A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency*

Total Page:16

File Type:pdf, Size:1020Kb

A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency* Theories of Delinquency / 753 A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency* JOHN P. H OFFMANN, Brigham Young University Abstract The history of criminological thought has seen several theories that attempt to link community conditions and individual-level processes. However, a comparative analysis of contextual effects has not been undertaken. This article estimates a multilevel model that examines the effects of variables derived from three delinquency theories. The results indicate that youths residing in areas of high male joblessness who experience stressful life events or little parental supervision are especially likely to be involved in delinquent behavior. The attenuating impact of school involvement on delinquency is more pronounced in urban environments low in male joblessness. These results suggest that examining the contextual implications of delinquency theories is important, but theories need to be developed with more attention to specific contextual processes. The search for macro-micro linkages and how they affect deviant and crimi- nal behavior has a substantial and notable history (Coleman 1990; Durkheim 1951 [1897]; Stark 1987). The history of criminological thought has seen Shaw and McKay’s seminal work on how social disorganization affects behavior at the individual level, especially with reference to the qualitative life histories * Support for this research was provided by National Institute on Drug Abuse grant 11293. An earlier version of this article was presented at the 2000 annual meeting of the American Society of Criminology, San Francisco, Calif. I thank Bob Bursik, Frank Cullen, Bob Agnew, David Greenberg, and an anonymous Social Forces reviewer for helpful suggestions on earlier drafts. I also appreciate the assistance and advice provided by Bob Johnson, Harvey Goldstein, Jon Rasbash, Ken Rasinski, Shaun Koch, and Jing Zhou. Please address all correspondence to John P. Hoffmann, Department of Sociology, 844 SWKT, Brigham Young University, Provo, UT 84602. E-mail: John- [email protected]. © The University of North Carolina Press Social Forces, March 2002, 81(3):753-785 754 / Social Forces 81:3, March 2003 that they collected (Bursik & Grasmick 1993; Shaw & McKay 1931, 1969); Merton’s discussions of opportunity structures and strain (Merton 1968, 1995); and Sutherland’s discourse on the links between differential association and differential social organization (Reinarman & Fagan 1988; Sutherland 1939, 1973 [1942]). Although attention to these processes suffered a period of theo- retical and empirical dormancy, the last ten to fifteen years or so has seen a resurgence of interest in how macroprocesses affect microlevel social relation- ships. At least two motivating factors underlie this resurgence. First, Shaw and McKay’s (1969) social disorganization theory has been revisited and found to have merit. A number of studies indicate that aspects of social or community disorganization, a macrolevel construct, either affect individual behavior indirectly through micro relations or condition the impact of individual-level factors on delinquent and criminal behavior (Bursik & Grasmick 1993; Elliott et al. 1996; Sampson & Groves 1989; Taylor 1997; Veysey & Messner 1999; Yang & Hoffmann 1998). A key theoretical proposition is that socially disorganized communities are less able to control the general behavior of residents, thus affecting delinquent and criminal behavior via attenuated social control processes (Kornhauser 1978; Shaw & McKay 1931). The resurgence of social disorganization theory has prompted others to describe potential macro-micro linkages that elaborate several important theories of delinquency. These include elaborations of conflict and control processes in the development of delinquent behavior (Colvin & Pauly 1983; Hagan 1989), differential association and social learning theory to account for structural influences on learning and peer affiliations (Akers 1998; Reinarman & Fagan 1988), and the variable distribution of strains across types of communities (Agnew 1999). Second, recently developed statistical models, drawn primarily from educational research, now allow precise empirical attention to how macrolevel (contextual) variables condition the impact of explanatory variables on a variety of outcomes of interest to the criminological community. Recent studies have examined whether school- and community-level factors affect the relationship between demographic, family, and peer factors and various measures of delinquent behavior, drug use, violence, victimization, and fear of crime (Elliott et al. 1996; Hoffmann 2002; Perkins & Taylor 1996; Rountree, Land & Miethe 1994; Sampson, Raudenbush & Earls 1997). For instance, research suggests that community disorganization attenuates informal social control, which is then negatively related to adolescent deviant behavior (Elliott et al. 1997). Community disorganization may also have a direct impact on individual-level deviant behavior, even net of the effects of individual-level control mechanisms (Gottfredson, McNeil & Gottfredson 1991; Simcha-Fagan & Schwartz 1986; Taylor 1997). A limitation of this research has been its conceptual focus on linking social disorganization at the contextual level and social control or bonding mechanisms Theories of Delinquency / 755 at the individual level (Bursik & Grasmick 1993; Elliott et al. 1997; Sampson, Raudenbush & Earls 1997; Yang & Hoffmann 1998). Although the links between social disorganization and individual-level bonds are appealing and theoretically elegant, recent discussions of how other delinquency theories may be elaborated to include macro-micro connections offer a promising avenue for