation was the collective work of all of us. Gracias mamá y papá, por su apoyo y presencia durante este proceso tan árduo y largo. Gracias mas que nada por sus sacrificios, ya que sin ellos nunca hubiera logrado realizar mis sueños. Los quiero mucho. Gracias también a mis hermanas. Las adoro y doy gracias a la vida por haberme otorgado unas personas tan bonitas como ustedes. Thank you to my new family, too – Ellen, Steve, Stacey, Libby, Tim, and Emily. Although our kinship has been brief as of yet, I love you as my own. You have been an essential part of this process as much as anyone else.

1 Views expressed herein are those of the authors and have neither been reviewed nor approved by the granting agency.

2 Levels of human development indicate the well-being of a country and consider measures such as life expectancy, literacy, education, and standards of living. iv

TABLE OF CONTENTS

List of Tables ...... vii List of Figures ...... x Abstract ...... xi 1. INTRODUCTION ...... 1 Purpose of Study ...... 4 Research Questions ...... 5 Significance of Study ...... 5 Outline of Study ...... 7 2. LITERATURE REVIEW ...... 8 Literature Search ...... 9 Operationalization of Important Terms ...... 10 Cross-national Themes ...... 12 Cross-national Trends ...... 29 Conclusion ...... 32 3. METHODS ...... 35 Research Questions ...... 35 Conceptual Framework ...... 36 Data…...... 37 Countries Selected ...... 45 Methodology ...... 46 4. RESULTS ...... 51 Descriptive Analyses ...... 51 Multilevel Analyses ...... 54 A Closer Look at the Immigrant Achievement Gap ...... 58 Research Questions ...... 60 Summary ...... 62 5. DISCUSSION ...... 65 Conclusion ...... 73 6. CONCLUSION ...... 74

Restatement of the Problem ...... 74 Summary of the Study ...... 75 Research Questions ...... 75 Significance of Study ...... 75 Review of the Literature ...... 77 Methodology ...... 78 Summary of Results ...... 80 Limitations ...... 82 Future Research ...... 85 APPENDICES ...... 113 A. PARTICIPATING COUNTRIES ...... 113 B. MISSING DATA ...... 117 C. DESCRIPTIVE TABLES ...... 122 D. HUMAN SUBJECTS IN RESEARCH APPROVAL ...... 200 REFERENCES ...... 202 BIOGRAPHICAL SKETCH ...... 211

LIST OF TABLES

2.1 Articles selected for inclusion and search criteria ...... 88

2.2 A multilevel conceptual framework of the immigrant student experience ...... 90

3.1 Country selection for TIMSS ...... 92

3.2 TIMSS countries and respective sample and immigrant sub-sample sizes ...... 92

3.3 Top immigrant countries ...... 93

3.4 TIMSS variables in analysis ...... 94

3.5 Country selection for PIRLS ...... 95

3.6 PIRLS countries and respective sample and immigrant sub-sample sizes ...... 95

3.7 PIRLS variables in analysis ...... 96

3.8 Country categorization ...... 98

3.9 Top origin countries for selected countries, 2005-2008 ...... 98

4.1 TIMSS descriptive data and mean scores on mathematics by country ...... 99

4.2 PIRLS descriptive data and mean scores on reading by country ...... 101

4.3 TIMSS descriptive statistics ...... 103

4.4 PIRLS descriptive statistics ...... 104

4.5 Three-level analysis of TIMSS data (fully unconditional model) ...... 105

4.6 Three-level analysis of TIMSS data (final model) ...... 105

4.7 Interpretation of TIMSS variables in final model ...... 106

4.8 Three-level analysis of PIRLS data (fully unconditional model) ...... 107

4.9 Three-level analysis of PIRLS data (final model) ...... 107

4.10 Interpretation of PIRLS variables in final model ...... 108

4.11 Mean achievement of students in mathematics across countries by immigrant status ...... 109

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4.12 Mean achievement of students in reading across countries by immigrant status ...... 110

4.13 Magnitude & direction of mathematics immigrant achievement gap across countries by group ...... 111

4.14 Magnitude & direction of reading immigrant achievement gap across countries by group .... 112

A.1 Countries participating in TIMSS 2007 4th grade ...... 113

A.2 Benchmarking participants in TIMSS 2007 4th grade ...... 114

A.3 Countries and regions participating in PIRLS 2006 ...... 115

B.1 TIMSS missing data for descriptive analysis ...... 117

B.2 TIMSS missing data for multilevel analysis ...... 118

B.3 PIRLS missing data for descriptive analysis ...... 119

B.4 PIRLS missing data for multilevel analysis ...... 121

C.1 School-level descriptive data for TIMSS variables ...... 122

C.2 Descriptive data for TIMSS teacher-derived categorical variables ...... 126

C.3 Descriptive data for TIMSS teacher-derived binary variables ...... 131

C.4 Descriptive data for TIMSS teacher-derived scale variables ...... 133

C.5 Student-level descriptive data for TIMSS categorical variables ...... 134

C.6 Student-level descriptive data for TIMSS binary variables ...... 144

C.7 Student-level descriptive data for TIMSS scale variables ...... 148

C.8 School-level descriptive data for PIRLS variables ...... 149

C.9 Descriptive data for PIRLS teacher-derived categorical variables ...... 151

C.10 Descriptive data for PIRLS teacher-derived binary variables ...... 154

C.11 Descriptive data for PIRLS teacher-derived scale variables ...... 159

C.12 Student-level descriptive data for PIRLS categorical variables ...... 161

C.13 Student-level descriptive data for PIRLS binary variables ...... 164

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C.14 Descriptive data for PIRLS parent-derived categorical variables ...... 169

C.15 Descriptive data for PIRLS parent-derived binary variables ...... 196

LIST OF FIGURES

2.1 Conceptual framework for studying immigrant children ...... 89

3.1 Conceptual framework ...... 91

4.1 Mean scores on mathematics ...... 100

4.2 Mathematics immigrant achievement gap by type of immigrant country ...... 100

4.3 Mean scores on reading ...... 101

4.4 Reading immigrant achievement gap by type of immigrant country ...... 102

ABSTRACT

Immigration is a rapidly-growing global phenomenon. Although many countries devote significant resources to investigate the outcomes of adult immigrants, both governments and researchers have given much less attention to the outcomes of younger immigrants. With this study, I aim to increase our understanding of immigrant student achievement, first through a synthesis of the existing evidence in the form of an extensive literature review, and second, through a quantitative analysis of the so-called ‘immigrant achievement gap’. I examine the gap for fourth graders utilizing two cross-national assessments, the Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) via a cross-sectional multilevel analysis with students nested within schools nested within countries. First, I ask whether or not a gap exists for fourth graders as it has been largely found for older students. Second, I assess whether or not existing literature provides a good guide to explain variability in the gap. Third, I delve deeper into the gap by examining sub- groups of students in order to better understand the achievement of young immigrant students. Finally, I highlight cross-national trends that emerge from the findings, as previous literature has done. I find evidence of an immigrant achievement gap for both mathematics and reading, and that existing literature provides a good skeleton by which to examine the gap. Contrary to some of the existing literature, I find that the gap is larger in mathematics than in reading. Next, I find that student characteristics are strongly associated with student scores in both mathematics and reading. Findings corroborate research based on adolescent populations which suggests that, in general, students who are native, with native parents, who speak the language of testing, have better educated parents, and are of higher socioeconomic status, outperform their counterparts on these standardized academic assessments. Further, I find that the immigrant achievement gap is smaller or non-existent between the highest-achieving immigrant and native students, that there is no gender gap between immigrant boys and girls, that 2nd generation immigrants outperform 1st generation immigrants, and that students who immigrated between the ages of 1-5 outperform their younger and older counterparts in mathematics, suggesting some evidence for the ‘vulnerable age hypothesis’. I also find evidence which suggests that immigrant students attend lower quality schools, that the immigrant achievement gap is largest between the most

advantaged immigrant and native students, and that there is no difference in scores among immigrant students when the gap is analyzed by the language students speak at home. Concerning the multilevel analyses, I find few school- and country-level variables predict the immigrant achievement gap significantly. Other than peer effects (including percent economically disadvantaged and percent non-native-speaking students in schools), no school variables predicted either outcome. Corroborating extant evidence, findings suggest that attending high-achieving schools predicts both outcomes positively and significantly. Concerning the country-level, results indicate that countries with exclusionary policies, non- traditional settlement countries, and countries that attract low-skilled immigrants tend to have larger immigrant achievement gaps. However, only exclusionary/inclusionary policy as a variable was significantly predictive of the outcome and only for the mathematics model. Gross Domestic Product was significant in both models although the coefficient in both instances was zero. This study contributes to the current understanding of young immigrant students’ achievement by providing a synthesis of the extant literature as well as by comparing their mathematics and reading outcomes to those of their native counterparts. Although the variables utilized in this study are not all-encompassing of the extensive factors that have an effect on immigrant student achievement, they do provide a well-defined picture of what is associated with mathematics and reading outcomes. This study illuminates the current understanding of a number of dimensions for young immigrants – incoming resources, race/ethnicity, gender, student attitudes, and host culture variables (e.g., institutional- and school-related variations). It corroborates many of the findings from literature based on adolescent populations, suggesting cross-national trends that span a wide age range. However, dissimilar results also suggest that fourth-grade immigrants’ academic success is associated with influences that are different than those of adolescent immigrants on several dimensions. Many limitations of this study spur from the focused definition of who is an immigrant, which is only based on country of birth, and as such limits the generalizability of the conclusions. Further, the use of secondary data limits the range of variables that can be tested in the model and therefore excludes many factors that may be considered essential to include in statistical models predicting student achievement.

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CHAPTER 1

INTRODUCTION

Immigration is an issue that has been gaining attention across the world. According to the Organisation for Economic Co-operation and Development (OECD), international immigration has tripled worldwide in the past five decades, especially to developed nations such as the United States, which presently hosts one-fifth of all immigrants in the world. In this country, recent news concerning immigration questions the rights of immigrants, including a right to an education. Historically, the United States has operated on the notion that public schools have the obligation to educate all children that come to their doors, regardless of their citizenship or legal status (Plyer v. Doe, 1982). Soon, voters across various states might redefine this court holding mandating free access to public schools. Cross-nationally, immigration is a contentious issue because, as in the United States, it brings into question some of the most basic assumptions about who belongs in a society. The extent to which nations attend to the specific needs of immigrant children questions these assumptions as well. Nevertheless, immigration is an important activity, not only to address supply and demand of jobs, especially due to declining fertility rates and increased aging in some parts of the world (UNDP, 2009), but also as an issue of human rights, as many immigrants migrate due to reasons of political, racial, economic, and social strife. Education in and of itself “represents a key to upward mobility” and is “a gateway to the larger society” (Melia, 2004, p.125) and, thus, can have an enormous impact on the experience of both the immigrant and the receiving country. Further, schools can be socializing agents and “help transmit the norms and values that provide a basis for social cohesion…and set the stage for the integration of immigrant groups into the economic system” as well as into society at large (OECD, 2006, p. 16). Thus, the success of immigrants—particularly children—in a receiving country largely depends on the education they are provided. If not provided equal access to educational opportunities, they have diminished prospects as adults and many may become a permanent part of the underclass (Suárez-Orozco & Suárez-Orozco, 2000). In addition, second, third, and subsequent generations of immigrants may be less likely to succeed without the proper educational, economic, and social opportunities. The so-called ‘immigrant achievement gap’

raises important questions about issues of quality of education as it pertains to immigrant students, especially as existing evidence demonstrates that these students perform less well than their counterparts in measures of mathematics, science, and reading. Accordingly, immigration presents serious challenges to countries receiving large numbers of immigrants; issues related to education are no exception. Internationally, immigration has experienced unprecedented growth. Having tripled worldwide since the 1960’s, this phenomenon of human movement has turned previously ethnically-homogeneous countries into immigrant nations (OECD, 2010). At the beginning of this century, about 3 percent of the world’s population, or 175 million people, resided in a nation other than their country of origin (UN, 2002). Coupled with the administrative and logistical challenges of admitting large groups of people across a border, legally or illegally, nations all over the world are faced with issues of language policy, labor market integration, and human rights, as well as issues arising from a multitude of ethnicities, cultures, ideas, and religions coming together under the same national roof, so to speak. Although increased attention is being paid to all the above issues, comparatively less attention has been provided to how immigrant children are integrated into schools (OECD, 2010). Yet, despite the significant obstacles created by immigration, immigrants provide receiving countries with immense benefits (UN, 2002; UNDP, 2009). This is partly due to the nature of immigrants themselves. Most move to countries with a higher level of human development2 than that of their native country, tend to move voluntarily, to improve their livelihood, and tend to be of working age (UNDP, 2009). They are different than people who do not migrate. For example, a study comparing people who migrate to the United States to those who remain in their native country has found that immigrants tend to be more highly educated than their counterparts (Feliciano, 2005a). This trend is known as self-selection, and is also observed internationally with immigrants possessing higher income-earning capacity than non- immigrants from the same country (UNDP, 2009). Due to the high costs attached to migrating across countries, it is also possible that those who move may have more resources at their disposal and may be more willing to take the risks that may come with immigration itself (UNDP, 2009). Further, immigrants are “healthier and [more] productive than natives” with

2 Levels of human development indicate the well-being of a country and consider measures such as life expectancy, literacy, education, and standards of living.

similar education characteristics as those native to the receiving country (UNDP, 2009, p. 27). Finally, immigrants either move with their children or have children at significant rates, enough that in about half of OECD countries, for example, immigrant children and children of immigrants make up 10 percent or more of the young adult population (OECD, 2010). This makes the issue of examining the quality of education provided to immigrant children even more pressing both for the benefit of immigrant as well as the receiving country. Specifically, the theoretical basis for this study lies in an idea that takes root in the field of economics – that education can yield individual benefits – but in that it can also provide benefits for nations as a whole. In 1958, Jacob Mincer wrote a seminal article that sought to examine the source of unequal incomes. The research of the time, he noted, was shifting toward understanding such inequality from studying people’s individual abilities as one such source. The capabilities that had a direct effect on the lifetime earnings of individuals, he argued, were the result of education and experience. While such a suggestion is probably not surprising to many today, decades ago the idea that education was an investment, much like investments in physical capital, and that it could have an effect on economic growth, was novel (Blaug, 1972; Mincer, 1958; Schultz, 1962). The link between education and income lies in the notion that workers who are better trained (presumably through some form of education) are worth more in terms of their earning potential because they are more productive. This idea is now widely known as Human Capital Theory (HCT). While the link between education, productivity, and economic growth is one that is still, to this day, highly debated (see for example Easterly, 2001), and based on correlational data, the argument that education has at least some positive economic benefits for society is one that is more widely accepted (Hanushek & Kimko, 2000; Psacharopoulos & Patrinos, 2004). Therefore, governments may have a direct interest in human capital and, thus, on specific inputs to education that would maximize economic growth. Beyond considering the economic benefits of education, providing all children with access to a quality education is also an issue of human rights and reflective upon societies’ willingness to follow international calls for universal access to education, such as those found in the United Nations Universal Declaration of Human Rights (United Nations, 2012). But, what are the consequences of a system that provides education inputs inequitably? For example, the high correlation between parents’ educational attainment and their children’s

educational attainment may mean some children are provided the opportunity to acquire more human capital and a better education than others by virtue of the place in society into which they were born. In the case of immigrant students, if not provided opportunities to acquire human capital, they may be less likely to succeed in the receiving country, which may negatively impact economic growth and societies at large. This is especially alarming in countries where a significant portion of the population are immigrants, as in Australia, Canada, Luxembourg, New Zealand, and Switzerland where over one in five are foreign-born, and in the United States, Austria, Belgium, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom where a little over one in ten people are foreign-born (OECD, 2008; Rong & Brown, 2002; Suárez- Orozco, Suárez-Orozco, & Sattin-Bajaj, 2010). For this reason, it is important to closely analyze possible differential inputs (e.g., school resources) and outputs (e.g., measures of achievement) of schooling. While analyzing the long-term consequences of inequitable education systems is beyond the scope of this study, examining outcomes such as test scores may be very informative for future conversations about the quality of education provided to immigrant students. Purpose of this Study The objective of this study is twofold. First, I aim to increase our understanding of immigrant student achievement through a synthesis of the existing evidence in the form of an extensive literature review. Second, I quantitatively examine the so-called ‘immigrant achievement gap’, which in this study refers to the difference in outcome scores between native and immigrant students. An extensive body of literature has widely documented this gap (see for example Ammermuller, 2007; Heus, Dronkers, & Levels, 2009; Ma, 2003; OECD, 2010; Portes & MacLeod, 1996; Portes & MacLeod, 1999; Rangvid, 2007; Rangvid, 2010; Zinovyeva, Felgueroso, & Vazquez, 2008), although less so for younger students and in the subject of reading. Therefore, the second part of this study builds on the first to examine the immigrant achievement gap for fourth graders utilizing two cross-national assessments, the Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS). First, I ask whether or not a gap exists for fourth graders as it has been largely found for older students. Second, I assess whether or not existing literature provides a good guide to explain variability in the gap, if it exists. Third, I delve deeper into the gap by examining sub-groups of students in order to better understand the achievement of young

immigrant students. Finally, I highlight cross-national trends that emerge from the findings, as previous literature has done. Research Questions The first section of this study is in the form of a synthesis of research that analyzes the achievement of immigrant students. As such, it follows these guiding questions: 1) What themes emerge from the literature on immigrant students and what factors does the literature find are important when examining immigrant student achievement? 2) Does the literature provide any evidence for cross-national trends in immigrant student achievement?

The second section of this study is quantitative, and aims to increase existing understanding of the educational experience of immigrant fourth graders by asking the following research questions:

1) Is there evidence of an immigrant achievement gap for fourth graders in mathematics, and reading?

2) Does the immigrant achievement gap vary across schools and across countries, controlling for student-level variables? Does that variability remain significant after teacher, school, and country predictors are entered in the model? 3) Is the gap generally homogeneous across levels of socioeconomic status, language, generation, and achievement? Significance of Study This study contributes to the existing body of research in various ways. First, via the literature review, it contributes by providing a cross-national synthesis of the evidence regarding immigrant student achievement. While individual studies of immigrant students provide a strong background on the topic, no comprehensive review currently exists that examines cross-national research across several disciplines and methodologies. As evidenced in the literature search (see Literature Search) student achievement for younger children (e.g., elementary) is not expansively covered in special issues on immigration or journals specifically geared to issues of immigration. While the literature is growing, issues such as employment and earnings outcomes, immigrant adjustment and adaptation, discrimination, historicity, and other similar topics are given precedence. Although these are undoubtedly important topics, relative to the expansive coverage of the aforementioned subjects, the educational achievement of young immigrants has received

less attention in the literature. This study echoes scholars’ calls to increase our understanding of how “schools in different societies treat immigrant children and children of immigrants,” (Editorial, 2011, p. 4) and to establish a common theoretical language with which to understand and discuss immigrant student achievement (Portes, 1997). Next, and as the literature review shows, only a few studies on immigrant students cross- nationally utilize multilevel techniques (see for example Christensen, 2004 and Levels & Dronkers, 2008). The majority of studies conducted to date relied on single-level regressions with test scores as the outcome. They report associations between immigrant students’ birth status and student characteristics such as language spoken at home. Yet, most discuss their findings in terms of whether or not these student characteristics explain performance differences between native and immigrant students. This approach limits our understanding. Unless the immigrant achievement gap is modeled as an outcome, one cannot speak of the immigrant achievement gap being explained, per se. Shared variance among variables in a single-level regression is not equivalent to variance explained on the immigrant achievement gap. Therefore, if the significance of the immigrant achievement gap changes in sign, magnitude, or otherwise, in a single-level model after entering other student-level predictors, this signifies that the variables in the model share variance on predicting the outcome, not that they explain variance for each other. This study models the immigrant achievement gap as a school-level phenomenon and therefore moves toward developing a model for statistically explaining the difference in performance between native and immigrant students, or the immigrant achievement gap. Further, most studies have focused on either mathematics and/or science for eighth graders and fifteen year-olds. To this author’s knowledge, only two studies have utilized the PIRLS (see Schnepf, 2007; 2008). It is possible that the well-documented gap may be different for students who are younger. First, because we can be more certain that their age of entry is at a younger age than adolescents, tapping into evidence indicating an inverse relation between achievement of immigrant students and age of entry would suggest a smaller or no gap for fourth graders. Second, using datasets with younger populations allows the researcher to analyze a more heterogeneous population due to the higher dropout rates experienced with older students. Thus, a gap for fourth graders may be different than a gap for teenagers because assessments of the latter may capture only those students who have not dropped out of school, or the best, brightest, and most advantaged in a given country. What is known about young immigrant children is

limited, as more is known about “the educational attainment and labor market outcomes for new adult arrivals and adolescents [while there is] less understanding of the progress made by very young children in immigrant families and the influence of family resources on these children’s outcomes” (Glick & Hohmann-Marriott, 2007, p. 374). The specific aim of the second section of this study is to add to the existing body of evidence by providing research on young populations on the subjects of mathematics and reading using multilevel cross-national methods. Outline of Study Chapter 1 outlines the significance of the current study. Chapter 2 presents a synthesis of extant literature on immigrant student achievement. Chapter 3 describes the research design and methodology for the PIRLS/TIMSS 4th grade study. Chapter 4 addresses the four research questions with the analysis, followed by the findings and discussion in Chapter 5. Chapter 6 concludes by providing a summary of the literature review and findings from the quantitative analysis, describing how the current study contributes to the body of related research, discussing limitations, and suggesting future steps for researchers interested in continuing the work on immigrant student achievement.

CHAPTER 2

LITERATURE REVIEW

This chapter is a review of the literature on immigrant student achievement. Overall, it seeks to synthesize the evidence on factors that researchers have examined in order to understand the immigrant achievement gap based on cross-national evidence. It seeks to synthesize quantitative studies whose aim have been to account for the unexplained variance on immigrant and native student outcomes by controlling for student- and school-level variables. Additionally, it includes qualitative studies that aim to explicate the different sources of the immigrant achievement gap. In this effort to solidify cross-national research on immigrant student achievement, it is important to seek trends on the topic across nations. As a result, the review intentionally overlooks important nation- or region-specific (or otherwise) issues such as the politics of language and civic/citizenship education policy, religious education, refugee issues, nationhood, et cetera3. In other words, the focus is a specific attempt to generalize across countries in order to identify trends that may be useful in understanding the overall phenomenon of immigration and its corollaries. Most of the literature included in this chapter, as the previous chapter briefly noted, is based on adolescents, as that is the population that has received the most attention in the immigrant student achievement literature. For example, to this author’s knowledge, only two studies on this topic have utilized the PIRLS (see Schnepf, 2007; 2008), and none have utilized the fourth-grade TIMSS. Therefore, it becomes even more important to analyze younger children. The quantitative section of this study tests hypotheses based on existing literature, in order to explore whether or not findings differ for a younger population. Fourth graders have, by fiat, all entered a receiving country at a younger age. This may have important implications, not only on the size of the immigrant achievement gap, but also on the relationships between the gap and school- and country-level factors. This study aims to strengthen the current understanding of what influences young immigrant students’ achievement. Hypotheses tested in the quantitative

3 For a review of these issues see Witenstein, M. & Luschei, T. F. (2011, May). Conceptualizing a comparative review of immigrant education. Paper presented at the Annual Meeting of the Comparative and International Education Society, Montreal, Canada.

section of this study are based on the themes and factors discussed in this chapter, to the extent that corresponding variables exist in the datasets. The chapter begins with a description of the literature search and of the inclusion criteria applied to studies included in the review. It follows with a conceptualization of important terms and a discussion of factors that influence the immigrant achievement gap, how these have been used to observe their relationship to immigrant student achievement, and the respective findings. Factors discussed are traditional background factors such as socioeconomic status (SES), family structure, gender, and immigrant-specific factors such as culture, generation, age of arrival, and country of origin. Thereafter, the review moves to how factors of receiving countries may affect the gap; these factors include history of immigration, recruitment policies, institutional and school-related variations, as well as other considerations such as social capital, community, and peer effects. Next, a section on evidence mainly derived from qualitative studies follows, describing findings on variables that are not easily quantified, such as parent-teacher relationships, student-teacher-school leader relationships, legal status, and gender. Finally, the review concludes with general findings and trends derived from the literature on immigrant student achievement, thereby addressing the second guiding question. Literature Search The first phase of the search consisted of a thorough investigation of the published and unpublished literature (including reports by multilateral organizations such as the Organisation for Economic Co-operation and Development, OECD) using primarily Google Scholar, as this search engine tends well to the inter-disciplinary nature of this topic. An additional search of the databases ERIC (CSA), JSTOR, and AcademicSearch was performed thereafter. The search terms in these databases were the following: ‘immigrant’ AND ‘TIMSS’ OR ‘PISA’ OR ‘PIRLS’. All terms were selected to capture cross-national quantitative studies. This search was performed with no alternative combinations of the terms and no explicit geographical and/or cultural restrictions. Only multi-country studies were included in this first phase, while single- country studies were reviewed in the second phase (see Table 2.1). Only articles in English were accepted for inclusion. Namely, all searches were restricted to English-language documents but their geographic origin varied as some were from German institutes of study, for example. No time range was specified for the search. Lastly, only articles that dealt with young immigrant student populations were included (i.e., adult education is not part of this study). Twenty-three

articles fulfilled all the inclusion criteria during this first phase. Table 2.1 displays the literature search. During the second phase, the author aimed to include both qualitative and quantitative studies as well as single-country studies. For this aspect of the search, no specifications for type of test/data used were made. The author first referred to the reference lists of the selected articles from the first phase to find additional quantitative and qualitative articles relevant to the immigrant achievement gap, as operationalized in this study (see Operationalization of Important Terms). Next, international journals specifically targeting the topic of young immigrants in special issues were investigated for relevant articles (see Table 2.1). The author first sought out special issues dealing with immigration through Google Scholar, then browsed the articles within the special issues, and selected articles that dealt with issues directly related with young immigrant student achievement, as per the search terms. Then, five academic journals specifically dealing with issues of migration were searched for articles pertinent to immigrant student achievement, also per the search terms (see Table 2.1). Finally, the author evaluated all of the articles selected for Witenstein and Luschei’s (2011) comparative analysis of immigrant education and included those that provided insights into understanding the immigrant achievement gap. This second phase yielded an additional 43 articles that were included in the review. The time range of the 66 selected published and unpublished articles spans the years 1978 to 2011. Operationalization of Important Terms The study of immigrants is one that encompasses a much larger set of issues than the issue of immigrant student education. Furthermore, the issue of immigrant student education itself is an umbrella term for issues ranging from the topic of this study – immigrant student achievement –, to language policy, acculturation/assimilation programs, civic education et cetera. Therefore, it is critical to note that this study is a small, albeit important, part of a larger set of issues. As such, this study is very focused in its goals and definitions. Immigrant student achievement is defined in this literature review as pertaining to student test scores, grade point average (GPA), and attainment, although achievement may also include issues of enrollment, graduation, dropout, and other important school-related measures at any level of education. This study does not dismiss the importance of complimentary measures of achievement that may not be easily be quantified. Examples may include levels of acculturation, belongingness,

discrimination, and adaptation to a foreign environment, all of which are essential factors to consider when evaluating an immigrant’s success in a receiving country. Further, the term ‘immigrant’, as used in this study and in international datasets such as PIRLS and TIMSS, is very specific, defined as those students who were ‘not born in the country of testing’, whereas ‘native’ students are the counterparts who ‘were born in the country of testing’. This is a limited definition of an immigrant and automatically excludes internal migrants4, and narrowly categorizes them based solely on place of birth, creating a very unidimensional definition. For example, according to the World Bank, a ‘migrant’ may be classified based on country of birth, country of citizenship, last country of previous residence, duration of time spent away from birthplace or last place of previous residence, purpose of their stay (vista type), or ethnicity (Parsons, Skeldon, Walmsley, & Winters, 2007). Further, although this study’s definition is accurate, the term ‘immigrant’ is denoted and connoted differently in different regions, both across and within countries. What this means is that in some countries one may always considered an immigrant (i.e., not a citizen) unless there is proof of ancestry in the receiving country (Buchmann & Parrado, 2006; OECD, 2006); by way of contrast, in the United States, any child born on American soil is a citizen. In many cases not strictly related to citizenship status, generations of people may be considered immigrants, and may even identify as such. For example, in the United States it is not uncommon for an Italian or Irish family, for example, to consider themselves and strongly identify with their immigrant history. Finally, this study does not delve into special groups of immigrants such as migrant and refugee students. Therefore, although the immigrant student population discussed in this study could very well include these special groups, the author makes no attempt to draw inferences particular to the two groups as examination of their specific situations are beyond the scope of this study. Still, place of birth cannot be ignored as an important defining factor of immigrants, as the literature in this chapter demonstrates, when tested in models of achievement, it is always significant across different subjects and populations. Therefore, place of birth itself may have an effect on achievement, or it may act as a proxy for something else that creates a disadvantage in the form of the immigrant achievement gap, such as unequal access to educational opportunities or incongruence with host culture.

4 According to the United Nations Development Programme, almost four times as many migrants move within countries than across countries (UNDP, 2009).

Cross-national Themes The author identified the themes discussed below during the review of the articles found in the Literature Search. First, articles found during the first phase formed the skeleton for the themes outlined below. Next, the articles identified during the second phase supplemented and enriched the information found within the previously-identified themes. This second set of articles generated only three new sections – ‘Age of arrival’, ‘Social capital, community and peer effects’, and the final section entitled ‘Further evidence’. Although it was not used as a guide in identifying the themes for this chapter, the multilevel conceptual framework presented by Suárez-Orozco and Suárez-Orozco (2000) which is based on research from the United States is a helpful guide in understanding and visualizing how the cross-national themes may fit together (see Figure 2.1). Suárez-Orozco and Suárez-Orozco (2000) note that in order to understand the immigrant experience of children in the United States, one must consider its multidimensional nature with care not to reduce complex processes into “disciplinary clichés” (p. 21) such as categorizing children based solely on factors such as parental background. This literature review echoes that view. Upon presenting the cross-national themes below, the author provides a table describing how they may fit into Suárez-Orozco and Suárez-Orozco’s conceptual framework. The purpose is two-fold. First, it aims to connect the immigrant literature mainly based in the United States, to the cross-national literature presented in this chapter. Second, it aims to answer Suárez-Orozco and Suárez-Orozco’s call to form “better theoretical understandings of multiple paths taken by immigrants in their long-term adaptations” (p. 32) by creating a conceptual framework that is globally-minded and applicable to immigrant experiences across the world. Student background. Overwhelmingly, studies identify student background variables as one of the most important explanatory sources for student scores. Current research on immigrant students indicates that background variables are very highly correlated with measures of achievement (Ammermueller, 2007; Christensen, 2004; Entorf & Lauk, 2008; Heus, Dronkers, & Levels, 2009; Marks, 2005; OECD, 2004; Persson, 1978; Schnepf, 2004; Schnepf, 2007). Findings indicate that immigrants tend to be disproportionately found in the low end of the SES distribution in many countries (Christensen, 2004; Schnepf, 2004). The general findings of these studies indicate that students who are native, with native parents, who speak the language of testing, have better educated parents, are of higher SES (as measured by number of books in the

home, income et cetera, as noted below), live with fewer people in the home, but with both parents, and have a higher number of possessions that indicate social capital in the home such as books, computers, and desks, perform higher in assessments than their counterparts (Ammermuller, 2007; Heus, Dronkers, & Levels, 2009; Ma, 2003; Portes & MacLeod, 1996; Portes & MacLeod, 1999; Rangvid, 2007; Rangvid, 2010; Zinovyeva, Felgueroso, & Vazquez, 2008). Further, the gap tends to be larger in reading than in mathematics or science (Ammermueller, 2007). In order to quantify the construct of SES, authors have used indices such as the ‘International Socio-Economic Index of Occupational Status’ (ISEI), the ‘Economic, Social and Cultural Status’ (ESCS), and the Ganzeboom index, or more specific variables such as number of books in the home as well as other resources such as computers, cars, and televisions, access to the internet, and parent’s occupation. Other demographic variables such as family structure and number of siblings have also been examined (Buchmann & Parrado, 2006; Schnepf, 2007). Background factors explain little variance in some countries, particularly those in Northern Europe, although there are exceptions (Buchmann & Parrado, 2006; Marks, 2005). Driessen and Dekkers (2009) highlight that in European countries: Knowledge of ‘surface variables’ such as parental educational and occupational level certainly is not enough; what is lacking is insight into the underlying processes and conditions that create group differences and that might be altered or manipulated to reduce these differences effectively. (p. 311) This highlights what most of the research in this area acknowledges – immigrants are not the same across countries. Even within countries, immigrants can be vastly different. For example, although most immigrants to the United States tend to have low human capital, “one fourth of all physicians, 40% of engineers, and one third of people with doctorates” (Roberts, 2010 as cited in Suárez-Orozco, Suárez-Orozco, & Sattin-Bajaj, 2010, p. 540) and about a third of United States’ Nobel Prize winners are immigrants or children of immigrants (Suárez-Orozco, 2001). Moreover, when analyzed across different levels of SES, researchers find that although there is a gap on average, immigrant students of lower SES fare worse than native students of similar SES in various assessments, and the former are more likely to attend lower SES schools than the latter. This is especially true in those countries with the largest achievement gaps such as Germany and Switzerland (Christensen, 2004; Schnepf, 2004). In other words, in countries

where the gap is small, low SES immigrant students perform similarly to native low SES students; in countries where that gap is large, low-SES immigrant students do much worse than low-SES native students. While there is a gap between higher SES immigrant and higher SES native students, it is smaller than the gap for lower SES immigrant students, especially in those countries with smaller gaps (Rangvid, 2007; Schnepf, 2004). There is also evidence that the gap is smaller at the higher end of the test score distribution and larger at the lower end (Ammermueller, 2007; Meunier, 2011). Stated differently, high-achieving immigrants demonstrate a smaller gap than low-achieving immigrants when compared to native students who achieve similarly. While gender (i.e., sex) has been examined in many of the cross-national studies, research generally finds that the gender gap for immigrants is similar to that of natives (Christensen, 2004; Entorf & Lauk, 2008; OECD, 2006; Rangvid, 2007; Schnepf, 2007; Zinovyeva, Felgueroso, & Vazquez, 2008). In other words, males outperform females in mathematics and science, while the opposite is true for reading regardless of immigrant status. Single-country studies corroborate this finding (Ammermueller, 2007; Driessen & Dekkers, 1997; Ma, 2003). For instance, Rangvid (2010) found that girls from Pakistan living in Denmark perform on par with native girls with similar SES. In the United States, generational differences for boys and girls have been documented, where the gap between the first and third generation is smaller for girls than boys in mathematics (Crosnoe & López Turley, 2011). Such studies suggest that there may be some differences in gender performance when considering various background factors. Culture. While information on a student’s ethnicity is generally not available in PISA, TIMSS, or PIRLS, many countries gather this information independently. Research in the United States leads the literature when it comes to measures of culture. Variables such as race and ethnicity have been found to account for much variability on student outcomes. Further, some research finds that, in the United States, immigrant reading gaps remain significant after controlling for SES but not after controlling for race and ethnicity (Portes & MacLeod, 1996; Warren, 1996). Also, Glick and Hohmann-Marriott (2007) found that racial and ethnic differences remained even after controlling for family background and language proficiency. Therefore, race and ethnicity seem to be very important in the United States and may also be so in other countries.

Language predicts outcomes for natives and immigrants alike (Ma, 2003; Marks, 2005). Namely, students who report not speaking the language of testing at home generally achieve at relatively lower levels regardless of whether or not they were born in the country of testing (OECD, 2004; OECD, 2006). Language spoken at home has been found to significantly predict reading scores, but not mathematics or science scores in some instances (Ma, 2003) while the opposite has been found by others (Rangvid, 2007), suggesting differential prediction patterns across subjects. Differences may also exist across countries of origin, where controlling for language nullifies the gap for immigrants from some countries but not others (Rangvid, 2007). Still, in general, even after accounting for language, immigrant gaps remain (Entorf & Minoiu, 2005; OECD, 2006). Proficiency in the native language may also play a part in achievement as some studies suggest cross-linguistic transfer of literacy skills (see Dressler & Kamil, 2006), and differential relationships across generations between measures of proficiency in the native language and assessment outcomes (Buriel & Cardoza, 1988). Bilingualism may also have a positive effect on achievement (Golash-Boza, 2005). Generational gaps. Overall, studies find that 2nd generation immigrants outperform 1st generation immigrants, although both generations typically lag behind native students (Buchmann & Parrado, 2006; Christensen, 2004; Heus, Dronkers, & Levels, 2009; Levels, Dronkers, & Kraaykamp, 2008; Marks, 2005; OECD, 2004; OECD, 2006; Pong, 2009; Rangvid, 2007; Schneeweis, 2006; Schnepf, 2004). Although definitions vary by study, in general 2nd generation students are those who were born in the country of testing but whose parents (at least one) were born outside of the country. Conversely, 1st generation immigrants are those who were born outside the country of testing, as were their parents. Some studies also include the 1.5- generation, or students who arrived in the receiving country before the age of 12, and have therefore been socialized primarily in the receiving culture (see for example Rumbaut, 2004; see Christensen, 2004; Heus, Dronkers, & Levels, 2008 for alternative definitions). Often, definitions of generations are supplemented by whether or not the national language of the receiving country is spoken at home. This is an important consideration as many immigrants originate from former colonies (see Countries Selected section for an example of these) and therefore speak the same language as spoken in the receiving country. When the language variable is controlled in these cases, the gap remains significant. This is not the case in countries whose immigrants tend to arrive with little knowledge of the national language (Entorf

& Minoui, 2005; Schnepf, 2007). Therefore, the addition of this variable may allow the researcher to identify gaps between groups of immigrants who come from former colonies and those who do not. Given a thorough understanding of the patterns of immigration in a given country, such differentiation may prove useful in explaining the gaps between immigrant students who arrive knowing the language of testing and those who do not. Regardless of how researchers operationalize these definitions, the time spent in the country tends to impact student achievement positively so that the longer the student has been in the country of testing, the better he or she will perform, as demonstrated in analyses that control for age of arrival (Cortes, 2006; OECD, 2006) discussed in detail below. Likewise, those students who have spent all their lives in the country of testing should theoretically outperform those who have recently arrived. This is not only due to language, as those born in the country are more likely to be familiar with the language of testing, but also because the more an immigrant student resembles the native student, the smaller the achievement gap (OECD, 2006). Yet, some evidence exists to the contrary, particularly in the United States. Research investigating the effect of generation on immigrant achievement in the United States finds that 1.5 generation students perform better than subsequent generations (Buchmann & Parrado, 2006; Kao, 2004; Pong & Hao, 2007) and sometimes similarly to natives (Glick & Hohmann-Marriott, 2007), and that second generation immigrants underperform in comparison to first generation immigrants, so the aforementioned assumption may not hold in all instances (Crosnoe & López Turley, 2011; Glick & Hohmann-Marriott, 2007; Lee, 2001). For example, Pong (2009) found similar results in Hong Kong; when controlling for grade level, first generation students performed better than later generations. Many cite disillusionment and assimilation as sources for these differences (see section on Further Evidence). Otherwise, cross-nationally, while on average it seems 2nd generation immigrants perform better than 1st generation immigrants and are less likely to attend segregated schools, they still do not perform on par with native students (Levels & Dronkers, 2008). Age of arrival. Research investigating the age at which an immigrant arrived in the receiving country is linked to studies investigating generational gaps. Generally, it suggests an inverse relationship between age of arrival of both the parent and the student and achievement, although much less is known with regard to the age of arrival of the parent (Cortes, 2006; Dumon, 1974; Glick & Hohmann-Marriott, 2007). Studies have found that first and second

generation individuals perform similarly when the first-generation individual arrived before having attended formal schooling (Cortes, 2006; Glick & Hohmann-Marriott, 2007). Immigrating as an adolescent (versus as a child) tends to have a negative effect on overall educational attainment (Chiswick & DebBurman, 2004; Leslau, Krausz, & Nussbaum, 1995). This may be related to the multiple transitions adolescents undergo in a short period of time (Alitolppa & Niitamo, 2004) and the ease of acculturation for younger students (Dumon, 1974), where they are more adaptable to new environments than their older counterparts. Further evidence indicates that those who enter as younger immigrants (under 18) tend to attain a higher level of education than older immigrants (ages 18 and older; Leslau, Krausz, & Nussbaum, 1995). Research from studies on student mobility investigating the ‘vulnerable age hypothesis’ has had mixed results. This hypothesis is manifested in a curvilinear relationship indicating that students who move residences between the age range of 6-11 tend to achieve lower than higher and lower age ranges. However, research across countries is inconclusive with some finding the complete opposite relationship, and some finding age 7 as the vulnerable age of arrival for immigrants. Some research has documented higher drops in achievement in verbal than mathematics measures as well as differential drops across different ethnicities (Cahan, Davis, & Staub, 2001). Other related research deals with ‘academic redshirting’, a situation that occurs when students are strategically retained by the school or the parent to allow students to spend longer periods of time in a given grade or subject (see Appleyard & Amera, 1978; Pong, 2009). Immigrant students are considered ‘redshirts’ either when they are held back in the receiving country, or when they begin school at a later age in their country of origin, a fact that becomes a disadvantage in a receiving country where native students begin school at an earlier age. Studies that control for age and grade of the student have found that these ‘redshirts’ perform as well as their younger counterparts even showing evidence of catching up (Pong, 2009; Zinovyeva, Felgueroso, & Vazquez, 2008). Yet, ‘redshirts’ may be at a disadvantage in measures of social adjustment such as self-esteem and social behavior (Pong, 2009). Country of origin. In general, researchers strongly emphasize the fact that immigration is not random. Rather, some countries specifically attract high-skilled workers whose education will resemble or even surpass that of the native worker. Their children may be more likely to either perform on par or outperform native students. Therefore, the educational experience of

immigrant students may be more dependent on their individual circumstances of immigration rather than on the education provided in the receiving country. When the country of origin variable is available (as it was in some countries for PISA 2003) it has been found to be a significant predictor of mathematical achievement, showing immigrants from specific regions (such as Australia, Eastern Europe, and South Asia) achieve higher scores than those from other regions (such as Latin America, Northern Africa, and Southern Europe), especially when the language of testing is spoken at home (Levels & Dronkers, 2008; OECD, 2006). In single- country studies, decomposition of immigrant groups by country of origin supports the notion that some immigrants arrive with higher human capital than others, a fact that has educational repercussions (Driessen, 2001; Driessen & Dekkers, 1997; Leslau, Krausz, & Nussbaum, 1995; Rangvid, 2007). Perhaps the most expansive research on differences spurring from country of origin exists in literature originating in the United States, where it is found that Asian, Eastern, and Western European students outperform Latino/Hispanic students (Campbell, Hombo & Mazzeo, 2000; Educational Testing Service, 2009; Glick & Hohmann-Marriott, 2007; Gonzalez, 2002; Kao, 2004; NAEP 2003a; 2003b; Pong & Hao, 2007) with important within-ethnic-group differences (Glick & Hohmann-Marriott, 2007; Portes & MacLeod, 1996; Portes & MacLeod, 1999). The United States has been found to have a larger immigrant achievement gap than other traditional countries of immigration due to the fact that it attracts low-skilled immigrants (Buchmann & Parrado, 2006; Entorf & Minoiu, 2005). The country of origin of the student may also impact aforementioned generational gaps (Rangvid, 2007). Although the country of origin variable is largely absent from international datasets and is not commonly analyzed cross-nationally, there are a few exceptions. For example, macro-level differences related to country of origin (such as religion, economic development, quality of resources, and political stability) and country of destination (such as policies towards immigrants, quality of resources, teacher shortage, and student to teacher ratios) have also been examined and have been found to explain variance in mathematics and science achievement after controlling for student-level characteristics (Heus, Dronkers, & Levels, 2009; Levels, Dronkers & Kraaykamp, 2008). These authors emphasize that due to the selective nature of immigration, in order to properly understand immigrant gaps it is important to account and ‘disentangle’ origin and country of destination effects.

