FINDING FOREVER HOMES:
EXAMINING PARENTAL MOTIVES AND PREFERENCES IN ADOPTION
by
RACHEL J. HAMMEL
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy
Department of Sociology
CASE WESTERN RESERVE UNIVERSITY
May, 2017
CASE WESTERN RESERVE UNIVERISTY SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of Rachel Hammel
Candidate for the degree of Doctor of Philosophy
Committee Chair Gary Deimling, Department of Sociology
Committee Member Brian Gran, Department of Sociology
Committee Member Sue Hinze, Department of Sociology
Committee Member Carol Musil, Frances Payne Bolton School of Nursing
Date of Defense February 27, 2017
*We also certify that written approval has been obtained for any proprietary material contained herein.
TABLE OF CONTENTS
Pages
List of Tables 2
Life of Figures 4
Abstract 5
Chapter 1: Introduction, Significance & Background 7
Chapter 2: Conceptual & Theoretical Orientations 26
Chapter 3: Research Questions, Conceptual Models & Hypotheses 36
Chapter 4: Research Design & Methodology 49
Chapter 5: Analysis Plan 1 Results 72
Chapter 6: Analysis Plan 2 Results 79
Chapter 7: Analysis Plan 3 Results 117
Chapter 8: Summary of Findings & Discussion 134
Appendix 151
Bibliography 196
1 LIST OF TABLES
Chapter Pages Table 1. Overview of Research Questions, Aims, Hypotheses and 3 41 Supporting Theories and Literature Table 2. Age of Potential Mothers Seeking to Adopt and Not Seeking to 4 52 Adopt Table 3. Race of Potential Mothers Seeking to Adopt and Not Seeking to 4 53 Adopt Table 4. Marital Status of Potential Mothers Seeking to Adopt and Not 4 54 Seeking to Adopt Table 5. Income of Potential Mothers Seeking to Adopt and Not Seeking 4 55 to Adopt Table 6. Education Level of Potential Mothers Seeking to Adopt and Not 4 56 Seeking to Adopt Table 7. Number of Pregnancies of Potential Mothers Seeking to Adopt 4 57 and Not Seeking to Adopt Table 8. Have the Potential Mothers Seeking to Adopt and Not Seeking 4 58 to Adopt Had a Live Birth(s) Table 9. Potential Mothers Seeking to Adopt and Not Seeking to Adopt 4 58 Wanting A/Another Baby Some Time in the Future Table 10. Respondent's Type of Adoption (N=2,089) 4 59 Table 11. Why Respondents Choose to Adopt (N=2,089) 4 60 Table 12. Why Respondents That Adopted from Foster Care Chose this 4 61 Mode of Adoption (N=763) Table 13a. Adoption Intentions by Maternal Age, Income, Education Level 5 73 and Number of Pregnancies Table 13b. Adoption Intentions by Maternal Race, Marital Status, Has had 5 74 a Live Birth and Wants A/Another Child Table 14. Odds of Seeking to Adopt (N=441) 5 77 Table 15. Desired Characteristics in an Adoptive Child (N=113) 6 81 Table 16. Respondent Flexibility in Adoptive Child Characteristics 6 83 (N=113) Table 17. Respondent's Overall Flexibility in Adoptive Child 6 84 Characteristics (N=87) Table 18. Correlations with Flexibility Variables and Characteristics of 6 86 Women Seeking to Adopt (N=86-89) Table 19a. Flexibility in Child Gender by Maternal Race, Has had Live 6 88 Birth(s) and Wants A/Another Child (N=88) Table 19b. Flexibility in Child Gender by Maternal Marital Status (N=87) 6 88 Table 19c. Correlations of Flexibility in Child Gender by Maternal Age, 6 89 Income, Education Level and Number of Pregnancies (N=88) Table 20a. Flexibility in Child Age by Maternal Race, Has had Live 6 90 Birth(s) and Wants A/Another Child (N=89) Table 20b. Flexibility in Child Age by Maternal Marital Status (N=89) 6 90 Table 20c. Correlations of Flexibility in Child Age by Maternal Age, 6 91 Income, Education Level and Number of Pregnancies (N=89) Table 21a. Flexibility in Child Race by Maternal Race, Has had Live 6 92 Birth(s) and Wants A/Another Child (N=87) Table 21b. Flexibility in Child Race by Maternal Marital Status (N=86) 6 92 Table 21c. Correlations of Flexibility in Child Age by Maternal Age, 6 93