research (cf. Agnew 1999; Akers 1998; Reinarman & Fagan 1988; Simcha- Fagan & Schwartz 1986). In this article, I draw upon three major theories of delinquent behavior — social control, strain, and differential association/social learning — to elabo- rate the community context of adolescent involvement in delinquency.1 The goal is to determine whether some of the key individual-level relationships expressed by these theories vary across U.S. communities and, if so, whether community characteristics condition these relationships. To provide motiva- tion for this goal, the following section reviews these three theories with a clear eye toward discussing how their implied relationships might be conditioned by community characteristics. This discussion is followed by an empirical analy- sis designed to test hypotheses concerning the contextual effects of delinquency theories. Macro-Micro Context of Delinquency Theories A key goal of the sociological enterprise, and the criminological initiatives that it engendered, has been to describe how group processes and environmental conditions affect individual-level behavior (Durkheim 1982 [1895]; Hechter 1987). Important criminological inquiries drawn from this interest include the following: Why do residents of certain urban regions tend to engage in more delinquent and criminal behavior than residents of other areas? (Shaw & McKay 1931, 1969; Stark 1987). What ecological characteristics affect the probability of gang formation or individual delinquent behavior? (Short 1997). What community factors affect the fear of victimization or actual victimization? (Perkins & Taylor 1996; Rountree, Land & Miethe 1994). A variety of explanations have been proposed to answer questions such as these. The following discussion addresses three of these explanations: social control (bonding) theory, strain theory, and differential association theory. Although these theories focus primarily on individual-level processes, all are amenable to contextual elaboration. SOCIAL CONTROL THEORY Although its individual-level processes are well known due to the work of Hirschi (1969), several observers argue that social control theory’s macro-micro linkages are demonstrated in early criminological work (Kornhauser 1978; 756 / Social Forces 81:3, March 2003 Sampson & Groves 1989). Community disorganization, for instance, is thought to attenuate bonding mechanisms by making supervision and interpersonal attachments more tenuous (Elliott et al. 1997; Shaw & McKay 1931; Simcha- Fagan & Schwartz 1986). One might also ask whether community disorganization weakens the ability of social bonds to circumscribe delinquent behavior: In communities characterized by residential instability and heterogeneity and a high proportion of broken and/or single parent families [i.e., community disorganization], the likelihood of effective socialization and supervision is reduced and it becomes difficult to link youths to the wider society through institutional means. (Bursik & Grasmick 1983:37) Empirical research supports the notion that the impact of social bonds varies by type of community and that disorganized communities negatively affect the ability of social bonds to reduce delinquent behavior. Attachment to parents and peers, for instance, has a differential impact on delinquent be- havior that depends on the type of community within which it occurs (Krohn, Lanza-Kaduce & Akers 1984; see, however, Reinarman & Fagan 1988). More- over, community disorganization reduces social support structures and thus attenuates effective parenting, an important source of successful socialization and conventional bonding (Peeples & Loeber 1994; Sampson & Laub 1994; Simons et al. 1997; Yang & Hoffmann 1998). In general, social bonds such as attachment and involvement
Recommended publications
  • Elect New Council Members
    Volume 43 • Issue 3 IMS Bulletin April/May 2014 Elect new Council members CONTENTS The annual IMS elections are announced, with one candidate for President-Elect— 1 IMS Elections 2014 Richard Davis—and 12 candidates standing for six places on Council. The Council nominees, in alphabetical order, are: Marek Biskup, Peter Bühlmann, Florentina Bunea, Members’ News: Ying Hung; 2–3 Sourav Chatterjee, Frank Den Hollander, Holger Dette, Geoffrey Grimmett, Davy Philip Protter, Raymond Paindaveine, Kavita Ramanan, Jonathan Taylor, Aad van der Vaart and Naisyin Wang. J. Carroll, Keith Crank, You can read their statements starting on page 8, or online at http://www.imstat.org/ Bani K. Mallick, Robert T. elections/candidates.htm. Smythe and Michael Stein; Electronic voting for the 2014 IMS Elections has opened. You can vote online using Stephen Fienberg; Alexandre the personalized link in the email sent by Aurore Delaigle, IMS Executive Secretary, Tsybakov; Gang Zheng which also contains your member ID. 3 Statistics in Action: A If you would prefer a paper ballot please contact IMS Canadian Outlook Executive Director, Elyse Gustafson (for contact details see the 4 Stéphane Boucheron panel on page 2). on Big Data Elections close on May 30, 2014. If you have any questions or concerns please feel free to 5 NSF funding opportunity e [email protected] Richard Davis contact Elyse Gustafson . 6 Hand Writing: Solving the Right Problem 7 Student Puzzle Corner 8 Meet the Candidates 13 Recent Papers: Probability Surveys; Stochastic Systems 15 COPSS publishes 50th Marek Biskup Peter Bühlmann Florentina Bunea Sourav Chatterjee anniversary volume 16 Rao Prize Conference 17 Calls for nominations 19 XL-Files: My Valentine’s Escape 20 IMS meetings Frank Den Hollander Holger Dette Geoffrey Grimmett Davy Paindaveine 25 Other meetings 30 Employment Opportunities 31 International Calendar 35 Information for Advertisers Read it online at Kavita Ramanan Jonathan Taylor Aad van der Vaart Naisyin Wang http://bulletin.imstat.org IMSBulletin 2 .