In the same vein, Song and Róbert (2010) highlight the importance of understanding the history of immigration in different countries when assessing how country of origin relates to generational gaps. They note that comparing two groups of immigrants, say one which immigrated a generation prior to the other, even if both groups originate from the same country, may mean comparing two groups that may have had entirely dissimilar circumstances for immigrating, and are therefore very different. One group might have immigrated with high levels of education and the other with low levels of education, a fact that will dictate whether the gap favors immigrant students or not. Therefore, the gap might be imprecisely interpreted. It may, in fact, be explained by differing characteristics of shifting patterns of immigration, typified by the needs of the labor market and the selective requirements for immigrants in the receiving countries, rather than explained by the educational system of the receiving country itself. However, until information on the country of origin is collected more often in the international datasets, such questions will not be easily answered. History of immigration of receiving country. Although not analyzed by many, some authors argue that much of the immigrant achievement gap can be explained by the receptivity of receiving countries toward immigrants (OECD, 2006; Suárez-Orozco, Suárez-Orozco, & Sattin- Bajaj, 2010). For example, Heus, Dronkers, and Levels (2010) found a positive effect of longer history of migration of a receiving country on science achievement perhaps suggesting a relationship between such history and preparedness to address education-related issues for immigrants. Similarly, Buchmann and Parrado (2006) found that those countries that are traditional settlement countries (or traditional countries of immigration), such as the United States, Australia, New Zealand, and Canada, tend to have more inclusionary policies toward immigrants whereas non-traditional settlement countries tend to have exclusionary policies. They find that after controlling for student background, immigrants who reside in countries with exclusionary policies have a larger achievement gap than those who live in countries with inclusionary policies. In fact, they find that demographic variables do not nullify the gap in Northern European countries, due to exclusionary policies, and possibly discrimination unrelated to demographic variables. Others have cited similar findings where immigrant students in countries classified as traditional settlement countries perform better than immigrant students from countries with a more recent history of immigration (Entorf & Lauk, 2008; Entorf & Minoiu, 2005; Levels,

Dronkers & Kraaykamp, 2008; Schneeweis, 2006; Schnepf, 2004). This may be largely due to unpreparedness of school systems to educate students with immigrant-specific needs (Alitolppa & Niitamo, 2004). An important exception is the United States, a traditional settlement country that attracts low-skilled workers and, therefore, exhibits a larger immigrant achievement gap than the other traditional settlement countries that tend to attract high-skilled workers, such as New Zealand, Australia, and Canada. Another source of difference may be found in policies of integration, or how systems approach the integration of immigrants. For example, these can aim to assimilate, exclude, or acculturate immigrants with multicultural approaches (Alitolppa & Niitamo, 2004; Crul & Schneider, 2009; OECD, 2006). These may have an effect both on how immigrant students feel they are perceived by natives as well as on institutional structures such as language policies, tracking, and funds made available for immigrant-specific needs, which would consequently affect achievement. Recruitment policies of receiving countries. As previously noted, another very important source of difference can be found in the receiving country’s history and selectivity of immigrants. Whereas many countries with a smaller immigrant achievement gap tend to recruit high-skilled immigrants, others with a larger achievement gap attract migrant workers, both legal and illegal, who are likely to have lower levels of education and income. Those countries with high-selectivity of immigrants tend to have smaller achievement gaps than those who attract lower-skilled immigrants (Christensen, 2004; Entorf & Minoiu, 2005; Ma, 2003; OECD, 2004; Schneeweiss, 2006; Schnepf, 2007; Schnepf, 2008; Song & Róbert, 2010). Therefore, the achievement gap may be incorrectly interpreted as a result of the education system of the receiving country when, in fact, it is dictated by background variables of immigrants and circumstances of migration (Feliciano, 2005b). Institutional and school-related variations. Researchers have suggested differences across schools as important in explaining differences in student achievement (Brewer & Goldhaber, 1997; Fuller, 1987; Fuller & Clarke, 1994; Gamoran & Long, 2006; Hanushek, 1971; Hanushek, 1986; Heyneman & Loxley, 1983; Wöβmann, 2003). Characteristics of schools and countries have been utilized to understand immigrant achievement gaps (Buchmann & Parrado, 2006; Christensen, 2004; Levels & Dronkers, 2008; Marks, 2005; Rangvid, 2007; Schnepf, 2007; Wöβmann, 2003), although they generally explain a small amount of variance in comparison to student background variables (Pong, 2009; Rangvid, 2007; Zinovyeva,

Felgueroso, & Vazquez, 2008). Some of these characteristics include teacher characteristics, school resources, school size, type, climate, and location of the school, as well as student to teacher ratio, and peer characteristics such as their SES. The findings for these variables are largely mixed with some studies finding positive relations, some negative, and some none, suggesting largely inconsistent findings for school effects (see Dronkers & Levels, 2007; Heus, Dronkers, & Levels, 2009; Marks, 2005; Pong, 2009; Rangvid, 2007; Zinovyeva, Felgueroso, & Vazquez, 2008). One definitively important source of explanation is peer effects (see Social capital, community and peer effects section below), or the extent to which high achieving children have an effect on low achieving children (or vice versa) when attending the same school (Hanushek et al., 2003; Zimmer & Toma, 2000). Thus, the significance of institutional and school-related variables diverges substantially from country to country, and no general trends are evident (Christensen, 2004; Schneeweis, 2006). Concerning school composition, some studies find that immigrant students are more likely than native students to attend schools with other immigrant students (Entorf & Lauk, 2008; OECD, 2006; Rangvid, 2007; Rangvid, 2010; Schnepf, 2004; Zinovyeva, Felgueroso, & Vazquez, 2008). In some countries, attending school with immigrants has a negative impact on achievement, while in others it has either a positive or no impact (Entorf & Lauk, 2008; Levels & Dronkers, 2008; Schnepf, 2004; Schnepf, 2007; Zinovyeva, Felgueroso, & Vazquez, 2008). Further, immigrant students are sometimes more likely to attend schools with less favorable characteristics, such as fewer resources, lower average achievement, and a poor disciplinary climate (Christensen, 2004; Entorf & Lauk, 2008; Marks, 2005; OECD, 2006; Pong & Hao, 2007; Rangvid, 2007). Research in the United States provides evidence that there may be important differences across ethnic groups, where Latino students are likelier than Asian students to attend segregated and problematic schools (Crosnoe & López Turley, 2011). Cross-national research indicates segregation may be higher in non-traditional countries of immigration (Entorf & Lauk, 2008). Once more, research examining institutional and school-related factors is inconclusive. Other evidence suggests that while immigrant students are likely to attend schools with better indices of traditional school resources, such as class size and student to teacher ratios, they are less likely to attend schools with a culture of achievement as measured by teacher expectations, encouragement, and pressure to achieve (Rangvid, 2007; Zinovyeva, Felgueroso, &

Vazquez, 2008). Notably, in related studies, including these school-level predictors accounted for 40-74% (variant across subjects) of the outcome, after controlling for student background. A culture of achievement in a school may mark the difference between native and immigrant students’ academic achievement. Another institutional difference is in how students are tracked into different types of schools according to perceived ability. In some countries, such as Germany, France and Switzerland, students are tracked across different types of schools. In an analysis on the effect of comprehensive (i.e., tracked within schools or not at all) versus ability-differentiated (i.e., tracked across schools), Entorf and Lauk (2008) found that the latter exacerbate the gap between high and low SES students. Heus, Dronkers, and Levels (2010) found that the performance of higher SES (not low SES) students is positively affected by comprehensive education systems. Heus, Dronkers, and Levels (2009) found that, even after controlling for parental education, immigrant students in countries with highly stratified education systems (i.e., have high levels of tracking) score lower than immigrants in more comprehensive systems, but that immigrants in moderately stratified systems performed better than those in comprehensive systems. Zinovyeva, Felgueroso, and Vazquez (2008) found immigrant students in Spain perform worse in geographic areas with high segregation. In countries like the United States, students are more often tracked within schools, with minorities more likely to be tracked to lower levels (see Argys, Brewer, & Rees, 1996; Newlon, Romano, & Epple, 2002). These systems of tracking have also been found to disproportionately track immigrant students into lower level tracks (Crul & Holdaway, 2009; Educational Testing Service, 2009; Entorf & Minoiu, 2005; Fotiu, Cheong & Raudenbush, 1998; Heus, Dronkers, & Levels, 2009; Schneeweis, 2006; Schnepf, 2007), and often relegate immigrant students to racial, poverty, and linguistic isolation, or what may be termed as ‘triple segregation’ (Suárez Orozco, Suárez Orozco, & Sattin-Bajaj, 2010). Social capital, community, and peer effects. Membership and affiliations with social networks may have a positive effect on achievement to the extent that these networks provide immigrants with social capital and the benefits coupled with it. For immigrants, the literature indicates that three types of networks can afford access to such resources – family, ethnic group, and the receiving country itself (Alitolppa & Niitamo, 2004). While family can provide the immigrant student with important resources to succeed, it can be limited insofar as dissonant

acculturation occurs, which “brings competing norms, values and practices [between the native and new cultures] to the social structure of a family, [and] severs the ties between children and adults” (Alitolppa & Niitamo, 2004, p.91) possibly affecting students’ achievement negatively (Appleyard & Amera, 1978; Dumon, 1974; Lee, 2001; Portes, Fernández-Kelly, & Haller, 2009; Portes, & MacLeod, 1996). Neighborhoods can also provide negative social capital when they are deficient in important resources conducive to high achievement. For example, Appleyard and Amera (1978) hypothesized that the isolation of Greek children in Australian Greek communities was a source of underachievement due to their lack of exposure to the English language. Ethnic groups can also be a source of positive social capital by providing both a system of strong ethnic ties as well as act as a conduit to the outside community of the receiving country (Portes & MacLeod, 1996). In a study of Vietnamese youth in Norway, Fekjær and Leirvik (2011) found that the Vietnamese community held high expectations for students and this, in turn, was much more responsible than parental education for the students’ ability to outperform natives. Other studies of Vietnamese youth have found similar strong ethnic expectations (Zhou & Bankston III, 2001). The expectations of parents, identified as social capital by the authors, also seemed to play a role. Levels, Dronkers, and Kraaykamp (2008) investigated community effects finding that the higher the socioeconomic capital of the community of the immigrant, the higher their mathematics achievement. In addition, the size of the community, a proxy for immigrants’ access to social networks, had a positive effect on students’ achievement. Concerning social capital in the receiving country, other studies of neighborhoods in which immigrants reside indicate that immigrants tend to live in neighborhoods with less social capital. For example, in the United States, Pong and Hao (2007) found that Latino immigrants are likely to live in neighborhoods with many peers who are neither in school nor working, with low proportions of two-parent households, and with high proportions of foreign-born and Limited English Proficient (LEP) individuals. They also found notable differences within and across ethnic groups, noting for example, that after controlling for neighborhood and school effects, Mexican immigrants no longer differed in their Grade Point Average (GPA) from non- Hispanic native Whites. Comparable results were found for other ethnic groups, which led the authors to conclude that immigrants can be more susceptible to neighborhood effects. Finally, the peers with which students interact can also affect achievement. In general, peer effects have been found to be significant predictors of immigrant student achievement as

discussed above (Entorf & Lauk, 2008; Portes & MacLeod, 1996; Rangvid, 2007; Zinovyeva, Felgueroso, & Vazquez, 2008). Across nations, peer effects of native students, or attending schools with native students, has been found to have an effect more consistently than peer effects of immigrant students, or attending schools with immigrant students. The latter effect has only been found in traditional settlement countries (Entorf & Lauk, 2008). A separate measure of peer effects was exemplified by Cortes’ (2006) analysis of the effect of attending an ‘enclave’ school (defined by the author as an “immigrant receiving high school”, p. 121), in which she found that although it is associated with lower performance on mathematics and reading tests, after using propensity score matching techniques, there was no test score difference between immigrant students who attended ‘enclave’ schools and those who did not. Still, the students in ‘enclave’ schools were less likely than their counterparts to live in a two-parent home, tended to have parents that had lower levels of education and were less likely to own a home (Cortes, 2006). These ‘enclave’ schools also had more students eligible for Free and Reduced Price Lunch (FRPL), and were more likely to be inner city schools (Cortes, 2006). Further evidence. Additional themes emerge on factors that cannot be easily quantified and that are not typically measured in international datasets. For this reason, the following sections are mostly based on studies employing qualitative techniques. These are useful because they allow for a richer understanding of additional factors that may influence immigrant students’ achievement. Involvement and academic decisions. In a study of Dominican immigrants in New York City, and of Moroccan immigrants in the Netherlands, Crul and Holdaway (2009) found that immigrant parents were poorly informed on the matter of school choice and, therefore, did not seek to enroll their children in higher-quality schools. Heus, Dronkers, and Levels (2009) have argued similarly stating that such ‘strategic knowledge’ tends to be weaker in immigrant families (see also Pong, 2009). These authors also argue that teachers often lower their teaching standards or perpetuate ethnic-based stereotypes of immigrant children, thereby negatively affecting their educational outcomes, a notion put forth by others as well (Conchas, 2001; Lee, 2001). Differences between the culture of the immigrant student and the native teachers may also create problems for the educational success of students. For example, parent involvement is often cited as lacking for immigrant populations (Mclaughlin, Liljestrom, Lim, & Meyers, 2002), with important differences within immigrant groups (Driessen, 2001). This may be because the

culture of some immigrants prevents the parent from becoming involved as they see the teacher as the authority figure who knows what is best for the student. It may also be that parent involvement takes different forms in different cultures, such as encouragement, high expectations, and/or the value of hard work, as opposed to attending meetings with teachers, volunteering in schools, and helping with homework (Alitolppa & Niitamo, 2004; Crosnoe & López Turley, 2011; Fekjær & Leirvik, 2011; Lopez, 2001). For some immigrant groups, brothers and sisters help with homework more so than the parents (Driessen, 2001). Language differences can also inhibit communication between the parent and the teacher. Influence of schools and school personnel. In the absence of parent involvement due to lack of information or understanding of the new culture, other adult role models can step in and be a positive influence on immigrant students’ lives, thereby becoming a source of social capital (Gonzales, 2010; Portes, Fernández-Kelly, & Haller, 2009; Suárez-Orozco, Suárez-Orozco, & Sattin-Bajaj, 2010). Young students have pointed to teachers and guidance counselors as their motivating factor for excelling, encouraging immigrant students to pursue paths that would help them succeed academically and otherwise (Crul & Holdaway, 2009; Portes, Fernández-Kelly, & Haller, 2009). Organized programs that help immigrant students succeed may also affect student achievement. These may include English Speakers of Other Languages (ESOL), ethnic-based groups, path to college programs, or other programs that help immigrant students overcome their multifaceted disadvantages. However, teachers and schools can also have negative effects on students. In fact, evidence from the United States shows that schools can either be structural inhibitors or motivators for immigrant students, as “student success and failure is often determined by [the schools’] relative ability to form positive relationships [between] school personnel[,]…high-achieving peers” and immigrant students (Gonzalez, 2010, p. 472), underscoring the effect of both personnel (including teachers) and peers on achievement. Of note is the role tracking plays in this interchange between school structure and student success, as students placed into higher tracks are given more opportunities to forge strong relationships with school personnel and are also exposed to higher-achieving peers (Conchas, 2001; Gonzales, 2010; Lee, 2001). The opposite would be true for those students placed into lower tracks, a fact that underscores the compounding nature of disadvantage. Beyond the structural nuances of schools, being tracked into a ‘good class’ or a ‘good program’ may have a positive cognitive

impact on students, for whom applying and attending college, for example, becomes the ‘natural thing to do’, and yet another motivating factor to succeed (Gonzales, 2010). This research suggests that the way in which teachers and students mobilize resources is what has an effect on achievement (Cohen, Raudenbush, & Lowenberg Ball, 2003). In this scenario, tracking in and of itself would not have an effect; rather, what teachers offer students and how students respond to those opportunities is what impacts learning. Yet, this interplay becomes complicated in large schools, where resources are limited and have to be rationed across students, oftentimes provided with priority to students in higher tracks. This has obvious consequences for immigrant students who are less likely to be put into the higher-level tracks (Crul & Holdaway, 2009; Educational Testing Service, 2009; Entorf & Minoiu, 2005; Fotiu, Cheong & Raudenbush, 1998; Schneeweis, 2006; Schnepf, 2007;). Parent-student relationships. Many immigrant parents have very high aspirations for their children (Appleyard & Amera, 1978; Louie, 2001; Portes & MacLeod, 1996), hoping they will be the first in the family to attend college or get better jobs than they (the parents); however, few have adequate information to aid their children in navigating the educational system in order to succeed. High parental and student aspirations have also been observed elsewhere (Buriel & Cardoza, 1988; Driessen, 2001; Lee, 2001; Suárez-Orozco, Suárez-Orozco, & Sattin-Bajaj, 2010), with some suggesting variations across gender (Limage, 1985). Relationships between the parent and student as well as parenting styles have been investigated showing variation across ethnicities with authoritative and open-communication parental styles being more effective than authoritarian with closed communication (Fekjær & Leirvik, 2011; Portes, Fernández-Kelly, & Haller, 2009). A sense of dual responsibility has been documented in Chinese families, where the parent is responsible for securing an education for the student, while the student is responsible for high achievement for the parent (Louie, 2001). Motivations. While some studies find that new immigrants have very positive attitudes regarding schools and school personnel, as well as strong desires to succeed academically (OECD, 2006; Mclaughlin, Liljestrom, Hoon Lim, & Meyers, 2002), some have suggested disillusionment as a predicting source of prevailing gaps between natives and subsequent immigrant generations (OECD, 2006; Alitolppa & Niitamo, 2004; Lee, 2001, Leslau, Krausz, & Nussbaum, 1995). This may be due to disappointment in progress made in receiving country, poor returns on educational attainment, and feeling disconnected from the host culture, among

others. Such evidence may imply important differences in motivation across generations, where first or 1.5 generations are more motivated and see education as a means for social mobility and success more so than second and subsequent generations (Lee, 2001). This body of literature introduces the notion of human agency into the function of immigrant student achievement, stressing the fact that immigrant students can have a direct impact on their own achievement (Alitolppa & Niitamo, 2004), challenging otherwise deterministic notions. Many researchers specifically study two phenomena – the ‘immigrant paradox’ and the ‘model minority’ – both of which refer to low SES immigrant students who outperform natives, against all expectations (see for example Lee, 2001). Some of these studies find that the relationship between the parent and the student is at play, which is defined by high expectations on the part of the parent, and a ‘debt of gratitude’ on the part of the student for their parents’ hard work (Fekjær & Leirvik, 2011; Louie, 2001). For these students, parental education and SES do not explain high achievement, much to the contrary of the traditional functions described in earlier sections. Others propose cultural capital as the source for the unexpected success of some immigrants, stating that strong ethnic ties and pride can drive students to excel and make their ethnic group proud and represent them well (Lee, 2001; Portes, Fernández-Kelly, & Haller, 2009; Portes & MacLeod, 1996; Sarroub, 2001). Immigrant students’ culturally fixed responsibilities outside of school (either at home or at work) also transmit important cultural values that are prized in many countries, such as strong work ethic and responsibility. These may help immigrant students excel both in school as well as in the workplace (Lopez, 2001; Orellana, 2001), but may also create stress for students to the point of being detrimental (Lee, 2001; Louie, 2001). Legal status, migration, and discrimination. Lack of legal status may also have an influence on the achievement of immigrant students, as it can complicate their situation, and deny them academic, social, and economic opportunities (Alitolppa & Niitamo, 2004). Coupled with fear and anxiety of being deported or separated from family members who do not have proper legal status in the receiving country, “such psychological and emotional duress can take a heavy toll on [their] academic experiences” (Suárez Orozco, Suárez Orozco, & Sattin-Bajaj, 2010, p. 542). Other factors that may compound this problem include the psychological remnants of immigrating (Crosnoe & López Turley, 2011), which for many may have been under dangerous circumstances or a form of escape from dangerous environments such as war

(Alitolppa & Niitamo, 2004; García Coll & Szalacha, 2004). Experiencing discrimination (Driessen, 2001; García Coll & Szalacha, 2004; Lee, 2001; Rong & Brown, 2001), and poor ethnic relations and clashing cultural values in the receiving country (Alitolppa & Niitamo, 2004; Louie, 2001) may also negatively impact the achievement of immigrant students. Thus, negotiating their place in a new society may be a psychological burden, taking away focus and attention from learning, or it may be as motivation to achieve (Lee, 2001; Louie, 2001). Characteristics at entry. The educational experience of immigrant students prior to arrival in the receiving country is not often captured quantitatively. Yet, as a study of Somali immigrants in Helsinki demonstrates, having little to no experience with formal education in the sending country can have profound consequences on the success of immigrant students in the receiving country (Alitolppa & Niitamo, 2004). The psychological and physical health of immigrants at arrival and in the receiving country can also have an effect on achievement (Alitolppa & Niitamo, 2004; Concha, 2001; Crosnoe & López Turley, 2011; Fuligni & Hardaway, 2004). Considering the significant obstacles that immigrant students face, it is noteworthy that in the United States, young foreign-born populations sometimes report being healthier, and are less likely to report using illicit drugs than their native counterparts. Still, they may be more likely to suffer from depression and prolonged periods of sadness, as well as engage in high-risk behaviors such as early sexual activity (Fuligni & Hardway, 2004). Gender. While sex is a variable that is simple to capture quantitatively, gender and the cultural expectations ascribed to it is not and may have an effect on achievement, particularly if expectations are higher for boys than girls, and if girls have to find balance between home responsibilities (e.g., cleaning, cooking, helping raise children) and school (Alitolppa & Niitamo, 2004; Lee, 2001; Orellana, 2001). Yet a study of Vietnamese girls living in New Orleans found that the achievement of girls was higher than boys, specifically for those who identified the strongest with their ethnicity (Zhou & Bankston III, 2001). This study also presented census data that showed that among Vietnamese women aged 16-24, those who were unmarried dropped out of school at a lower rate than men, suggesting a balance between retaining and abandoning some cultural values, and an ensuing relationship of that balance with educational success. In this community, gender roles were redefined (although still tightly controlled and biased) for women such that they were expected to live up to the ideal of a “‘virtuous woman’, which calls not only for passive obedience but also for living up to higher behavioural standards than are expected of

men” (Zhou & Bankston III, 2001, p. 143). The high aspirations of this ethnic group had positive effects on the achievement of women. These multifaceted expectations and high achievement of women have also been observed in other ethnic groups (Lee, 2001; Sarroub, 2001). Still, one can imagine a situation where contradictory expectations (e.g., early marriage and/or house chores and education) may take a toll on academic achievement. Cross-national Trends It is important to note that there are not many findings that can be said to apply across all nations. Therefore, the following section encompasses general findings across the studies discussed above. For specific findings by country, the reader should refer to the specific study of interest. To reiterate an earlier point, this study is intentionally general as one of its aims is to identify themes that can guide researchers in understanding immigrant student achievement as a cross-national phenomenon. The immigrant experience, as discussed in reference to Suárez- Orozco and Suárez-Orozco’s (2000) conceptual framework, is multidimensional and complex, and certainly not prescriptive or stagnant. Therefore, according to the many factors present in an immigrant student’s life, a student with even the lowest human capital can be successful, while the exact opposite may also prove true. The background of the student seems to be one of the most important explanatory variables. In general, it explains much of the variance in immigrant student scores, although not all (see for example Entorf & Minoiu, 2005; Marks, 2005). Variables included under the general term of SES are number of books in the home, whether or not students have a computer, a calculator, a television, and a study desk (among other possessions), parents’ education, occupation and income, and country of origin, among many others. In general, the more support in the home and the better educated the parents are, the better students seem to perform. Yet, there are very discernable differences across the SES and achievement continuum so that high SES and high achieving students exhibit different gaps than their counterparts. The explanatory power of background variables may also vary by region, highlighting the heterogeneity of immigrants across, but also within, countries. Language, race and ethnicity may be most important to consider as attributes tied to the human capital of immigrants, and, as such, they may differentiate between high- and low-skilled workers and, thereby, dictate the direction of the immigrant achievement gap.

The generation of the student and language also appear to be important factors with the latter sometimes surpassing SES variables in importance. It is especially true where the influx of immigrants tends to be from countries which have a different language, such as Turkish immigrants moving to Germany versus Irish immigrants moving to the United States (Entorf & Minoui, 2005; Schnepf, 2007; Song & Róbert, 2010). Generally, students who were born in the receiving country or speak the national language tend to do better in assessments than students that either do not speak the language at home and/or were not born in the country of testing. The same naturally applies to the birthplace of the parents because those students whose parents were born in the receiving country are classified as native. One important exception is in those countries that are traditional immigrant countries and tend to attract highly skilled immigrants; in such countries a parent born outside of the country is positively associated with achievement scores, although SES indicators are more important than place of birth in general (Entorf & Minoiu, 2005). Therefore, individual circumstances of immigration are essential to understand when interpreting results of this research. Characteristics of the sending and receiving countries have found to provide insights in understanding the immigrant achievement gap, but are less widely studied than student background measures. All of these variables, however, seem to have important nuances across ethnicities, generations, SES, and gender, which require researchers to understand well the populations being investigated. Reporting distributions of immigrant students for these variables as well as differing functions across these variables (such as high versus low SES girls or Asian boys versus Latino boys) may be very helpful in better understanding the gap. Further, as it has already been stated, understanding migration patterns is key in understanding which variables to test and how to interpret the data. International reports from multilateral organizations such as the OECD are very helpful here. Finally, evidence based on qualitative studies provides extremely useful insights. It suggests functions based on variables that are not easily quantified. For example, this review has shown that there is much that schools can do to either help or hinder students’ success. One of the commonly cited findings is the role adult mentors can play in a young immigrant’s life by stepping in and fulfilling roles that parents cannot due to an inability to speak the language of the country or a lack of understanding of the education system. Other important variables are gender expectations and lack of legal status. Girls are often held to different standards than boys, a fact

that may have both positive and negative outcomes for girls. Lack of legal status may deny students opportunities if they are denied social services in the receiving country such as education, but also health and economic services. Table 2.2 incorporates the cross-national themes presented in this chapter into Suárez- Orozco and Suárez-Orozco’s (2000) conceptual framework. Once more, this has two goals - to connect the immigrant literature based in the United States to the cross-national literature, and to strengthen the theoretical understanding of the immigrant student experience. It is evident that not all of Suárez-Orozco and Suárez-Orozco’s (2000) themes are represented in this literature review and vice versa. The themes from this review not discussed in Suárez-Orozco and Suárez-Orozco’s conceptual framework are ‘Generation gaps’, ‘Country of origin’, ‘History of immigration of receiving country’, ‘Recruitment policies of receiving country’, and ‘Involvement and academic decisions’. Nevertheless, while they may not fit neatly into Suárez-Orozco and Suárez-Orozco’s framework, one can argue that ‘Country of origin’ may fit into the ‘Race’ category if it is re-conceptualized as ‘Race/ethnicity/culture’, as it more or less involves the attributes that a student possesses due to the country from which they originate. Similarly, Suárez-Orozco and Suárez-Orozco suggest that one of the variables of the host culture that affects student outcomes is public opinion about immigrants. The themes ‘History of immigration of receiving country’, and ‘Recruitment policies of receiving country’ could be placed into that category as both affect immigrants on a macro-level scale. Parental involvement, a topic discussed in this review under the theme ‘Involvement and academic decisions’ is not explicitly discussed in Suárez-Orozco and Suárez-Orozco’s framework but could fit into the ‘Family Cohesion’ category. Finally, differential outcomes of the same group across generations is not presented in Suárez-Orozco and Suárez-Orozco’s framework (although it is discussed in their chapter), but could be conceptualized as an incoming resource of the student in that the generation to which he or she belongs may have an effect on academic outcomes. In the same vein, this review does not explicitly discuss a number of variables that Suárez-Orozco and Suárez-Orozco cover. These include occupational opportunities, neighborhood safety, transnational contacts, language of origin maintenance, contact with members of country of origin, TV/radio exposure, race, and extracurricular activities such as sports or work. These are important themes. The perceived (and actual) opportunities provided to immigrant students upon receiving an education weighs on students’ notion of the importance of

school. Also, their ability to secure a job and perform well are important factors in their success. Although discussed in the sense of belongingness and identification to an ethnic group, this review perhaps does not sufficiently stress the important influence that maintaining contact with kin in and from the country of origin has on the adaptation and well-being of immigrant students. This also includes maintenance of native language, as it allows for that connection to prevail. Further, how the student perceives that their native language is viewed by the host society can also have important effects on their well-being. Next, although the influence of neighborhoods is discussed in the review, their safety is not, and it is undoubtedly an important determinant of schooling outcomes. Finally, race is an interesting category because the term itself is rarely used with the same connotation across countries. For this reason, it may be best conceptualized in future discussions under the category of ‘Culture’, as it is in this review. Conclusion This chapter has synthesized the existing evidence on cross-national research of immigrant student achievement. It has delineated factors that have been widely used in the literature as well as the respective findings. While no single function seems to exist in explaining immigrant achievement gaps, important themes have emerged. Specifically, the background of the student seems essential to control for in most countries. The levels of education, language, and SES of the family, for example, are important descriptors of immigrants. It is therefore likely intuitive that these variables are important in understanding immigrant student achievement. Furthermore, this chapter has referenced Suárez-Orozco and Suárez-Orozco’s (2000) multilevel conceptual framework and has merged into it the themes from this review to form a cross- national framework that is applicable to immigrant students’ experiences across the world. As Suárez-Orozco and Suárez-Orozco aptly note, the variables or categories in the framework “are the major vectors that structure the schooling experiences and outcomes of immigrant youth” and the framework should therefore stress the need to view immigrant students’ experience as multidimensional and fluid. This study specifically looks at a population of fourth graders. The literature discussed in this chapter overwhelmingly refers to adolescents. Therefore, although the hypotheses for the next section of the study are based on existing findings, it is possible that different patterns will emerge because the population is younger. For example, the literature on age of entry and on the ‘vulnerable age hypothesis’ both suggest, albeit inconclusively, that younger students adapt more

easily to new environments and fare better academically than those students who enter a receiving country at an older age. This may result in smaller or no gaps for fourth graders in different countries. Moreover, it may result in different relationships between the factors within the aforementioned themes and achievement. For example, on one hand, because children’s social influences are mostly dominated by their home environment and family, it is possible that background factors may increase in importance. On the other hand, because they are very impressionable, they may be more influenced by school factors. This study aims to begin laying the groundwork in the understanding of young immigrant students’ achievement by examining both differences and similarities with the literature that focuses on their older counterparts. Future studies may continue to consider important background variables of the student, the social context of the school and the country, but also delve a bit deeper into functions of education for immigrant students. This review has demonstrated that immigrant students are not all the same, and fourth graders are no exception. For example, there are different functions for adolescent immigrants across different levels of SES or achievement. Some evidence presented in this chapter begs the question, are there larger gaps at the lower end of the distribution than at the higher end? Do some types of instruction work better for immigrants than natives? Does school matter more for them? Some evidence exists of differential school effects for immigrants versus natives (Pong & Hao, 2007). Most important is the understanding that the experiences and outcomes of immigrant students cannot be prescribed according to a neat formula that includes only a few of the variables discussed in the framework. The conceptual framework must be viewed as a system in which the student is the center. Exploring the answers to these questions may allow researchers to paint a more accurate picture of immigrant student achievement. Moreover, it may allow the identification of policy factors that may be manipulated to increase the likelihood that immigrant students will receive a better education. This, after all, is the most promising route for education researchers who want to effect change through research. The study of immigrant student achievement, a neglected area in the study of immigrants (Suárez-Orozco & Suárez-Orozco, 2000), is important as it has the potential to contribute to the general understanding of the immigrant experience. It is an issue that needs increased attention, as this chapter’s review has found via the Literature Search that most journals and special issues dedicated to the topic of immigration cover relatively little to no material on immigrant student achievement. Not paying attention to this issue, as immigration

worldwide continues to rise, may create a situation where generations of immigrants become a permanent part of the underclass, or “locked into low-skill service jobs without much promise of status mobility”, and in which societies accept a permanent system of inequality (Suárez-Orozco & Suárez-Orozco, 2000, p. 18). Such are the warnings of theorists who propose downward assimilation of immigrants as a real threat to societies (Portes, Fernández-Kelly, & Haller, 2009).