2 Income, Education Level and Number of Pregnancies (N=87) Table 22a. Flexibility in Child Race by Maternal Race, Has had Live 6 94 Birth(s) and Wants A/Another Child (N=89) Table 22b. Flexibility in Child Disability by Maternal Marital Status 6 94 (N=88) Table 22c. Correlations of Flexibility in Child Disability by Maternal Age, 6 95 Income, Education Level and Number of Pregnancies (N=89) Table 23a. Flexibility in Number of Child in Sibling Group by Maternal 6 96 Race, Has had Live Birth(s) and Wants A/Another Child (N=89) Table 23b. Flexibility in Number of Children in Sibling Group by Maternal 6 96 Marital Status (N=88) Table 23c. Correlations of Flexibility in Number of Children in Sibling 6 97 Group by Maternal Age, Income, Education Level and Number of Pregnancies (N=89) Table 24a. Flexibility in Child Characteristics by Maternal Race, Has had 6 98 Live Birth(s) and Wants A/Another Child (N=87) Table 24b. Flexibility in Child Characteristics by Maternal Marital Status 6 98 (N=86) Table 24c. Correlations of Overall Flexibility in Child Characteristics and 6 99 Maternal Age, Income, Education Level and Number of Pregnancies (N=87) Table 25. Flexibility in Child Gender by Maternal Demographic 6 101 Characteristics Table 26. Flexibility in Child Race by Maternal Demographic 6 103 Characteristics Table 27. Flexibility in Child Age by Maternal Demographic 6 105 Characteristics Table 28. Flexibility in Child Disability by Maternal Demographic 6 108 Characteristics Table 29. Flexibility in Number of Children in Sibling Group by Maternal 6 111 Demographic Characteristics Table 30. Overall Flexibility in Child Characteristics by Maternal 6 113 Demographic Characteristics Table 31. International Adoption and Respondent's Reason for Adoption 7 120 Table 32. Foster Care Adoption and Respondent's Reason for Adoption 7 122 Table 33. Private Domestic Adoption and Respondent's Reason for 7 124 Adoption Table 34. Odds of Adopting via International Adoption (N=2089) 7 127 Table 35. Odds of Adopting via Foster Care (N=2089) 7 129 Table 36. Odds of Adopting via Private Domestic Adoption (N=2089) 7 132 Table 37. Summary of Results by Research Question and Aim 8 135
3 LIST OF FIGURES
Chapter Pages Figure 1. Racial Composition of Children in Foster Care Awaiting 1 13 Adoption versus Children Adopted from Foster Care Figure 2. Age of Children in Foster Care Awaiting Adoption versus 1 17 Children Adopted from Foster Care Figure 3. Full Conceptual Model 3 43 Figure 4. Conceptual Model as Related to Women Currently 3 46 Seeking to Adopt Figure 5. Conceptual Model as Related to Adoptive Parents 3 48
4 Finding Forever Homes:
Examining Parental Motives and Preferences in Adoption
Abstract
by
RACHEL J. HAMMEL
Introduction: The overall aim of the study is to examine the motives and preferences of both adoptive parents and women currently seeking to adopt to predict their desire to adopt the children within the U.S. foster care system. This aim is addressed through the following research questions: Why do people adopt? Who is currently seeking to adopt?
How flexible are women currently seeking to adopt in the type of child they are seeking to adopt? What role do parental demographics play in how flexible women currently seeking to adopt are in the type of child they are seeking to adopt? Why do adoptive parents select their adoption type?
Methods: Two national random sample datasets were selected for this project. The
National Survey of Family Growth Cycle VI contained interviews with women currently seeking to adoption and asked questions relating to their flexibility in child characteristics. The National Survey of Adoptive Parents surveyed adoptive parents on their type of adoption and motivations. The national secondary datasets lent themselves to a complex multivariate analysis.
Results: The results show that the average woman seeking to adopt is older, nonwhite, married or separated and wants a/another child. Overall, respondents show flexibility in child race, gender and the number of children in a sibling group, but inflexibility in age and disability status. Respondent's race, age and education are not significant predictors of flexibility in any of the child characteristics. A higher number of pregnancies and a
5 lower annual income predict overall flexibly in child characteristics. Motivations for adoption include: Wanting to expand their families and having friends who adopted are significant predictors of adopting internationally; Altruism is a significant predictor of adoption from foster care, and; Infertility and wanting a sibling for an existing child are significant predictors of adopting via private domestic adoption.