    [Show full text]
  • This Thesis Has Been Submitted in Fulfilment of the Requirements for a Postgraduate Degree (E.G. Phd, Mphil, Dclinpsychol) at the University of Edinburgh
    This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Sociology and Statistics in Britain, 1830-1990 Plamena Yankova Panayotova A thesis submitted to the Department of Sociology in conformity with the requirements for the degree of Doctor of Philosophy The University of Edinburgh 2018 2 Declaration I hereby confirm that this doctoral thesis has been written entirely by myself, is solely the product of my own work unless otherwise specified, and has not been submitted for any other degree or professional qualification. Signed: Plamena Yankova Panayotova Dated: 17th July 2018 3 4 Acknowledgements My interest in the history of sociology and statistics in Britain began when I was still an undergraduate student; at a time when I had more curiosity to find out about these subjects than actual experience in studying them. Back then, I had been studying sociology for three years but knew little about its development in this country.
    [Show full text]
  • Do Algorithms Homogenize Students' Achievements in Secondary School Better Than Teachers' Tracking Decisions?
    education policy analysis archives A peer-reviewed, independent, open access, multilingual journal epaa aape Arizona State University Volume 23 Number 62 July 5th, 2015 ISSN 1068-2341 Do Algorithms Homogenize Students’ Achievements in Secondary School Better Than Teachers’ Tracking Decisions? Florian Klapproth University of Luxembourg Luxembourg Citation: Klapproth, F. (2015). Do algorithms homogenize students’ achievements in secondary school better than teachers’ tracking decisions? Education Policy Analysis Archives, 23(62). http://dx.doi.org/10.14507/epaa.v23.2007 Abstract: Two objectives guided this research. First, this study examined how well teachers’ tracking decisions contribute to the homogenization of their students’ achievements. Second, the study explored whether teachers’ tracking decisions would be outperformed in homogenizing the students’ achievements by statistical models of tracking decisions. These models were akin to teachers’ decisions in that they were based on the same information teachers are supposed to use when making tracking decisions. It was found that the assignments of students to the different tracks made either by teachers or by the models allowed for the homogenization of the students’ achievements for both test scores and school marks. Moreover, the models’ simulations of tracking decisions were more effective in the homogenization of achievement than were the tracking decisions, if the students assigned to the different tracks were at the center of the achievement distribution. For the remaining students, there was no significant difference found between teachers’ tracking decisions and the models’ simulations thereof. The reason why algorithms produced more homogeneous groups was assumed to be due to the higher consistency of model decisions compared to teacher decisions.