CHAPTER 3

METHODS

Research Questions This chapter describes the quantitative and second section of the current study. It utilizes the TIMSS and PIRLS datasets to analyze the achievement of fourth graders, both immigrant and native. The literature presented in Chapter 2 forms the basis for the hypotheses, to the extent the datasets include key factors suggested as influential. As previously mentioned, they are based on literature that overwhelmingly examines adolescent immigrants and thus will strengthen existing literature on immigrant student achievement by increasing existing understanding of factors that influence young immigrant students’ achievement. This portion of the study explores the following research questions: 1) Is there evidence of an immigrant achievement gap for fourth graders in mathematics and reading? 2) Does the immigrant achievement gap vary across schools and across countries, controlling for student-level variables? Does that variability remain significant after teacher, school, and country predictors are entered in the model? 3) Is the gap generally homogeneous across levels of socioeconomic status, language, generation, and achievement? Following previous research presented in the literature review, the author expects to find the following: a significant mean difference between native and immigrant students in mathematics and reading outcomes (hereafter referred to as the ‘immigrant achievement gap’). The author also expects that the immigrant achievement gap will vary across schools and countries, and that teacher, school, and country characteristics will be useful in predicting unexplained variance. Next, the author expects to observe the following between country-level variables and the immigrant achievement gap: a negative relationship with Gross Domestic Product (GDP) showing larger gaps in countries with lower GDP, a positive relationship with Gini showing smaller gaps in countries with lower Gini coefficients, a negative mean difference showing larger gaps in countries with a more recent history of immigration, and a negative mean difference showing larger gaps in countries with more exclusionary policies towards immigrants. Finally,

the author expects that the immigrant achievement gap will vary across levels of SES, language, generation, and achievement: highest in lower levels of SES, higher for immigrant students who do not speak language of testing, highest for the first generation, and highest at lower levels of achievement. Conceptual Framework For the purpose of this analysis, immigrant is defined as a student not born in the country of testing, and is a variable derived from a question in both datasets that asks the student whether or not they were born in the country of testing (“yes=0” or “no=1” question). The outcome variable used for these analyses at level 1 is the students’ reading or mathematics scores. Because the goal of the second section of this study is to better understand the immigrant achievement gap, the outcome used at level 2 is the mean difference between native and immigrant students in mathematics and reading outcomes, and the outcome at level 3 is the mean immigrant achievement gap for the average school. In technical terms, this model aims to account for the unexplained variance on the immigrant achievement gap by controlling for student-, school-, and country-level variables. The conceptual framework designed by Buchmann and Hannum (2001) is useful in understanding this model. Although the discussion that accompanied their framework is not directly related to the study at-hand, the framework is very useful in understanding the logic behind this study. The logic supporting the current study is well represented in the framework on Figure 3.1 because this study considers macro-structural forces (e.g., Gini coefficient, history of immigration), family and school factors (e.g., immigrant status, available resources in school), and their effect on student achievement. These sections are highlighted in blue to emphasize that although this study’s model is thorough, it deliberately excludes other important factors. Further, the relative contribution of each set of factors varies depending on the country being analyzed as well as the specific population of students. Therefore, and because no study has successfully established a function that is invariant across samples by which to predict the contribution of school and background factors on achievement, this study does not aim to constrain any relationship as positive, negative, or zero. Further, because it is also out of the scope of this study, it does not test whether or not the associations are reciprocal. While multivariate models that utilize structural equation modeling techniques may test bi-directionality, the current study is interested first and foremost on the influence of the aforementioned variables on the immigrant

achievement gap, not vice versa. Thus, the adopted model is a simple model that tests the relative contribution of school and background factors in the explanation of the immigrant achievement gap of fourth grade students. Data This section details the datasets that are utilized in this study, beginning with a general description of both assessments followed by a specific outline of both the TIMSS and the PIRLS. Each section contains an overview of country selection for each dataset, an outline of the variables utilized, and a description of data preparation. Overview. The Trends in International Mathematics and Science Study (TIMSS) is an assessment of mathematics and science implemented by the International Association for the Evaluation of Educational Achievement (IEA) every four years. The goal of TIMSS is to evaluate achievement in science and mathematics and the context in which learning occurs across participating countries. The last available data, from the 2007 implementation, which sampled fourth and eighth graders, is used for this study. The Progress in International Reading Literacy Study (PIRLS) is an assessment of student proficiency in reading comprehension and of the context in which children learn to read. Like the TIMSS, it is implemented by the IEA with the goal to evaluate achievement and the context in which learning occurs across participating countries. However, it is implemented every five years and only to fourth graders. The last available data, from 2006, is used in this study. The TIMSS fourth grade assessments were implemented in 37 countries and 7 benchmarking participants (all of which are cities, states, or provinces, not countries; Mullis, Martin, & Foy, 2008) while the PIRLS was implemented in 40 countries (see Country selection for overview of which countries were included in this study; Mullis, Martin, Kennedy, & Foy, 2007). Data Collection. Data collection involved questionnaires for principals and teachers, and tests and questionnaires for students. For the PIRLS, parents also answered questions about students. Sampling. Sampling occurred in a two-stage stratified cluster design where schools were selected in the first stage, and whole classrooms within those schools in the second stage. Schools were selected with probability proportional to size, while classrooms were generally selected with equal probability. This is not the equivalent of a Simple Random Sample (SRS) in which every child has an equal probability of being sampled. For this reason, analyses using

TIMSS or PIRLS that aim to make inferences about a general population (as opposed to only the students sampled) must employ weights and proper variance estimation procedures. The weights are the inverse of the probability of selection for a student in a country and account not only for characteristics of the sample but also for the selection procedure – the stratification or disproportional sampling of subgroups, and adjustments for non-response – so that the selection probability for each student is known. Each dataset contains three types of weights – school, classroom, and student, the latter being the product of the other two weights. The variance estimation procedures adjust for the sampling error associated with sampling, the sample size, and the variability within the population of sampled students. Because this is not at SRS, the uncertainty related to the sampling procedure is higher because clusters sampled (i.e., schools and classrooms) are likely to contain students that are much more similar to each other than students randomly sampled from a given population (Martin, Mullis, & Kennedy, 2007; Olson, Martin, & Mullis, 2008). In other words, students sampled by clusters are less representative of a larger population than students sampled randomly and independently from the clusters in which they exist. The variance estimation procedures utilized in the TIMSS and PIRLS are Jackknife Repeated Replication (JRR), which estimates the standard error for any statistic derived from the data as well as adjusts for the variation between the five plausible values (see sections on Outcome scores as well as Software for additional information). Sample. Students are sampled in the equivalent of the fourth year of formal schooling, which in most countries was fourth grade, ensuring that the average age of tested children was no lower than 9.5. These objective measures are put in place due to the comparative nature of the TIMSS and PIRLS studies, so as to assess “achievement at the same point in schooling across countries” (Mullis, Martin, Kennedy, & Foy, 2007, p. 28). However, because in some countries students start school at a younger age, some students were assessed at fifth grade as their fourth graders would be too young for the assessment. Therefore, referring to this study as a study of fourth graders, much like the PIRLS and TIMSS developers do, is for ease of comparison across countries. Almost all countries considered their entire population of fourth graders as eligible for the study, typically excluding no more than 5% of their population by choosing to leave out hard to reach schools or resource-intensive to test such as small or remote schools. Most countries sampled 150 schools and one to two classrooms within each school, explicitly designed so that each country would have at least 4,000 students sampled for PIRLS and 4,500 for TIMSS.

Therefore, this allows for a multilevel analysis of students nested within schools only, because some schools only sampled one classroom per school (see Methodology). Outcome scores. The TIMSS and PIRLS are designed so that not every student is administered all the available questions in order to be able to implement a wider set of questions without overwhelming any one student (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). For this reason, items on each test are assigned to test booklets according to a matrix sampling plan, and outcome scores are in the form of plausible values. Specifically, developers of the tests employ Item Response Theory (IRT) to estimate latent proficiency parameters for each examinee, generate a distribution of possible scores in combination with background information, and draw five random values from each examinee’s distribution (Martin, Mullis, & Kennedy, 2007; Olson, Martin, & Mullis, 2008). This procedure generates five plausible values, which compose the outcome scores for both assessments. While content area scores are available for both the TIMSS and PIRLS, only the overall mathematics and reading scores are utilized in this study, each standardized by developers to a mean of 500 and a standard deviation of 100. Reliability of surveys. For PIRLS, reliability for most countries as measured by Cronbach’s alpha was between .80 and .90, with the median reliability across all countries at .88. For TIMSS, reliability for most countries as measured by Cronbach’s alpha was between .80 and .90, with the median reliability across all countries at .83. The coefficient is the median KR-20 reliability across the 12 test booklets and PIRLS reader for PIRLS, and across the 14 test booklets for TIMSS (Mullis, Martin, Kennedy, & Foy, 2007; Mullis, Martin, & Foy, 2008). Software. Three types of software are utilized in this study. First, the International Data Base (IDB) Data Analyzer (IDB Analyzer, 2009), a plug-in for SPSS developed by the IEA Data Processing and Research Center, is used to merge student files with teacher, parent, and school files. Further, it is used in conjunction with SPSS (SPSS, 2008) to compute basic descriptive statistics for immigrant students, their teachers, classrooms, and schools. The IDB Data Analyzer is able to work with SPSS to account for the complex design of the TIMSS and PIRLS (see section on Data). Second, HLM software (Raudenbush & Bryk, 2002) is utilized for the multilevel part of the analysis. TIMSS. Country selection. For this study, countries were selected according to a combination of sources. First, they were selected by following migration reports produced by the United Nations

(UN, 2002) and the OECD (2003), which identify countries with the largest stock, percentage, and inflow of immigrants. The reports were selected purposefully so that they reflected migration inflows before the year of testing for TIMSS (i.e., 2007). More recent reports were thus not reviewed because they would not reflect population changes that would have affected the samples of TIMSS for the year of interest. From these, only the top immigrant countries found in the TIMSS dataset with the largest stock, highest percentage, and inflow of immigrants were selected for consideration. Then, those countries listed across each of the three categories at least two times were selected for this analysis. Next, the TIMSS data was evaluated to ensure that the immigrant subsample composed at least 10% of the entire sample. Table 3.1 shows the countries that were evaluated according to each report and the final countries selected for this study. Table 3.2 shows the selected countries with their corresponding valid sample sizes and the percentage of immigrants within that sample. In total, seven countries and one province were selected for analysis. For the United Kingdom both England and Scotland were selected as they are the only two countries in the Kingdom that participated in TIMSS; for the country of Canada, the province of Ontario was selected due to not only having the highest number of immigrant students of all the Canadian provinces sampled, but also because it receives fifty percent of the immigrant population in that country (Citizenship and Immigration Canada, 2007). The logic behind the selection of countries based on external reports follows the cross- national nature of this study. Other studies particularly interested on immigrant students have selected countries either based on history of immigration, or sample size from the dataset of interest. First, the chosen selection process did, in fact, include the four traditional settlement countries (US, Canada, New Zealand, Australia). Second, while the latter method is valid, the intent of this study is to find cross-national trends on immigrant students and as such, selecting countries based on the students sampled for the TIMSS may risk giving more attention to countries that do not have a large population of immigrants according to population estimates, but that happened to sample classes with many immigrant students. For example, a list of the countries from TIMSS with the top immigrant student populations is not representative of the top immigrant countries according to the selection process (based on international reports) employed above (see Table 3.3). As noted above, sample sizes of each selected country were evaluated in order to ensure that the countries selected according to the external reports were, in fact, immigrant countries.

Variables. The variables were selected according to the hypotheses and guided by the factors discussed in the literature review. Variables were selected by the author if thought to be representative of factors suggested as influential by the literature. For example, whether the school offers enrichment mathematics was selected because it is one of various variables that serves as a proxy to examine the quality of the schools that immigrant students attend. The literature would suggest immigrant students are less likely to attend high quality schools. To provide another example, some literature suggests immigrant students attend less safe schools than native students; this explains the selection of the variables related to school climate and being safe at school. For the multilevel analysis, variables in the school level are derived from principal and teacher questionnaires, while variables at the student level are derived from student questionnaires (see Methodology). The majority of these variables are not tested in the multilevel model; rather, they are utilized for the descriptive part of the study in order to better understand the context of immigrant students’ home and school lives. Those that are directly tested in the multilevel model are marked with an asterisk (see Table 3.4). For the regression analyses, all categorical variables were coded to k-1 dummy variables. Preparing data for analysis. The weight used for the descriptive analysis was the total student weight as student data was merged with teacher, and school data separately to perform the analyses. For the multilevel analysis, student data composed the first level, while teacher- and principal-derived data composed the second level, and the country data composed the third level. Teacher data was thus aggregated at the school level for this part of the analysis. The weights employed in the multilevel analysis were a decomposition of the total student weight. Two sets of weights were computed, one for the student level and another for the school level. The student level weight was composed from the class and student factors and their respective adjustments. The school level weight was simply a product of the school factor and the school adjustment, which are the appropriate weights for multilevel models using student and school variables (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). The country level does not have a weight, as their selection into the sample of countries participating in TIMSS was not random, resulting in inferences made at the country level being only applicable to countries selected in this study (see Limitations). In order to link students to schools, their school ID (IDSCHOOL), unique within but not across countries, was concatenated with the country ID (IDCNTRY) to create a unique school ID for every child in the dataset (Foy & Olson, 2009). Concerning the

variance estimation, the JKK procedure was performed for the descriptive analyses when using the IDB Data Analyzer in conjunction with SPSS. However, this procedure cannot be done when using HLM as it is not part of the software’s platform. Yet, this is not a limitation of this study, as: In HLM analysis there is no need for replication, since [one is] actually computing the coefficients within each of the clusters, and then summarizing these. This is analogous, although not the same, as replication where [one takes] one or more clusters out and recalculates [the] statistics. (Eugene Gonzales, personal communication, October 19, 2011). Finally, descriptive analyses were performed on all variables to assess the extent of missing values. For the multilevel analyses, if more than ten percent of the data were missing for any one variable, that variable was considered either for mean imputation or eliminated from the analysis. No variable met this criterion for the TIMSS analysis (see Appendix B). Otherwise, missing data was handled via listwise deletion. No imputation was performed for the descriptive part of the analysis. PIRLS.

Country selection. For this study, countries were selected according to a combination of sources. First, countries were selected following migration reports by the United Nations (UN, 2002) and the OECD (2003), which identify countries with the largest stock, percentage, and inflow of immigrants. The reports were selected purposefully so that they reflected migration inflows before the year of testing for PIRLS (i.e., 2006). More recent reports were thus not reviewed because they would not reflect population changes that would have affected the sample of PIRLS for the year of interest. From these, only the top immigrant countries found in the PIRLS dataset with the largest stock, highest percentage, and inflow of immigrants were selected for consideration. Then, those countries listed across each of the three categories at least two times were selected for this analysis. Next, the PIRLS data was evaluated to ensure that the immigrant subsample composed at least 7% of the entire sample. This was lower than the criteria for TIMSS because the immigrant sub-samples were much smaller for PIRLS and utilizing the criteria of 10% would leave only two countries for the analysis. Table 3.5 shows the countries that were evaluated according to each report and the final countries selected for this study. Table 3.6 shows the selected countries with their corresponding valid sample sizes and the percentage

of immigrants within that sample. In total, four countries and one province were selected for analysis. For the United Kingdom, England was selected as it was the only country in the Kingdom that participated in PIRLS and fulfilled the criteria of having a sufficient immigrant sample size; for Canada, the province of Ontario was selected because it receives fifty percent of the immigrant population in that country (Citizenship and Immigration Canada, 2007). The logic behind the selection of countries based on external reports mirrors the selection for TIMSS and follows the cross-national nature of this study.

Variables used in analysis. The variables were selected according to the hypotheses and guided by the factors discussed in the literature review. Variables were selected by the author if thought to be representative of factors suggested as influential by the literature. For example, whether the school offers enrichment reading instruction and time spent on reading instruction were selected because they were two of various variables that served as a proxy to examine the quality of the schools that immigrant students attend. The literature would suggest immigrant students are less likely to attend high quality schools. Teacher variables mostly serve this end as well. To provide another example, some literature suggests lower parental involvement for immigrant students; this explains the selection of the variables related to home-school involvement and parents’ engagement on reading activities with the child. For the multilevel analysis, variables in the school level are derived from principal and teacher questionnaires, while variables at the student level are derived from parent and student questionnaires (see Methodology). The majority of these variables were not tested in the multilevel model; rather, they were utilized for the descriptive part of the study in order to better understand the context of immigrant students’ home and school lives (see Table 3.7). Those that were directly tested in the multilevel model are marked with an asterisk. For the regression analyses, all categorical variables were coded to k-1 dummy variables. Preparing data for analysis. The weight used for the descriptive analysis was the total student weight as student data was merged with parent, teacher, and school data separately to perform the analyses. For the multilevel analysis, student data composed the first level, while teacher- and principal-derived data composed the second level, and the country data composed the third level. Teacher data was thus aggregated at the school level for this part of the analysis. The weights employed in the multilevel analysis are a decomposition of the total student weight. Two sets of weights were computed, one for the student level and another for the school level.

The student level weight was composed from the class and student factors and their respective adjustments. The school level weight was simply a product of the school factor and the school adjustment, which are the appropriate weights for multilevel models using student and school variables (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). The country level does not have a weight, as their selection into the sample of countries participating in PIRLS was not random, resulting in inferences made at the country level being only applicable to countries selected in this study (see Limitations). In order to link students to schools, their school ID (IDSCHOOL), unique within but not across countries, was concatenated with the country ID (IDCNTRY) to create a unique school ID for every child in the dataset (Foy & Olson, 2009). Concerning the variance estimation, the JKK procedure was performed for the descriptive analyses when using the IDB Data Analyzer in conjunction with SPSS. However, this procedure cannot be done when using HLM as it is not part of the software’s platform. Yet, this is not a limitation of this study, as: In HLM analysis there is no need for replication, since [one is] actually computing the coefficients within each of the clusters, and then summarizing these. This is analogous, although not the same, as replication where [one takes] one or more clusters out and recalculates [the] statistics. (Eugene Gonzales, personal communication, October 19, 2011). Finally, descriptive analyses were performed on all variables to assess the extent of missing values. For the multilevel analyses, if more than ten percent of the data were missing for any one variable, that variable was considered either for mean imputation or eliminated from the analysis. Four variables met this criterion for PIRLS (see Appendix B for how they were handled). Otherwise, missing data was handled via listwise deletion. No imputation was performed for the descriptive part of the analysis. Importantly, SES variables were not considered as student-level controls for the multilevel reading models. Because the multilevel model in this study pools country estimates into a cross-national estimate, and because the United States did not participate in the survey from which the SES variables were derived, including them in the model would have eliminated all the United States data via listwise deletion. This would have meant a reduction of over 50% of the sample for the multilevel reading model (see Appendix B) and would have limited inferences made to only the four remaining countries selected for PIRLS (Canada (Ontario),

Israel, New Zealand, and England). Because one of the central aims of this study is to make cross-national inferences about the immigrant achievement gap, the author believed the inclusion of the United States as an important country of immigration surpassed that of the inclusion of student-level SES variables. To the extent that the exclusion of these variables as student-level controls biases the model through omitted variable bias, the final model chosen for reading may be misspecified. The author acknowledges this as a limitation of the reading model. Countries selected A note must be made about the countries that were selected for analysis, so that interpretations of the results are done with context in mind. The process through which the countries were selected allows general inferences to be made about what may be termed as ‘immigrant countries’. However, these immigrant countries are not alike. According to a report by the OECD (2006), immigrant countries may be distinguished in the literature into four categories: traditional settlement countries, European states with post-war labor recruitment, European states with migration related to their colonial histories and post-war labor recruitment, and new immigration countries. Table 3.8 categorizes the countries selected for this study according to those four groups. The classification is based on characteristics of the immigration histories of each country. Whereas the traditional settlement countries have, since their formation, accepted a large number of immigrants and continue to do so, the other three groups of countries have less experience accepting immigrants. This has implications on the policies each country adopts toward immigrants. Traditional settlement countries are more experienced in matters of immigration, and therefore have well-established recruitment policies, as well as policies of integration, all which create a more streamlined and effective system of immigration. As Buchmann and Parrado (2006) have argued, these countries also tend to have inclusionary policies toward immigrants. On the contrary, the other three groups have less experience and thus vary largely in their approach toward immigrants. Therefore, not only does each type of country attract a specific type of immigrant, but through their immigration policies, each has differing effects on immigrants themselves, including on their academic achievement. European states with post-war labor recruitment first transitioned into being immigrant countries after World War II, when they recruited low-skilled laborers to compensate for labor shortages, and expected these immigrants to be temporary migrants. This mentality still has

implications in the policies they adopt toward immigrants, as they tend to be less inclusive toward their integration into the native culture than traditional settlement countries. The third group of countries differs from the second only in that their colonial pasts tend to attract immigrants from former colonies and are thus more likely to speak the native language. Both of these groups have recently adopted policies of recruitment that model those of traditional settlement countries, yet still largely attract low-skilled labor. Finally, new immigration countries have become so due to various reasons including emigration (i.e., return migration). For the countries in this study that fall into this category, ethnic-based as opposed to economic motivations attract immigrants (OECD, 2011). Specifically, while Ukraine receives immigrants from other former-Soviet Union territories, Israel receives immigrants who are Jewish from all parts of the world. To better understand the results presented in the next chapter, Table 3.9 lists the top origin countries of immigrants for all the countries selected during the period of time in which the TIMSS and PIRLS utilized in this study were conducted (2005-2008). While the country of origin of individual immigrant students is not available as a variable for either TIMSS or PIRLS, it is certain that their origins span many more countries than those listed below. However, considering the fact that this is a cross-national study, the results are representative of a wide range of countries, as the top origin countries listed below represent virtually all continents in the world. Therefore, while the lack of weights at the country level limits generalization of the findings to only the countries selected for this study and even though the countries selected for this study are not all-encompassing of all the countries in the world that may be classified as ‘immigrant countries’, findings are representative both of all four categories of immigrant countries as well as of many origin countries. Methodology A multilevel model was estimated assuming students are nested within schools nested within countries. Although information on teachers and classrooms is collected, the data does not allow for additional sub-levels of nesting because some schools only sampled one classroom per school, equating the classroom level to the school (Martin, Mullis, & Kennedy, 2007; Olson, Martin, & Mullis, 2008). Further, test developers do not recommend adding a teacher level in multilevel analyses as the teachers who respond to questionnaires in TIMSS and PIRLS respond with the specific sampled students in mind, and are not representative of other teachers in other

schools (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). Therefore, while student-derived variables were treated as characteristics of the student, principal- and teacher-derived variables composed the school level, as per test developers’ specifications (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). Variables for the country level are not existent in the data and were gathered by the author from international reports and other external sources. The analysis first begins with a descriptive analysis of immigrant students, their teachers, classrooms, and schools. The aim is to better understand the context in which fourth grade immigrants live and go to school, as less is known about young students in the immigrant student achievement literature. This part of the analysis aims to fill some of that gap. The analysis then proceeds to testing a three-level intercept- and slopes-as-outcomes model by employing hierarchical linear modeling. The student level estimates the immigrant achievement gap, first unconditionally and then by entering student-level predictors in the model as controls. The school level tests whether the immigrant achievement gap varies across schools, and which school-level predictors significantly explain variability in those differences across schools, after controlling for student-level characteristics. The third level examines variability across countries on the mean gap and whether country-level predictors help significantly explain that variability. Fully unconditional models were estimated in order to estimate the grand mean, its corresponding confidence interval, as well as the outcome variability at each level:

Level 1: Yijk = π0jk + π1jkIMMIGRANT1ijk + eijk

Yijk = π0jk + π1jkIMMIGRANT1ijk + π2jkSTUDENT2ijk + eijk

Level 2: π1jk = β10k + r1jk

π1jk = β10k +β11kSCHOOL1jk + β12kPEERS2jk + β13kTEACHER3jk + r1jk

Level 3: β10k = γ100 + u10k

β10k = γ100 + γ101GDP1k + γ102GINI2k + γ103HISTIMM3k + γ104POLICY4k + u10k

Level 1 represents the student level, where Yijk is the outcome (mathematics or reading) for student i in school jk, π0jk is the intercept or the expected achievement on the outcome for native students (coded 0) in school jk, controlling for the effect of the covariate. π1jk represents the immigrant achievement gap or the mean difference on the outcome between immigrant (defined as not having been born in the country of the test application) and native students, and π2jk

represents the effect of the student-level controls in the model. eijk is the error term (residual) associated with student i in school jk, controlling for the effect of the covariates. Level 2 represents the school level, where β10k is the mean achievement difference between immigrant and native students across schools, β11k is the effect of a vector of variables (SCHOOL1jk) representing the relationship between principal-derived characteristics and the outcome (π1jk), and β12k is the effect of a variable (PEERS2jk) representing the relationship between the mean school achievement on the test and the outcome (π1jk). The subsequent vector (β13k) represents the effect of teacher-derived variables and the outcome (π1jk). r1jk is the unique effect of school jk on mean immigrant achievement gap, holding all vectors constant. Level 3 represents the country level, where γ100 is the intercept term or the grand mean, γ101 is the effect of GDP on the outcome

(β10k), γ102 is the effect of Gini coefficient on the outcome (β10k), γ103 is the mean difference between countries with and without a history of immigration, and γ104 is the mean difference between countries with exclusionary and inclusionary policies toward immigrants based on

Buchmann and Parrado’s classification (2006). u10k is the unique effect of country k on the mean immigrant achievement gap, holding all vectors constant. This model assumes eijk ∼ independently N(0,σ2) for i = 1,…,n students in school jk, j = 1,…,n schools and k = 1,…,n countries, and r1jk and u1k ∼ independently N(0, τ10). Predictors were entered into the model according to the research questions: 1) Is there evidence of an immigrant achievement gap for fourth graders in mathematics, and reading? First, immigrant student birth status was entered, in accordance with the first research question.

In the model, the immigrant achievement gap is π1jk and is quantified as the mean difference in outcome scores between immigrant and native students. Therefore, once it was entered first in the model, it signified the immigrant achievement gap, and the next level aimed to explain variability in this slope (mean difference) across schools by entering other predictors. Student- level controls were entered in the model in a stepwise model fitting technique with forward selection, thus considering the relative importance and statistical significance of each, so that only those that were most important were retained for level 2. This was done by examining their significance and variance explained after entering each set of predictors as follows:

a. Language b. Parents’ birth status c. SES indicators d. Sex 2) Does the immigrant achievement gap vary across schools and across countries, controlling for student-level variables? Does that variability remain significant after teacher, school, and country predictors are entered in the model? Next, an unconditional model was tested to assess whether or not the gap varied across schools. Then, predictors were entered in the model with the goal to explain that variability. Because many of the variables are categorical, entering all predictors of interest simultaneously would make interpretation of the model difficult. Therefore, a stepwise model fitting technique with forward selection was used to assess the relative importance of each predictor. Vectors were tested in the order they are in the model and only significant predictors were kept for the subsequent steps. Variability in the gap was evaluated before entering each of the subsequent vectors. Additional vectors were not entered in the model if there was no more variability to explain after controlling for the previous vector, as the gap would have theoretically been explained. A similar process was conducted for the third level, where country-level predictors were entered in a stepwise model fitting technique with forward selection. Unexplained variance was assessed after entering each predictor, only entering the subsequent predictor if significant variability was yet to be explained in the model. Multicollinearity was a concern for some variables. For this reason, correlations between the variables in each vector were assessed in order to determine if any of these variables should be eliminated from the model. Correlations higher than or equal to r=.80 were the threshold for elimination. The only sets of variables that were highly correlated were parent’s individual birth status with the constructed variable measuring both parents’ birth status5. For this reason, the author only entered the constructed variable in both models. 3) Is the gap generally homogeneous across levels of socioeconomic, language, generation, and achievement?

5 For TIMSS, these variables are AS4GMBRN, AS4GFBRN, with ASDGBORN (r=.84, .91); for PIRLS, these variables are ASBGBRNM, ASBGBRNF, with ASDGBRN (r=.88, .93).

Next, the analysis moved to examining level 1 only and without student-level controls in order to better understand how immigrant achievement varies across different levels of student characteristics. This was done in two ways. First, it was done by examining mean differences on mathematics and reading scores by comparing immigrant students separately, so as to assess where differences within the immigrant student population lie. Then, it was done by analyzing the immigrant achievement gap for different sub-groups of students – across SES, language, generation, and achievement. To test the latter characteristic, a categorical variable was created according to the four international benchmarks created by test developers indicating whether the student achieved an advanced (625-1000), high (550-624), intermediate (475-549), or low (0- 474) score on the outcome. This analysis more clearly describes how the immigrant achievement gap varies across these student characteristics independent of any other factors.

CHAPTER 4

RESULTS

This chapter first begins with a descriptive account of immigrant students, their teachers and schools. It focuses on differences between native and immigrant students across a number of variables (see section on Data). It verbally combines descriptive information for both TIMSS and PIRLS so as to provide a broader picture of these immigrant countries and the students within them. The chapter then moves to presenting the results of the multilevel mathematics and reading models, respectively. Descriptive Analyses An immigrant achievement gap, represented by the mean difference between scores of immigrant and native students on the respective assessment, exists in all but one country – Canada, in reading (see Tables 4.1 and 4.2, Figures 4.1-4.4). Overwhelmingly, it was significant and negative. Only in New Zealand did immigrant students perform higher in reading than native students. Thus, across these immigrant countries an immigrant achievement gap existed, indicating that, on average, native students outperformed immigrant students on both mathematics and reading. The magnitude of the gap was larger for mathematics than for reading and it was larger in non-traditional settlement countries (see Table 3.8) for both subjects (see Figures 4.2 and 4.4). The United States represented an exception as it had a relatively large immigrant achievement gap in comparison to other traditional settlement countries. The following section compares immigrant and native students based on their corresponding responses to the survey questionnaires. Although all the variables from Tables 3.4 and 3.7 were analyzed, the section only discusses variables for which there was a discernible difference between native and immigrant students. Descriptive information for all the variables is included in the form of tables in Appendix C.

Schools. More immigrant than native students attended schools with a larger proportion of both economically disadvantaged and language minority students. In addition, more immigrant than native students attended schools with medium to low attendance and with medium/low number of resources available. In some instances, the number of immigrant students who attended these schools was three (Canada (Ontario), US) to five (England) times the number

of native students who attended these schools+6. There were no clear patterns related to attendance in schools that offered either enrichment mathematics/reading instruction, or remedial mathematics, or had a high index of home-school involvement. In some instances, more immigrants attended schools offering these programs and services while in others more native students attend these schools. More immigrant students in Ontario, Israel and New Zealand+ attended a school in which a principal had a high perception of school climate. Otherwise, there was little difference between immigrant and native students on variables pertaining to services offered in schools. A larger number of native students had teachers who indicated their perception of school climate was high++. In general, more native students attended schools in which the principal+ or the teacher++ had a high perception of school safety.

Teachers. Overall, there were no discernible differences of teacher age, certification, whether or not they have a full teaching certificate+, work full time+, level of formal education, class size++, average number of years teaching altogether, or in fourth grade+, whether teacher feels well prepared on mathematics topics++, or time spent on mathematics as percent of total instruction time++, average time spent on either language or reading instruction per week in students’ classrooms+, and whether or not they scored high on the index of career satisfaction+. In some countries, more immigrant students had “higher-quality”7 teachers, whereas the opposite was true in other cases. For the most part, teachers of immigrant and native students did not differ in the extent to which they studied different areas of emphases, except for language learning. More teachers of immigrant students indicated they studied that as an area of emphasis+. In three countries (Canada (Ontario), Germany, Scotland), more teachers of immigrant students indicated their perception of adequate work conditions was high, and the opposite was true in three others (England, Ukraine, US), while the others (Australia, New Zealand) show no difference++. Students. More immigrant students indicated they sometimes or never speak the language of testing at home, although a number of native students indicated the same, highlighting the fact that many students born in the country of testing spoke a different language at home. Most native students spoke the language of testing before starting school+; yet, a large

6 +Only based on PIRLS data; ++Only based on TIMSS data. 7 The literature is inconclusive concerning these variables (see Literature Review). Therefore, the author urges the reader to make careful conclusions regarding whether or not having a younger/older, more/less experienced teacher et cetera signifies having a “high-quality” teacher.

majority (68%) of immigrant students fell under the same category, indicating many immigrant students arrived in schools speaking the language of testing+. A higher percent of native students indicated they have over 100 books at home. At times, almost twice as many natives as immigrants fell into this category, as is the case in Germany++. There was no discernible difference in the index of possessions between native and immigrant students++, or students’ average on the index of home educational resources+, except in New Zealand where more immigrant students had a ‘high’ average+. The United States had the largest differences with fewer immigrants reporting owning the items listed in the surveys. Although more immigrant students indicated that their fathers and mothers were born outside of the country of testing, a good number of natives indicated this as well. For example, about one fifth to one third of native students in these countries indicated their mother and/or father were not born in the country of testing. Furthermore, in a constructed variable measuring whether both parents were born in the country of testing, about 10 to 20% of native students in all countries had at least one parent who was not born in the country of testing. Across these immigrant countries, students immigrated in more or less equal proportions (~30%) between the age ranges of younger than age one, one to five, or older than 5 ++. In general, fewer immigrant students felt safe at school. In almost every country, more immigrants indicated to have a high positive affect toward mathematics++, although the percentages on average were very similar for both groups with over 65% of all students indicating a high positive affect. Across countries, there was no difference in scores of reading self-concept except in Canada (Ontario), and New Zealand, where immigrants scored higher. In contrast, in almost every instance, just as many immigrant as native students scored high on the index of student reading attitudes+. The exception was New Zealand and Ontario, where more immigrant students fell into that category. Yet in every instance, fewer immigrant students indicated having high self-confidence in learning mathematics. Parents8. In almost every instance, a lower number of immigrant students’ parents indicated students engaged in specific educational activities before starting school. Yet, in many cases, more parents of immigrant students indicated they or another adult in the home presently engaged in educational activities with the child every or almost every day. In all countries, more

8 Information is based on home surveys that were only administered for PIRLS and in which the United States did not participate.

native students’ scored high on the index of early home literacy activities, while more immigrant students attended ISCED level 09. On average, there was no difference in the length of time either group of students attended ISCED level 0. More fathers and mothers of immigrant students had acquired ISCED level 5A, which would be equivalent to a Bachelor’s degree in the United States. Also, a much higher percentage of immigrant students’ parents had an education level of university or higher, sometimes twice as many as native parents, as is the case in New Zealand. As a whole, more immigrant students’ parents’ highest occupation level was that of a professional, while in every instance fewer parents of immigrants students’ highest occupation level was that of a small business owner. Yet, fewer fathers of immigrant students were employed full-time. The same is not true of mothers, as there was no discernible difference between mothers of immigrant and native students. Finally, more immigrant students’ parents reported that their families are very or somewhat well off in comparison to other families, although the difference was small. Multilevel Analyses Before presenting the results for the mathematics and reading models, a couple of notes must be made. First, the extent of missingness was assessed before fitting any multilevel models. Appendix B presents the extent of missingness for the variables utilized in this section of the study. Because HLM software employs listwise deletion based on levels 2 and 3, the following analyses only consider those students for whom there was school data on any of the variables tested. Tables 4.3 and 4.4 present the descriptive statistics for all the variables tested in the multilevel models. Even after listwise deletion, the sample size for both TIMSS and PIRLS remained high. To further assist the reader in understanding the multilevel analyses, one additional note must be made. Because the immigrant achievement gap is a school-level phenomenon, in that no individual student him or herself can exhibit an immigrant achievement gap, only predictors at the school level can be used to predict the immigrant achievement gap. That being stated, by controlling for student-level factors, one can control for any shared variance that other student characteristics may have with immigrant students’ birth status. Yet, because the outcome is a test score on mathematics or reading, any shared variance is on the outcome itself, not on the immigrant achievement gap. Therefore, it is important not to erroneously interpret a decrease in

9 Preschool, nursery, kindergarten, early childhood facility.

significance of immigrant student status after entering other student characteristics in the model as those subsequent predictors ‘explaining the gap’ because the gap is not the outcome until level 2. It is, however, possible to say that immigrant student birth status and any other student characteristics share variance, are related, or have an association. Although it may be interesting to examine the immigrant achievement gap unconditionally in a multilevel format (i.e., without student-level controls), statistical adjustment of this sort is important for a couple of reasons: First, because persons are not usually assigned at random to organizations, failure to control for background may bias the estimates of organization effects. Second, if these level-1 predictors (or covariates) are strongly related to the outcome of interest, controlling for them will increase the precision of any estimates of organizational effects and the power of hypothesis tests by reducing unexplained level-1 error variance…(Raudenbush & Bryk, 2002, p. 111) For this reason, student-level controls were entered in the both the reading and mathematics models as a first step according to theory as indicated in the section on Methodology. The final mathematics and reading models differ somewhat (see Table 4.6 and 4.9). No teacher-derived variables significantly predict the outcome for either model. Similarly, most principal-derived variables did not predict the outcome. Next, once a level-2 model was established, the variance on the average immigrant achievement gap across schools was modeled. Country-level predictors were then entered in the model and kept only if they significantly predicted the outcome. The final models are presented on Tables 4.6 and 4.9. TIMSS Results. The largest percentage in variability (73%) of the gap lies at level 2, between schools; a smaller percentage (27%) lies at level 3, between countries. The immigrant achievement gap

(π1jk) significantly varies across schools and countries. On average, an immigrant achievement gap exists, evident by the significant mean difference between the scores of immigrant and native students, which corroborates the descriptive analyses from the previous section. While on average, immigrant students in these countries score 510 on overall mathematics, native students perform, on average, 39 points higher, controlling for other student characteristics. The student- level controls indicate that students who always or almost always speak the language of testing before starting school, and who come from more economically-affluent homes (as indicated by

the possessions index and number of books owned at home) score higher on average than their counterparts. Specifically, a student who is native, frequently speaks the language of testing at home and owns over 100 books scores a 549 on overall mathematics in comparison to its counterpart who scores between 474 and 490, on average, depending on how many books they own (see Table 4.7). This is a difference of about three quarters of a standard deviation. A significant and positive relationship between the number of possessions at home and the mathematics outcome indicates that for every one unit increase on scores in this index, the mathematics score increases 12 points. The difference between a student who owns all five possessions listed in the TIMSS questionnaire and a student who owns none would, therefore, signify a 60 point difference or about a third of a standard deviation. Added to the estimates made between more and less affluent students above, a native affluent student with all possessions could score a 609 in comparison to an immigrant less affluent student with no possessions who could score 474 and 490, on average. At the most extreme, this could signify a difference of over 1.75 standard deviations. In general, boys outscore girls by about 11 points. Next, school-level predictors indicate that the average immigrant achievement gap is larger in schools in which over 90% of students are native speakers of the language of testing; specifically, schools in which a majority of students are native speakers exhibit a gap that is 16 points higher than schools where fewer than 90% of students are native speakers. This is about a quarter of a standard deviation difference on mathematics scores. Next, a significant and positive relationship between a school’s mean score on mathematics and the immigrant achievement gap indicates that for every one unit increase in the mean mathematics achievement in a school, the gap decreases by about a point. In other words, the higher the mean achievement on mathematics of a school, the lower the immigrant achievement gap. Concerning country-level predictors, in comparison to immigrant countries which tend to have more inclusionary policies toward immigrants (i.e., traditional settlement countries) according to the literature (Buchmann & Parrado, 2006), students in Northern European countries, which tend to have more exclusionary policies, experience a higher gap than their counterparts. Therefore, controlling for all other variables in the model, the gap between immigrant and native students is 39 points in traditional settlement countries, 54 points in Northern European countries, and 100 points in Ukraine, which is the only country that is neither in Northern Europe nor a traditional settlement country. These amount to a difference of a half to

a full standard deviation on mathematics scores. Finally, although GDP exhibits a significant and negative relationship with the immigrant achievement gap, the coefficient is zero. Therefore, although theoretically those countries with lower GDP have a larger immigrant achievement gap, the difference per unit change is less than one. PIRLS Results. The largest percentage in variability (87%) of the gap lies at level 2, between schools; a smaller percentage (13%) lies at level 3, between countries. The immigrant achievement gap

(π1jk) significantly varies across schools and countries. On average, an immigrant achievement gap exists and is evident by the significant mean difference between the scores of immigrant and native students, thus corroborating the findings from the descriptive analyses. While on average, immigrant students in these countries score 520 on overall reading, native students perform, on average, 25 points higher, controlling for other student characteristics. The student-level controls indicate that students who spoke the language of testing before starting school and students whose parents are both native to the country of testing score 6 and 12-13 points higher on average than their counterparts respectively (see Table 4.9). Therefore, a native student who speaks the language of testing before starting school and whose parents are native scores about 545 on overall reading in comparison to its counterpart who scores about 502, on average. This is a difference equivalent to a half of a standard deviation. In general, girls outscore boys by about 12 points. Next, school-level predictors indicate that the average immigrant achievement gap is lower in schools in which 50% of students are economically-disadvantaged; specifically, the gap for schools in which a majority of students are economically-disadvantaged is 26 points lower than for schools that have fewer economically-disadvantaged students. This is a difference equivalent to a third of a standard deviation on reading scores. In fact, controlling for all other variables in the model, the gap is non-existent in schools in which a majority of students are economically disadvantaged. Next, a significant and positive relationship between a school’s mean score on reading and the immigrant achievement gap indicates that for every one unit increase in overall reading achievement of a school, the gap decreases by a point and a half. In other words, the higher the mean achievement on reading of a school, the lower the immigrant achievement gap. Finally, although GDP exhibits a significant negative relationship with the immigrant achievement gap, the coefficient is zero. Therefore, although theoretically those

countries with lower GDP have a larger immigrant achievement gap, the difference per unit change is less than one. A Closer Look at the Immigrant Achievement Gap This section specifically addresses the third research question. It further delves into understanding where the immigrant achievement gap originates and among which groups. First, it examines mean differences on mathematics and reading scores by comparing immigrant students separately, so as to assess where differences within the immigrant student population lie. In other words, it compares immigrant students to immigrant students. Then, the immigrant achievement gap is analyzed for different sub-groups of students. The results indicate that, for mathematics, immigrant students perform equivalently regardless of sex, or frequency with which they speak the language of testing at home. Immigrant students with either one or both immigrant parents score higher in comparison to immigrant students whose parents are both native. Yet, the opposite is the case for native students, who score significantly lower if either one or both parents are immigrants than if both parents are native. There are additional differences across immigrants’ achievement by other characteristics. For example, in comparison to immigrants who own over 100 books at home, those who own fewer books score lower (see Table 4.11). Further, those immigrant students who arrive in the country of testing either when they were older than five or younger than one year of age score lower than immigrant students who arrived between the ages of 1 through 5. The differences in achievement within the group of native students tend to be higher in magnitude, possibly suggesting greater disparities across these student characteristics for these students or that they are a less homogeneous group than immigrant students as a whole. Still, native students outperform immigrant students in every category. Finally, it is possible to assess the existence of generational gaps (see Table 4.11). Adopting the definition that 2nd generation students are defined as native students who have at least one immigrant parent and 1st generation students are immigrant students with immigrant parents, a generation gap is evident in mathematics where 2nd generation students outperform 1st generation students by 35 to 61 points. For reading, the results indicate that immigrant students perform equivalently regardless of whether or not they speak the language of testing at home before starting school or whether, at the time of testing, they always or sometimes/never speak the language of testing at home.

Although there is a difference between immigrant boys and girls, it is small. The same is true between immigrant students who have one or both immigrant parents in comparison to those who have two native parents. Similar to the results for mathematics, the differences in achievement within the group of native students are higher in magnitude, possibly suggesting greater disparities across these student characteristics for these students or that they are a less homogeneous group than immigrant students as a whole. In fact, for native students, the language they speak before starting school and the frequency with which they speak it at home signifies a difference in scores, with native and frequent speakers of the language of testing scoring higher than their counterparts. As previously noted, this is not the case for immigrants for whom language does not create a difference in scores. Still, native students outperform immigrant students in every category. Finally, it is possible to assess the existence of generational gaps (see Table 4.12). Adopting the definition that 2nd generation students are defined as native students who have at least one immigrant parent and 1st generation students are immigrant students with immigrant parents, a generation gap is evident in reading where 2nd generation students outperform 1st generation students by 17 to 20 points. Next, an analysis of the immigrant achievement gap for mathematics across sub-groups of students indicates the presence of an immigrant achievement gap across almost all groups analyzed (see Table 4.13). Overall, it is significant and negative indicating that regardless of group, immigrant students underperform in mathematics in comparison to native students. The gap is greater in magnitude for boys, students who own over 100 books at home, and students who always or almost always speak the language of testing at home, suggesting a greater disparity within groups of more advantaged students in comparison to their counterparts. The gap is also greater the more possessions a student indicates to have at home, being insignificant for those who own none and peaking at 1 and 4 possessions, where the difference in scores between immigrant and native students is between 50 and 61 points, respectively. Finally, there is no immigrant achievement gap for students who reach the two highest benchmarks in mathematics (i.e., scored at least a 550 in overall mathematics). The gap increases in magnitude the lower the students score, being highest between native and immigrant students who reached the low benchmark (i.e., scored no higher than a 474 in overall mathematics). Finally, an analysis of the immigrant achievement gap for reading across sub-groups of students indicates the presence of an immigrant achievement gap across most of the groups

analyzed (see Table 4.14). Overall, it is significant and negative indicating that regardless of group, immigrant students underperform in reading in comparison to native students. The gap is greater for girls as well as for students who spoke the language of testing before starting school, who more frequently speak it at home, and who have one immigrant parent. On the contrary, there is no immigrant achievement gap between students with two native parents while there is a small gap for those students with two immigrant parents. For students who did not speak the language of testing before starting school, the direction of the gap indicates that immigrant students score 16 points higher than their native counterparts who do not speak the language of testing before starting school. As previously noted, the gap is in the opposite direction for those who do speak the language of testing before starting school, indicating native students outperform immigrant students. Finally, there is no immigrant achievement gap for students who reach the three highest benchmarks in reading (i.e., scored at least a 475 in overall reading). The gap is existent but marginally significant and small for the remaining group of students who fall in the lowest category (i.e., scored no higher than a 474 in overall mathematics). Overall, this suggests that there is no difference across students when classified by the benchmark they reached. Research Questions Following previous research presented in the literature review, I expected to find a significant immigrant achievement gap for fourth graders in both subjects. I also expected that the immigrant achievement gap would vary across schools and countries, that teacher, school, and country characteristics would be useful in predicting unexplained variance and that characteristics of immigrant students’ peers and of the countries would significantly explain variance on the outcome. Next, I expected to observe the following: a negative relationship with Gross Domestic Product (GDP) showing larger gaps in countries with lower GDP, a positive relationship with Gini showing smaller gaps in countries with lower Gini coefficients, a negative mean difference showing larger gaps in countries with a more recent history of immigration, and a negative mean difference showing larger gaps in countries with more exclusionary policies towards immigrants. Finally, I predicted that the immigrant achievement gap would vary across levels of SES, language, generation, and achievement: highest in lower levels of SES, higher for immigrant students who do not speak language of testing, highest for the first generation, and

highest at lower levels of achievement. The results are presented with their corresponding research questions. 1) Is there evidence of an immigrant achievement gap for fourth graders in mathematics, and reading? An immigrant achievement gap is evident for fourth graders in both mathematics and reading for all countries except Canada (Ontario) in reading. In addition, the reading gap in New Zealand is positive and indicates that immigrant students outperform native students in that country. 2) Does the immigrant achievement gap vary across schools and across countries, controlling for student-level variables? Does that variability remain significant after teacher, school, and country predictors are entered in the model? The unconditional models for the immigrant achievement gap indicate that the gap significantly varies across all levels. Predictors were entered in the order they were in the model, following a stepwise model fitting technique with forward selection. Most predictors are not significant in either model. For mathematics, controlling for student-level variables, the mean score of schools on the outcome, the percent of students who speak the language of testing as their native language, the type of policy that a country has toward immigrants (Buchmann & Parrado, 2006), as well as the country’s GDP significantly predict the gap. For reading, controlling for student- level variables, the mean score of schools on the outcome, the percent of students who are economically disadvantaged, and the country’s GDP significantly predict the gap. Still, with one exception, variability at every level remains significant even after predictors are entered in the model suggesting that variance in the gap is not completely accounted for by the predictors tested in this study. For the reading model, variability at level 3 becomes insignificant once GDP is entered in the model, indicating this variable accounts for all the variance on the outcome at that level. Importantly, this signifies that 13% of the variance is accounted for by GDP in the reading model. 3) Is the gap generally homogeneous across levels of socioeconomic status, language, generation, and achievement? The gap is generally heterogeneous across levels of socioeconomic status, language, generation, and achievement. First, the results of this research question indicate that there are important differences within the group of immigrant students. For example, while there is no gender or language gap, the number of books owned, parents’ birth status, and generation signifies a

difference between immigrants in mathematics and reading. Second, when analyzing the immigrant achievement gap by sub-groups of students, although native students still always outperform immigrant students, the gap does not exist across the highest levels of achievement in either mathematics or reading. In other words, the gap is a phenomenon that exists primarily at the lowest levels of achievement for both mathematics and reading. For the most part, the gap exists across all other sub-groups of students, although it largely varies in magnitude. To specifically address the author’s predictions concerning this research questions, the gap was generally: lower in lower levels of SES, lowest for those students who do not speak language of testing, highest for the first generation, and highest at lower levels of achievement. Summary In this section of the study I aim to increase our understanding of the achievement gap between the performance of immigrants students compared to that of native students based on an analysis of the mathematics and reading scores of fourth graders across a number of immigrant countries. First, I descriptively analyze characteristics of immigrant students, their parents, their teachers, and their schools. Second, I seek to explain variability in the immigrant achievement gap through a cross-sectional multilevel analysis controlling for student-, school-, and country- level variables. I find an immigrant achievement gap for both mathematics and reading for most countries, as indicated by a significant mean difference on outcome scores between native and immigrant students. Overall, native students outperform immigrant students in both mathematics and reading. Further, with the exception of the United States, the gap tends to be smaller in traditional settlement countries than in the other types of countries. More immigrant students attend what may be termed ‘lower quality’ schools, or those that have a larger proportion of both economically disadvantaged and language minority students, medium to low resources, medium to low attendance, and in which their principals and teachers have a lower perception of school climate and school safety. Teachers of immigrant students do not seem to be very different on the measured characteristics than teachers of native students. Immigrant students tend to come from less advantaged backgrounds or have fewer resources at home. More immigrants indicate to not speak the language of testing at home and have foreign parents. However, for both subjects, a good number of native students indicate this as well. Immigrant students indicate to have positive attitudes toward mathematics and reading, yet indicate to have lower confidence in mathematics than their native counterparts. Concerning

the home environment of immigrant students, while fewer engage in specific educational activities before starting school, just as many or more than native students engage in educational activities in the present. Further, a larger percentage of immigrant students attend ISCED level 0. Finally, immigrant parents tend to be more highly educated than native parents as well as tend to have held a professional-level job as their highest occupation level. The multilevel analysis indicates that the immigrant achievement gap varies significantly across schools, and countries, with most of the variability found between schools. However, few of the variables tested in the model significantly predict this variability. In the end, the mean score of students’ schools on the outcome as well as characteristics of peers such as language spoken and SES significantly predict the gap, so that the gap is smaller for students attending schools with a higher average on the outcome, but it is larger for schools which have more advantaged students in terms of student composition such as language spoken and SES. At the country level, GDP significantly and negatively predicts the mean gap, although the coefficient is close to zero for both models. For mathematics, countries with more inclusionary policies (Buchmann & Parrado, 2006) toward immigrants exhibit a lower average immigrant achievement gap. Finally, a closer analysis of the immigrant achievement gap that analyzes differences within immigrant students as well as across different sub-groups of students suggests that it is far from homogeneous. On the one hand, immigrant boys and girls perform equivalently in both mathematics and reading; the same is true between immigrants who speak the language of testing before starting school and those who do not, and between those who speak it always/almost always or sometimes/never. In other words, there is no gender or language gap for immigrant students. On the other hand, the number of possessions in the home, parents’ birth status, and the age at which immigrants arrived does create differences within immigrants. As a side note, these differences are larger in magnitude within native students perhaps suggesting greater disparities between native students across these variables. When analyzing the magnitude and direction of the immigrant achievement gap across different sub-groups of students, it is evident that native students almost always outperform immigrant students with one exception; for those students who reached the highest benchmarks on both reading and mathematics, there is no immigrant achievement gap, possibly suggesting that the gap is a phenomenon only present for the lowest- performing students.

Most of the hypotheses are retained, finding a significant immigrant achievement gap for fourth graders in both subjects that varies across schools and countries. Further, teacher, school, and country characteristics are useful in predicting unexplained variance on the outcome, and characteristics of immigrant students’ peers and of the countries also significantly explain variance on the outcome. Importantly, however, most variables at level 2 and 3 are not significant in the model. While I expected to observe the following: a negative relationship with Gross Domestic Product (GDP) showing larger gaps in countries with lower GDP, a positive relationship with Gini showing smaller gaps in countries with lower Gini coefficients, a negative mean difference showing larger gaps in countries with a more recent history of immigration, and a negative mean difference showing larger gaps in countries with more exclusionary policies towards immigrants, only the hypothesis regarding GDP is retained for both models, while the final hypothesis (concerning type of policy) is retained only in the mathematics model. Finally, I had posited that the immigrant achievement gap would vary across levels of SES, language, generation, and achievement. This is retained, although the direction of the prediction was incorrect in two instances. While I had predicted that the gap would be highest in lower levels of SES, and higher for students who do not speak the language of testing, the opposite is the case. The other two predictions are correct, finding the gap is highest for the first generation, and highest at lower levels of achievement.

CHAPTER 5

DISCUSSION

The primary aim of this study is to conceptualize and examine background, school, and country effects for immigrant students, both across the globe as well as specifically in a number of immigrant countries. This study argues that education is the primary agent for the integration and success of immigrants as a whole, but principally of immigrant children. One indicator by which to evaluate the success of immigrant students in a receiving country is the immigrant achievement gap, a gauge of how well immigrant students perform in assessments in comparison to their native counterparts. International evidence indicates that, on average, native students outperform immigrant students in mathematics, science, and reading. Researchers have highlighted the important questions such findings raise about the quality of the education provided to immigrant children. What is more, a special challenge is presented for those countries termed as immigrant countries, or those that have historically received a large influx of immigrants for various reasons. Therefore, this study intends to add to the understanding not only of the nature of the immigrant achievement gap, but the characteristics of teachers, schools, and countries associated with it so that countries across the globe may be better prepared to tackle the challenges of providing a good education to immigrant children. This study looks at a population of immigrant fourth graders, as much less is known about young immigrants in comparison to what is known about adolescent and adult immigrants. To this end, and in order to find similarities and differences across different populations of immigrants, this chapter cross- references the results from the previous chapter with the literature presented in chapter 2, which is based primarily on research associated with adolescent populations. First, this study finds results similar to the existing literature. For example, although student background variables cannot be utilized to explain variability related to the immigrant achievement gap, as it is a school-level phenomenon, they are strongly associated with student scores in both mathematics and reading. This study corroborates the findings for adolescent populations which indicate that, in general, students who are native, with native parents, who speak the language of testing, have better educated parents, are of higher SES (as measured by number of books in the home and number of possessions that indicate social capital in the home

such as computers and desks), outperform their counterparts on standardized academic assessments. Additionally, extant evidence indicates that the immigrant achievement gap is smaller at the higher end of the test score distribution and larger at the lower end. Stated differently, high-achieving immigrants demonstrate a smaller gap than low-achieving immigrants when compared to native students who achieve similarly. This is also the case for fourth graders, so that there is no significant mean difference in mathematics or reading scores between those students who reach the highest performance benchmarks. The results of this study also corroborate the existence of a gender gap favoring males in mathematics and females in reading. However, there is one important caveat – immigrant students of both genders perform equivalently in both reading and mathematics so that the gender gap is evident only for native students10. Further, the magnitude of the gap varies so that it is larger for boys than girls in mathematics, and larger for girls than boys in reading. While language spoken has not been found to be associated with mathematics scores in some instances, this is not the case for fourth graders as language variables were associated with both mathematics and reading outcomes. Next, studies have found that 2nd generation immigrants outperform 1st generation immigrants, although both generations typically lag behind native students. Results of this study suggest this is true for younger students in both subjects, although it is much smaller in reading. Next, research from studies on student mobility investigating the ‘vulnerable age hypothesis’ has had mixed results. This study tends to support those that have found a vulnerable age of immigration, finding a vulnerable age for mathematics (for which such data is available) that suggests students arriving between the ages of 1-5 outperform those who arrived before and after that age range. Still, while this may be taken as support for the vulnerable age hypothesis, students are not asked about other age ranges beyond 5, which are the ages that the hypothesis has generally referred to, so it is difficult to corroborate or contradict findings based on adolescents. Nevertheless, it provides initial evidence for a vulnerable age for younger students (e.g., fourth grade/elementary school). Results from the descriptive analyses support the notion that immigrant students attend lower quality schools. More immigrant than native students attend schools with a larger proportion of both economically disadvantaged and language minority students, have medium to

10 An OECD (2006) study using PISA data also failed to find a gender gap for adolescent immigrant students.

low attendance, and have medium/low number of resources available. In general, more native students attend schools in which the principal or the teacher has a high perception of school safety. Also, a larger proportion of native students have teachers who indicated their perception of school climate is high. Finally, fewer immigrant students feel safe at school. Yet, while some contrasting evidence based on adolescents indicates that immigrant students are more likely to attend schools with better indices of traditional school resources, such as relatively lower class sizes, and that immigrants are less likely to attend schools with a culture of achievement as measured by teacher expectations, encouragement, and pressure to achieve, results from this study indicate that there was no discernible difference between the schools that fourth-grade immigrant and native students attended across those types of variables. Lastly, some evidence suggests that parent involvement is lower among immigrant populations. Findings in this study do not support this, as the schools that immigrant students attended had similar home-school involvement indices to those that native students attended. Although this study cannot provide causal evidence for the immigrant achievement gap, it does suggest that immigrant students attend ‘lower quality’ schools. The inclusion of country-level variables in the multilevel analysis as well as the descriptive analysis comparing different categories of countries provides some insight supporting findings from previous research. While results from this study comparing the unconditional immigrant achievement gap across countries support the notion that immigrants who reside in countries with exclusionary policies have a larger achievement gap in both subjects than those who live in countries with inclusionary policies (Buchmann & Parrado, 2006), after controlling for student- and school-level variables, the type of policy at the country level only predicts variability in the gap for mathematics, not for reading. Similarly, this study corroborates findings that immigrant students in traditional settlement countries perform higher than immigrants in the other categories of countries, with the United States being an exception as gaps are larger for this country than any other traditional settlement country. Otherwise, this statement is especially true for reading (see Figure 4.4). Yet, as a country-level variable, it did not explain significant variance in the gap for either mathematics or reading. Descriptive results also support previous findings that those countries attracting high-skilled immigrants tend to have a smaller gap (in both subjects) than those attracting low-skilled laborers.

There are a number of explanations why non-traditional settlement countries exhibit a larger immigrant achievement gap. To begin, historical evidence suggests that traditional settlement countries have much more experience managing the phenomenon of immigration and its accompanying challenges (OECD, 2006), perhaps including the provision of a quality education to immigrant students, as well as a more inclusionary system of integration, and a better system of recruitment that attracts high-skilled immigrants. All of these factors could yield a smaller or non-existent immigrant achievement gap. Although many non-traditional settlement countries have modeled the policies of recruitment and integration of traditional settlement countries, they have a long way to go in reducing their immigrant achievement gaps. These gaps may be a function of the fact that non-traditional settlement countries still attract low-skilled laborers, albeit their pronounced efforts to do otherwise, or the gaps may be a function of these countries’ inexperience attending to the needs of immigrants. In the future, these trends may change. On one hand, if non-traditional settlement countries succeed in attracting high-skilled immigrants, their immigrant achievement gaps may decrease in magnitude or switch directions. Alternately, if they improve their systems of education so that immigrant students attend higher quality schools, for example, they may also manage to decrease those gaps. The findings in this study also differ in some important ways from those related to adolescent immigrants. Some of the literature has indicated a relatively larger immigrant achievement gap in reading than in mathematics. This study, however, indicates the gap in reading is much smaller in magnitude and significance than that in mathematics (see Figures 4.2 and 4.4). Prior evidence also suggested that immigrants of lower SES fared worse than native students of similar SES. This is only partly substantiated. Although there is an immigrant achievement gap across all levels of SES, the gap is largest in the highest SES group suggesting higher disparities for the most advantaged students. Stated differently, the advantage that native students have over immigrant students is largest among the most advantaged. This is also true at the school level, where the immigrant achievement gap is larger at schools that have fewer economically-disadvantaged students and fewer non-native language speakers. These schools can be considered more advantaged, at least in terms of the composition of their student population. In fact, the gap in reading is practically non-existent in schools in which over half of their students are economically-disadvantaged. The gap is also associated with language variables; yet, immigrants perform equivalently regardless of language spoken indicating that

speaking the native language either before starting school or while in school is associated with performance differences across native, but not immigrant students. This may suggest that language gaps are only present for 2nd generation students, who are native to the country of testing but may not be native speakers of its language. Previous studies have included teacher and school characteristics such as various teacher characteristics, school resources, school size, type, climate, and location of the school, as well as student to teacher ratio, and peer characteristics such as their SES. The findings for these variables have been largely mixed with some studies finding positive relations, some negative, and some no relation, suggesting largely inconsistent findings for school effects. Overall, this study finds no evidence that any of these school characteristics predict the gap except for SES and language spoken by peers in the school. However, consistent with extant evidence, one definitively important source of explanation is peer effects or the extent to which high achieving children have an effect on low achieving children when attending the same school. This is manifested in a positive and significant relationship between the average school score on the outcome and the immigrant achievement gap, for both subjects. It is possible that the insignificance of most level-2 variables is due to the fact that they measure ‘traditional’ school resources, the overwhelming majority of which have not been consistently found to predict student test scores in a number of meta-analyses (Greenwald, Hedges, & Laine, 1996; Hanushek, 1986; Hanushek, 1989; Hanushek, 1989; Hanushek, 1997; Hanushek, Kain, & Rivkin, 1998). As the literature review in this study indicates, in order to understand the immigrant student experience, researchers must consider its multidimensional nature. Although the variables in the TIMSS and the PIRLS are not all-encompassing of the extensive factors that have an effect on immigrant student achievement, they do provide a well-defined picture of what is associated with the outcomes of native and immigrant fourth graders in immigrant countries. Following the conceptual framework presented on chapter 2, this study illuminates current understanding of a number of dimensions for young immigrants – incoming resources, race/ethnicity, gender, student attitudes, and host culture variables (e.g., institutional- and school- related variations). It corroborates many of the findings from literature based on adolescents, suggesting that many of the cross-national trends presented in chapter 2 do span a wide age range. However, the dissimilar results suggest that fourth-grade immigrants’ academic success is

associated with influences that are different than those of adolescent immigrants on several dimensions. Nonetheless, an overwhelming number of factors presented in the conceptual framework could not be tested in the quantitative analyses of this study because corresponding variables were not captured by either TIMSS or PIRLS. Therefore, although this study adds to the understanding of the context in which fourth grade immigrants attend schools, additional research is needed to capture a broader picture of this context. In this endeavor, single-country studies may be most helpful as they are more manageable and may be able to capture the full context in better detail, along the lines of the conceptual framework. Nevertheless, this study allows for a more sophisticated understanding of what is associated with the immigrant achievement gap at the school and country levels. These variables are arguably much more malleable than student characteristics, and, thus, lend themselves to policy considerations that can effect change for immigrant students. A fundamental aim of this study has also been to answer a central question ‘what is the immigrant achievement gap?’ Based on existing evidence as well as the results from this study, a couple of generalizations may be made in order to begin generating a workable definition for the immigrant achievement gap with the end goal to create a common language to facilitate future discussions on this topic. First, the gap is a cross-national phenomenon, as it has been found to exist in most countries, regardless of the subject, year of assessment, age, or history of immigration of the country. While the gap for three traditional settlement countries – New Zealand, Canada, and Australia – has sometimes been in the opposite direction or non-existent, overall the gap is an issue in all other countries. Yet, it varies in magnitude. Cross-national studies such as this one which pool estimates across a number of countries find a sizeable gap in both mathematics and reading; however, the range is substantial across the countries analyzed, ranging from a positive 14 points to a negative 58 points in reading, and a negative 24 points to a negative 70 points in mathematics. Country-level predictors other than GDP and type of policy toward immigrants for the mathematics model were not found to significantly predict the gap in this study. Therefore, although this study provides some evidence that the lower the GDP of the country, the higher the immigrant achievement gap will be, and that countries with inclusionary policies (Buchmann & Parrado, 2006) tend to have a smaller gap, additional research is needed in order to improve our understanding of why some countries have larger gaps than others.

Second, the gap is an issue for both younger and older populations. Evidence from this study supports the existing literature on adolescents by finding that native students largely outperform immigrant students in measures of mathematics and reading. However, this study is ill-equipped to directly compare the magnitude and significance of the gap across these populations. The most ideal study to accomplish such a task would be one that utilizes populations from the same countries, during the same year, for the same assessment/subject (e.g., immigrant countries, 2007, TIMSS) utilizing data for both younger and older immigrant students. This would yield much more comparable information about which groups exhibit a relatively larger or smaller gap. A raw comparison across different studies, as well as the current study, would be highly speculative as the countries analyzed are different as are the populations of students. For example, a cursory analysis performed by the author11 suggests that the gap for adolescents in mathematics (TIMSS) in the same year as the assessment used in this study (i.e., 2007) is significant and negative, indicating immigrant students score 27 points lower12. This is much lower than the unconditional gap of 49 points for fourth graders. Moreover, an analysis of reading data for PISA 2006 indicates the gap is non-existent between second generation and native students, and significant and negative between first generation and native students, indicating immigrant first-generation adolescent students score 21 points lower than native students in reading13. This is also much lower than the unconditional achievement gap of 33 points for fourth graders. This preliminary evidence would indicate the gap is larger for younger students. Still a much more in-depth comparison should be performed employing at the very least student-level controls. Third, the gap does differ across subjects. Although, as previously stated, it is typically present regardless of the subject, this study finds that the gap for mathematics is much larger than the gap for reading. Overall, studies comparing different subjects also find dissimilar gaps in magnitude across subjects. For this study, it is possible that the gap for reading is smaller for a

11 Author utilized the International Data Explorer developed by the National Center for Education Statistics available freely online. 12 Analysis excludes Germany and New Zealand as data was unavailable. 13 Analysis performed utilizing the International Data Explorer developed by the National Center for Education Statistics and including United Kingdom as a whole according to PISA data- collection protocol.

couple of reasons. First, mathematics assessments for fourth graders may have more word problems, increasing the demands for reading comprehension. A cursory evaluation of the TIMSS released items suggests that the mathematics test does contain a large number of word problems. According to the TIMSS Technical Report, about half of the questions in the assessment are constructed response (Olson, Martin, & Mullis, 2008). These may be more difficult for non-native speakers to answer. Relatedly, reading passages in PIRLS tell stories with context. The mathematical word problems do not give clues to non-native-speaking readers who do not know the meaning of all of the words in the text. Context may allow immigrant students to decipher the meaning of the text without necessarily understanding all of the words in the passages. Second, it is also possible that immigrant students may be poorly prepared in mathematics. In other words, there may be larger disparities in the rigor of mathematic concepts and knowledge taught when comparing origin countries to receiving countries, making immigrant students less prepared for the mathematics assessments of the receiving country. Although that much can be stated about the immigrant achievement gap, there is still more to learn. This study finds associations with the gap both at the school and the country level, finding very few of the variables tested and captured by TIMSS and PIRLS associated with the immigrant achievement gap. A large number of dimensions based on the conceptual framework presented in chapter 2 could not be tested. Further, because the gap remained significant both at level 2 and 3, it is possible that even the variables utilized for this study are flawed and did not properly capture the dimensions they were intended to test. For example, whether a school offered enrichment reading did not significantly predict the gap. This could mean all immigrant students have as much access to such a resource as native students. Yet, it could also mean that although immigrant and native students attend schools with enrichment programs in equal proportions, immigrant students are less likely to actually have access to these resources, a fact that would exacerbate, and perhaps explain variance on the reading gap if captured differently. In the end, two facts must be taken into account – most of the dimensions in the conceptual framework were not evaluated and there is still significant variability to be explained at levels 2 and 3 for both the reading and the mathematics models. Therefore, there is much more to learn about what predicts the immigrant achievement gap both at the school and country levels, and the conceptual framework should be a useful guide to do so in future studies.

Other questions remain. For example, is the immigrant achievement gap a phenomenon of external immigrants only? As it was discussed in chapter 2, by virtue of the definition of an immigrant student adopted in this study and used in the TIMSS and PIRLS, the exclusion of internal migrants is inevitable. Do immigrants who move within countries exhibit achievement gaps? This might be a difficult question to answer, as data on internal migrants is scant in most countries. In addition, patterns of immigration are not static. Is the gap time-variant? Does it change as patterns of immigration change? This may be possible to assess via a meta-analysis of various datasets across a range of time. Finally, this study has defined the immigrant achievement gap as a mean difference in scores between immigrant and native students, but can it also signify a difference in literacy, numeracy, graduation rates et cetera? For ease of comparison and in the quest to establish a common language in the field of immigrant student education, it may be useful to limit the definition to difference in test scores. Other gaps may thus be termed ‘immigrant literacy gap’, ‘immigrant numeracy gap’, or ‘immigrant graduation gap’, just to provide a few examples. Conclusion This chapter has discussed the results from chapter 4 by cross-referencing them to the existing literature based primarily on studies of adolescent populations. It presents supporting evidence that, in many ways, younger immigrant students resemble their older counterparts. Yet, it also suggests that they may differ in important ways. Further, the functions by which to understand the immigrant achievement gap, by analyzing associated variables at the school and country level, yield a better understanding of what may be the causes for the achievement differences between native and immigrant students. Still, many of the dimensions from the conceptual framework presented in chapter 2 have not been evaluated, leaving much to learn about immigrant students, their schools, teachers, and home environments. Some generalizations can be made about what the immigrant achievement gap is – it is cross-national, but variant in magnitude across countries, a phenomenon of both younger and older immigrants, variant across subjects, and associated with some school- and country-level variables. In the end, there is still much to learn about what the immigrant achievement gap means, from its definition to its causes.

CHAPTER 6

CONCLUSION

Restatement of the problem Immigration is a phenomenon that is increasing both in absolute numbers as well as in importance throughout the world. It is an important activity, as it addresses issues that arise due to declining fertility rates and increased aging in some parts of the world (UNDP, 2009). It is also an issue of human rights, as many immigrants migrate due to reasons of political, racial, economic, and social strife. Yet, it is a contentious issue because it brings into question some of the most basic assumptions about who belongs in a society. The extent to which nations attend to the specific needs of immigrant children questions these assumptions as well. It is important for societies to provide a quality education to immigrants as it provides a gateway for success in the receiving society both economically and socially, undoubtedly yielding benefits for both native and immigrant individuals, as well as society as a whole. If not provided equal access to educational opportunities, first and subsequent generations of immigrants may have diminished prospects as adults and many may become a permanent part of the underclass (Suárez-Orozco & Suárez-Orozco, 2000). The so-called ‘immigrant achievement gap’ raises important questions about issues of quality of education as it pertains to immigrant students, especially as existing evidence demonstrates that these students underperform in comparison to native students in measures of mathematics, science, and reading. Immigration presents serious challenges to countries receiving large numbers of immigrants; issues related to education are no exception. Although, in the literature, increased attention is being paid to the issues that immigration itself creates, comparatively less attention has been provided to how immigrant children are integrated into schools (OECD, 2010). The consequence of a system that provides education inputs to immigrants inequitably is an important empirical question worthy of further study. Such a situation may be especially alarming in countries where a significant portion of the population is comprised of immigrants. Examining outcomes such as test scores may be very informative for future conversations about the quality of education provided to immigrant students.

Summary of the Study The objective of this study is twofold. First, I aim to increase our understanding of immigrant student achievement through a synthesis of the existing evidence in the form of an extensive literature review. Second, I aim to quantitatively examine the so-called ‘immigrant achievement gap’. Although extensive literature has widely documented this gap, much less evidence exists for younger students and in the subject of reading. Therefore, the second section of this study extends from the first to examine the immigrant achievement gap for fourth graders utilizing two cross-national assessments, the TIMSS and the PIRLS. Research Questions The first section of this study is a synthesis of research that analyzes the academic achievement of immigrant students. As such, it follows these guiding questions: 1) What themes emerge from the literature on immigrant students and what factors does the literature find are important when examining immigrant student achievement? 2) Does the literature provide any evidence for cross-national trends in immigrant student achievement?

The second section of this study is quantitative, and aims to increase our understanding of the educational experience of immigrant fourth graders by asking the following research questions:

1) Is there evidence of an immigrant achievement gap for fourth graders in mathematics, and reading?

expansively covered in special issues on immigration or journals specifically geared to issues of immigration. While the literature is growing, issues such as employment and earnings outcomes, immigrant adjustment and adaptation, discrimination, historicity, and other similar topics are given precedence. Although these are undoubtedly important topics, relative to the expansive coverage of the aforementioned subjects, the educational achievement of young immigrants has received less attention in the literature. This study echoes calls of scholars to increase our understanding of how “schools in different societies treat immigrant children and children of immigrants,” (Editorial, 2011, p. 4) and to establish a common theoretical language with which to understand and discuss immigrant student achievement (Portes, 1997). Next, and as the literature review shows, only a few studies on immigrant students cross- nationally have utilized multilevel techniques (see for example Christensen, 2004). The majority of studies conducted to date relied on single-level regressions with test scores as the outcome. They report associations between immigrant students’ birth status and student characteristics such as language spoken at home. Yet, most discuss their findings in terms of whether or not these student characteristics explain performance differences between native and immigrant students. This approach limits our understanding. Unless the immigrant achievement gap is modeled as an outcome, one cannot speak of the immigrant achievement gap being explained, per se. Shared variance among variables in a single-level regression is not equivalent to variance explained on the immigrant achievement gap. Therefore, if the significance of the immigrant achievement gap changes in sign, magnitude, or otherwise, in a single-level model after entering other student-level predictors, this signifies that the variables in the model share variance on predicting the outcome, not that they explain variance for each other. This study modeled the immigrant achievement gap as a school-level phenomenon and therefore moves the field toward developing a model for statistically explaining the difference in performance between native and immigrant students, or the immigrant achievement gap. Further, most studies have focused on either mathematics and/or science for eighth graders and fifteen year-olds. Only two studies have utilized the PIRLS (see Schnepf, 2007; 2008). It is possible that the well-documented gap may be different for students who are younger. First, because we can be more certain that their age of entry is at a younger age than adolescents, tapping into evidence indicating an inverse relation between achievement of immigrant students and age of entry would suggest a smaller or no gap for fourth graders.

Second, using datasets with younger populations allows the researcher to analyze a more heterogeneous population due to the higher dropout rates experienced with older students. Thus, a gap for fourth graders may be different than a gap for teenagers because assessments of the latter may capture only those students who have not dropped out of school, or the best, brightest, and most advantaged in a given country. Although it was not the focus of this study, a cursory analysis conducted by the author comparing the gap in the same year and subject for populations of fourth graders and adolescents (see previous chapter) provides some evidence that a younger population may be more heterogeneous and thus exhibit a larger gap. What is known about young immigrant children has been limited, as more is known about “the educational attainment and labor market outcomes for new adult arrivals and adolescents [while there is] less understanding of the progress made by very young children in immigrant families and the influence of family resources on these children’s outcomes” (Glick & Hohmann-Marriott, 2007, p. 374). The specific aim of the second section of the current study is to add to the existing body of evidence by providing research on young populations on the subjects of mathematics and reading using multilevel cross-national methods. Review of the Literature The background of the student seems to be one of the most important explanatory variables according to existing literature. In general, it explains much of the variance in immigrant student scores, although not all. Yet, there are very discernible differences across the SES and achievement continuum so that high SES and high achieving students exhibit different gaps than their counterparts. The explanatory power of background variables may also vary by region, highlighting the heterogeneity of immigrants across, but also within, countries. The generation of the student and language also appear to be important factors with the latter at times surpassing SES variables in importance. It is especially true where the influx of immigrants tends to be from countries where a different language is spoken. Generally, students who were born in the receiving country and speak the national language tend to do better in assessments than students that either do not speak the language at home and/or were not born in the country of testing. The same naturally applies to the birthplace of the parents because those students whose parents were born in the receiving country are classified as native. One important exception is in those countries that are traditional immigrant countries and tend to attract highly skilled immigrants; in such countries a parent born outside of the country is positively associated with

achievement scores. Therefore, individual circumstances of immigration are essential to understand when interpreting results of this research. Characteristics of the sending and receiving countries have found to provide insights in understanding the immigrant achievement gap, but are less widely studied than student background measures. All of these variables, however, seem to have important nuances across ethnicities, generations, SES, and gender, which require researchers to understand well the populations being investigated. Further, understanding migration patterns is key in understanding which variables to test and how to interpret the data. Finally, further evidence suggests functions based on variables that are not easily quantified. For example, the literature review found evidence that there is much that schools can do to either help or hinder students’ success. One of the commonly cited findings was the role adult mentors can play in a young immigrant’s life by stepping in and fulfilling roles that parents cannot due to an inability to speak the language of the country or a lack of understanding of the education system. Other important variables were gender expectations and lack of legal status. Girls are often held to different standards than boys, a fact that may have both positive and negative outcomes for girls. Lack of legal status may deny students opportunities if they are denied social services in the receiving country such as education, but also health and economic services. Methodology This study first synthesizes the existing evidence on factors that researchers have examined in order to understand the immigrant achievement gap based on cross-national evidence, and presents it in the form of a literature review. It includes quantitative studies whose aim was to account for the unexplained variance on immigrant student outcomes by controlling for student- and school-level variables as well as qualitative studies that aimed to explicate the different sources of the immigrant achievement gap. The quantitative section of this study tests hypotheses based on the existing literature, in order to explore whether or not findings differed for a younger population. Thus, hypotheses tested in the quantitative section of this study are based on the themes and factors found in the literature, to the extent that corresponding variables were available in the datasets. The literature search consists of a thorough investigation of the published and unpublished literature. All the search terms used were selected to capture cross-national studies of young immigrant student populations (i.e., adult education was not part of this study). The

time range of the 66 selected published and unpublished articles spans the years 1978 to 2011. The author identified the themes in the literature review during the review of the articles found in the Literature Search. Upon discussing the cross-national themes, the author provides a table describing how they may fit into Suárez-Orozco and Suárez-Orozco’s (2000) conceptual framework. The purpose is two-fold. First, it aims to connect the immigrant literature mainly based in the United States, to the cross-national literature presented in chapter 2. Second, it aims to answer Suárez-Orozco and Suárez-Orozco’s call to form “better theoretical understandings of multiple paths taken by immigrants in their long-term adaptations” (p. 32) by creating a conceptual framework that is globally-minded and applicable to immigrant experiences across the world. The second section of this study is a quantitative cross-sectional multilevel analysis of secondary data. It analyzes data from the PIRLS and the TIMSS. The analysis first begins with a descriptive analysis of immigrant students, their homes, their teachers, and schools, to better understand the context in which fourth grade immigrants live and go to school. The analysis then tests a three-level intercept- and slopes-as-outcomes model by employing hierarchical linear modeling in order to address the three research questions. The student level estimates the immigrant achievement gap, first unconditionally and then by entering student-level predictors in the model as controls. The school level tests whether the immigrant achievement gap varies across schools, and which school-level predictors significantly explain variability in those differences across schools, after controlling for student-level characteristics. The third level examines variability in the mean gap across countries and whether country-level predictors help to explain that variability. The following models are tested, one for mathematics and one for reading:

Level 1: Yijk = π0jk + π1jkIMMIGRANT1ijk + eijk

Yijk = π0jk + π1jkIMMIGRANT1ijk + π2jkSTUDENT2ijk + eijk

Level 2: π1jk = β10k + r1jk

π1jk = β10k +β11kSCHOOL1jk + β12kPEERS2jk + β13kTEACHER3jk + r1jk

Level 3: β10k = γ100 + u10k

β10k = γ100 + γ101GDP1k + γ102GINI2k + γ103HISTIMM3k + γ104POLICY4k + u10k

Thus, a multilevel model is estimated assuming students are nested within schools nested within countries. Although information on teachers and classrooms is collected, the data did not allow for additional sub-levels of nesting because some schools only sampled one classroom per school, equating the classroom level to the school (Martin, Mullis, & Kennedy, 2007; Olson, Martin, & Mullis, 2008). Further, test developers do not recommend adding a teacher level as the teachers who respond to questionnaires in TIMSS and PIRLS respond with the specific sampled students in mind, and are not representative of other teachers in other schools (Rutkowski, Gonzalez, Joncas, & von Davier, 2010). Therefore, while student-derived variables were treated as characteristics of the student, teacher- and principal-derived variables composed the school level. Variables for the country level were not existent in the data and were gathered by the author from international reports and other external sources. Summary of Results The descriptive section of the study provides a clearer picture of the context in which immigrant students attend schools as well as illuminates upon their background characteristics. First, more immigrant students attend what may be termed ‘lower quality’ schools, or those that have a larger proportion of both economically disadvantaged and language minority students, medium to low resources, medium to low attendance, in which their principals and teachers have a lower perception of school climate and school safety. Teachers of immigrant students do not seem to be very different on the measured characteristics than teachers of native students. Further, immigrant students tend to come from less advantaged backgrounds or have fewer resources at home. More immigrants indicated to not speak the language of testing at home and to have foreign parents. However, for both subjects, a good number of native students also indicated they have foreign parents. Immigrant students indicated to have positive attitudes toward mathematics and reading, yet indicated to have lower confidence in mathematics than their native counterparts. Concerning the home environment of immigrant students, while fewer engaged in specific educational activities before starting school, just as many or more than native students engaged in educational activities in the present. Further, a larger percentage of immigrant students attended ISCED level 0. Finally, immigrant parents tended to be more highly educated than native parents, and to have held a professional-level job as their highest occupation level.