    [Show full text]
  • Longitudinal and Life Course Studies: International Journal
    VOLUME 3 | ISSUE 2 | MAY 2012 ISSN 1757-9597 Longitudinal and Life Course Studies: International Journal Published by Society for Longitudinal and Life Course Studies Inside this issue G Special Section: transition to adulthood in the UK, the US and Finland G Father involvement, family poverty and adversity G Study profile: Panel Study of Income Dynamics G Social class returns to higher education debate Co-sponsors: Promoting Longitudinal and Life Course Studies EDITORIAL BOARD EDITORIAL COMMITTEE Executive Editor John Bynner, Institute of Education, UK Health Sciences Section Editor - Michael Wadsworth, MRC Unit for Lifelong Health and Ageing London, UK Associate Editors - David Blane, Imperial College Medical School, UK - Scott Montgomery, Karolinska University Hospital, Sweden Social and Economic Sciences Section Editor - Robert Erikson, University of Stockholm, Sweden Associate Editors - Paul Gregg, University of Bath, UK - John Hobcraft, University of York, UK - Jan Smit, VU Medical Centre, Netherlands Statistical Sciences and Methodology Section Editor - Harvey Goldstein, University of Bristol, UK Associate Editor - Bianca de Stavola, London School of Hygiene and Tropical Medicine, UK Development and Behavioural Sciences Section Editor - Barbara Maughan, Institute of Psychiatry, Kings College, UK Associate Editors - Lars Bergman, University of Stockholm, Sweden - Amanda Sacker, University of Essex, UK BOARD MEMBERS Mel Bartley, University College London, UK; Nick Buck, University of Essex, UK; Richard Burkhauser, Cornell University,
    [Show full text]
  • The Royal Statistical Society Getstats Campaign Ten Years to Statistical Literacy? Neville Davies Royal Statistical Society Cent
    The Royal Statistical Society getstats Campaign Ten Years to Statistical Literacy? Neville Davies Royal Statistical Society Centre for Statistical Education University of Plymouth, UK www.rsscse.org.uk www.censusatschool.org.uk [email protected] twitter.com/CensusAtSchool RSS Centre for Statistical Education • What do we do? • Who are we? • How do we do it? • Where are we? What we do: promote improvement in statistical education For people of all ages – in primary and secondary schools, colleges, higher education and the workplace Cradle to grave statistical education! Dominic Mark John Neville Martignetti Treagust Marriott Paul Hewson Davies Kate Richards Lauren Adams Royal Statistical Society Centre for Statistical Education – who we are HowWhat do we we do: do it? Promote improvement in statistical education For people of all ages – in primary and secondary schools, colleges, higher education and theFunders workplace for the RSSCSE Cradle to grave statistical education! MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE How do we do it? Funders for the RSSCSE MTB support for RSSCSE Where are we? Plymouth Plymouth - on the border between Devon and Cornwall University of Plymouth University of Plymouth Local attractions for visitors to RSSCSE - Plymouth harbour area The Royal Statistical Society (RSS) 10-year
    [Show full text]
  • Measuring-Success-League-Tables-In
    oldstein G UCCESS and Harvey Harvey and Beth Foley League tablesLeague in the public sector S MEASURING MEASURING SUCCESS League tables in the public sector Beth Foley and Harvey Goldstein Measuring Success League tables in the public sector Beth Foley and Harvey Goldstein March 2012 Steering Group: Stephen Ball David Bartholomew Colin Crouch Harvey Goldstein (Chair) THE BRITISH ACADEMY 10 –11 Carlton House Terrace London SW1Y 5AH www.britac.ac.uk Registered Charity: Number 233176 © The British Academy 2012 Published March 2012 ISBN 978-0-85672-600-2 Designed by Soapbox, www.soapbox.co.uk Printed by Smith & Watts 3 Contents Acknowledgements 5 About the authors 6 Executive summary 7 Introduction 14 1 Education 21 Schools 21 Technical and analytical issues 23 Usability issues 26 Political, ethical and societal issues 28 Issues for further research 32 Summary of issues and recommendations 33 2 Higher education 35 Technical and analytical issues 36 Usability issues 39 Political, ethical and societal issues 41 Identifying solutions 43 Policy issues 43 Issues for further research 46 Summary of issues and recommendations 47 3 Crime and policing 48 Technical and analytical issues 50 Usability issues 53 Political, ethical and societal issues 54 Issues for further research 57 Summary of issues and recommendations 57 4 General conclusions and recommendations 59 Rankings as a tool for improvement 59 Who should produce comparative rankings? 60 Technicalities 60 4 Measuring Success // British Academy Policy Centre Monitoring 60 Summary of recommendations 62 General 62 Education 63 Higher education 64 Policing 64 References 66 Appendix A: Alternatives to league tables 69 Appendix B: Forum participants 74 British Academy Policy Centre publications 75 British Academy Policy Centre // Measuring Success 5 Acknowledgements We would like to thank Stephen Finnigan, Rob Copeland, Mick Brookes, Ann Mroz and Aaron Porter who were interviewed in the early stages of the project.