For the multilevel section of the study, I hypothesize a significant mean difference between native and immigrant students in mathematics and reading outcomes, that the gap varies across schools and countries, and that teacher, school, and country characteristics are useful in predicting unexplained variance on the gap. I predict that characteristics of immigrant students’ peers and of the countries would explain significant variance on the outcome. Next, I also expect to observe the following: a negative relationship with Gross Domestic Product (GDP) showing larger gaps in countries with lower GDP, a positive relationship with Gini showing smaller gaps in countries with lower Gini coefficients, a negative mean difference showing larger gaps in countries with a more recent history of immigration, and a negative mean difference showing larger gaps in countries with more exclusionary policies towards immigrants. Finally, I predict that the immigrant achievement gap would vary across levels of SES, language, generation, and achievement: highest in lower levels of SES, higher for immigrant students who do not speak language of testing, highest for the first generation, and highest at lower levels of achievement. An immigrant achievement gap is found for fourth graders in both mathematics and reading for all countries except Canada (Ontario) in reading. In addition, the reading gap in New Zealand is positive and indicates that immigrant students outperform native students in that country. The unconditional model for the immigrant achievement gap indicates that the gap significantly varies across all levels. Most predictors are not significant in the model. For mathematics, controlling for student-level variables, the mean score of schools on the outcome, the percent of students who spoke the language of testing as their native language, the type of policy that a country has toward immigrants, as well as the country’s GDP significantly predict the gap. For reading, controlling for student-level variables, the mean score of schools on the outcome, the percent of students who were economically disadvantaged, and the country’s GDP significantly predict the gap. Still, with one exception, variability at every level remains significant even after predictors are entered in the model suggesting that variance in the gap is not completely accounted for by the predictors tested in this study. For the reading model, variability at level 3 becomes insignificant once GDP is entered in the model, indicating this variable accounts for all the variance on the outcome at that level or 13% of the total variance. The gap is generally heterogeneous across levels of socioeconomic status, language, generation, and achievement. First, the results indicate that there are important differences within the group of immigrant students. For example, while there is no gender or language gap, the

number of books owned, parents’ birth status, and generation mark a difference between immigrants in mathematics and reading. Second, when analyzing the immigrant achievement gap by sub-groups of students, although native students still always outperform immigrant students, the gap does not exist across the highest levels of achievement in both mathematics and reading. In other words, the gap is a phenomenon mostly existent at the lowest levels of achievement for both mathematics and reading. Finally, for the most part, the gap is existent across all other sub- groups of students, although it largely varies in magnitude. Limitations First, as discussed in the section on the Operationalization of Important Terms, this study is a small, albeit important, part of a larger set of issues. It is very focused in its goals and definitions as it defines immigrant student achievement, both in the literature review as well as the quantitative analysis, as pertaining to student test scores, grade point average (GPA), and attainment, even though achievement may also include issues of enrollment, graduation, dropout, and other important school-related measures at any level of education. This study does not dismiss the importance of complimentary measures of achievement that may not be easily be quantified. Examples may include levels of acculturation, belongingness, discrimination, and adaptation to a foreign environment, all of which are essential factors to consider when evaluating an immigrant’s success in a receiving country. Further, while the usefulness of test scores and GPA in identifying subject-specific deficits cannot be underestimated, they are a double-edged sword because these measures can categorize immigrants in ways that may deny them later opportunities (e.g., college admission) without paying special attention to their specific needs. Further, the term ‘immigrant’, as used in this study and in the international datasets is very specific and limited. Although this study’s definition is accurate, the term ‘immigrant’ is denoted and connoted differently in different regions, both across and within countries. Additionally, this study does not delve into special groups of immigrants such as migrant and refugee students. Therefore, although the immigrant student population discussed in this study could very well include these special groups, the author makes no attempt to draw inferences particular to the two groups as examination of their specific situations is beyond the scope of this study. Still, place of birth cannot be ignored as an important defining factor of immigrants, as the

literature and this study demonstrate, when tested in models of achievement, it is consistently significant across different subjects and populations. Next, this study does not only exclude internal migrants as data is not generally collected on within-country immigration, it further excludes the most recent of external immigrants to the countries selected for the quantitative section of the study. All countries participating in the TIMSS and PIRLS are allowed to make exclusions related to the level of mastery a student has in the language of testing. Some examples include if the student has had insufficient instruction in the language of testing or if the student is a language learner with less than one year of native language instruction. These students may be excluded from the assessment. This, of course, creates issues at least as it pertains to the generalizability of the findings based on the language spoken variables, as this study may only have included immigrants with a certain minimum level of proficiency on the language of testing. Still, one must wonder how valid the test scores would be for immigrants who do not yet have the ability to comprehend the questions presented in either assessment. There are also limitations in the variables available in the TIMSS and PIRLS. First, the TIMSS does not gather data on parents’ education, income, or occupation. Next, while the PIRLS does gather this data, the home-questionnaire which is the source for this information is not implemented in the United States and utilizing it in the multilevel models would signify a 50% loss of sample size. Therefore, the multilevel models do not include key SES variables that may be considered essential to control at the student level in any educational study. Nevertheless, existent literature in the field of education has found evidence that the SES of the school is more important in understanding achievement differences among students than the individual SES of the students themselves (see for example Sirin, 2005). Further limitations arise when considering the limited scope of the variables available in the assessments especially as it pertains to the conceptual framework presented in chapter 2. Most of the dimensions were not tested in any way due to the limitations inherent in analyzing secondary data. For example, no data was collected about the neighborhoods in which students live, nor about intergenerational conflicts in immigrant families, limiting what could be said about these dimensions’ association with the immigrant achievement gap. However, this study does add significant knowledge to the currently-limited understanding about fourth grade immigrants.

Next, TIMSS and PIRLS sample whole classrooms, which may bias estimates depending on how students are tracked across and within schools in each country. As chapter 2 suggested, immigrant students are likely to be found in lower level tracks and/or lower-quality schools, which would mean that in those countries in which this is true, student attributes such as country of birth may be more highly correlated with classroom and/or school characteristics that are detrimental to achievement than in other countries where tracking either does not exist or is more subtle. This is a limitation of the data and thus the study at-hand. Also, although this study has the potential to substantially add to the body of literature, it does not show ‘how’ school resources or family background affect achievement (Buchmann & Hannum, 2001; Gamoran & Long, 2006;). For example, “the ways in which resources are used is more consequential for achievement than the presence or absence of resources" (Gamoran & Long, 2006, p. 8) and how teachers mobilize resources within the school could potentially be more informative than whether or not a school or a teacher has specific resources and attributes. Additionally, by utilizing the HCT as a main theoretical basis for which to analyze the effectiveness of countries in targeting the educational needs of immigrant students, this study makes certain assumptions about the impact of schooling on both the acquisition of human capital as well as its subsequent effect on economic growth. While the relation between education and economic growth is an important one to analyze, it is simply beyond the scope of this analysis. This study further assumes that the main function of schooling is student achievement, while others may argue that its function is to create citizens, to socialize children, or to maintain a stable society (Heyneman, 2005). Also, the TIMSS and PIRLS collect data on many of the variables that have been inconclusively related to achievement (see Hanushek, 1986; Hanushek, 1989; Hanushek, 1997; Hanushek, Kain, & Rivkin, 1998; Hanushek & Rivkin, 2004). In this, this study has the same limitation as previous studies. Finally, much like existing research, this study is not experimental. It is subject to misspecification and selection bias (Krueger, 2003). These findings must, of course, be understood in the context of the countries selected for quantitative analysis in this study. As previously discussed, the logic behind the selection of the countries follows its cross-national nature. So as to make meaningful inferences about a specific group of countries, the quantitative section of this study only refers to what have been termed as ‘immigrant countries’. But, these immigrant countries have very specific attributes that make

them very different from other countries. They are developed countries, scoring high on the Human Development Index (HDI) (>.7), having medium to high equality (as measured by the Gini coefficient), and having a high GDP (in the tens of thousands). Findings may have been different had this study included all (or more of) the countries who participated in the TIMSS and PIRLS. Further, because countries self-select for participation in both the TIMSS and PIRLS, country-level weights for multilevel analyses are not available, further creating an issue of generalizability in this study to only the immigrant countries included in this study. Still, as discussed in the Countries Selected section in Chapter 3, the countries are highly representative of the four types of immigrant countries as well as a wide range of origin countries, spanning virtually all continents in the world. Future Research At least two issues will continue to affect immigration – globalization and its increasing politicization. Coupled with these are a number of interesting changes that researchers must consider. First, international boundaries will continue to become more and more blurred. How this will change the definition of immigration has yet to be determined. For example, will European Union (EU) citizens who seek work within the EU always be considered immigrants? Evidence suggests that the top sending countries to European nations are other European countries. How will EU immigrants differ from other immigrants across the world? Further, because of the ageing and declining fertility rates in developed countries, immigration will undoubtedly continue to play an important role in meeting labor shortages, and in financing health and pension systems in deficit (OECD, 2011). Will this need change the perception of immigration for national populations as a necessary process? Will this create a need for more comprehensive systems of recruitment to either allow immigrants to become a permanent part of society, or allow them the liberty to travel back home at will? Second, the troubled global economy has created austere economic environments in many countries. As a result, citizens are electing progressively more extreme representatives that promise change, which in some parts includes severe restrictions on immigration influxes (Mudde, 2012). How will such changes affect immigrant outcomes, including student achievement? Will globalization counteract these new limitations on immigration and how? How will these change the functions that affect the achievement of immigrant students as discussed in this study? Will the immigrant countries discussed in the second section of this study continue to increase their efforts in attracting high-

skilled labor despite the aforementioned inhospitableness toward immigrants? Should they succeed in attracting high-skilled immigrants, this will likely affect the immigrant achievement gap. This study finds evidence that immigrant students are attending ‘lower quality’ schools, yet international scholars have called for societies to invest in the integration of immigrants (OECD, 2011). To what extent will this study, as well as others that echo its results, have an effect on the policies that nations adopt to this end? Future studies should aim to further compare adolescents and fourth graders by analyzing populations from the same test application, such as 4th and 8th graders in mathematics from the year 2007. Such a study would allow for a better comparison of these two populations by being able to directly compare the magnitude of gaps as well as the strength of associated variables. While it is possible to compare other findings with those from this study, it is not possible to compare the coefficients from the analyses to those from other studies as they may not be comparable populations. Future data collection efforts must consider the conceptual framework discussed on chapter 2. While the TIMSS and PIRLS have allowed this study to better understand the context in which fourth grade immigrants attend school, they are limited in the dimensions they capture. There is still much more to learn about fourth grade immigrants and their multidimensional experiences. Developers of these assessments may consider collecting more rich data related to immigrants, as it is an issue that affects more and more countries each day. To conclude, there are numerous areas requiring future study in the field of immigrant student achievement. For example, little to no research has been done on higher education outcomes for immigrant students. Next, asylum seekers, migrant populations (those who migrate seasonally), and refugee populations may be very different from what has been a more generally- defined population of immigrants. Research on these populations’ educational outcomes is scant. Also, while the current study examined differences in test scores, equally important outcomes include difference in graduation rates, college attendance, dropout, and enrollment. In the same vein, constructs that are more difficult to quantify such as levels of acculturation and belongingness need to be considered. Qualitative studies may be best suited for this task, and must be conducted in order to unearth a better understanding of such factors and how they affect academic achievement for immigrant students. Likewise, developers of the surveys utilized in this study may consider gathering data on immigrant students that is more in-depth and provides

much more information beyond a student’s place of birth. After all, that is one item in the plethora of factors that affects immigrant student achievement, as this study has shown. Finally, and not to say that this list is exhaustive of future paths for research, but further comparisons of people who immigrate and people who stay in their country of origin may add to the general understanding of who, in fact, is ‘the immigrant’. Some evidence suggests that those who immigrate have higher levels of human capital than those who remain, but this evidence is limited and leaves room for researchers to delve even further into the phenomenon of immigration.

Table 2.1 Articles Selected for Inclusion and Search Criteria Phase No. Articles Sources Search Terms First 23 Google Scholar, ERIC (CSA), immigrant’ AND ‘TIMSS’ OR JSTOR, AcademicSearch ‘PISA’ OR ‘PIRLS’ Second 43 Single-country studies and Educational achievement, reference lists (from first phase) outcomes, or performance Google Scholar - special issues on Special issue immigrant immigration14 education; Educational achievement, outcomes, or performance Immigration/migration journals15 Educational achievement, outcomes, or performance Witenstein and Luschei’s (2011) Educational achievement, comparative analysis outcomes, or performance

14 Includes the Peabody Journal of Education – Special Issue on Immigration, two editions of the Future of Children: Children of immigrant families, and Immigrant Children, the International Journal of Comparative Sociology Special Issue: Immigration, Social Problems: Special Issue on Immigration, Race, and Ethnicity in America, and Social Science Quarterly Virtual Issue: Immigration on the One Year Anniversary of Arizona SB 1070. 15 These include the Journal of Ethnic and Migration Studies, the International Migration Review, the Journal of International Migration and Integration, the International Migration journal, and the Journal of Migration and Refugee Issues.

Figure 2.1. Conceptual framework for studying immigrant children. Original figure presented in Suárez-Orozco & Suárez-Orozco (2000).

Table 2.2 A Multilevel Conceptual Framework of the Immigrant Student Experience Suárez-Orozco & Suárez-Orozco Themes Cross-national Themes Incoming Resources Student Background Culture Age of arrival Characteristics at entry Host Culture Variables Institutional and school-related variations Social capital, community, and peer effects Influence of schools and school personnel Social Support Networks Social capital, community, and peer effects Influence of schools and school personnel Legal status, migration, and discrimination Family Cohesion Involvement and academic decisions Parent-student relationships Motivations Maintenance of Culture of Origin – Peer Orientation Social capital, community, and peer effects Teacher Expectations Influence of schools and school personnel Race – Gender Gender Student’s Attitudes, Perceptions, and Motivations Behaviors Legal status, migration, and discrimination

Figure 3.1. Conceptual Framework. Original figure presented in Buchmann & Hannum, 2001, Annual Review of Sociology, 27, p. 79

Table 3.1 Country Selection for TIMSS UN (2002)* UN (2002)* OECD (2003)** Final Selection Stock Percentage Inflow Australia Australia Australia Australia Canada Canada Austria Canada, Ontario Germany Kazakhstan Canada Germany Iran (Islamic Republic of) Kuwait Germany Ukraine Italy Latvia Italy UK-England Kazakhstan New Zealand Japan UK-Scotland Russian Federation Singapore Netherlands United States Ukraine Ukraine New Zealand New Zealand United Kingdom United Kingdom United States United States Note. *UN (2002) International Migration Report, two adjacent columns based on stock of immigrants and immigrants as percent of total population, respectively; **OECD (2003) Trends in International Migration, based on inflows of foreigners.

Table 3.2 TIMSS Countries and Respective Sample and Immigrant Sub-sample Sizes Countries Selected Valid N Percent Immigrants

Australia 4051 15.7 Canada, Ontario 3419 21.7 Germany 4511 10.0 Ukraine 4191 12.7 UK-England 4253 13.9 UK-Scotland 3885 13.0 United States 7769 19.2 New Zealand 4866 25.7

Table 3.3 Top Immigrant Countries TIMSS Final Selection Russian Federation Australia Yemen Canada, Ontario Kuwait Germany Armenia Ukraine El Salvador UK-England New Zealand UK-Scotland Hong Kong SAR United States Chinese Taipei New Zealand United States Colombia

Table 3.4 TIMSS Variables in Analysis

Variable Name Description Type Country GDP* Gross Domestic Product Scale GINI* Gini coefficient Scale HISTIMM* History of immigration Binary POLICY* Exclusionary/inclusionary policies+ Binary School AC4GSBED* Percent economically-disadvantaged students in school Categorical (4) AC4GNALA* Percent students who have language of test as native language Categorical (4) AC4MSOEM School offers enrichment mathematics Binary AC4MSORM School offers remedial mathematics Binary ACDGAS Index-Level of attendance at school Categorical (3) ACDSRMI* Index-Available mathematics resources in school Categorical (3) ACDGPPSC Index-Principal’s perception of school climate Categorical (3) PEERS* Mean school score on mathematics Scale Teacher AT4GAGE Age of teacher Categorical (6) AT4GTAUT* Years teaching Scale AT4GTLCE Teacher has teaching certificate Binary AT4GFEDC* Level of formal education completed Categorical (6) ATDMTTOV Index-Teacher feels very well prepared on mathematics topics Scale ATDMSTUD* Index-Class size for mathematics instruction Categorical (3) ATDGTPSC Index-Teacher’s perception of school climate Categorical (3) ATDMTAWC Index-Math teacher’s perception of adequate work conditions Categorical (3) ATDGTPSS Index-Teacher’s perception of school safety Categorical (3) ATDMPTIT* Index-Time spent on math as percent of total instruction time Scale Student ITSEX* Sex of student Binary AS4GOLAN* How often student speaks language of testing at home Categorical (4) AS4GBOOK* Number of books in home Categorical (5) AS4GTH01-05* Type of possessions at home++ Binary AS4GMBRN* Mother born in country Binary AS4GFBRN* Father born in country Binary AS4GBORN* Student born in country Binary AS4GBRNC* If not born in country, age student arrived in country of testing Categorical (3) ASDGBORN* Index-Both parents born in country Categorical (3) ASDMPATM Index-Student’s positive affect toward mathematics Categorical (3) ASDMSCM Index-Student’s self-confidence in learning mathematics Categorical (3) Categorical (3) ASDGPBSS Index-Student’s perception of being safe at school

Table 3.4 – continued TIMSS Variables in Analysis

Variable Name Description Type Dependent ASMMAT01-05 Five plausible values mathematics Scale Note. +Based on classification by Buchmann and Parrado (2006). ++ Index tested in model constructed by author as an average of total possessions.

Table 3.5 Country Selection for PIRLS UN (2002)* UN (2002)* OECD (2003)** Final Selection Stock Percentage Inflow Canada Canada Austria Canada, Ontario France Israel Belgium Israel Germany Jordan Canada New Zealand Iran (Islamic Republic of) Kuwait France UK-England Israel Latvia Germany United States Italy New Zealand Italy Poland Oman Netherlands Russian Federation Singapore New Zealand United Kingdom United Kingdom United States United States Note. *UN (2002) International Migration Report, two adjacent columns based on stock of immigrants and immigrants as percent of total population, respectively; **OECD (2003) Trends in International Migration, based on inflows of foreigners.

Table 3.6 PIRLS Countries and Respective Sample and Immigrant Sub-sample sizes Countries Selected Valid N Percent Immigrants

Canada, Ontario 3803 10.0 Israel 3622 7.2 New Zealand 6005 14.1 UK-England 3979 8.1 United States 5037 7.5

Table 3.7 PIRLS Variables in Analysis

Variable Name Description Type Country GDP* Gross Domestic Product Scale GINI* Gini coefficient Scale HISTIMM* History of immigration Binary + POLICY* Exclusionary/inclusionary policies Binary School ACBGPST1* Percent economically-disadvantaged students in school Categorical (4) ACBGPST3* Percent students do not speak language of test as 1st language Categorical (4) ACDGASR* Index-Principal’s perception of availability school resources Categorical (3) ACDGHSI Index-Home-school involvement Categorical (3) ACDGPPSC Index-Principal’s perception of school climate Categorical (3) ACDGPPSS Index-Principal’s perception of school safety Categorical (3) PEERS* Mean school score on reading Scale Teacher ATBGERCN* School offers enrichment reading instruction Binary ATBGACTH* Time spent on language instruction per hour++ Scale ATBGACTM* Time spent on language instruction per minute++ Scale ATBGRINH* Time spent on reading instruction per hour++ Scale ATBGRINM* Time spent on reading instruction per minute++ Scale ATBGTAUG* Number of years teaching altogether Scale ATBG4TAU* Number of years teaching fourth grade Scale ATBGAGE Age of teacher Categorical (6) ATBGHLE* Teacher’s highest level of formal education Categorical (6) ATBGTCR Teacher is certified to teach Binary ATBGTCR1 Type of license or certificate Categorical (3) ATBGEAR1-9 Extent to which teacher studied specific area (out of 9) Categorical (3) ATBGWRK Teacher works full or part time Binary ATDGTCS Index teacher career satisfaction Categorical (3)

Table 3.7 – continued PIRLS Variables in Analysis

Variable Name Description Type Student ITSEX* Sex of student Binary ASBGLNG1* Student spoke language of testing before starting school Binary ASBGLNGH* How often student speaks language of testing at home Categorical (3) ASBGBOOK Number of books in home Categorical (5) ASBGTA1-6 Type of possessions at home Binary ASBGBRN1* Student born in country Binary ASBGBRNM* Mother born in country Binary ASBGBRNF* Father born in country Binary ASDHHER Index-Home educational resources Categorical (3) ASDGBRN* Index-Both parents born in country Categorical (3) ASDGSATR Index-Student’s reading attitudes Categorical (3) ASDGSRSC Index-Student’s reading self-concept Categorical (3) ASDGSSS Index-Perception of safety in school Categorical (3) Parent ASBHHA01-10 Freq. child engaged in activities before start school (out of 10) Categorical (3) ASBH0ATT Child attended ISCED level 0 Binary ASBH0HLO Length of time child attended ISCED level 0 Categorical (5) ASBHDOT1-T6 Freq. parent engages in activity with child (out of 6) Categorical (4) ASBHLEDF Highest education father Categorical (7) ASBHEMPF Employment situation father Categorical (3) ASBHLEDM Highest education mother Categorical (7) ASBHEMPM Employment situation mother Categorical (3) ASBHMJF Main job father Categorical (11) ASBHMJM Main job mother Categorical (11) ASBHWELL Compared to others, how well-off family is financially Categorical (5) ASDHEDUP Index-Parents’ highest education level Categorical (5) ASDHEHLA Index-Early home literacy activities Categorical (3) ASDHPEMP Index-Parent’s employment situations Categorical (4) ASDHOCCP Index-Parents’ highest occupation level Categorical (6) Dependent ASRREA01-05 Five plausible values reading Scale Note. +Based on classification by Buchmann and Parrado (2006). ++ Variables combined by author into two variables signifying time spent per week on reading or language instruction.

Table 3.8 Country Categorization Category Countries 1. Traditional settlement countries Australia, Canada, New Zealand, United States 2. European states with post-war labor Germany recruitment 3. European states with migration related to England, Scotland their colonial histories and post-war labor recruitment 4. New immigration countries Ukraine, Israel

Table 3.9 Top Origin Countries for Selected Countries, 2005-2008 Country Origin Countries Australia UK, New Zealand, India, China, Philippines Canada (Ontario) China, India, Philippines Germany Turkey, Yugoslavia, Italy, Greece, Poland, Turkey, Romania Ukraine Russian Fed., Belarus, Kazakhstan, Uzbekistan, Rep. of Moldova United Kingdom Australia, China, France, Germany, India United States Mexico, China, Philippines, India, Cuba New Zealand United Kingdom, China, India, South Africa, Ireland Israel Frmr USSR countries, Morocco, Romania, North America, Ethiopia

Table 4.1 TIMSS Descriptive Data and Mean Scores on Mathematics by Country Immigrant Percent Mean Mean Mean Country Percent status (s.e.) score (s.d.) difference Australia Native 84.32 0.85 523.12 78.20 -36.94*** Immigrant 15.68 0.85 486.19 94.69 Canada (Ontario) Native 78.34 1.93 517.57 65.29 -23.65*** Immigrant 21.66 1.93 493.92 72.26 England Native 86.08 0.74 551.33 81.13 -64.91*** Immigrant 13.92 0.74 486.42 91.35 Germany Native 90.02 0.54 532.88 67.59 -48.96*** Immigrant 9.98 0.54 483.92 68.14 New Zealand Native 74.33 0.87 503.51 78.11 -38.39*** Immigrant 25.67 0.87 465.12 98.38 Scotland Native 86.98 0.77 503.90 74.36 -69.77*** Immigrant 13.02 0.77 434.13 80.89 Ukraine Native 87.26 0.79 479.38 79.62 -62.76*** Immigrant 12.74 0.79 416.62 85.42 United States Native 80.83 0.74 541.70 69.64 -62.72*** Immigrant 19.17 0.74 478.98 76.22 Int’l avg. Immigrant 83.52 0.35 519.18 74.24 -51.01*** Native 16.48 0.35 468.16 83.42 Note. Native students are reference group. *** p < .001.

Figure 4.1. Mean Scores on Mathematics

Key Category 1 Native students Category 2 score higher Category 3 Category 4 Int'l avg

Figure 4.2. Mathematics Immigrant Achievement Gap by Type of Immigrant Country

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Table 4.2 PIRLS Descriptive Data and Mean Scores on Reading by Country Immigrant Percent Mean Mean Mean Country Percent status (s.e.) score (s.d.) difference Canada (Ontario) Immigrant 10.01 1.08 553.14 70.82 -3.68 Native 89.99 1.08 556.82 70.36 England Immigrant 8.12 0.73 486.92 98.18 -58.39*** Native 91.88 0.73 545.31 83.43 Israel Immigrant 7.17 0.62 504.03 99.66 -15.34* Native 92.83 0.62 519.38 96.10 New Zealand Immigrant 14.09 0.77 545.98 84.93 14.27*** Native 85.91 0.77 531.71 86.30 United States Immigrant 7.51 0.60 506.62 76.10 -37.79*** Native 92.49 0.60 544.40 72.27 Int’l avg. Immigrant 9.38 0.35 519.34 85.94 -20.18*** Native 90.62 0.35 539.52 81.69 Note. Native students are reference group. *** p < .001. * p < .05.

Figure 4.3. Mean Scores on Reading

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Key Category 1 Category 2 Category 3 Category 4 Int'l avg Native students Immigrant score higher students score higher

Figure 4.4. Reading Immigrant Achievement Gap by Type of Immigrant Country Note. Canada (Ontario) mean difference not significant.

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Table 4.3 TIMSS Descriptive Statistics Student Level Variable n Mean s.d. Min Max ASMMAT01 33818 511.06 80.88 164.58 816.42 ASMMAT02 33818 510.45 81.06 113.56 826.48 ASMMAT03 33818 511.23 80.29 149.82 849.54 ASMMAT04 33818 510.51 81.09 130.22 782.24 ASMMAT05 33818 510.69 80.53 164.48 811.09 ITSEXR 33818 0.5 0.5 0 1 SGOLANR3 32998 0.14 0.35 0 1 SGBOOKR1 32767 0.31 0.46 0 1 SGBOOKR2 32767 0.35 0.48 0 1 SGMBRNR 32469 0.22 0.42 0 1 SGFBRNR 32363 0.24 0.43 0 1 SGBORNR 32778 0.17 0.37 0 1 SGBRNCR1 5385 0.35 0.48 0 1 SGBRNCR2 5385 0.31 0.46 0 1 SGBORNR1 33818 0.15 0.36 0 1 SGBORNR2 33818 0.16 0.36 0 1 POSSESS 31911 4.27 1.02 0 5 School Level CGSBEDR 1384 0.19 0.39 0 1 CGNALAR 1384 0.4 0.49 0 1 CSRMIR 1384 0.51 0.5 0 1 PEERS 1384 507.64 45.52 290.71 659.45 AT4GTAUT 1384 15.7 9.95 0 46 ATDMPTIT 1384 17.36 4.15 0 40 TGFEDCR1 1384 0.06 0.24 0 1 TGFEDCR2 1384 0.29 0.45 0 1 TMSTUDR1 1384 0.2 0.4 0 1 TMSTUDR2 1384 0.04 0.21 0 1 Country Level GDP 8 37954.25 14964.67 3083 47418 GINI 8 33.29 5.72 27 45 HISTIMM 8 0.5 0.53 0 1 POLICYNE 8 0.38 0.52 0 1 POLICYO 8 0.13 0.35 0 1

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Table 4.4 PIRLS Descriptive Statistics Student Level Variable n Mean s.d. Min Max ASRREA01 19868 531.24 84.63 152.35 794.54 ASRREA02 19868 531.39 84.99 162.87 801.89 ASRREA03 19868 531 84.89 88.66 810.51 ASRREA04 19868 530.85 84.94 155.2 873.37 ASRREA05 19868 531.32 84.88 123.03 831.95 ITSEXR 19857 0.49 0.5 0 1 SGLNG1R 19244 0.09 0.29 0 1 SGLNGHR 18510 0.35 0.48 0 1 SGBRN1R 19181 0.1 0.3 0 1 SGBRNMR 17570 0.26 0.44 0 1 SGBRNFR 16834 0.28 0.45 0 1 SHHERR 9689 0.19 0.39 0 1 SGBRNR1 18003 0.19 0.39 0 1 SGBRNR2 18003 0.21 0.41 0 1 SHEDUPR1 9491 0.38 0.49 0 1 SHEDUPR2 9491 0.09 0.29 0 1 School Level CGPST1R 763 0.23 0.42 0 1 CGPST3R 763 0.13 0.33 0 1 CGASRR 763 0.25 0.43 0 1 PEERS 763 530.14 49.59 310.24 655.36 TGRINR1 763 5.96 3.64 0 20.5 TGACTMR1 763 8.25 2.7 3 16 ATBGTAUG 763 12.46 8.83 0 41 ATBG4TAU 763 5.12 4.77 0 36 TGERCNR 763 0.43 0.49 0 1 TGHLER1 763 0.15 0.36 0 1 TGHLER2 763 0.3 0.46 0 1 Country Level GDP 5 34171.23 9717.85 21412.14 43625.6 GINI 5 37.3 5.05 32.1 45 HISTIMM 5 0.4 0.55 0 1 POLICYNE 5 0.2 0.45 0 1 POLICYO 5 0.2 0.45 0 1

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Table 4.5 Three-Level Analysis of TIMSS Data (Fully Unconditional Model) Fixed Effect Coefficient s.e. t Ratio

Average student score, π0jk 526.28 10.90 48.30*** Immigrant achievement gap, π1jk -48.76 3.18 -15.34*** Random Effect Variance Component df χ2 p Value Student (level 1), eijk 3869.48 School (level 2), r0jk 651.12 1162 5409.77 0.000 School (level 2), r1jk 1100.31 1160 2446.58 0.000 Country (level 3), u00k 354.07 7 417.35 0.000 Country (level 3), u10k 527.75 7 205.93 0.000 Variance Decomposition of Immigrant Achievement Gap (Percentage by Level) Level 2 72.77 Level 3 27.23

Table 4.6 Three-Level Analysis of TIMSS Data (Final Model) Fixed Effect Coefficient s.e. t Ratio

Average student score, π0jk 548.61 6.71 81.80***

IMMIGRANT VECTOR, π1jk SGBORNR -39.40 7.83 5.03** Level 2 Predictors

CGNALAR 15.70 2.43 6.47*** PEERS 0.98 0.03 29.48*** Level 3 Predictors

GDP -.00 -.00 -2.91* POLICYNE -15.38 3.07 -5.01** POLICYO -60.81 22.46 -2.71* STUDENT VECTOR, π 2jk SGOLANR3 -7.69 1.82 -4.22** POSSESS 11.64 0.49 23.93*** SGBOOKR1 -28.44 1.11 -25.67*** SGBOOKR2 -11.61 1.00 -11.62*** ITSEXR -10.86 1.15 -9.48*** Variance Random Effect Component df χ2 p Value Student (level 1), eijk 3868.62 School (level 2), r0jk 649.89 1162 5407.60 0.000 School (level 2), r1jk 1114.81 1160 2456.49 0.000 Country (level 3), u00k 348.48 7 417.28 0.000 Country (level 3), u10k 300.37 4 133.64 0.000 * p ≤ .05. ** p ≤ .01. *** p ≤ .001.

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Table 4.7 Interpretation of TIMSS Variables in Final Model Variable Description Type Reference Category name SGBRN1R Student born in country (Yes=0, No=1) Binary Native student Speaks language How often student speaks language of testing at SGOLANR3 Binary almost/almost home (Always/Alm always=0, Some/Never=1) always POSSESS Index of possessions at home Scale n/a Over 100 books at SGBOOKR1 Number of books in home (>100=0, 0-25=1) Dummy home Over 100 books at SGBOOKR2 Number of books in home (>100=0, 26-100=1) Dummy home ITSEXR Sex of student (Boy=0, Girl=1) Binary Boy Percent students who have language of test as CGNALAR Binary native language (>90%=0, <90%=1) Over 90% PEERS Mean school score on mathematics Scale n/a GDP Gross Domestic Product Scale n/a Type of policy (Inclusionary policy=0, POLICYNE Dummy Exclusionary policy=1) Inclusionary policy Type of policy (Inclusionary policy=0, Other POLICYO Dummy type of policy=1) Inclusionary policy

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Table 4.8 Three-Level Analysis of PIRLS Data (Fully Unconditional Model) Fixed Effect Coefficient s.e. t Ratio

Average student score, π0jk 545.30 1.58 344.18*** Immigrant achievement gap, π1jk -32.74 2.83 -11.58*** Variance Random Effect Component df χ2 p Value Student (level 1), eijk 4470.98 School (level 2), r0jk 1165.89 542 4017.83 0.000 School (level 2), r1jk 468.29 542 848.69 0.000 Country (level 3), u00k 23.00 4 15.90 0.004 Country (level 3), u10k 71.22 4 22.23 0.000 Variance Decomposition of Immigrant Achievement Gap (Percentage by Level) Level 2 86.80 Level 3 13.20

Table 4.9 Three-Level Analysis of PIRLS Data (Final Model) Fixed Effect Coefficient s.e. t Ratio

Average student score, π0jk 545.34 2.82 193.22***

IMMIGRANT VECTOR, π1jk SGBRN1R -25.05 7.81 -3.21^ Level 2 Predictors

CGPST1R 25.61 5.39 4.75*** PEERS 1.55 0.26 5.85*** Level 3 Predictors

GDP -.00 -.00 -3.72^ STUDENT VECTOR, π 2jk SGLNG1R -5.99 2.58 -2.32* SGBRNR1 -12.25 1.95 -6.29*** SGBRNR2 -13.63 2.47 -5.52*** ITSEXR 12.16 1.39 8.78*** Random Effect Variance Component df χ2 p Value Student (level 1), eijk 4294.17 School (level 2), r0jk 1126.36 517 3265.71 0.000 School (level 2), r1jk 3763.58 515 1780.14 0.000 Country (level 3), u00k 23.60 4 16.29 0.003 Country (level 3), u10k 4.36 3 3.53 0.317 ^ p ≤ .1. * p ≤ .05. ** p ≤ .01. *** p ≤ .001.

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Table 4.10 Interpretation of PIRLS Variables in Final Model Variable Reference Description Type name Category SGBRN1R Student born in country (Yes=0, No=1) Binary Native student Student spoke language of testing before starting Student spoke SGLNG1R Binary school (Yes=0, No=1) language Both native SGBRNR1 Index-Both parents born in country (Both=0, One=1) Dummy parents Index-Both parents born in country (Both=0, Both native SGBRNR2 Dummy Neither=1) parents ITSEXR Sex of student (Boy=0, Girl=1) Binary Boy Percent economically-disadvantaged students in school CGPST1R Binary Less than 50% (< 50%=0, >50%=1) PEERS Mean school score on reading Scale n/a GDP Gross Domestic Product Scale n/a

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Table 4.11 Mean Achievement of Students in Mathematics Across Countries by Immigrant Status Variable Category Mean s.d. t Sex Native Boy 523.55 76.18 -7.87*** Girl 515.00 72.00 Immigrant Boy 469.59 84.57 -1.09 Girl 466.25 81.37 Freq. speak language Native Alw/Alm alw 520.85 73.37 -11.62*** Some/Never 492.85 77.21 Immigrant Alw/Alm alw 469.12 82.21 -0.61 Some/Never 466.80 84.70 Books owned Native 0-25 486.33 72.41 -33.98*** 26-100 521.96 68.56 -15.36*** Over 100 541.87 70.81 RG Immigrant 0-25 447.18 76.26 -11.28*** 26-100 478.90 80.10 -3.14** Over 100 491.46 87.50 RG Age arrived Immigrant Older than 5 461.42 83.19 -7.05*** 1 to 5 489.28 83.84 RG Younger than 1 457.24 76.49 -8.26*** Parents born in country Native Both 520.25 72.57 RG Either 514.41 76.99 -3.48***

Neither 513.56 78.57 -2.76**

Immigrant Both 452.84 76.94 RG

Either 462.96 84.17 2.54*

Neither 478.57 84.10 7.22*** Note. K-1 dummy variables were compared to the RG (reference group). *** p < .001. ** p < .01. *p < .05.

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Table 4.12 Mean Achievement of Students in Reading Across Countries by Immigrant Status Variable Category Mean s.d. t Sex Native Boy 531.31 83.47 10.32*** Girl 547.91 78.81 Immigrant Boy 513.64 86.76 2.45* Girl 525.50 84.05 Student spoke language of testing before starting school Native Yes 541.92 80.72 -9.62*** No 501.59 87.75 Immigrant Yes 522.07 86.41 -0.85 No 517.78 81.53 Freq. speak language Native Always 545.31 80.75 -6.19*** Some/Never 534.79 77.92 Immigrant Always 518.64 85.76 0.76 Some/Never 523.03 81.30 Parents born in country Native Both 545.12 80.26 RG Either 539.55 83.72 -2.59** Neither 528.83 78.87 -6.37*** Immigrant Both 543.19 86.55 RG Either 523.34 92.86 -1.74^ Neither 520.08 81.35 -2.43* Note. K-1 dummy variables were compared to the RG (reference group). *** p < .001. ** p < .01. *p < .05.

110

Table 4.13 Magnitude & Direction of Mathematics Immigrant Achievement Gap Across Countries by Group Group Category Coefficient t Sex Boy -53.97 -23.62*** Girl -48.75 -18.88*** Books owned 0-25 -39.14 -15.58*** 26-100 -43.06 -14.99*** Over 100 -50.41 -15.32*** Freq. speak language Always/Alm always -51.74 -25.26*** Some/Never -26.04 -6.90*** Index of possessions 0 -23.93 -1.28 1 -61.01 -5.28*** 2 -43.49 -5.90*** 3 -48.97 -11.38*** 4 -51.23 -15.61*** 5 -38.66 -16.27*** Benchmark reached+ Advanced 1.28 0.22 High -1.75 -1.03 Intermediate -5.26 -4.03*** Low -21.21 -11.74*** Note. + Advanced 625-1000, High 550-624, Intermediate 475-549, Low 0-474. Native students are the reference group. *** p < .001.

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Table 4.14 Magnitude & Direction of Reading Immigrant Achievement Gap Across Countries by Group Group Category Coefficient t Sex Boy -17.67 -4.77*** Girl -22.41 -6.16*** Student spoke language of testing before starting school Yes -19.85 -6.09*** No 16.19 2.77* Freq. speak language Always -26.68 -5.33*** Some/Never -11.75 -3.87*** Parents born in country Both -1.92 -0.22 Either -16.21 -2.26* Neither -8.76 -2.87** Benchmark reached Advanc ed -2.84 -0.46 High -1.44 -0.69 Intermediate -1.57 -0.83 Low -6.49 -1.77^ *** p < .001. ** p < .01. *p < .05.

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APPENDIX A

PARTICIPATING COUNTRIES

Table A.1 Countries Participating in TIMSS 2007 4th Grade Countries ISO Code Numeric Code Algeria DZA 12 Armenia ARM 51 Australia AUS 36 Austria AUT 40 Chinese Taipei TWN 158 Colombia COL 170 Czech Republic CZE 203 Denmark DNK 208 El Salvador SLV 222 England ENG 926 Georgia GEO 268 Germany DEU 276 Hong Kong SAR HKG 344 Hungary HUN 348 Iran, Islamic Rep. of IRN 364 Italy ITA 380 Japan JPN 392 Kazakhstan KAZ 398 Kuwait KWT 414 Latvia LVA 428 Lithuania LTU 440 Mongolia MNG 496 Morocco MAR 504 Netherlands NLD 528 New Zealand NZL 554 Norway NOR 578 Ontario, Canada COT 9132 Qatar QAT 634 Russian Federation RUS 643 Scotland SCO 927 Singapore SGP 702 Slovak Republic SVK 703 Slovenia SVN 705

113

Table A.1 – continued Countries Participating in TIMSS 2007 4th Grade Countries ISO Code Numeric Code Sweden SWE 752 Tunisia TUN 788 Ukraine UKR 804 United States USA 840 Yemen YEM 887

Table A.2 Benchmarking Participants in TIMSS 2007 4th Grade Countries ISO Code Numeric Code Alberta, Canada CAB 9134 Basque Country, Spain BSQ 3724 British Columbia, Canada CBC 9135 Dubai, UAE ADU 7841 Massachusetts, US UMA 12500 Minnesota, US UMN 12700 Ontario, Canada COT 9132

114

Table A.3

Countries and Regions Participating in PIRLS 2006

Country ISO Code Numeric Code Austria AUT 40 Belgium (Flemish) BFL 956 Belgium (French) BFR 957 Bulgaria BGR 100 Canada, CAB 9134 Canada, British Columbia CBC 9135 Canada, Nova Scotia CNS 9136 Canada, Ontario COT 9132 Canada, Quebec CQU 9133 Chinese Taipei TWN 158 Denmark DNK 208 England ENG 926 France FRA 250 Georgia FEO 268 Germany DEU 276 Hong Kong SAR HKG 344 Hungary HUN 348 Iceland ISL 352 Iceland IS5 9352 Indonesia IDN 360 Iran, Islamic Rep. of IRN 364 Israel ISR 376 Italy ITA 380 Kuwait KWT 414 Latvia LVA 428 Lithuania LTU 440 Luxembourg LUX 442 Macedonia, Republic of MKD 807 Moldova, Republic of MDA 498 Morocco MAR 504 Netherlands NLD 528 New Zealand NZL 554 Norway NOR 578 Norway* NO5 9578 Poland POL 616 Qatar QAT 634 Romania ROM 642 Russian Federation RUS 643

115

Table A.3 – continued

Countries and Regions Participating in PIRLS 2006

Country ISO Code Numeric Code Scotland SCO 927 Singapore SGP 702 Slovak Republic SVK 703 Slovenia SVN 705 South Africa ZAF 710 Spain ESP 724 Sweden SWE 752 Trinidad and Tobago TTO 780 United States USA 840 Note. For their own purposes as an additional effort, Iceland and Norway administered PIRLS 2006 to small samples of their fifth grade students. These data are included in the international database as separate files.

116

APPENDIX B

MISSING DATA

Table B.1 TIMSS Missing Data for Descriptive Analyses Variable Name Description Missing (%) Country GDP Gross Domestic Product 0% GINI Gini coefficient 0% HISTIMM History of immigration 0% POLICY Exclusionary/inclusionary/other policies 0% School

AC4GSBED Percent economically-disadvantaged students in school 7.50% AC4GNALA Percent students who have language of test as native language 4.60% AC4MSOEM School offers enrichment mathematics 6.90% AC4MSORM School offers remedial mathematics 5.40% ACDGAS Index-Level of attendance at school 5.20% ACDSRMI Index-Available mathematics resources in school 5.70% ACDGPPSC Index-Principal’s perception of school climate 4.90% PEERS Mean school score on mathematics 0% Teacher

AT4GAGE Age of teacher 5.80% AT4GTAUT Years teaching 7.10% AT4GTLCE Teacher has teaching certificate 6.10% AT4GFEDC Level of formal education completed 7.30% ATDMTTOV Index-Teacher feels very well prepared on mathematics topics 23.90% ATDMSTUD Index-Class size for mathematics instruction 28.80% ATDGTPSC Index-Teacher’s perception of school climate 6.90% ATDMTAWC Index-Math teacher’s perception of adequate work conditions 6.80% ATDGTPSS Index-Teacher’s perception of school safety 6.30% ATDMPTIT Index-Time spent on math as percent of total instruction time 30.20% Student

ITSEX Sex of student 0% AS4GOLAN How often student speaks language of testing at home 2.60% AS4GBOOK Number of books in home 3.30% AS4GTH01-05 Type of possessions at home 2.9-3.7% POSSESS Index of possessions at home 5.60% AS4GMBRN Mother born in country 4.10% AS4GFBRN Father born in country 4.40% AS4GBORN Student born in country 3.20%

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Table B.1 – continued TIMSS Missing Data for Descriptive Analyses Variable Name Description Missing (%) AS4GBRNC If not born in country, age student arrived in country of testing+ 84.10% ASDGBORN Index-Both parents born in country 3.40% ASDMPATM Index-Student’s positive affect toward mathematics 3.70% ASDMSCM Index-Student’s self-confidence in learning mathematics 4.10% ASDGPBSS Index-Student’s perception of being safe at school 23.80% Note. +Data logically applicable only to immigrant students

Table B.2 TIMSS Missing Data for Multilevel Analyses Variable Name Description Missing (%) GDP Gross Domestic Product 0% GINI Gini coefficient 0% HISTIMM History of immigration 0% POLICY Exclusionary/inclusionary/other policies 0% School Percent economically-disadvantaged students in school-over or under AC4GSBED 50% 7.30% AC4GNALA Percent students who have language of test as native language 4.20% ACDSRMI Index-Available mathematics resources in school-High or Med/Low 5.40% PEERS Mean school score on mathematics 0% Teacher AT4GTAUT Years teaching 0.60% AT4GFEDC Level of formal education completed 0.30% ATDMSTUD Index-Class size for mathematics instruction 0.80% ATDMPTIT Index-Time spent on math as percent of total instruction time 1.60% Student ITSEX Sex of student 0% AS4GOLAN How often student speaks language of testing at home 2.90% AS4GBOOK Number of books in home 3.50% AS4GMBRN Mother born in country 4.40% AS4GFBRN Father born in country 4.60% AS4GBORN Student born in country 3.40% ASDGBORN Index-Both parents born in country 3.60% POSSESS Index of possessions at home 5.92%

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Table B.3 PIRLS Missing Data for Descriptive Analyses

Variable Name Description Missing (%) Country GDP Gross Domestic Product 0% GINI Gini coefficient 0% HISTIMM History of immigration 0% POLICY Exclusionary/inclusionary/other policies 0% School

ACBGPST1 Percent economically-disadvantaged students in school 8.80% ACBGPST3 Percent students do not speak language of test as 1st language 8.40% ACDGASR Index-Principal’s perception of availability school resources 7.20% ACDGHSI Index-Home-school involvement 5.50% ACDGPPSC Index-Principal’s perception of school climate 6.00% ACDGPPSS Index-Principal’s perception of school safety 6.10% PEERS Mean school score on reading 0% Teacher

ATBGERCN School offers enrichment reading instruction 7.10% TGACTMR1+ Hours spent on language instruction per week 27.89% TGRINR1+ Hours spent on reading instruction per week 15.18% ATBGTAUG Number of years teaching altogether 5.70% ATBG4TAU Number of years teaching fourth grade 6.00% ATBGAGE Age of teacher 5.90% ATBGHLE Teacher’s highest level of formal education 5.90% ATBGTCR Teacher is certified to teach 5.30% ATBGTCR1 Type of license or certificate 23.30% ATBGEAR1-9 Extent to which teacher studied specific area (out of 9) 9.30-10% ATBGWRK Teacher works full or part time 5.60% ATDGTCS Index teacher career satisfaction 6.50% Student

ITSEX Sex of student 0% ASBGLNG1 Student spoke language of testing before starting school 3.60% ASBGLNGH How often student speaks language of testing at home 7.40% ASBGBOOK Number of books in home 5.90% ASBGTA1-6 Type of possessions at home 4-4.3% ASBGBRN1 Student born in country 4% ASBGBRNM Mother born in country 4.20% ASBGBRNF Father born in country 4.20% ASDHHER Index-Home educational resources 51.10% ASDGBRN Index-Both parents born in country 9.90% ASDGSATR Index-Student’s reading attitudes 3.60% ASDGSRSC Index-Student’s reading self-concept 3.70% ASDGSSS Index-Perception of safety in school 3.10%

119

Table B.3 – continued PIRLS Missing Data for Descriptive Analyses

Variable Name Description Missing (%) Parent

ASBHHA01-10 Freq. child engaged in activities before start school (out of 10) 49.8-50.1% ASBH0ATT Child attended ISCED level 0 49.60% ASBH0HLO Length of time child attended ISCED level 0 59% ASBHDOT1-T6 Freq. parent engages in activity with child (out of 6) 49.9-50.1% ASBHLEDF Highest education father 53% ASBHEMPF Employment situation father 52.90% ASBHLEDM Highest education mother 52.70% ASBHEMPM Employment situation mother 53% ASBHMJF Main job father 54.50% ASBHMJM Main job mother 54.50% ASBHWELL Compared to others, how well-off family is financially 51% ASDHEDUP Index-Parents’ highest education level 51.90% ASDHEHLA Index-Early home literacy activities 49.80% ASDHPEMP Index-Parent’s employment situations 53.50% ASDHOCCP Index-Parents’ highest occupation level 52.20% Note. +Author-constructed variables which combine ATBGACTM with ATBGACTH, and ATBGRINM with ATBGRINH.

120

Table B.4 PIRLS Missing Data for Multilevel Analyses Variable Name Description Missing (%) Imputation GDP Gross Domestic Product 0% GINI Gini coefficient 0% HISTIMM History of immigration 0% POLICY Exclusionary/inclusionary/other policies 0% School

Percent economically-disadvantaged students in school- ACBGPST1 <>50% 8.90% Percent students do not speak language of test as 1st ACBGPST3 language-><50% 8.90% Index-Principal’s perception of availability school ACDGASR resources 7.50% PEERS Mean school score on reading 0% Teacher

ATBGERCN School offers enrichment reading instruction 5.20% Hours spent on reading instruction per week (combination Mean of TGRINR of two above) 15.18% nearby 2 pts TGRINR1* Hours spent on reading instruction per week-mean imputed 0.08% Hours spent on language instruction per week Mean of TGACTMR (combination of two above) 27.89% nearby 2 pts Hours spent on language instruction per week-mean TGACTMR1* imputed 0.08% ATBGTAUG Number of years teaching altogether 0.00% ATBG4TAU Number of years teaching fourth grade 0.00% ATBGHLE Teacher’s highest level of formal education 0.00% Student

ITSEX Sex of student 0.00% ASBGLNG1 Student spoke language of testing before starting school 3.60% ASBGLNGH How often student speaks language of testing at home 7.40% How often student speaks language of testing at home- Always, Sometimes/Never 7.40% ASBGBRN1 Student born in country 4.00% ASBGBRNM Mother born in country 12.00% Eliminated ASBGBRNF Father born in country 15.60% Eliminated ASDGBRN Index-Both parents born in country 9.90% Note. *Mean-imputed variables were used in analysis.

121

APPENDIX C

DESCRIPTIVE TABLES

Table C.1

School-level Descriptive Data for TIMSS Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students who attend a school where over half of students are economically disadvantaged Australia Native 11.82 2.78 Immigrant 20.67 5.70 Canada (Ontario) Native 13.65 3.21 Immigrant 37.64 6.79 England Native 14.55 2.82 Immigrant 22.95 4.96 Germany Native 13.36 2.25 Immigrant 22.95 4.72 New Zealand Native 20.61 1.72 Immigrant 30.05 2.69 Scotland Native 12.57 2.62 Immigrant 20.10 4.58 Ukraine Native 3.56 1.51 Immigrant 8.91 4.02 United States Native 37.99 2.76 Immigrant 60.26 3.27 International Average Native 16.01 0.89 Immigrant 27.94 1.68 Percent of students who attend a school where over 90% of students speak language of test as native language Australia Native 64.16 4.01 Immigrant 51.96 5.10 Canada (Ontario) Native 63.81 4.50 Immigrant 37.43 5.16 England Native 70.08 3.88 Immigrant 57.17 4.87 Germany Native 46.90 3.08 Immigrant 29.00 3.71 New Zealand Native 70.07 2.78 Immigrant 49.31 3.94

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Table C.1 – continued

School-level Descriptive Data for TIMSS Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students who attend a school where over 90% of students speak language of test as native language Scotland Native 87.50 3.18 Immigrant 83.55 4.37 Ukraine Native 59.11 3.39 Immigrant 52.29 4.05 United States Native 66.50 3.06 Immigrant 45.12 3.35 International Average Native 66.02 1.25 Immigrant 50.73 1.54

Percent of students who attend a school that offers enrichment mathematics

Australia Native 58.93 3.75 Immigrant 54.63 5.84 Canada (Ontario) Native 31.76 4.79 Immigrant 20.84 5.34 England Native 61.60 4.00 Immigrant 63.73 5.16 Germany Native 38.31 3.08 Immigrant 41.35 4.20 New Zealand Native 69.59 3.22 Immigrant 71.56 3.40 Scotland Native 49.88 5.17 Immigrant 50.84 6.08 Ukraine Native 6.17 2.18 Immigrant 5.06 2.04 United States Native 51.81 3.29 Immigrant 47.23 3.50 International Average Native 46.01 1.34 Immigrant 44.40 1.64

Percent of students who attend a school that does not offer remedial mathematics

Australia Native 26.92 3.55 Immigrant 23.90 4.74 Canada (Ontario) Native 33.79 4.80 Immigrant 40.43 6.72 England Native 8.29 2.42 Immigrant 5.88 1.92 Germany Native 26.70 2.64 Immigrant 22.82 3.67

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Table C.1 – continued

School-level Descriptive Data for TIMSS Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students who attend a school that does not offer remedial mathematics

New Zealand Native 32.33 3.51 Immigrant 31.04 3.56 Scotland Native 8.75 2.60 Immigrant 6.24 2.39 Ukraine Native 18.04 2.82 Immigrant 21.20 3.84 United States Native 30.08 3.19 Immigrant 28.94 3.81 International Average Native 23.11 1.16 Immigrant 22.56 1.44

Percent of students who attend a school with medium to low attendance

Australia Native 68.67 4.13 Immigrant 69.51 6.01 Canada (Ontario) Native 56.43 5.07 Immigrant 65.52 6.67 England Native 65.22 4.52 Immigrant 69.47 5.05 Germany Native 35.24 3.64 Immigrant 47.62 4.53 New Zealand Native 61.62 3.50 Immigrant 67.61 3.83 Scotland Native 48.30 4.05 Immigrant 53.95 4.74 Ukraine Native 53.29 4.07 Immigrant 56.35 5.94 United States Native 76.56 3.25 Immigrant 87.30 2.60 International Average Native 58.17 1.44 Immigrant 64.67 1.79

Percent of students who attend a school with a high number resources available

Australia Native 57.50 5.00 Immigrant 56.82 5.94 Canada (Ontario) Native 38.94 4.00 Immigrant 31.06 5.52 England Native 54.06 4.60 Immigrant 47.32 5.49

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Table C.1 – continued

School-level Descriptive Data for TIMSS Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students who attend a school with a high number resources available

Germany Native 56.47 3.90 Immigrant 51.03 5.12 New Zealand Native 54.91 3.43 Immigrant 55.34 3.61 Scotland Native 60.57 3.86 Immigrant 64.34 4.96 Ukraine Native 15.51 2.72 Immigrant 9.73 2.47 United States Native 50.43 3.57 Immigrant 43.75 4.02 International Average Native 48.55 1.39 Immigrant 44.93 1.69 Percent of students who attend a school where principal has a high perception of school climate Australia Native 51.16 4.34 Immigrant 46.01 4.99 Canada (Ontario) Native 39.91 4.91 Immigrant 42.92 7.76 England Native 45.32 4.50 Immigrant 43.53 5.52 Germany Native 13.25 2.65 Immigrant 11.53 3.34 New Zealand Native 50.45 3.26 Immigrant 44.41 3.87 Scotland Native 48.91 4.83 Immigrant 47.29 5.55 Ukraine Native 2.50 1.26 Immigrant 2.49 1.33 United States Native 50.18 3.15 Immigrant 38.14 3.32 International Average Native 37.71 1.34 Immigrant 34.54 1.70

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Table C.2

Descriptive Data for TIMSS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher is specific age range

Australia Native Under 30 21.20 4.04 30-49 46.42 3.98 Over 50 32.38 3.67 Immigrant Under 30 21.71 3.91 30-49 51.97 4.52 Over 50 26.33 3.38 Canada (Ontario) Native Under 30 12.08 2.60 30-49 66.81 4.56 Over 50 21.10 4.07 Immigrant Under 30 14.42 4.36 30-49 62.48 6.43 Over 50 23.10 4.63 England Native Under 30 31.39 3.87 30-49 52.27 4.12 Over 50 16.34 3.00 Immigrant Under 30 32.54 4.60 30-49 51.43 4.31 Over 50 16.03 3.48 Germany Native Under 30 8.87 1.92 30-49 34.98 2.88 Over 50 56.15 3.09 Immigrant Under 30 8.44 2.54 30-49 36.59 4.63 Over 50 54.97 4.56 New Zealand Native Under 30 27.17 2.70 30-49 51.96 2.36 Over 50 20.87 2.27 Immigrant Under 30 28.51 3.08 30-49 51.22 3.06 Over 50 20.28 2.56 Scotland Native Under 30 23.05 3.66 30-49 43.78 3.64 Over 50 33.16 3.47 Immigrant Under 30 20.52 3.96 30-49 45.04 4.26 Over 50 34.44 4.33

126

Table C.2 – continued

Descriptive Data for TIMSS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher is specific age range

Ukraine Native Under 30 8.00 1.89 30-49 69.34 3.19 Over 50 22.66 2.93 Immigrant Under 30 7.13 2.10 30-49 73.18 3.60 Over 50 19.69 3.36 United States Native Under 30 18.24 2.09 30-49 50.20 2.39 Over 50 31.56 2.20 Immigrant Under 30 17.15 2.29 30-49 52.98 3.15 Over 50 29.87 2.95 International Average Native Under 30 18.75 1.05 30-49 51.97 1.23 Over 50 29.28 1.11 Immigrant Under 30 18.80 1.23 30-49 53.11 1.54 Over 50 28.09 1.32

Percent of students whose teacher has completed specific level of formal education

Australia Native 5B or less 7.53 1.71 5A 49.50 4.12 5A (2nd degree) or higher 42.96 4.12 Immigrant 5B or less 6.10 1.42 5A 56.01 5.06 5A (2nd degree) or higher 37.89 4.97 Canada (Ontario) Native 5B or less 2.97 1.19 5A 76.17 3.82 5A (2nd degree) or higher 20.86 3.70 Immigrant 5B or less 1.43 0.70 5A 80.97 3.97 5A (2nd degree) or higher 17.60 3.86 England Native 5B or less 9.38 2.14 5A 55.91 4.64

127

Table C.2 – continued

Descriptive Data for TIMSS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher has completed specific level of formal education

5A (2nd degree) or higher 34.71 4.23 Immigrant 5B or less 11.18 2.42 5A 55.41 5.07 5A (2nd degree) or higher 33.41 4.85 Germany Native 5B or less 0.33 0.33 5A 99.67 0.33 Immigrant 5B or less 1.03 1.03 5A 98.97 1.03 New Zealand Native 5B or less 25.34 2.46 5A 66.15 2.76 5A (2nd degree) or higher 8.51 1.24 Immigrant 5B or less 23.68 2.46 5A 64.32 3.06 5A (2nd degree) or higher 12.00 2.16 Scotland Native 5A 70.36 4.02 5A (2nd degree) or higher 29.64 4.02 Immigrant 5A 63.08 4.93 5A (2nd degree) or higher 36.92 4.93 Ukraine Native 5B or less 17.26 2.91 5A 81.79 2.96 5A (2nd degree) or higher 0.96 0.67 Immigrant 5B or less 22.82 4.83 5A 76.94 4.80 5A (2nd degree) or higher 0.24 0.24 United States Native 5B or less 0.23 0.24 5A 48.42 2.76 5A (2nd degree) or higher 51.35 2.73 Immigrant 5A 42.58 3.31

128

Table C.2 – continued

Descriptive Data for TIMSS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher has completed specific level of formal education

5A (2nd degree) or higher 57.42 3.31 International Average Native 5B or less 7.88 0.61 5A 68.50 1.21 5A (2nd degree) or higher 23.62 1.08 Immigrant 5B or less 8.28 0.78 5A 67.29 1.46 5A (2nd degree) or higher 24.44 1.27 Percent of students whose class size is specific number of students Australia Native 1 to 19 18.76 3.03 20 to 32 79.31 3.03 33 or More 1.93 1.24 Immigrant 1 to 19 16.16 3.75 20 to 32 82.04 3.85 33 or More 1.79 1.29 Canada (Ontario) Native 1 to 19 16.41 3.01 20 to 32 79.44 3.31 33 or More 4.15 1.07 Immigrant 1 to 19 24.71 6.53 20 to 32 70.39 7.09 33 or More 4.90 3.51 England Native 1 to 19 7.73 1.84 20 to 32 80.49 3.04 33 or More 11.78 2.37 Immigrant 1 to 19 10.81 2.94 20 to 32 78.97 3.99 33 or More 10.21 2.87 Germany Native 1 to 19 21.30 2.55 20 to 32 78.70 2.55 Immigrant 1 to 19 22.60 3.98 20 to 32 77.40 3.98 New Zealand Native 1 to 19 13.54 2.22 20 to 32 80.44 2.43 33 or More 6.01 1.76 Immigrant 1 to 19 10.66 2.15 20 to 32 85.05 2.80

129

Table C.2 – continued

Descriptive Data for TIMSS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose class size is specific number of students

33 or More 4.30 1.80 Scotland Native 1 to 19 16.30 2.75 20 to 32 78.96 2.99 33 or More 4.74 1.52 Immigrant 1 to 19 17.52 3.95 20 to 32 76.44 4.40 33 or More 6.04 3.02 Ukraine Native 1 to 19 30.66 3.45 20 to 32 64.81 3.55 33 or More 4.54 1.34 Immigrant 1 to 19 27.61 3.80 20 to 32 65.30 4.74 33 or More 7.09 3.14 United States Native 1 to 19 26.63 2.79 20 to 32 68.21 3.00 33 or More 5.15 1.40 Immigrant 1 to 19 24.13 2.72 20 to 32 71.09 2.97 33 or More 4.78 1.22 I nternational Average Native 1 to 19 18.92 0.97 20 to 32 76.29 1.06 33 or More 4.79 0.52 Immigrant 1 to 19 19.28 1.39 20 to 32 75.83 1.56 33 or More 4.89 0.85

130

Table C.3

Descriptive Data for TIMSS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose teacher has a teaching certificate Australia Native 96.31 1.22 Immigrant 96.87 1.23 Canada (Ontario) Native 99.95 0.05 Immigrant 99.96 0.04 England Native 98.01 0.59 Immigrant 98.68 0.57 Germany Native 99.67 0.33 Immigrant 98.97 1.03 New Zealand Native 98.74 0.54 Immigrant 98.86 0.49 Scotland Native 100.00 0.00 Immigrant 100.00 0.00 Ukraine Native 100.00 0.00 Immigrant 100.00 0.00 United States Native 98.58 0.74 Immigrant 99.55 0.33 International Average Native 98.91 0.21 Immigrant 99.11 0.23

Percent of students whose teacher's perception of school climate is high

Australia Native 35.83 3.62 Immigrant 32.67 4.76 Canada (Ontario) Native 26.28 4.26 Immigrant 22.53 6.16 England Native 37.27 4.05 Immigrant 33.11 4.24 Germany Native 18.06 2.97 Immigrant 12.09 2.62 New Zealand Native 37.50 2.12 Immigrant 33.72 3.52 Scotland Native 49.62 3.44 Immigrant 39.42 4.41 Ukraine Native 15.77 2.98 Immigrant 13.80 3.24 United States Native 40.31 2.86 Immigrant 26.97 2.67 International Average Native 32.58 1.18 Immigrant 26.79 1.45

131

Table C.3 – continued

Descriptive Data for TIMSS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose teacher's perception of adequate work conditions is high Australia Native 11.19 2.53 Immigrant 11.74 2.68 Canada (Ontario) Native 13.32 3.55 Immigrant 15.49 6.14 England Native 18.37 3.28 Immigrant 17.90 3.92 Germ any Native 8.95 2.14 Immigrant 11.75 3.05 New Zealand Native 17.60 2.31 Immigrant 17.90 2.54 Scotland Native 12.00 2.77 Immigrant 14.68 4.03 Ukraine Native 13.22 2.69 Immigrant 10.65 3.02 United States Native 25.69 2.66 Immigrant 22.02 2.76 International Average Native 15.04 0.98 Immigrant 15.26 1.30

Percent of students whose teacher's perception of school safety is high

Australia Native 86.73 2.18 Immigrant 79.97 4.60 Canada (Ontario) Native 86.41 3.21 Immigrant 82.27 5.32 England Native 87.18 2.36 Immigrant 76.38 3.29 Germany Native 91.83 1.64 Immigrant 86.00 3.66 New Zealand Native 87.64 1.63 Immigrant 82.15 2.62 Scotland Native 87.68 2.57 Immigrant 86.22 3.91 Ukraine Native 85.31 2.93 Immigrant 79.84 4.30 United States Native 83.45 2.10 Immigrant 66.67 3.55 International Average Native 87.03 0.84 Immigrant 79.94 1.41

132

Table C.4

Descriptive Data for TIMSS Teacher-derived Scale Variables

Immigrant Country Mean Mean (s.d.) Status Students' teacher's average years of teaching Australia Native 16.80 11.15 Immigrant 16.02 10.60 Germany Native 21.76 12.05 Immigrant 21.90 12.62 New Zealand Native 11.55 9.76 Immigrant 11.33 10.41 Ukraine Native 22.39 8.67 Immigrant 21.46 8.70 United States Native 14.02 10.43 Immigrant 13.25 10.28 England Native 10.75 9.64 Immigrant 10.85 10.04 Scotland Native 14.71 11.55 Immigrant 14.99 10.87 Canada (Ontario) Native 13.46 8.86 Immigrant 12.20 8.57 International Average Native 15.68 10.26 Immigrant 15.25 10.26 Mean index - students' teacher feels very well prepared on mathematics topics Australia Native 80.34 25.60 Immigrant 81.98 23.92 Germany Native 61.88 33.92 Immigrant 64.02 31.56 New Zealand Native 77.02 27.56 Immigrant 76.99 27.30 Ukraine Native 85.87 21.58 Immigrant 82.22 23.70 United States Native 90.45 16.75 Immigrant 89.04 18.63 England Native 89.33 18.55 Immigrant 87.91 20.41 Scotland Native 90.74 16.75 Immigrant 88.74 18.39 Canada (Ontario) Native 88.81 15.86 Immigrant 90.18 13.36 International Average Native 83.06 22.07 Immigrant 82.63 22.16

133

Table C.4 – continued

Descriptive Data for TIMSS Teacher-derived Scale Variables

Immigrant Country Mean Mean (s.d.) Status Mean index - students' teacher reported time spent on math as percent of total instruction time Australia Native 17.41 5.67 Immigrant 18.29 6.39 Germany Native 17.35 3.23 Immigrant 17.22 3.42 New Zealand Native 15.89 4.29 Immigrant 15.79 4.05 Ukraine Native 17.17 3.10 Immigrant 17.31 3.37 United States Native 16.22 5.91 Immigrant 16.63 6.41 England Native 19.02 2.88 Immigrant 19.13 2.40 Scotland Native 19.08 3.85 Immigrant 18.95 3.85 Canada (Ontario) Native 18.03 4.80 Immigrant 17.49 4.95 International Average Native 17.52 4.22 Immigrant 17.60 4.35

Table C.5

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students who speak language of testing at home - frequency

Australia Native Always 81.42 1.29 Almost always 11.40 0.87 Sometimes/Never 7.18 0.91 Immigrant Always 64.09 3.23 Almost always 15.56 2.16 Sometimes/Never 20.34 2.42 Canada (Ontario) Native Always 64.34 1.91 Almost always 25.47 1.64 Sometimes/Never 10.18 0.88 Immigrant Always 39.01 3.48

134

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students who speak language of testing at home - frequency

Almost always 29.77 2.99 Sometimes/Never 31.22 2.54 England Native Always 79.94 1.24 Almost always 15.69 1.12 Sometimes/Never 4.37 0.44 Immigrant Always 56.89 3.40 Almost always 20.32 2.39 Sometimes/Never 22.79 2.46 Germany Native Always 79.50 0.87 Almost always 14.57 0.65 Sometimes/Never 5.93 0.52 Immigrant Always 41.75 2.72 Almost always 32.88 2.47 Sometimes/Never 25.37 2.27 New Zealand Native Always 80.62 0.92 Almost always 12.16 0.67 Sometimes/Never 7.21 0.58 Immigrant Always 53.79 1.90 Almost always 17.52 1.20 Sometimes/Never 28.69 1.59 Scotland Native Always 80.41 1.03 Almost always 12.83 0.71 Sometimes/Never 6.76 0.80 Immigrant Always 64.26 2.77 Almost always 13.35 1.82 Sometimes/Never 22.39 2.06 Ukraine Nati ve Always 54.87 2.69 Almost always 19.02 1.14 Sometimes/Never 26.11 2.21 Immigrant Always 60.81 3.43 Almost always 15.58 1.82 Sometimes/Never 23.61 2.90 United States Native Always 78.20 0.96 Almost always 13.51 0.63 Sometimes/Never 8.30 0.58 Immigrant Always 49.73 1.94 Almost always 17.08 1.20

135

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students who speak language of testing at home - frequency

Sometimes/Never 33.18 1.67 International Average Native Always 74.91 0.53 Almost always 15.58 0.35 Sometimes/Never 9.50 0.36 Immigrant Always 53.79 1.03 Almost always 20.26 0.74 Sometimes/Never 25.95 0.81

Percent of students - number of books owned at home

Australia Native 0-25 17.38 0.94 26-100 37.20 0.98 Over 100 45.43 1.14 Immigrant 0-25 29.43 2.63 26-100 32.47 2.12 Over 100 38.10 2.86 Canada (Ontario) Native 0-25 21.94 1.48 26-100 35.20 1.36 Over 100 42.86 1.75 Immigrant 0-25 37.21 3.52 26-100 29.79 2.65 Over 100 33.00 2.74 England Native 0-25 23.84 1.25 26-100 34.12 1.06 Over 100 42.04 1.43 Immigrant 0-25 40.30 2.31 26-100 30.04 2.30 Over 100 29.65 2.37 Germany Na tive 0-25 31.19 1.37 26-100 36.41 1.05 Over 100 32.41 1.22 Immigrant 0-25 55.49 3.26 26-100 27.06 2.55 Over 100 17.45 2.28 New Zealand Native 0-25 24.24 0.84 26-100 35.35 0.83 Over 100 40.41 1.07

136

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students - number of books owned at home

Immigrant 0-25 37.79 1.72 26-100 30.09 1.45 Over 100 32.12 1.44 Scotland Native 0-25 30.04 1.34 26-100 33.55 1.07 Over 100 36.41 1.43 Immigrant 0-25 42.94 3.15 26-100 27.86 2.49 Over 100 29.20 3.02 Ukraine Native 0-25 40.78 1.53 26-100 38.31 1.07 Over 100 20.91 1.16 Immigrant 0-25 51.29 2.56 26-100 29.71 2.02 Over 100 19.00 1.72 United States Native 0-25 30.98 0.97 26-100 35.18 0.64 Over 100 33.84 0.94 Immigrant 0-25 49.24 1.58 26-100 28.53 1.24 Over 100 22.24 1.19 International Average Native 0-25 27.55 0.44 26-100 35.66 0.36 Over 100 36.79 0.46 Immigrant 0-25 42.96 0.95 26-100 29.44 0.76 Over 100 27.59 0.81

Age range at which immigrant student arrived in country of testing

Australia Immigrant Older than 5 39.18 3.59 Immigrant 1 to 5 30.46 2.69 Immigrant Younger than 1 30.36 2.92 Canada (Ontario) Immigrant Older than 5 30.79 2.89 Immigrant 1 to 5 37.84 2.54 Immigrant Younger than 1 31.37 2.96 England Immigrant Older than 5 32.98 2.32 Immigrant 1 to 5 30.96 2.19

137

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Age range at which immigrant student arrived in country of testing

Immigrant Younger than 1 36.06 2.97 Germany Immigrant Older than 5 31.52 2.16 Immigrant 1 to 5 35.08 2.38 Immigrant Younger than 1 33.41 2.53 New Zealand Immigrant Older than 5 44.20 1.62 Immigrant 1 to 5 32.21 1.55 Immigrant Younger than 1 23.59 1.43 Scotland Immigrant Older than 5 37.83 2.56 Immigrant 1 to 5 19.51 2.19 Immigrant Younger than 1 42.66 2.94 Ukraine Immigrant Older than 5 36.52 2.77 Immigrant 1 to 5 19.32 1.95 Immigrant Younger than 1 44.16 3.06 United States Immigrant Older than 5 29.42 1.37 Immigrant 1 to 5 31.02 1.28 Immigrant Younger than 1 39.57 1.40 Internationa l Average Immigrant Older than 5 35.30 0.88 Immigrant 1 to 5 29.55 0.76 Immigrant Younger than 1 35.15 0.92

Percent of students whose parents were born in country of testing

Australia Native Both parents 62.94 1.84 Only one parent 21.21 0.97 Neither parent 15.85 1.52 Immigrant Both parents 27.68 2.51 Only one parent 22.52 2.17 Neither parent 49.80 3.27 Canada (Ontario) Native Both parents 60.63 1.96 Only one parent 18.57 0.97 Neither parent 20.80 1.89 Immigrant Both parents 23.04 3.56 Only one parent 11.53 1.54 Neither parent 65.43 3.95 England Native Both parents 78.67 1.38 Only one parent 15.24 1.00 Neither parent 6.09 0.74 Immigrant Both parents 41.95 2.95 Only one parent 17.54 1.82

138

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose parents were born in country of testing

Neither parent 40.51 3.18 Germany Native Both parents 74.74 1.37 Only one parent 11.59 0.72 Neither parent 13.66 0.91 Immigrant Both parents 31.57 2.75 Only one parent 17.30 1.97 Neither parent 51.13 2.75 New Zealand Native Both parents 70.03 1.03 Only one parent 19.28 0.82 Neither parent 10.69 0.70 Immigrant Both parents 29.67 1.79 Only one parent 20.83 1.30 Neither parent 49.49 1.85 Scotland Native Both parents 88.05 0.71 Only one parent 9.94 0.65 Neither parent 2.01 0.32 Immigrant Both parents 58.54 2.55 Only one parent 15.62 1.86 Neither parent 25.84 2.27 Ukraine Native Both parents 80.22 1.04 Only one parent 14.13 0.74 Neither parent 5.65 0.72 Immigrant Both parents 51.08 2.61 Only one parent 22.66 1.94 Neither parent 26.26 2.43 United States Native Both parents 76.41 0.97 Only one parent 11.34 0.44 Neither parent 12.25 0.82 Immigrant Both parents 40.27 1.76 Only one parent 21.59 1.33 Neither parent 38.14 1.92 International Average Native Both parents 73.96 0.48 Only one parent 15.16 0.29 Neither parent 10.87 0.38 Immigrant Both parents 37.98 0.93 Only one parent 18.70 0.62 Neither parent 43.33 0.99

139

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Student's positive affect toward mathematics

Australia Native High 66.27 1.66 Medium 16.26 0.79 Low 17.47 1.23 Immigrant High 65.16 2.46 Medium 17.23 1.91 Low 17.60 1.91 Canada (Ontario) Native High 57.60 1.68 Medium 17.86 1.03 Low 24.53 1.52 Immigrant High 63.42 2.65 Medium 15.59 1.88 Low 20.99 2.28 England Native High 61.72 1.50 Medium 17.77 0.87 Low 20.52 1.10 Immigrant High 64.84 2.23 Medium 12.68 1.54 Low 22.48 1.77 Germany Native High 69.40 0.99 Medium 16.25 0.61 Low 14.35 0.72 Immigrant High 75.61 1.92 Medium 14.88 1.80 Low 9.51 1.56 New Zealand Native High 64.39 1.03 Medium 17.68 0.70 Low 17.93 0.88 Immigrant High 69.42 1.84 Medium 18.50 1.53 Low 12.08 1.14 Scotlan d Native High 57.80 1.33 Medium 18.10 0.79 Low 24.11 1.20 Immigrant High 63.51 2.21 Medium 14.93 1.80 Low 21.56 2.24 Ukraine Native High 86.73 0.80 Medium 7.98 0.52

140

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Student's positive affect toward mathematics

Low 5.29 0.53 Immigrant High 83.37 2.02 Medium 11.44 1.81 Low 5.19 1.16 United States Native High 65.33 0.82 Medium 16.02 0.59 Low 18.65 0.67 Immigrant High 67.09 1.39 Medium 16.70 0.99 Low 16.21 0.95 International Average Native High 66.15 0.45 Medium 15.99 0.27 Low 17.86 0.36 Immigrant High 69.05 0.75 Medium 15.24 0.60 Low 15.70 0.60

Student's self-confidence in learning mathematics

Australia Native High 65.23 1.26 Medium 25.26 0.94 Low 9.50 0.77 Immigrant High 57.17 3.91 Medium 31.09 3.18 Low 11.73 1.87 Canada (Ontario) Native High 64.94 1.54 Medium 25.42 1.23 Low 9.64 0.76 Immigrant High 54.86 2.05 Medium 33.02 2.46 Low 12.12 2.02 England Native High 65.41 1.00 Medium 25.08 0.85 Low 9.51 0.75 Immigrant High 59.12 2.63 Medium 28.99 2.23 Low 11.89 1.67 Germany Native High 70.78 0.88 Medium 19.97 0.78

141

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Student's self-confidence in learning mathematics

Low 9.24 0.51 Immigrant High 59.84 2.50 Medium 27.68 1.84 Low 12.48 1.92 New Zealand Native High 53.29 0.83 Medium 35.79 0.84 Low 10.92 0.57 Immigrant High 47.92 1.71 Medium 39.44 1.52 Low 12.64 1.15 Scotland Native High 67.56 1.12 Medium 23.47 1.08 Low 8.97 0.65 Immigrant High 60.16 2.65 Medium 28.47 2.56 Low 11.37 1.59 Ukraine Native High 57.38 1.07 Medium 32.65 0.97 Low 9.97 0.68 Immigrant High 41.03 2.51 Medium 42.34 2.56 Low 16.63 2.34 United States Native High 69.94 0.83 Medium 20.15 0.64 Low 9.92 0.45 Immigrant High 56.72 1.54 Medium 31.63 1.38 Low 11.65 0.87 International Average Native High 64.32 0.39 Medium 25.97 0.33 Low 9.71 0.23 Immigrant High 54.60 0.90 Medium 32.83 0.81 Low 12.56 0.61

142

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Student's perception of being safe at school

Australia Native High 30.55 1.28 Medium 44.10 1.32 Low 25.35 1.46 Immigrant High 26.66 2.70 Medium 42.01 2.89 Low 31.32 2.49 Canada (Ontario) Native High 33.86 1.22 Medium 42.08 1.08 Low 24.06 1.11 Immigrant High 25.91 1.84 Medium 43.69 2.37 Low 30.40 2.63 England Native High 33.32 1.16 Medium 43.43 1.01 Low 23.25 0.90 Immigrant High 25.52 1.91 Medium 38.83 2.61 Low 35.65 2.30 Germany Native High 55.34 1.23 Medium 33.32 0.92 Low 11.34 0.68 Immigrant High 43.11 2.65 Medium 37.88 2.85 Low 19.00 1.80 New Zealand Native High 26.32 0.94 Medium 42.55 1.00 Low 31.13 1.06 Immigrant High 21.85 1.46 Medium 38.95 1.51 Low 39.20 1.98 Scotland Native High 41.43 1.29 Medium 39.65 1.00 Low 18.91 1.10 Immigrant High 30.17 2.35 Medium 35.66 2.43 Low 34.17 3.02 Ukraine Native High 52.60 1.41 Medium 37.34 1.07

143

Table C.5 – continued

Student-level Descriptive Data for TIMSS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Student's perception of being safe at school

Low 10.06 0.79 Immigrant High 46.49 2.97 Medium 37.02 2.19 Low 16.48 2.03 International Average Native High 39.06 0.46 Medium 40.35 0.40 Low 20.59 0.39 Immigrant High 31.39 0.88 Medium 39.15 0.93 Low 29.46 0.89 Note. United States did not administer the questions utilized in constructing this index.