    [Show full text]
  • Harvey Goldstein
    HARVEY GOLDSTEIN Harvey Goldstein 30 October 1939 – 9 April 2020 elected Fellow of the British Academy 1996 by JOHN GRAY Fellow of the Academy IAN DIAMOND Fellow of the Academy FIONA STEELE Fellow of the Academy Harvey Goldstein died in April 2020 aged 80, an early victim of the Coronavirus pandemic. During the course of his career he made getting on for 400 published contributions to the development of statistical methods and to the substantive analysis of various educational, social and medical issues. He is particularly noted for his pioneering contributions to the development and dissemination of multilevel modelling and for his sustained critiques of so-called league tables of institutional performance. Biographical Memoirs of Fellows of the British Academy, XIX, 357–376 Posted 2 October 2020. © British Academy 2020. HARVEY GOLDSTEIN Professional life and contributions In 1977 the London Institute of Education (IoE) advertised a Chair of Statistical Methods and appointed Harvey Goldstein to it. At first glance this was a routine appointment—several professorial positions were being created across the Institute and a new head of department was needed for the Department of Measurement, Analysis and Computing. However, it was an unusual appointment in at least two respects. First, Harvey had been a lecturer at the London Institute of Child Health and, whilst he had already published quite extensively, relatively little of his output had had much to say about educational matters. And second, Educational Research was still in the process of establishing itself as an area of enquiry with its own distinc- tive approaches. Some of the major US graduate schools of education had a dedicated chair in statistics but there had never previously been such a chair in the UK.
    [Show full text]
  • Evaluating Educational Changes: a Statistical Perspective
    Evaluating educational changes: a statistical perspective Harvey Goldstein* Abstract The paper explores some of the issues involved in evaluating educational policy initiatives. It gives examples of how research findings can be evaluated and draws lessons for the ways in which policymakers can interact usefully with researchers. It argues that while central government’s use of research evidence is often highly selective and concerned with its own perceived short term interests, a broader view of the research process is more productive and beneficial. The issues of class size, school league tables and the effects of homework are studied in detail and the often provisional nature of research evidence is emphasised as well as the uncertainty surrounding the findings of individual studies. Keywords: Educational research. Educational policy. Class size research. League tables. Homework. Avaliando mudanças educacionais: uma perspectiva estatística Resumo O artigo explora questões que aparecem ao se avaliar iniciativas de políticas educacionais. O artigo dá exemplos de como resultados de pesquisas devem ser avaliados e tira lições de como formuladores de políticas podem interagir efetivamente com pesquisadores. O artigo argumenta que enquanto o uso que o governo faz da evidência da pesquisa é frequentemente seletivo e preocupado com seus interesses de curto prazo, uma visão mais geral do processo de pesquisa é mais produtivo e benéfico. As questões do tamanho da turma, ranqueamento de escolas e os efeitos do dever de casa são estudadas em detalhe e a natureza frequentemente provisória da evidência da pesquisa é enfatizada assim como a incerteza em volta dos resultados de estudos individuais. Palavras-chave: Pesquisa educacional.
    [Show full text]
  • Harvey Goldstein Memoria
    Radical Statistics 2020 Editorial to Section: Memories of Harvey Goldstein (30 October 1939 – 9 April 2020) "In dark times there will be singing" Brecht We statisticians may be hardened by continuous examination of death statistics. We may sneer uneasily at the statement questionably attributed to Stalin: “One death is a tragedy; a million deaths is a statistic”. But tragedy hits hard and we are impacted as much as others when one who dies is one of us, especially when it is a person so dear as Harvey Goldstein. Harvey always wore his eminence lightly: soft, quizzical, humourous. This shines out in a gem of an interview (Goldstein 2017). He was with Radical Statistics before our first meeting on 30 January 1975. The inaugural issue of Radical Statistics Journal (then called ‘Newsletter’) included his call (Goldstein 1975) to establish a Radstats study-group on educational research. Harvey was also with us at our most recent meeting on 29th February 2020, cogent and stimulating as usual, and with no sign of slowing down. His wisdom is already greatly missed in the statistical discussions around the disease that sadly floored him. Goldstein, Harvey. 2017. Interview with Harvey Goldstein. https://discover.ukdataservice.ac.uk/QualiBank/Document/?id=q-92053089-af34-4f09-b08c- 51c2279ca6b2&q=goldstein#relatedfiles. Goldstein, Harvey. 1975. “Radical Statistics Study Group on Educational Research.” Radical Statistics Newsletter, February. https://www.radstats.org.uk/no001/reports.pdf. Harron, Katie, George Leckie, Bill Browne, James Carpenter, Fiona Steele, and Tim Cole. 2020. “Professor Harvey Goldstein at 80.” Significance. Statistics Making Sense 17 (1): 41– 41.