Table C.6

Student-level Descriptive Data for TIMSS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Possessions at home - percent of students who have a calculator

Australia Native 94.39 0.51 Immigrant 90.63 1.50 Canada (Ontario) Native 95.68 0.70 Immigrant 92.41 1.19 England Native 94.93 0.50 Immigrant 92.16 1.26 Germany Native 93.91 0.49 Immigrant 81.83 2.78 New Zealand Native 92.75 0.49 Immigrant 88.76 1.09 Scotland Native 93.25 0.56 Immigrant 88.31 1.47 Ukraine Native 81.13 0.90 Immigrant 75.06 2.45 United States Native 93.49 0.39 Immigrant 85.18 1.21 International Average Native 92.44 0.21 Immigrant 86.79 0.61

144

Table C.6 – continued

Student-level Descriptive Data for TIMSS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Possessions at home -percent of students who have a computer

Australia Native 95.11 0.58 Immigrant 92.38 1.44 Canada (Ontario) Native 96.01 0.47 Immigrant 94.87 1.00 England Native 95.18 0.43 Immigrant 91.27 1.11 Germany Native 94.19 0.47 Immigrant 87.39 1.63 New Zealand Native 91.87 0.52 Immigrant 89.79 1.08 Scotland Native 95.05 0.53 Immigrant 89.78 1.79 Ukraine Native 40.53 1.33 Immigrant 38.92 2.51 United States Native 91.68 0.44 Immigrant 84.44 1.19 International Average Native 87.45 0.23 Immigrant 83.61 0.55

Possessions at home - percent of students who have a study desk

Australia Native 85.64 1.14 Immigrant 81.40 1.86 Canada (Ontario) Native 82.26 1.25 Immigrant 82.10 2.56 England Native 82.75 0.87 Immigrant 77.36 2.27 Germany Native 98.26 0.23 Immigrant 95.66 1.24 New Zealand Native 80.42 0.77 Immigrant 77.67 1.43 Scotland Native 80.78 0.88 Immigrant 75.94 2.03 Ukraine Native 88.22 0.86 Immigrant 84.22 2.28 United States Native 80.06 0.79 Immigrant 69.86 1.30 International Average Native 84.80 0.32 Immigrant 80.53 0.68

145

Table C.6 – continued

Student-level Descriptive Data for TIMSS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Possessions at home - percent of students who have a dictionary

Australia Native 91.00 0.93 Immigrant 87.64 1.54 Canada (Ontario) Native 89.17 0.90 Immigrant 87.02 2.21 England Native 90.38 0.77 Immigrant 85.57 1.81 Germany Native 94.97 0.51 Immigrant 90.63 1.85 New Zealand Native 91.00 0.66 Immigrant 85.89 1.20 Scotland Native 87.92 0.80 Immigrant 83.43 2.02 Ukraine Native 89.69 0.82 Immigrant 82.89 2.21 United States Native 86.59 0.68 Immigrant 77.58 1.48 Intern ational Average Native 90.09 0.27 Immigrant 85.08 0.64

Possessions at home - percent of students who have an internet connection

Australia Native 84.52 0.90 Immigrant 79.62 2.10 Canada (Ontario) Native 88.85 0.87 Immigrant 88.14 2.34 England Native 87.37 0.74 Immigrant 79.92 1.72 Germany Native 82.60 0.81 Immigrant 69.39 2.48 New Zealand Native 79.04 1.00 Immigrant 72.75 1.77 Scotland Native 86.67 0.71 Immigrant 77.51 2.45 Ukraine Native 24.19 1.10 Immigrant 23.38 2.34 United States Native 80.13 0.81 Immigrant 67.23 1.70 International Average Native 76.67 0.31 Immigrant 69.74 0.76

146

Table C.6 – continued

Student-level Descriptive Data for TIMSS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students whose mother was born in the country of testing

Australia Native 75.63 1.74 Immigrant 41.50 3.42 Canada (Ontario) Native 71.42 1.90 Immigrant 29.96 3.84 England Native 88.26 1.00 Immigrant 52.66 3.17 Germany Native 81.54 1.11 Immigrant 39.33 2.73 New Zealand Native 81.80 0.84 Immigrant 41.87 1.75 Scotland Native 94.77 0.45 Immigrant 67.87 2.34 Ukraine Native 89.00 0.85 Immigrant 64.58 2.67 United States Native 83.67 0.87 Immigrant 53.73 1.93 International Average Native 83.26 0.42 Immigrant 48.94 0.99

Percent of students whose father was born in the country of testing

Australia Native 72.35 1.58 Immigrant 36.99 2.65 Canada (Ontario) Native 69.41 2.08 Immigrant 28.03 3.75 England Native 85.94 1.13 Immigrant 50.22 3.08 Germany Native 80.50 1.17 Immigrant 42.08 2.78 New Zealand Native 79.13 0.92 Immigrant 39.26 2.01 Scotland Native 93.20 0.57 Immigrant 65.96 2.50 Ukraine Native 87.05 0.91 Immigrant 61.77 2.50 United States Native 82.16 0.91 Immigrant 50.11 1.75 International Average Native 81.22 0.44 Immigrant 46.80 0.95

147

Table C.7

Student-level Descriptive Data for TIMSS Scale Variables

Immigrant Country Mean Mean (s.d.) Status

Index of possessions - student's mean number of possessions at home

Australia Native 4.56 0.87 Immigrant 4.38 1.06 Canada (Ontario) Native 4.59 0.77 Immigrant 4.54 0.86 England Native 4.56 0.85 Immigrant 4.32 1.07 Germany Native 4.64 0.77 Immigrant 4.24 1.08 New Zealand Native 4.37 1.04 Immigrant 4.23 1.14 Scotland Native 4.52 0.89 Immigrant 4.23 1.13 Ukraine Native 2.77 1.40 Immigrant 2.62 1.46 United States Native 4.39 1.04 Immigrant 3.95 1.30 International Average Native 4.30 0.95 Immigrant 4.06 1.14

148

Table C.8

School-level Descriptive Data for PIRLS Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students who attend a school where over half of students are economically disadvantaged Canada (Ontario) Native 13.00 3.62 Immigrant 22.20 6.15 England Native 16.82 3.51 Immigrant 30.91 6.44 Israel Native 21.08 3.07 Immigrant 27.66 5.58 New Zealand Native 16.63 2.07 Immigrant 14.42 2.66 United States Native 35.82 4.11 Immigrant 53.63 5.60 International Average Native 20.67 1.50 Immigrant 29.76 2.44 Percent of students who attend a school where more than half of students do not speak the language of testing as first language Canada (Ontario) Native 8.09 2.69 Immigrant 24.48 7.78 England Native 6.43 2.14 Immigrant 31.95 8.45 Israel Native 24.88 3.34 Immigrant 27.12 5.96 New Zealand Native 4.30 1.12 Immigrant 6.77 2.54 United States Native 8.48 2.15 Immigrant 22.76 6.27 International Average Native 10.44 1.07 Immigrant 22.62 2.92 Percent of students who attend a school where the principal's perception of availability of school resources is high Canada (Ontario) Native 76.99 4.90 Immigrant 69.16 7.48 England Native 80.91 3.80 Immigrant 79.70 6.26 Israel Native 38.00 4.15 Immigrant 31.55 5.64 New Zealand Native 85.14 2.55 Immigrant 89.83 2.45

149

Table C.8 – continued

School-level Descriptive Data for PIRLS Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students who attend a school where the principal's perception of availability of school resources is high United States Native 81.55 2.76 Immigrant 77.05 5.36 International Average Native 72.52 1.67 Immigrant 69.46 2.54 Percent of students who attend a school where the index of home-school involvement is high Canada (Ontario) Native 96.01 1.93 Immigrant 95.74 2.23 England Native 27.76 4.23 Immigrant 23.27 4.67 Israel Native 75.44 3.22 Immigrant 77.18 4.83 New Zealand Native 66.32 3.45 Immigrant 70.41 4.41 United States Native 92.89 1.91 Immigrant 89.96 3.36 International Average Native 71.69 1.38 Immigrant 71.31 1.80 Percent of students who attend a school where principal has a high perception of school climate Canada (Ontario) Native 50.54 5.37 Immigrant 61.38 7.02 England Native 70.12 3.64 Immigrant 66.32 6.11 Israel Native 54.40 4.18 Immigrant 56.31 6.30 New Zealand Native 70.50 3.14 Immigrant 75.26 4.11 United States Native 71.22 3.75 Immigrant 61.54 6.13 International Average Native 63.35 1.83 Immigrant 64.16 2.69 Percent of students who attend a school where principal has a high perception of school safety Canada (Ontario) Native 69.96 5.20 Immigrant 69.65 7.07 England Native 91.14 1.89 Immigrant 83.98 5.19

150

Table C.8 – continued

School-level Descriptive Data for PIRLS Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students who attend a school where principal has a high perception of school safety Israel Native 47.12 4.33 Immigrant 41.29 5.76 New Zealand Native 76.67 2.80 Immigrant 76.06 4.03 United States Native 77.50 3.44 Immigrant 67.77 6.21 International Average Native 72.48 1.66 Immigrant 67.75 2.57

Table C.9

Descriptive Data for PIRLS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher is specific age range

Canada (Ontario) Native Under 30 18.20 4.12 30-49 62.09 5.71 Over 50 19.72 4.21 Immigrant Under 30 15.24 4.63 30-49 60.52 8.08 Over 50 24.24 6.76 England Native Under 30 29.86 3.89 30-49 46.81 4.30 Over 50 23.33 3.74 Immigrant Under 30 29.99 6.14 30-49 46.60 6.35 Over 50 23.40 5.22 Israel Native Under 30 7.07 1.69 30-49 75.31 3.78 Over 50 17.62 3.43 Immigrant Under 30 6.31 2.51 30-49 66.75 6.24 Over 50 26.94 6.12 New Zealand Native Under 30 22.06 2.17 30-49 52.47 2.83

151

Table C.9 – continued

Descriptive Data for PIRLS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher is specific age range

Over 50 25.46 2.42 Immigrant Under 30 20.21 2.40 30-49 55.78 3.75 Over 50 24.01 3.46 United States Native Under 30 20.70 2.78 30-49 54.17 3.19 Over 50 25.13 3.46 Immigrant Under 30 25.53 4.63 30-49 51.42 5.78 Over 50 23.04 4.48 International Average Native Under 30 19.58 1.38 30-49 58.17 1.83 Over 50 22.25 1.57 Immigrant Under 30 19.46 1.92 30-49 56.22 2.77 Over 50 24.33 2.39

Percent of students whose teacher has completed specific level of formal education

Canada (Ontario) Native 5B or less 2.31 1.37 5A 35.63 4.56 5A (2nd degree) or higher 62.06 4.47 Immigrant 5B or less 0.69 0.69 5A 32.74 6.66 5A (2nd degree) or higher 66.57 6.68 England Native 5B or less 14.24 3.22 5A 62.96 4.32 5A (2nd degree) or higher 22.80 3.49 Immigrant 5B or less 9.90 3.68 5A 69.04 6.34 5A (2nd degree) or higher 21.06 5.22 Israel Native 5B or less 19.18 3.51 5A 80.51 3.52

152

Table C.9 – continued

Descriptive Data for PIRLS Teacher-derived Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose teacher has completed specific level of formal education

5A (2nd degree) or higher 0.31 0.31 Immigrant 5B or less 23.95 5.83 5A 75.42 5.84 5A (2nd degree) or higher 0.63 0.63 New Zealand Native 5B or less 29.02 2.59 5A 61.13 2.58 5A (2nd degree) or higher 9.85 1.39 Immigrant 5B or less 26.98 3.75 5A 60.80 3.95 5A (2nd degree) or higher 12.22 1.95 United States Native 5B or less 0.92 0.67 5A 53.60 4.70 5A (2nd degree) or higher 45.48 4.69 Immigrant 5A 59.34 5.07 5A (2nd degree) or higher 40.66 5.07 International Average Native 5B or less 13.13 1.13 5A 58.77 1.80 5A (2nd degree) or higher 28.10 1.50 Immigrant 5B or less 12.31 1.58 5A 59.47 2.53 5A (2nd degree) or higher 28.23 2.02

153

Table C.10

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students who attend a school that offers enrichment reading instruction

Canada (Ontario) Native 25.67 4.32 Immigrant 28.81 6.92 England Native 18.75 3.32 Immigrant 21.61 4.56 Israel Native 51.26 3.98 Immigrant 44.05 5.72 New Zealand Native 35.78 3.14 Immigrant 38.06 4.25 United States Native 52.10 4.76 Immigrant 57.27 4.61 International Average Native 36.71 1.77 Immigrant 37.96 2.37

Percent of students whose teacher is certified to teach

Canada (Ontario) Native 100.00 0.00 Immigrant 100.00 0.00 England Native 100.00 0.00 Immigrant 100.00 0.00 Israel Native 100.00 0.00 Immigrant 100.00 0.00 New Zealand Native 100.00 0.00 Immigrant 99.87 0.13 United States Native 98.47 0.74 Immigrant 98.60 0.91 International Average Native 99.69 0.15 Immigrant 99.69 0.18

Percent of students whose teacher has a full teaching license or certificate

Canada (Ontario) Native 98.66 0.66 Immigrant 97.19 1.62 Israel Native 92.17 2.11 Immigrant 86.50 5.87 New Zealand Native 85.14 2.06 Immigrant 86.80 2.49 United States Native 92.45 1.95 Immigrant 93.37 1.52

154

Table C.10 – continued

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students whose teacher has a full teaching license or certificate

International Average Native 92.11 0.90 Immigrant 90.97 1.69 *Note. Question not administered or data not available in England

Percent of students whose teacher studied language as an area of emphasis

Canada (Ontario) Native 62.89 4.54 Immigrant 67.03 6.76 England Native 66.94 4.13 Immigrant 67.29 5.84 Israel Native 34.03 4.54 Immigrant 36.88 6.51 New Zealand Native 47.58 3.37 Immigrant 48.57 3.17 United States Native 56.15 3.52 Immigrant 53.87 4.95 International Average Native 53.52 1.81 Immigrant 54.73 2.50 Percent of students whose teacher studied literature as an area of emphasis

Canada (Ontario) Native 55.36 4.83 Immigrant 54.42 6.87 England Native 62.43 3.58 Immigrant 64.06 5.24 Israel Native 44.36 4.96 Immigrant 44.34 6.86 New Zealand Native 47.25 3.01 Immigrant 49.25 3.58 United States Native 56.51 3.91 Immigrant 59.28 5.89 International Average Native 53.18 1.84 Immigrant 54.27 2.60 Percent of students whose teacher studied pedagogy or teaching reading as an area of emphasis Canada (Ontario) Native 56.30 4.98 Immigrant 52.78 7.70 England Native 51.10 4.11

155

Table C.10 – continued

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose teacher studied pedagogy or teaching reading as an area of emphasis Immigrant 53.83 7.08 Israel Native 33.83 3.98 Immigrant 34.66 6.08 New Zealand Native 59.12 2.58 Immigrant 61.23 3.69 United States Native 65.98 3.01 Immigrant 68.68 4.91 International Average Native 53.27 1.71 Immigrant 54.24 2.71

Percent of students whose teacher studied psychology as an area of emphasis

Canada (Ontario) Native 42.07 4.11 Immigrant 47.30 7.26 England Native 25.07 3.68 Immigrant 31.79 6.52 Israel Native 6.85 2.30 Immigrant 4.99 2.10 New Zealand Native 22.43 2.40 Immigrant 23.83 2.52 United States Native 23.00 3.15 Immigrant 23.30 4.64 International Average Native 23.88 1.43 Immigrant 26.24 2.26 Percent of students whose teacher studied remedial reading as an area of emphasis Canada (Ontario) Native 18.08 3.77 Immigrant 20.22 5.53 England Native 11.39 2.66 Immigrant 16.40 5.99 Israel Native 14.94 3.02 Immigrant 12.08 3.66 New Zealand Native 13.93 1.56 Immigrant 13.61 3.09 United States Native 27.08 3.65 Immigrant 29.68 5.17

156

Table C.10 – continued

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose teacher studied remedial reading as an area of emphasis International Average Native 17.08 1.36 Immigrant 18.40 2.16

Percent of students whose teacher studied reading theory as an area of emphasis

Canada (Ontario) Native 26.29 4.57 Immigrant 35.57 7.22 England Native 21.01 3.63 Immigrant 17.12 4.43 Israel Native 22.59 3.48 Immigrant 19.23 4.09 New Zealand Native 35.95 2.53 Immigrant 34.09 3.83 United States Native 39.72 3.83 Immigrant 45.14 4.69 International Average Native 29.11 1.64 Immigrant 30.23 2.24 Percent of students whose teacher studied language development as an area of emphasis Canada (Ontario) Native 31.43 4.82 Immigrant 32.94 6.98 England Native 35.60 4.15 Immigrant 38.60 6.63 Israel Native 16.04 3.13 Immigrant 15.02 3.63 New Zealand Native 42.49 2.76 Immigrant 40.43 3.90 United States Native 39.58 3.60 Immigrant 37.63 4.49 International Average Native 33.03 1.68 Immigrant 32.92 2.38 Percent of students whose teacher studied special education as an area of emphasis Canada (Ontario) Native 27.55 4.62 Immigrant 27.96 5.71 England Native 10.70 2.09 Immigrant 15.02 3.72

157

Table C.10 – continued

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose teacher studied special education as an area of emphasis Israel Native 13.31 3.02 Immigrant 14.75 3.80 New Zealand Native 14.08 2.05 Immigrant 14.76 2.85 United States Native 10.96 2.24 Immigrant 10.01 2.80 International Average Native 15.32 1.33 Immigrant 16.50 1.75 Percent of students whose teacher studied language learning as an area of emphasis Canada (Ontario) Native 19.51 4.31 Immigrant 23.69 6.80 England Native 7.47 2.21 Immigrant 13.65 3.68 Israel Native 6.02 1.97 Immigrant 4.64 1.91 New Zealand Native 13.00 1.94 Immigrant 15.65 2.73 United States Native 10.99 2.32 Immigrant 17.44 4.48 International Average Native 11.40 1.21 Immigrant 15.01 1.91

Percent of students whose teacher works full time

Canada (Ontario) Native 97.85 1.19 Immigrant 98.62 0.97 England Native 91.40 2.60 Immigrant 88.64 3.94 Israel Native 89.46 2.93 Immigrant 93.04 2.34 New Zealand Native 97.17 0.82 Immigrant 98.78 0.60 United States Native 98.40 0.94 Immigrant 98.85 0.72 International Average Native 94.85 0.86 Immigrant 95.59 0.96

158

Table C.10 – continued

Descriptive Data for PIRLS Teacher-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students whose teacher scored high on career satisfaction index

Canada (Ontario) Native 81.02 3.97 Immigrant 76.40 6.84 England Native 67.24 3.59 Immigrant 69.58 4.35 Israel Native 80.87 3.14 Immigrant 80.66 4.67 New Zealand Native 68.47 2.61 Immigrant 72.50 3.05 United States Native 72.67 3.31 Immigrant 72.98 3.90 International Average Native 74.05 1.50 Immigrant 74.43 2.12

Table C.11

Descriptive Data for PIRLS Teacher-derived Scale Variables

Immigrant Country Mean Mean (s.d.) Status Average time spent on language instruction (in hours per week) in student's classroom Canada (Ontario) Native 8.31 2.80 Immigrant 9.02 3.53 England Native 7.09 1.90 Immigrant 6.79 1.62 Israel Native 6.46 2.24 Immigrant 6.56 2.42 New Zealand Native 9.22 2.88 Immigrant 8.80 2.81 United States Native 9.11 3.14 Immigrant 9.15 3.06 International Average Native 8.04 2.59 Immigrant 8.06 2.69

159

Table C.11 – continued

Descriptive Data for PIRLS Teacher-derived Scale Variables

Immigrant Country Mean Mean (s.d.) Status Average time spent on reading instruction (in hours per week) in students’ classroom Canada (Ontario) Native 5.89 3.43 Immigrant 5.89 3.70 England Native 3.35 2.72 Immigrant 3.13 1.96 Israel Native 4.61 3.67 Immigrant 4.30 3.22 New Zealand Native 5.71 2.93 Immigrant 5.70 3.10 United States Native 8.83 4.37 Immigrant 9.08 4.66 International Average Native 5.68 3.42 Immigrant 5.62 3.33

Students’ teacher's average number of years teaching altogether

Canada (Ontario) Native 11.71 8.28 Immigrant 12.47 8.78 England Native 12.09 10.40 Immigrant 11.35 9.14 Israel Native 16.30 9.07 Immigrant 18.29 9.19 New Zealand Native 12.50 10.75 Immigrant 12.27 10.26 United States Native 12.20 9.25 Immigrant 10.49 9.38 International Average Native 12.96 9.55 Immigrant 12.97 9.35 Student's teacher's average number of years teaching fourth grade

Canada (Ontario) Native 3.96 3.03 Immigrant 4.34 3.15 England Native 4.27 4.16 Immigrant 4.06 4.25 Israel Native 4.75 4.40 Immigrant 4.68 4.19 New Zealand Native 5.88 6.20 Immigrant 5.57 5.54

160

Table C.11 – continued

Descriptive Data for PIRLS Teacher-derived Scale Variables

Immigrant Country Mean Mean (s.d.) Status Students’ teacher's average number of years teaching fourth grade United States Native 6.87 6.51 Immigrant 5.70 6.00 International Average Native 5.15 4.86 Immigrant 4.87 4.63

Table C.12

Student-level Descriptive Data for PIRLS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status Percent of students - number of books owned at home Canada (Ontario) Native 0-25 21.55 1.31 26-100 33.20 1.07 Over 100 45.24 1.57 Immigrant 0-25 37.30 3.25 26-100 27.77 2.87 Over 100 34.92 3.68 England Native 0-25 24.98 1.09 26-100 29.88 0.91 Over 100 45.15 1.43 Immigrant 0-25 34.11 3.00 26-100 30.23 3.09 Over 100 35.66 3.76 Israel Native 0-25 31.33 1.24 26-100 34.51 0.96 Over 100 34.16 1.45 Immigrant 0-25 43.91 3.48 26-100 28.20 3.05 Over 100 27.89 3.66 New Zealand Native 0-25 24.00 0.93 26-100 32.33 0.88 Over 100 43.67 1.11 Immigrant 0-25 32.21 2.12 26-100 30.86 1.97 Over 100 36.92 2.02 United States Native 0-25 32.34 1.87

161

Table C.12 – continued

Student-level Descriptive Data for PIRLS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status Percent of students - number of books owned at home 26-100 33.51 1.07 Over 100 34.16 1.42 Immigrant 0-25 44.91 2.78 26-100 26.17 2.07 Over 100 28.92 2.50 International Average Native 0-25 26.84 0.59 26-100 32.68 0.44 Over 100 40.47 0.63 Immigrant 0-25 38.49 1.32 26-100 28.65 1.19 Over 100 32.86 1.43

Percent of students whose parents were born in country of testing

Canada (Ontario) Native Both parents 50.30 2.73 Only one parent 19.34 1.00 Neither parent 30.36 3.15 Immigrant Both parents 2.26 0.95 Only one parent 7.38 2.21 Neither parent 90.36 2.58 England Native Both parents 76.14 1.41 Only one parent 16.61 0.80 Neither parent 7.25 1.04 Immigrant Both parents 7.18 1.50 Only one parent 21.22 3.28 Neither parent 71.60 3.90 Israel Native Both parents 66.32 1.40 Only one parent 17.44 0.77 Neither parent 16.24 1.20 Immigrant Both parents 11.56 2.30 Only one parent 14.06 2.22 Neither parent 74.38 3.27 New Zealand Native Both parents 64.87 0.95 Only one parent 24.04 0.74 Neither parent 11.09 0.69 Immigrant Both parents 6.97 1.13 Only one parent 18.88 1.80 Neither parent 74.15 2.13 United States Native Both parents 70.95 1.58 Only one parent 15.92 0.74

162

Table C.12 – continued

Student-level Descriptive Data for PIRLS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students whose parents were born in country of testing

Neither parent 13.13 1.21 Immigrant Both parents 8.77 1.82 Only one parent 15.37 2.24 Neither parent 75.87 2.82 I nternational Average Native Both parents 65.72 0.77 Only one parent 18.67 0.37 Neither parent 15.61 0.76 Immigrant Both parents 7.35 0.72 Only one parent 15.38 1.07 Neither parent 77.27 1.34 Percent of students in each category of the index on student reading attitudes

Canada (Ontario) Native High 46.13 1.42 Medium 41.82 1.05 Low 12.05 0.93 Immigrant High 51.74 2.87 Medium 38.94 3.10 Low 9.32 2.48 England Native High 40.23 1.40 Medium 44.53 1.14 Low 15.24 0.79 Immigr ant High 39.11 3.59 Medium 47.17 3.44 Low 13.72 1.94 Israel Native High 42.92 1.31 Medium 48.38 1.13 Low 8.70 0.73 Immigrant High 38.89 3.66 Medium 48.29 3.96 Low 12.82 2.28 New Zealand Native High 47.69 1.16 Medium 44.82 1.02 Low 7.49 0.39 Immigrant High 55.18 1.82 Medium 39.56 1.98 Low 5.26 0.84 United States Native High 40.21 1.35 Medium 46.01 1.06 Low 13.79 0.72

163

Table C.12 – continued

Student-level Descriptive Data for PIRLS Categorical Variables

Immigrant Country Category Percent Percent (s.e.) Status

Percent of students in each category of the index on student reading attitudes

Immigrant High 40.02 2.34 Medium 45.04 2.56 Low 14.95 2.04 International Average Native High 43.43 0.60 Medium 45.11 0.48 Low 11.45 0.33 Immigrant High 44.99 1.32 Medium 43.80 1.38 Low 11.21 0.89

Table C.13

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Student spoke language of testing before starting school

Canada (Ontario) Native 89.79 0.91 Immigrant 61.52 3.83 England Native 97.36 0.34 Immigrant 63.94 3.24 Israel Native 96.30 0.36 Immigrant 68.53 3.03 New Zealand Native 95.13 0.39 Immigrant 72.41 1.95 United States Native 95.45 0.41 Immigrant 71.29 3.14 International Average Native 94.81 0.24 Immigrant 67.54 1.39

Student always speaks language of testing at home

Canada (Ontario) Native 64.78 1.71 Immigrant 25.47 3.02 England Native 78.93 1.05 Immigrant 35.45 3.71 Israel Native 59.96 1.05

164

Table C.13 – continued

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Student always speaks language of testing at home

Immigrant 26.43 2.97 New Zealand Native 77.31 0.90 Immigrant 49.81 2.09 United States Native 74.81 1.30 Immigrant 30.11 3.65 International Average Native 71.16 0.55 Immigrant 33.45 1.40

Possessions at home - percent of students who have a PC

Canada (Ontario) Native 93.43 0.60 Immigrant 92.42 1.96 England Native 93.35 0.54 Immigrant 88.92 1.84 Israel Native 88.00 0.76 Immigrant 83.29 2.23 New Zealand Native 89.31 0.55 Immigrant 90.96 1.27 United States Native 88.56 0.81 Immigrant 79.95 2.58 International Average Native 90.53 0.30 Immigrant 87.11 0.91

Possessions at home - percent of students who have a study desk/table

Canada (Ontario) Native 80.80 0.97 Immigrant 80.97 2.34 England Native 75.01 1.11 Immigrant 76.77 3.29 Israel Native 88.46 0.74 Immigrant 84.46 2.63 New Zealand Native 76.48 0.85 Immigrant 82.05 1.25 United States Native 75.48 1.16 Immigrant 69.78 2.70 International Average Native 79.25 0.44 Immigrant 78.81 1.13

165

Table C.13 – continued

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Possessions at home - percent of students who have their own books

Canada (Ontario) Native 91.48 0.61 Immigrant 90.61 1.61 England Native 91.99 0.64 Immigrant 89.00 1.84 Israel Native 81.95 0.81 Immigrant 79.33 3.00 New Zealand Native 91.79 0.57 Immigrant 92.33 1.13 United States Native 88.36 0.98 Immigrant 79.05 2.28 International Average Native 89.11 0.33 Immigrant 86.06 0.93

Possessions at home - percent of students who have the daily newspaper

Canada (Ontario) Native 65.16 1.39 Immigrant 68.15 3.00 England Native 66.45 1.15 Immigrant 59.39 3.76 Israel Native 61.17 1.09 Immigrant 49.67 3.11 New Zealand Native 69.71 1.02 Immigrant 69.59 1.88 United States Native 53.67 1.43 Immigrant 42.45 3.47 International Average Native 63.23 0.55 Immigrant 57.85 1.39

Possessions at home - percent of students who have their own room

Canada (Ontario) Native 79.75 1.12 Immigrant 65.29 3.49 England Native 77.51 1.17 Immigrant 64.98 2.47 Israel Native 66.37 1.14 Immigrant 67.50 2.86 New Zealand Native 77.33 0.72 Immigrant 71.35 2.07

166

Table C.13 – continued

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Possessions at home - percent of students who have their own room

United States Native 72.59 1.08 Immigrant 63.36 2.63 International Average Native 74.71 0.47 Immigrant 66.50 1.23

Possessions at home - percent of students who have their own mobile phone

Canada (Ontario) Native 19.07 0.91 Immigrant 19.41 2.32 England Native 64.75 1.31 Immigrant 52.39 3.07 Israel Native 55.46 1.31 Immigrant 62.56 3.56 New Zealand Native 33.26 0.91 Immigrant 30.50 2.37 United States Native 35.34 1.53 Immigrant 42.43 3.14 I nternational Average Native 41.57 0.55 Immigrant 41.46 1.31

Percent of students whose mother was born in the country of testing

Canada (Ontario) Native 64.44 3.07 Immigrant 4.54 1.45 England Native 88.99 1.19 Immigrant 17.42 2.97 Israel Native 76.91 1.32 Immigrant 18.86 2.91 New Zealand Native 82.81 0.80 Immigrant 17.04 1.72 United States Native 84.09 1.34 Immigrant 14.81 2.32 International Average Native 79.45 0.77 Immigrant 14.53 1.05

Percent of students whose father was born in the country of testing

Canada (Ontario) Native 60.76 3.16 Immigrant 7.61 2.17 England Native 87.83 1.31 Immigrant 19.92 3.03

167

Table C.13 – continued

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students whose father was born in the country of testing

Israel Native 76.50 1.25 Immigrant 20.43 3.07 New Zealand Native 79.43 0.93 Immigrant 16.33 1.58 United States Native 82.14 1.34 Immigrant 19.87 2.77 International Average Native 77.33 0.80 Immigrant 16.83 1.16 Percent of students whose average on index of home educational resources was high Canada (Ontario) Native 19.47 1.52 Immigrant 19.78 2.85 England Native 24.37 1.77 Immigrant 23.29 5.30 Israel Native 16.09 1.40 Immigrant 15.85 3.70 New Zealand Native 16.72 1.00 Immigrant 26.69 2.10 International Average Native 19.16 0.73 Immigrant 21.40 1.84 *Note. Data unavailable for US because it did not implement home survey from which this index is constructed

Percent of students who have a high reading self-concept

Canada (Ontario) Native 50.01 0.97 Immigrant 61.05 2.84 England Native 42.59 1.21 Immigrant 37.57 3.05 Israel Native 64.03 1.00 Immigrant 52.00 2.92 New Zealand Native 35.92 0.87 Immigrant 39.98 1.62 United States Native 51.87 0.82 Immigrant 48.88 2.96 International Average Native 48.88 0.44 Immigrant 47.90 1.22

168

Table C.13 – continued

Student-level Descriptive Data for PIRLS Binary Variables

Immigrant Country Percent Percent (s.e.) Status

Percent of students who have a high perception of school safety

Canada (Ontario) Native 39.60 1.67 Immigrant 31.89 3.12 England Native 37.73 1.62 Immigrant 24.91 3.56 Israel Native 25.49 1.17 Immigrant 27.82 2.95 New Zealand Native 37.33 1.21 Immigrant 38.75 1.82 United States Native 48.50 1.67 Immigrant 45.13 2.93 International Average Native 37.73 0.66 Immigrant 33.70 1.31

Table C.14

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency student visited libraries before starting school Canada (Ontario) Native Often 32.81 1.41 Sometimes 49.07 1.18 Never/Almost Never 18.12 1.06 Immigrant Often 31.61 3.63 Sometimes 52.64 3.46 Never/Almost Never 15.75 2.78 England Native Often 37.13 1.55 Sometimes 45.62 1.46 Never/Almost Never 17.25 1.02 Immigrant Often 30.25 5.02 Sometimes 42.09 5.53 Never/Almost Never 27.66 3.95 Israel Native Often 37.08 1.34 Sometimes 36.75 1.19 Never/Almost Never 26.16 1.25 Immigrant Often 27.50 3.14 Sometimes 33.98 4.28 Never/Almost Never 38.52 3.86

169

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - frequency student visited libraries before starting school

New Zealand Native Often 41.58 1.35 Sometimes 41.74 1.31 Never/Almost Never 16.68 1.09 Immigrant Often 40.93 2.29 Sometimes 40.85 2.12 Never/Almost Never 18.22 2.01 International Average Native Often 37.15 0.71 Sometimes 43.29 0.65 Never/Almost Never 19.55 0.55 Immigrant Often 32.57 1.83 Sometimes 42.39 2.02 Never/Almost Never 25.04 1.62 Percent of students in each category - length student attended ISCED level 0 Canada (Ontario) Native More than 3 yrs 16.24 1.23 2-3 yrs 39.17 1.24 Less than 2 yrs 44.60 1.51 Immigrant More than 3 yrs 17.82 3.15 2-3 yrs 39.47 3.89 Less than 2 yrs 42.71 4.34 England Native More than 3 yrs 10.87 0.93 2-3 yrs 40.15 2.04 Less than 2 yrs 48.98 2.14 Immigrant More than 3 yrs 18.12 3.75 2-3 yrs 44.31 5.05 Less than 2 yrs 37.57 4.97 Israel Native More than 3 yrs 68.36 1.46 2-3 yrs 23.87 1.21 Less than 2 yrs 7.77 1.06 Immigrant More than 3 yrs 55.22 4.15 2-3 yrs 32.96 4.50 Less than 2 yrs 11.82 2.90 New Zealand Native More than 3 yrs 30.64 1.11 2-3 yrs 51.55 1.21 Less than 2 yrs 17.82 0.92 Immigrant More than 3 yrs 29.76 2.51 2-3 yrs 44.46 2.37 Less than 2 yrs 25.79 2.14 International Average Native More than 3 yrs 31.52 0.60

170

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - length student attended ISCED level 0 2-3 yrs 38.68 0.74 Less than 2 yrs 29.79 0.74 Immigrant More than 3 yrs 30.23 1.72 2-3 yrs 40.30 2.04 Less than 2 yrs 29.47 1.88 Percent of students in each category - frequency parent/someone else in home engaged in listening to student read aloud Canada (Ontario) Native Every/Almost everyday 32.22 1.34 Once/Twice a week 46.54 1.16 Once/Twice a month 16.74 1.06 Never/Almost Never 4.50 0.47 Immigrant Every/Almost everyday 39.67 3.34 Once/Twice a week 44.23 3.29 Once/Twice a month 12.20 2.18 Never/Almost Never 3.91 1.27 England Native Every/Almost everyday 26.93 1.41 Once/Twice a week 48.20 1.76 Once/Twice a month 18.98 1.25 Never/Almost Never 5.89 0.67 Immigrant Every/Almost everyday 35.28 5.42 Once/Twice a week 47.26 5.42 Once/Twice a month 10.54 3.26 Never/Almost Never 6.91 3.10 Israel Native Every/Almost everyday 35.95 1.47 Once/Twice a week 38.46 1.07 Once/Twice a month 13.20 0.90 Never/Almost Never 12.39 0.80 Immigrant Every/Almost everyday 28.74 3.67 Once/Twice a week 41.43 4.50 Once/Twice a month 13.37 3.10 Never/Almost Never 16.46 3.62 New Zealand Native Every/Almost everyday 32.15 1.15 Once/Twice a week 42.62 1.01 Once/Twice a month 17.39 0.77 Never/Almost Never 7.84 0.48 Immigrant Every/Almost everyday 34.94 2.15 Once/Twice a week 41.64 2.17 Once/Twice a month 14.58 1.50 Never/Almost Never 8.85 1.76

171

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home engaged in listening to student read aloud International Average Native Every/Almost everyday 31.81 0.67 Once/Twice a week 43.96 0.64 Once/Twice a month 16.58 0.50 Never/Almost Never 7.65 0.31 Immigrant Every/Almost everyday 34.66 1.91 Once/Twice a week 43.64 2.02 Once/Twice a month 12.67 1.30 Never/Almost Never 9.03 1.31 Percent of students in each category - frequency parent/someone else in home engaged in talking to student about things they had done Canada (Ontario) Native Every/Almost everyday 72.99 1.46 Once/Twice a week 22.69 1.13 Once/Twice a month 3.67 0.55 Never/Almost Never 0.66 0.18 Immigrant Every/Almost everyday 65.49 2.56 Once/Twice a week 25.76 2.64 Once/Twice a month 6.88 1.51 Never/Almost Never 1.86 0.94 England Native Every/Almost everyday 82.63 1.01 Once/Twice a week 16.11 0.96 Once/Twice a month 1.19 0.28 Never/Almost Never 0.07 0.07 Immigrant Every/Almost everyday 68.33 3.84 Once/Twice a week 27.41 4.02 Once/Twice a month 2.30 1.44 Never/Almost Never 1.96 1.36 Israel N ative Every/Almost everyday 68.76 1.10 Once/Twice a week 24.14 1.05 Once/Twice a month 5.52 0.48 Never/Almost Never 1.58 0.28 Immigrant Every/Almost everyday 55.27 3.90 Once/Twice a week 29.38 3.58 Once/Twice a month 11.23 2.85 Never/Almost Never 4.13 1.56 New Zealand Native Every/Almost everyday 79.23 0.95 Once/Twice a week 18.49 0.89 Once/Twice a month 1.77 0.27 Never/Almost Never 0.51 0.15

172

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home engaged in talking to student about things they had done Immigrant Every/Almost everyday 71.82 2.18 Once/Twice a week 22.42 1.80 Once/Twice a month 3.65 0.93 Never/Almost Never 2.11 0.82 International Average Native Every/Almost everyday 75.90 0.57 Once/Twice a week 20.36 0.51 Once/Twice a month 3.04 0.21 Never/Almost Never 0.71 0.09 Immigrant Every/Almost everyday 65.23 1.61 Once/Twice a week 26.24 1.56 Once/Twice a month 6.02 0.91 Never/Almost Never 2.51 0.60 Percent of students in each category - frequency parent/someone else in home talked with student about what he/she is read on his/her own Canada (Ontario) Native Every/Almost everyday 38.70 1.26 Once/Twice a week 45.60 1.02 Once/Twice a month 12.67 0.66 Never/Almost Never 3.03 0.49 Immigrant Every/Almost everyday 45.55 3.69 Once/Twice a week 43.62 3.28 Once/Twice a month 8.52 1.98 Never/Almost Never 2.32 1.03 England Native Every/Almost everyday 34.09 1.54 Once/Twice a week 52.81 1.43 Once/Twice a month 10.79 0.88 Never/Almost Never 2.31 0.39 Immigrant Every/Almost everyday 40.20 4.44 Once/Twice a week 48.50 5.09 Once/Twice a month 8.89 2.84 Never/Almost Never 2.42 1.64 Israel Native Every/Almost everyday 36.12 1.53 Once/Twice a week 42.26 1.18 Once/Twice a month 14.59 0.98 Never/Almost Never 7.03 0.57 Immigrant Every/Almost everyday 34.06 4.01 Once/Twice a week 33.63 4.32 Once/Twice a month 18.27 3.07 Never/Almost Never 14.04 3.00

173

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home talked with student about what he/she is read on his/her own New Zealand Native Every/Almost everyday 32.22 1.15 Once/Twice a week 52.28 0.99 Once/Twice a month 12.38 0.70 Never/Almost Never 3.12 0.35 Immigrant Every/Almost everyday 36.85 2.35 Once/Twice a week 48.19 2.43 Once/Twice a month 11.66 1.30 Never/Almost Never 3.30 0.92 International Average Native Every/Almost everyday 35.28 0.69 Once/Twice a week 48.24 0.58 Once/Twice a month 12.61 0.41 Never/Almost Never 3.87 0.23 Immigrant Every/Almost everyday 39.16 1.85 Once/Twice a week 43.48 1.96 Once/Twice a month 11.83 1.20 Never/Almost Never 5.52 0.92 Percent of students in each category - frequency parent/someone else in home discussed student's classroom reading work with him/her Canada (Ontario) Native Every/Almost everyday 43.47 1.17 Once/Twice a week 39.22 1.12 Once/Twice a month 13.57 0.92 Never/Almost Never 3.75 0.43 Immigrant Every/Almost everyday 52.56 4.52 Once/Twice a week 34.46 3.52 Once/Twice a month 10.21 2.11 Never/Almost Never 2.77 1.09 England Native Every/Almost everyday 28.81 1.22 Once/Twice a week 46.56 1.22 Once/Twice a month 19.62 1.12 Never/Almost Never 5.01 0.55 Immigrant Every/Almost everyday 37.69 4.68 Once/Twice a week 44.75 5.04 Once/Twice a month 14.07 3.44 Never/Almost Never 3.49 1.70 Israel Native Every/Almost everyday 42.72 1.51 Once/Twice a week 31.33 1.30 Once/Twice a month 14.27 0.87 Never/Almost Never 11.69 0.70

174

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home discussed student's classroom reading work with him/her Immigrant Every/Almost everyday 42.37 4.07 Once/Twice a week 30.19 3.84 Once/Twice a month 12.37 3.01 Never/Almost Never 15.07 3.32 New Zealand Native Every/Almost everyday 29.06 1.01 Once/Twice a week 49.49 1.00 Once/Twice a month 17.03 0.79 Never/Almost Never 4.41 0.45 Immigrant Every/Almost everyday 32.12 2.07 Once/Twice a week 44.55 1.91 Once/Twice a month 18.02 1.84 Never/Almost Never 5.30 1.24 International Average Native Every/Almost everyday 36.01 0.62 Once/Twice a week 41.65 0.58 Once/Twice a month 16.12 0.47 Never/Almost Never 6.21 0.27 Immigrant Every/Almost everyday 41.19 1.99 Once/Twice a week 38.49 1.87 Once/Twice a month 13.67 1.34 Never/Almost Never 6.66 1.02 Percent of students in each category - frequency parent/someone else in home went to the library or a bookstore with the student Canada (Ontario) Native Every/Almost everyday 4.72 0.53 Once/Twice a week 22.63 1.48 Once/Twice a month 56.15 1.41 Never/Almost Never 16.51 0.97 Immigrant Every/Almost everyday 7.46 1.79 Once/Twice a week 42.09 4.67 Once/Twice a month 42.45 3.94 Never/Almost Never 8.00 2.38 England Native Every/Almost everyday 3.87 0.47 Once/Twice a week 15.68 0.98 Once/Twice a month 63.12 1.20 Never/Almost Never 17.33 1.02 Immigrant Every/Almost everyday 4.02 2.62 Once/Twice a week 22.81 3.81 Once/Twice a month 57.93 4.63 Never/Almost Never 15.24 3.21

175

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home went to the library or a bookstore with the student Israel Native Every/Almost everyday 10.51 0.76 Once/Twice a week 25.96 1.12 Once/Twice a month 35.72 1.40 Never/Almost Never 27.80 1.24 Immigrant Every/Almost everyday 15.07 2.82 Once/Twice a week 28.12 4.08 Once/Twice a month 32.79 4.20 Never/Almost Never 24.03 3.23 New Zealand Native Every/Almost everyday 5.47 0.44 Once/Twice a week 23.02 0.90 Once/Twice a month 56.64 1.12 Never/Almost Never 14.86 0.83 Immigrant Every/Almost everyday 6.66 1.06 Once/Twice a week 31.34 2.24 Once/Twice a month 51.76 2.41 Never/Almost Never 10.24 1.68 International Average Native Every/Almost everyday 6.15 0.28 Once/Twice a week 21.82 0.57 Once/Twice a month 52.91 0.64 Never/Almost Never 19.13 0.51 Im migrant Every/Almost everyday 8.30 1.09 Once/Twice a week 31.09 1.90 Once/Twice a month 46.23 1.94 Never/Almost Never 14.38 1.35 Percent of students in each category - frequency parent/someone else in home helped student with reading for school Canada (Ontario) Native Every/Almost everyday 37.26 1.38 Once/Twice a week 36.21 1.06 Once/Twice a month 16.26 0.89 Never/Almost Never 10.27 0.84 Immigrant Every/Almost everyday 49.17 3.87 Once/Twice a week 31.70 2.89 Once/Twice a month 8.20 2.00 Never/Almost Never 10.93 2.03 England Native Every/Almost everyday 25.64 1.22 Once/Twice a week 41.66 1.45 Once/Twice a month 19.45 1.21 Never/Almost Never 13.24 1.19

176

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - frequency parent/someone else in home helped student with reading for school Immigrant Every/Almost everyday 37.11 4.65 Once/Twice a week 42.81 4.49 Once/Twice a month 10.49 3.15 Never/Almost Never 9.59 3.16 Israel Native Every/Almost everyday 39.81 1.57 Once/Twice a week 25.39 1.10 Once/Twice a month 12.56 0.84 Never/Almost Never 22.24 1.05 Immigrant Every/Almost everyday 29.12 3.84 Once/Twice a week 30.53 4.77 Once/Twice a month 16.88 3.19 Never/Almost Never 23.48 3.30 New Zealand Native Every/Almost everyday 32.18 1.16 Once/Twice a week 35.99 0.93 Once/Twice a month 16.97 0.76 Never/Almost Never 14.86 0.78 Immigrant Every/Almost everyday 33.70 2.27 Once/Twice a week 33.03 2.28 Once/Twice a month 17.03 1.64 Never/Almost Never 16.24 2.01 International Average Native Every/Almost everyday 33.72 0.67 Once/Twice a week 34.81 0.57 Once/Twice a month 16.31 0.47 Never/Almost Never 15.15 0.49 Immigrant Every/Almost everyday 37.27 1.88 Once/Twice a week 34.52 1.88 Once/Twice a month 13.15 1.29 Never/Almost Never 15.06 1.35

Percent of students in each category - highest education father (in ISCED levels)

Canada (Ontario) Native Some (level 1/2)/no school 3.73 0.72 Level 2 5.81 0.59 Level 3 26.65 1.28 Level 4 34.20 1.36 Level 5A, 1st degree 15.97 1.33 Beyond level 5A, 1st degree 10.58 0.86 N/A 3.06 0.53

177

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - highest education father (in ISCED levels)

Immigrant Some (level 1/2)/no school 1.72 1.27 Level 2 1.96 0.97 Level 3 12.65 2.62 Level 4 21.20 3.21 Level 5A, 1st degree 32.09 3.50 Beyond level 5A, 1st degree 25.42 3.00 N/A 4.96 1.59 England Native Some (level 1/2)/no school 9.57 0.94 Level 2 13.98 0.96 Level 3 37.92 1.63 Level 4 2.05 0.42 Level 5A, 1st degree 28.94 1.53 Beyond level 5A, 1st degree 2.46 0.45 N/A 5.08 0.61 Immigrant Some (level 1/2)/no school 10.64 3.42 Level 2 11.36 3.58 Level 3 28.27 4.30 Level 4 3.00 1.60 Level 5A, 1st degree 32.72 5.96 Beyond level 5A, 1st degree 7.79 5.18 N/A 6.22 2.23 Israel Native Some (level 1/2)/no school 5.30 0.64 Level 2 10.70 1.24 Level 3 32.56 1.52 Level 4 12.12 0.82 Level 5B 8.37 0.64 Level 5A, 1st degree 15.44 1.03 Beyond level 5A, 1st degree 14.76 1.09 N/A 0.75 0.20 Immigrant Some (level 1/2)/no school 5.27 1.94 Level 2 7.77 2.47 Level 3 24.30 3.87 Level 4 11.36 3.10 Level 5B 13.46 3.25 Level 5A, 1st degree 14.59 3.15 Beyond level 5A, 1st degree 20.18 3.70 N/A 3.07 1.71

178

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - highest education father (in ISCED levels)

New Zealand Native Some (level 1/2)/no school 3.56 0.38 Level 2 17.69 0.76 Level 3 20.56 0.98 Level 4 29.09 1.24 Level 5B 6.17 0.61 Level 5A, 1st degree 12.58 0.94 Beyond level 5A, 1st degree 6.22 0.59 N/A 4.12 0.46 Immigra nt Some (level 1/2)/no school 2.80 0.69 Level 2 6.59 1.33 Level 3 7.96 1.31 Level 4 20.36 2.07 Level 5B 7.31 1.12 Level 5A, 1st degree 30.03 2.32 Beyond level 5A, 1st degree 22.16 2.08 N/A 2.80 0.99 International Average Native Some (level 1/2)/no school 5.54 0.35 Level 2 12.04 0.46 Level 3 29.42 0.69 Level 4 19.36 0.51 Level 5B 3.64 0.22 Level 5A, 1st degree 18.23 0.62 Beyond level 5A, 1st degree 8.50 0.39 N/A 3.26 0.24 Immigrant Some (level 1/2)/no school 5.11 1.05 Level 2 6.92 1.16 Level 3 18.30 1.62 Level 4 13.98 1.29 Level 5B 5.19 0.86 Level 5A, 1st degree 27.36 1.99 Beyond level 5A, 1st degree 18.89 1.83 N/A 4.26 0.84 Percent of students in each category - highest education mother (in ISCED levels)

Canada (Ontario) Native Some (level 1/2)/no school 2.80 0.76 Level 2 3.81 0.55 Level 3 27.94 1.60 Level 4 34.95 1.42 Level 5A, 1st degree 18.08 1.37

179

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - highest education mother (in ISCED levels)

Beyond level 5A, 1st degree 10.14 0.96 N/A 2.28 0.41 Immigrant Some (level 1/2)/no school 3.86 2.03 Level 2 2.67 1.14 Level 3 15.16 3.19 Level 4 22.69 3.18 Level 5A, 1st degree 32.76 4.46 Beyond level 5A, 1st degree 19.20 2.95 N/A 3.66 1.10 England Native Some (level 1/2)/no school 8.69 0.96 Level 2 13.36 0.84 Level 3 47.07 1.86 Level 4 3.47 0.51 Level 5A, 1st degree 24.47 1.72 Beyond level 5A, 1st degree 0.65 0.27 N/A 2.28 0.43 Immigrant Some (level 1/2)/no school 8.82 2.82 Level 2 12.65 4.93 Level 3 34.31 5.42 Level 4 6.46 2.73 Level 5A, 1st degree 30.01 6.04 Beyond level 5A, 1st degree 4.26 3.37 N/A 3.48 1.80 Israel Native Some (level 1/2)/no school 5.15 0.63 Level 2 8.24 1.01 Level 3 33.23 1.60 Level 4 11.45 0.73 Level 5B 8.87 0.75 Level 5A, 1st degree 19.00 1.19 Beyond level 5A, 1st degree 13.42 1.18 N/A 0.64 0.20 Immigrant Some (level 1/2)/no school 7.67 2.43 Level 2 6.53 2.12 Level 3 25.30 4.36 Level 4 15.20 3.26 Level 5B 4.43 2.06 Level 5A, 1st degree 20.65 3.52 Beyond level 5A, 1st degree 19.61 3.64 N/A 0.62 0.61

180

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - highest education mother (in ISCED levels)

New Zealand Native Some (level 1/2)/no school 2.82 0.31 Level 2 13.65 0.71 Level 3 31.11 1.02 Level 4 17.63 0.80 Level 5B 13.34 0.75 Level 5A, 1st degree 14.27 0.71 Beyond level 5A, 1st degree 5.08 0.65 N/A 2.10 0.31 Immigrant Some (level 1/2)/no school 1.39 0.47 Level 2 5.34 0.92 Level 3 17.20 1.62 Level 4 12.93 1.49 Level 5B 16.74 1.95 Level 5A, 1st degree 28.30 2.24 Beyond level 5A, 1st degree 16.06 1.77 N/A 2.03 0.71 International Average Native Some (level 1/2)/no school 4.86 0.35 Level 2 9.76 0.40 Level 3 34.84 0.77 Level 4 16.87 0.46 Level 5B 5.55 0.26 Level 5A, 1st degree 18.96 0.65 Beyond level 5A, 1st degree 7.32 0.42 N/A 1.82 0.17 Immigrant Some (level 1/2)/no school 5.43 1.07 Level 2 6.80 1.39 Level 3 22.99 1.96 Level 4 14.32 1.38 Level 5B 5.29 0.71 Level 5A, 1st degree 27.93 2.15 Beyond level 5A, 1st degree 14.79 1.51 N/A 2.45 0.58

Percent of students in each category - employment situation father

Canada (Ontario) Native Full-time 89.72 0.70 Part-time 2.30 0.43 Looking for a job 1.51 0.35 Other 3.90 0.40 N/A 2.58 0.36

181

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - employment situation father

Immigrant Full-time 82.06 2.93 Part-time 4.21 1.54 Looking for a job 6.26 1.86 Other 3.35 1.24 N/A 4.12 1.41 England Native Full-time 87.58 1.10 Part-time 2.45 0.47 Looking for a job 1.41 0.30 Other 5.15 0.63 N/A 3.41 0.57 Immigrant Full-time 76.54 3.68 Part-time 7.59 2.51 Looking for a job 1.94 1.17 Other 8.18 2.43 N/A 5.75 2.42 Israel Native Full-time 78.05 1.23 Part-time 6.72 0.66 Looking for a job 3.94 0.48 Other 8.10 0.61 N/A 3.19 0.34 Immigrant Full-time 69.85 4.73 Part-time 6.47 2.02 Looking for a job 4.84 2.04 Other 10.91 2.06 N/A 7.92 2.67 New Zealand Native Full-time 84.15 0.85 Part-time 2.74 0.32 Looking for a job 1.45 0.30 Other 6.49 0.58 N/A 5.17 0.48 Immigrant Full-time 82.80 1.98 Part-time 5.27 1.01 Looking for a job 3.01 0.85 Other 5.53 1.12 N/A 3.38 0.98 International Average Native Full-time 84.87 0.50 Part-time 3.55 0.24 Looking for a job 2.08 0.18 Other 5.91 0.28

182

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - employment situation father

N/A 3.59 0.22 Immigrant Full-time 77.81 1.74 Part-time 5.89 0.93 Looking for a job 4.01 0.78 Other 6.99 0.90 N/A 5.30 1.00

Percent of students in each category - employment situation mother

Canada (Ontario) Native Full-time 57.71 1.48 Part-time 20.61 1.05 Looking for a job 3.99 0.47 Other 11.40 0.96 N/A 6.28 0.59 Immigrant Full-time 48.34 3.63 Part-time 20.29 2.35 Looking for a job 9.09 1.77 Other 12.25 2.22 N/A 10.03 2.06 England Native Full-time 26.81 1.31 Part-time 48.67 1.60 Looking for a job 3.72 0.53 Other 15.53 1.05 N/A 5.26 0.68 Immigrant Full-time 28.81 4.56 Part-time 31.83 3.92 Looking for a job 7.56 2.86 Other 23.33 4.16 N/A 8.48 3.21 Israel Native Full-time 52.77 1.49 Part-time 19.82 1.23 Looking for a job 11.93 0.81 Other 8.70 0.67 N/A 6.79 0.79 Immigrant Full-time 53.99 4.57 Part-time 18.45 3.64 Looking for a job 9.02 2.81 Other 11.52 2.97 N/A 7.02 2.10

183

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - employment situation mother

New Zealand Native Full-time 33.34 1.23 Part-time 41.42 1.33 Looking for a job 3.81 0.42 Other 13.19 0.90 N/A 8.24 0.75 Immigrant Full-time 36.61 2.90 Part-time 30.01 2.37 Looking for a job 8.18 1.69 Other 14.61 1.98 N/A 10.58 1.66 International Average Native Full-time 42.66 0.69 Part-time 32.63 0.66 Looking for a job 5.86 0.29 Other 12.20 0.45 N/A 6.64 0.35 Immigrant Full-time 41.94 1.99 Part-time 25.14 1.58 Looking for a job 8.46 1.17 Other 15.43 1.48 N/A 9.03 1.17 Percent of students in each category - main job father Canada (Ontario) Native Never worked outside 0.59 0.17 Business owner 13.47 0.84 Clerk 1.90 0.36 Sales worker 5.93 0.62 Fishery worker 0.87 0.25 Trade worker 16.56 1.16 Operator 13.71 1.13 Laborers 6.46 0.56 Sen official 11.66 0.95 Professional 13.71 1.18 Technician 10.28 0.91 N/A 4.86 0.56 Immigrant Never worked outside 1.39 0.83 Business owner 11.00 1.97 Clerk 1.59 0.72 Sales worker 8.34 2.15 Fishery worker 0.05 0.05

184

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - main job father Trade worker 8.46 1.87 Operator 8.05 1.87 Laborers 5.98 1.68 Sen official 8.65 1.97 Professional 25.72 3.35 Technician 13.84 2.78 N/A 6.92 2.13 England Native Never worked outside 0.41 0.15 Business owner 13.42 1.01 Clerk 1.70 0.36 Sales worker 4.67 0.56 Fishery worker 1.07 0.32 Trade worker 14.96 1.13 Operator 8.35 0.80 Laborers 3.59 0.47 Sen official 16.33 1.13 Professional 21.29 1.33 Technician 7.92 0.77 N/A 6.30 0.84 Immigrant Never worked outside 0.99 1.03 Business owner 10.65 3.39 Clerk 2.73 1.72 Sales worker 7.44 2.63 Fishery worker 1.09 1.13 Trade worker 7.75 2.47 Operator 8.28 2.91 Laborers 3.96 2.03 Sen official 13.40 4.09 Professional 28.18 5.44 Technician 5.92 2.32 N/A 9.61 2.77 Israel Native Never worked outside 4.31 0.52 Business owner 10.69 0.70 Clerk 1.42 0.34 Sales worker 8.00 0.77 Fishery worker 2.62 0.52 Trade worker 13.62 0.97 Operator 7.55 0.66 Laborers 4.33 0.56

185

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - main job father Sen official 16.76 1.13 Professional 18.18 1.17 Technician 4.65 0.56 N/A 7.89 0.92 Immigrant Never worked outside 8.56 1.99 Business owner 8.87 2.51 Sales worker 9.31 2.95 Fishery worker 0.97 0.97 Trade worker 13.28 3.59 Operator 15.98 3.59 Laborers 5.43 2.19 Sen official 9.56 2.91 Professional 17.98 3.54 N/A 10.07 2.80 New Zealand Native Never worked outside 0.53 0.23 Business owner 16.50 0.79 Clerk 1.40 0.24 Sales worker 5.58 0.47 Fishery worker 7.93 1.24 Trade worker 16.54 0.92 Operator 7.58 0.65 Laborers 5.54 0.54 Sen official 10.76 0.75 Professional 15.54 1.04 Technician 5.89 0.49 N/A 6.21 0.56 Immigrant Never worked outside 0.84 0.70 Business owner 11.31 1.81 Clerk 1.96 0.68 Sales worker 4.08 0.97 Fishery worker 2.82 1.24 Trade worker 14.97 2.00 Operator 4.68 0.84 Laborers 2.44 0.61 Sen official 14.28 1.62 Professional 30.04 2.10 Technician 9.31 1.49 N/A 3.27 1.02

186

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - main job father International Average Native Never worked outside 1.46 0.15 Business owner 13.52 0.42 Clerk 1.60 0.16 Sales worker 6.04 0.31 Fishery worker 3.12 0.35 Trade worker 15.42 0.53 Operator 9.30 0.42 Laborers 4.98 0.27 Sen official 13.88 0.50 Professional 17.18 0.59 Technician 7.18 0.35 N/A 6.31 0.37 Immigrant Never worked outside 2.94 0.62 Business owner 10.46 1.25 Clerk 1.57 0.50 Sales worker 7.29 1.15 Fishery worker 1.23 0.49 Trade worker 11.12 1.29 Operator 9.25 1.26 Laborers 4.45 0.87 Sen official 11.47 1.41 Professional 25.48 1.90 Technician 7.27 0.98 N/A 7.47 1.15

Percent of students in each category - main job mother

Canada (Ontario) Native Never worked outside 2.53 0.43 Business owner 7.94 0.75 Clerk 14.33 0.73 Sales worker 12.80 0.78 Fishery worker 0.37 0.15 Trade worker 1.04 0.26 Operator 5.35 0.65 Laborers 5.61 0.64 Sen official 5.81 0.55 Professional 22.57 1.60 Technician 12.04 0.84 N/A 9.60 0.88

187

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - main job mother

Immigrant Never worked outside 5.68 1.89 Business owner 8.50 2.14 Clerk 10.60 2.45 Sales worker 9.18 2.08 Trade worker 2.40 1.21 Operator 2.74 1.23 Laborers 11.98 3.18 Sen official 2.57 1.31 Professional 21.96 3.30 Technician 15.93 2.29 N/A 8.46 2.01 England Native Never worked outside 3.14 0.50 Business owner 6.54 0.70 Clerk 17.28 0.96 Sales worker 11.50 0.86 Fishery worker 0.55 0.23 Trade worker 1.56 0.33 Operator 0.65 0.20 Laborers 4.34 0.58 Sen official 4.00 0.44 Professional 22.30 1.34 Technician 15.22 1.12 N/A 12.93 1.13 Immigrant Never worked outside 7.42 2.70 Business owner 6.85 2.66 Clerk 8.79 2.68 Sales worker 6.48 2.92 Trade worker 4.40 2.09 Operator 0.94 0.95 Laborers 8.17 3.04 Sen official 4.87 1.49 Professional 27.58 4.43 Technician 7.35 2.64 N/A 17.15 3.82 Israel Native Never worked outside 18.12 1.30 Business owner 4.24 0.54 Clerk 16.19 1.20 Sales worker 6.32 0.63 Fishery worker 0.64 0.22

188

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - main job mother

Trade worker 1.56 0.33 Operator 0.79 0.23 Laborers 4.41 0.50 Sen official 9.22 0.76 Professional 20.63 1.35 Technician 8.55 0.69 N/A 9.33 0.92 Immigrant Never worked outside 13.73 3.35 Business owner 4.25 1.75 Clerk 10.10 3.18 Sales worker 17.68 3.44 Fishery worker 0.89 0.90 Trade worker 1.71 1.36 Operator 1.52 1.05 Laborers 9.11 3.47 Sen official 9.57 2.65 Professional 18.13 3.82 Technician 5.66 2.47 N/A 7.66 2.40 New Zealand Native Never worked outside 2.64 0.41 Business owner 10.09 0.75 Clerk 16.90 0.93 Sales worker 12.54 0.71 Fishery worker 2.54 0.63 Trade worker 1.98 0.31 Operator 1.42 0.20 Laborers 6.33 0.57 Sen official 2.97 0.33 Professional 23.42 0.99 Technician 9.57 0.61 N/A 9.58 0.70 Immigrant Never worked outside 5.13 1.25 Business owner 6.74 1.25 Clerk 16.10 1.80 Sales worker 9.31 1.41 Fishery worker 0.19 0.19 Trade worker 1.59 0.80 Operator 1.13 0.44 Laborers 2.52 0.71

189

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - main job mother

Sen official 4.15 1.01 Professional 34.10 2.35 Technician 9.05 1.51 N/A 9.99 1.39 International Average Native Never worked outside 6.61 0.38 Business owner 7.20 0.34 Clerk 16.17 0.48 Sales worker 10.79 0.37 Fishery worker 1.02 0.18 Trade worker 1.54 0.15 Operator 2.05 0.19 Laborers 5.17 0.29 Sen official 5.50 0.27 Professional 22.23 0.67 Technician 11.34 0.42 N/A 10.36 0.46 Immigrant Never worked outside 7.99 1.22 Business owner 6.58 1.01 Clerk 11.40 1.29 Sales worker 10.66 1.29 Fishery worker 0.27 0.23 Trade worker 2.52 0.72 Operator 1.58 0.48 Laborers 7.94 1.41 Sen official 5.29 0.86 Professional 25.44 1.78 Technician 9.50 1.13 N/A 10.81 1.28 Percent of students in each category - how well-off financially the family is

Canada (Ontario) Native Very/somewhat well off 33.65 1.33 Average 58.11 1.30 Not very/at all well off 8.24 0.70 Immigr ant Very/somewhat well off 29.78 4.23 Average 57.69 3.65 Not very/at all well off 12.53 2.58 England Native Very/somewhat well off 28.65 1.76 Average 56.87 1.62 Not very/at all well off 14.48 1.04

190

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.) Percent of students in each category - how well-off financially the family is

Immigrant Very/somewhat well off 40.42 4.89 Average 43.40 5.10 Not very/at all well off 16.17 4.35 Israel Native Very/somewhat well off 38.42 1.26 Average 50.91 1.37 Not very/at all well off 10.67 0.84 Immigrant Very/somewhat well off 40.14 4.39 Average 42.86 5.42 Not very/at all well off 16.99 3.31 New Zealand Native Very/somewhat well off 32.40 1.41 Average 50.59 1.34 Not very/at all well off 17.01 0.80 Immigrant Very/somewhat well off 37.15 2.84 Average 49.15 2.49 Not very/at all well off 13.70 1.52 International Average Native Very/somewhat well off 33.28 0.73 Average 54.12 0.71 Not very/at all well off 12.60 0.43 Immigrant Very/somewhat well off 36.88 2.08 Average 48.28 2.16 Not very/at all well off 14.85 1.56

Percent of students in each category - parents' highest education level

Canada (Ontario) Native University or higher 37.80 2.09 Upper secondary or post secondary only 58.47 2.00 Lower secondary or less 3.73 0.83 Immigrant University or higher 69.31 3.98 Upper secondary or post secondary only 27.19 3.89 Lower secondary or less 3.50 1.98 England Native University or higher 36.78 1.82 Upper secondary or post secondary only 45.98 1.71 Lower secondary or less 17.24 1.34 Immigrant University or higher 53.12 5.23 Upper secondary or post secondary only 27.90 4.05 Lower secondary or less 18.98 4.30 Israel Native University or higher 40.65 1.96 Upper secondary or post secondary only 49.17 1.80 Lower secondary or less 10.18 1.14 Immigrant University or higher 47.86 4.65

191

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - parents' highest education level

Upper secondary or post secondary only 39.42 4.71 Lower secondary or less 12.72 2.88 New Zealand Native University or higher 28.02 1.40 Upper secondary or post secondary only 61.39 1.40 Lower secondary or less 10.59 0.77 Immigrant University or higher 60.16 2.56 Upper secondary or post secondary only 34.26 2.50 Lower secondary or less 5.58 0.82 International Average Native University or higher 35.82 0.92 Upper secondary or post secondary only 53.75 0.87 Lower secondary or less 10.43 0.52 Immigrant University or higher 57.61 2.11 Upper secondary or post secondary only 32.19 1.94 Lower secondary or less 10.19 1.40 Percent of students in each category - index of early home literacy activities

Canada (Ontario) Native High 72.46 1.26 Medium 22.61 0.88 Low 4.93 0.62 Immigrant High 60.41 4.02 Medium 30.29 3.79 Low 9.30 1.55 England Native High 84.81 1.06 Medium 13.38 0.89 Low 1.82 0.41 Immigrant High 67.19 5.25 Medium 26.90 4.75 Low 5.91 2.48 Israel Native High 73.85 1.17 Medium 21.41 1.03 Low 4.74 0.38 Immigrant High 54.03 3.95 Medium 35.72 3.35 Low 10.25 2.09 New Zealand Native High 74.92 1.00 Medium 21.00 0.87 Low 4.08 0.41 Immigrant High 69.03 2.38 Medium 24.48 2.00

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Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - index of early home literacy activities

Low 6.49 1.04 International Average Native High 76.51 0.56 Medium 19.60 0.46 Low 3.89 0.23 Immigrant High 62.66 2.02 Medium 29.35 1.81 Low 7.99 0.93 Percent of students in each category - parents' employment situation Canada (Ontario) Native Both work full-time for pay 51.83 1.47 At least one for full-time pay 43.55 1.45 Both less full-time for pay 0.67 0.23 Other situations 3.95 0.44 Immigrant Both work full-time for pay 43.63 3.29 At least one for full-time pay 45.93 3.98 Both less full-time for pay 5.19 1.71 Other situations 5.25 1.42 England Native Both work full-time for pay 24.08 1.30 At least one for full-time pay 68.79 1.40 Both less full-time for pay 1.78 0.29 Other situations 5.35 0.72 Immigrant Both work full-time for pay 23.76 4.22 At least one for full-time pay 63.43 4.51 Both less full-time for pay 2.64 1.39 Other situations 10.17 2.91 Israel Native Both work full-time for pay 44.90 1.62 At least one for full-time pay 41.40 1.35 Both less full-time for pay 5.33 0.59 Other situations 8.37 0.69 Immigrant Both work full-time for pay 42.71 5.41 At least one for full-time pay 36.88 5.33 Both less full-time for pay 6.47 2.37 Other situations 13.93 3.27 New Zealand Native Both work full-time for pay 28.60 1.19 At least one for full-time pay 62.85 1.22 Both less full-time for pay 1.34 0.25 Other situations 7.21 0.57 Immigrant Both work full-time for pay 30.58 2.74 At least one for full-time pay 58.82 2.78

193

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - parents' employment situation

Both less full-time for pay 3.20 0.84 Other situations 7.40 1.72 International Average Native Both work full-time for pay 37.35 0.70 At least one for full-time pay 54.15 0.68 Both less full-time for pay 2.28 0.19 Other situations 6.22 0.31 Immigrant Both work full-time for pay 35.17 2.02 At least one for full-time pay 51.27 2.13 Both less full-time for pay 4.38 0.84 Other situations 9.19 1.23

Percent of students in each category - parents' highest occupation level

Canada (Ontario) Native Professional 52.88 2.03 Small business owner 11.72 0.95 Clerical 18.95 1.08 Skilled worker 10.23 1.02 General laborer 3.04 0.46 Never outside home for pay 0.36 0.12 N/A 2.84 0.47 Immigrant Professional 57.73 4.28 Small business owner 10.40 2.35 Clerical 15.39 2.55 Skilled worker 7.27 1.85 General laborer 4.75 1.71 Never outside home for pay 0.77 0.60 N/A 3.70 1.43 England Native Professional 58.39 1.91 Small business owner 10.67 0.82 Clerical 15.81 1.06 Skilled worker 8.60 0.81 General laborer 1.83 0.34 Never outside home for pay 0.77 0.20 N/A 3.94 0.60 Immigrant Professional 57.34 5.10 Small business owner 8.21 2.99 Clerical 11.04 3.03 Skilled worker 12.62 2.54 General laborer 2.62 1.56

194

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - parents' highest occupation level

Never outside home for pay 3.34 1.96 N/A 4.82 1.99 Israel Native Professional 49.94 1.79 Small business owner 8.14 0.68 Clerical 14.36 1.15 Skilled worker 12.94 0.89 General laborer 2.90 0.42 Never outside home for pay 7.72 0.83 N/A 4.01 0.56 Immigrant Professional 41.24 5.30 Small business owner 7.38 2.32 Clerical 19.58 3.43 Skilled worker 14.01 3.39 General laborer 7.86 2.37 Never outside home for pay 6.83 2.43 N/A 3.09 1.57 New Zealand Native Professional 47.88 1.36 Small business owner 14.17 0.82 Clerical 18.93 0.88 Skilled worker 11.84 0.85 General laborer 3.01 0.35 Never outside home for pay 0.82 0.22 N/A 3.35 0.44 Immigrant Professional 63.35 2.33 Small business owner 8.99 1.33 Clerical 15.40 1.57 Skilled worker 6.20 1.31 General laborer 1.82 0.46 Never outside home for pay 2.11 0.91 N/A 2.13 0.66 International Average Native Professional 52.27 0.89 Small business owner 11.17 0.41 Clerical 17.01 0.52 Skilled worker 10.90 0.45 General laborer 2.69 0.20 Never outside home for pay 2.42 0.22 N/A 3.53 0.26 Immigrant Professional 54.91 2.21 Small business owner 8.75 1.16

195

Table C.14 – continued

Descriptive Data for PIRLS Parent-derived Categorical Variables

Immigrant Percent Country Category Percent Status (s.e.)

Percent of students in each category - parents' highest occupation level

Clerical 15.35 1.37 Skilled worker 10.02 1.20 General laborer 4.26 0.84 Never outside home for pay 3.26 0.83 N/A 3.44 0.75

Table C.15

Descriptive Data for PIRLS Parent-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose parents indicated they engaged in reading books often before starting school Canada (Ontario) Native 73.77 1.45 Immigrant 55.19 3.48 England Native 82.94 1.42 Immigrant 63.57 4.71 Israel Native 66.65 1.13 Immigrant 54.10 3.76 New Zealand Native 78.23 1.04 Immigrant 69.31 2.24 International Average Native 75.40 0.64 Immigrant 60.54 1.83 Percent of students whose parents indicated they engaged in telling stories often before starting school Canada (Ontario) Native 51.72 1.16 Immigrant 55.14 3.33 England Native 62.39 1.40 Immigrant 56.37 5.08 Israel Native 64.83 1.17 Immigrant 50.27 3.70 New Zealand Native 51.44 1.16 Immigrant 54.99 2.59 International Average Native 57.60 0.61 Immigrant 54.19 1.89

196

Table C.15 – continued

Descriptive Data for PIRLS Parent-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose parents indicated they engaged in singing songs often before starting school Canada (Ontario) Native 59.25 1.52 Immigrant 45.93 3.99 England Native 77.37 1.08 Immigrant 51.60 5.43 Israel Native 60.88 1.32 Immigrant 49.73 4.15 New Zealand Native 63.65 1.09 Immigrant 61.66 2.58 International Average Native 65.29 0.63 Immigrant 52.23 2.08 Percent of students whose parents indicated they engaged in playing with ABC- toys often before starting school Canada (Ontario) Native 55.79 1.44 Immigrant 43.28 4.37 England Native 64.13 1.19 Immigrant 51.88 5.20 Israel Native 57.29 1.26 Immigrant 39.43 3.96 New Zealand Native 52.01 1.31 Immigrant 48.16 2.25 International Average Native 57.31 0.65 Immigrant 45.69 2.04 Percent of students whose parents indicated they engaged in talking about things often before starting school Canada (Ontario) Native 66.79 1.30 Immigrant 56.63 2.45 England Native 80.58 1.12 Immigrant 74.85 4.44 Israel Native 72.11 1.12 Immigrant 52.67 3.85 New Zealand Native 72.59 1.04 Immigrant 69.97 2.12 International Average Native 73.02 0.58 Immigrant 63.53 1.68

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Table C.15 – continued

Descriptive Data for PIRLS Parent-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose parents indicated they engaged in talking about reading often before starting school Canada (Ontario) Native 37.59 1.18 Immigrant 36.61 2.60 England Native 50.73 1.33 Immigrant 35.36 4.61 Israel Native 51.82 1.48 Immigrant 36.37 3.33 New Zealand Native 40.60 1.04 Immigrant 42.33 2.69 International Average Native 45.19 0.64 Immigrant 37.67 1.70 Percent of students whose parents indicated they engaged in playing word games often before starting school Canada (Ontario) Native 34.40 1.17 Immigrant 29.07 2.91 England Native 48.90 1.18 Immigrant 40.02 4.82 Israel Native 50.55 1.23 Immigrant 33.69 4.09 New Zealand Native 38.26 1.17 Immigrant 38.34 2.03 International Average Native 43.03 0.59 Immigrant 35.28 1.81 Percent of students whose parents indicated they engaged in writing letters or words often before starting school Canada (Ontario) Native 50.70 1.44 Immigrant 48.66 3.41 England Native 60.87 1.32 Immigrant 55.99 4.40 Israel Native 57.93 1.41 Immigrant 48.02 4.26 New Zealand Native 52.52 1.14 Immigrant 51.13 2.51 International Average Native 55.51 0.67 Immigrant 50.95 1.86

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Table C.15 – continued

Descriptive Data for PIRLS Parent-derived Binary Variables

Immigrant Country Percent Percent (s.e.) Status Percent of students whose parents indicated they engaged in read aloud often before starting school Canada (Ontario) Native 48.65 1.29 Immigrant 43.58 3.96 England Native 57.92 1.32 Immigrant 49.05 5.00 Israel Native 54.13 1.51 Immigrant 43.27 4.23 New Zealand Native 54.24 1.13 Immigrant 52.00 2.19 International Average Native 53.73 0.66 Immigrant 46.97 1.99

Percent of students who attended ISCED level 0

Canada (Ontario) Native 50.91 1.52 Immigrant 72.12 3.71 England Native 92.61 0.84 Immigrant 86.96 3.12 Israel Native 92.14 1.24 Immigrant 95.78 1.54 New Zealand Native 96.10 0.48 Immigrant 93.16 1.18 International Average Native 82.94 0.55 Immigrant 87.00 1.31

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APPENDIX D

HUMAN SUBJECTS IN RESEARCH APPROVAL

Use of Human Subjects in Research - Approval Memorandum Human Subjects [[email protected]] Sent: Friday, July 22, 2011 10:45 AM To: Reta, Anabelle Cc: [email protected]

Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673 · FAX (850) 644-4392

APPROVAL MEMORANDUM

Date: 7/22/2011

To: Anabelle Reta

Address: University Center C, 4600 Dept.: EDUCATIONAL LEADERSHIP

From: Thomas L. Jacobson, Chair

Re: Use of Human Subjects in Research WHAT IS THE IMMIGRANT ACHIEVEMENT GAP? CONCEPTUALIZING AND EXAMINING BACKGROUND AND SCHOOL EFFECTS FOR IMMIGRANT STUDENTS GLOBALLY

The application that you submitted to this office in regard to the use of human subjects in the research proposal referenced above has been reviewed by the Human Subjects Committee at its meeting on 07/13/2011. Your project was approved by the Committee.

The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.

If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.

If the project has not been completed by 7/11/2012 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.

You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.

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By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.

Cc: Laura Lang, Advisor HSC No. 2011.6609

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BIOGRAPHICAL SKETCH

Anabelle Andon received a Ph.D. in Educational Leadership and Policy Studies with a focus on International Development Education from The Florida State University. She was an Institute of Education Sciences Fellow from 2009 to 2012 and a Foreign Language Area Studies Fellow in 2009. She has conducted research in the Amazonian Ecuador on issues of teacher training and bilingual education, as well as in the United States on issues ranging from professional development of elementary school teachers and principals on mathematics and science, to mathematics formative assessment for K-3 students in Florida. Her cross-national research ranges form investigating the correlates of teacher moonlighting in Brazil and Mexico, to issues related to immigrant student education across immigrant countries, and to a case study of policy borrowing from South to North exemplified by New York City’s conditional cash transfer program Opportunity NYC based on Mexico’s Oportunidades program. She is a native of Mexico City.

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