    [Show full text]
  • Print This Article
    VOLUME 8 | ISSUE 1 | JANUARY 2017 ISSN 1757-9597 Longitudinal and Life Course Studies: International Journal Published by Society for Longitudinal and Life Course Studies Special section: Transition to young adulthood Inside this issue Special section: Transition to young adulthood G Attitudes towards social inequality and social justice G Life satisfaction and engagement with studies and work G Socio-ecological model of education and employment transitions G Contact with the criminal justice system and health Individual papers G 30 years of UK post-16 labour market transitions G Selective attrition and compositional changes in Swedish longitudinal study LLCS EDITORIAL BOARD EDITORIAL COMMITTEE Executive Editor John Bynner, Institute of Education, UK Population and Health Sciences Section Editor - Scott Montgomery, Örebro University Hospital and Örebro University, Sweden Associate Editors - David Blane, Imperial College Medical School, UK - Rachel Knowles, Institute of Child Health, University College London, UK - Jan Smit, VU Medical Centre, Netherlands Social and Economic Sciences Section Editor - Richard Layte, Trinity College, Dublin, Ireland Associate Editors - Paul Gregg, University of Bath, UK - John Hobcraft, University of York, UK Statistical Sciences and Methodology Section Editors - Harvey Goldstein, University of Bristol, UK - Kate Tilling, University of Bristol, UK Development and Behavioural Sciences Section Editor - Jeylan Mortimer, University of Minnesota, USA Associate Editors - Lars Bergman, University of Stockholm, Sweden
    [Show full text]
  • Cross-Classified and Multiple Membership Structures in Multilevel Models: an Introduction and Review
    RESEARCH Cross-classified and Multiple Membership Structures in Multilevel Models: An Introduction and Review Antony Fielding University of Birmingham Harvey Goldstein University of Bristol Research Report RR791 Research Report No 791 Cross-classified and Multiple Membership Structures in Multilevel Models: An Introduction and Review Antony Fielding University of Birmingham Harvey Goldstein University of Bristol The views expressed in this report are the authors’ and do not necessarily reflect those of the Department for Education and Skills. © University of Birmingham 2006 ISBN 1 84478 797 2 ACKNOWLEGEMENTS The authors of this report would like to thank all the members and visiting fellows of the Centre for Multilevel Modelling, Graduate School of Education, and University of Bristol for the many discussions in the area which have informed this review. We are also extremely grateful to Professor Hywel Thomas and Dr Ian Davison of the University Of Birmingham School Of Education. The first effectively co-directed a project in which this review forms a part and was a constant source of advice. The second undertook valuable literature searches. Finally we are very grateful to the DfES researchers and officials for their patience and invaluable guidance. 1 CONTENTS 1 Introduction…………………………………………………….…………………... 3 2 Basic Multilevel Modelling in Hierarchical Social Structures …..……….……...... 4 2.1 Explanatory Models Using Multiple Regression.………….………………...... 4 2.2 Hierarchical Data Structures And Multilevel Models..………………….…...... 7 2.3 Basic Ideas Through An Example ……………………………………….…..... 9 2.4 An Example Of A Basic Two Level Variance Components Model.………....... 15 2.5 Extending Hierarchical Models…………………..……………….…………… 17 3 Cross-Classified Data Structures.……………………………………………………....... 20 3.1 The Nature Of Cross-Classifications And Their Effects……………………….
    [Show full text]
  • League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance
    League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance Harvey Goldstein; David J. Spiegelhalter Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 159, No. 3. (1996), pp. 385-443. Stable URL: http://links.jstor.org/sici?sici=0964-1998%281996%29159%3A3%3C385%3ALTATLS%3E2.0.CO%3B2-5 Journal of the Royal Statistical Society. Series A (Statistics in Society) is currently published by Royal Statistical Society. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/rss.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is an independent not-for-profit organization dedicated to and preserving a digital archive of scholarly journals. For more information regarding JSTOR, please contact [email protected]. http://www.jstor.org Mon May 14 09:57:04 2007 J. R. Statist. Soc. A (1996) 159, Part 3, pp. 385443 League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance By HARVEY GOLDSTEIN? and DAVID J.
    [Show full text]