6 CHAPTER 1: INTRODUCTION, SIGNIFICANCE & BACKGROUND
Introduction
The U.S. foster care system is inundated with children that are seeking permanent adoptive families. Prior research indicates that these waiting children often do not possess the characteristics that potential adoptive parents deem as most attractive (Barbell &
Freundlich, 2001; Brooks, James & Barth, 2002; Fisher, 2003, Hegar & Rosenthal, 2011;
Howard, Livingston Smith & Ryan, 2004; Kemp & Bondonyl, 2000; Snowden, 2008;
Zhang, 2011).
This study examines the motives and preferences of women currently seeking to adopt as well as parents who have already adopted to predict their desire to adopt children from the U.S. foster care system. This is accomplished through three specific aims. First, the characteristics of women seeking to adopt are compared to women who did not want to adopt. Second, characteristics of children that are preferred by women seeking to adopt are identified and the level of their flexibility in terms of the type of child they are seeking to adopt is examined. Third, the characteristics of families who have adopted are examined to understand their adoption motives and why they selected a specific form of adoption (international, foster care or private domestic). The ultimate goal of the study is to determine who is adopting, why they are adopting, what motivates them to their type of adoption, how flexible they are in their desired child-type and what types of adoptive families are more flexible in their desired child-type. To achieve the study goals, multivariate analysis is conducted on two randomly sampled national datasets.
7 Data from The National Survey for Family Growth Cycle VI (ICPSR, 2002) and
The National Survey of Adoptive Parents (2007) are utilized. The National Survey for
Family Growth Cycle VI (ICPSR, 2002), contains 7,643 interviews with women ages 15-
44 years of age. Of the women interviewed, 113 stated that they were currently seeking to adopt and were asked questions regarding their adoption plans. The National Survey of
Adoptive Parents (2007) collected 2,089 interviews with parents who had adopted children between the ages of 0 and 17. The dataset contains information on why the families adopted and the reason for selecting their particular mode of adoption
(international, foster care or private domestic).
The analysis focuses on the conceptual model derived from existing empirical research and theoretical underpinnings. Among these are the family life course developmental framework and social exchange and rational choice theories. The family life course developmental framework as well as social exchange and rational choice theories are utilized to identify the roles played by society, the extended family and the individuals seeking to adopt.
The family life course developmental framework illustrates the roles that society and extended family members play in a family’s willingness to adopt and the type of child they are seeking to adopt. The openness of others will likely lead to a more accepting adoptive family, as they receive less judgment from their community (Dannefer
& Settersten, 2010; Duvall, 1971; Elder & Johnson, 2002; Hagestad, 2002; Settersten,
2002; White & Klein, 2008).
Social exchange and rational choice theories offer a lens through which to view the family’s decision-making processes while deciding which child they should adopt.
8 Families evaluate the costs versus benefits in determining what children attributes matter the most to them and the range of attributes they are willing to be flexible with.
Ultimately, potential families are seeking the child that will best fit within their lifestyle and they desire to maximize their likelihood for success. Issues highlighted by theory along with existing literature, together depict the challenges and tough decisions that potential adoptive families encounter as they seek to grow their families (Coleman, 1990,
1988; Hechter, 1994; Nye, 1980; Sabatelli, 1994; Thibaut & Kelley, 1959; Turner, 2003).
Specific Aims
With a substantial number of children awaiting adoption from the U.S. foster care system it is critical that potential adoptive parents be identified for these adoptable children. By understanding the characteristics of the children available for adoption from foster care and the families interested in adopting, this study explores the desire for the children available for adoption in the U.S. foster care system. The study examines the individual motives and preferences that exist for women who are currently seeking to adopt. The overall goal of the study is to examine the preferences of both adoptive parents and women currently seeking to adopt to identify factors that predict if there is a desire to adopt the children in the U.S. foster care system. This is accomplished through three specific aims.
First, the attributes of women currently seeking to adopt are compared to women who did not want to adopt.
Second, the child traits preferred by women currently seeking to adopt are identified and their level of flexibility was gauged. Data from women currently seeking to adopt was used to examine the first two aims was obtained from the National Survey of
9 Family Growth Cycle VI (ICPSR, 2002). The data will be utilized to describe the characteristics of women currently seeking to adopt, describe their preferred child-type and gauge their level of flexibility in desired child-type, as well as to compare the characteristics of women currently seeking to adopt to those not wanting to adopt.
Lastly, the characteristics of families who have adopted are examined to understand their motives specific to their adoption type (international, foster care and private domestic). The National Survey of Adoptive Parents (CDC, 2007-2008) was utilized to describe the characteristics of families who have adopted and investigate their motivations specific to their type of adoption (international, foster care or private domestic) over other forms.
Combined, these elements provide a comprehensive examination to determine if there is a demand for the children in foster care based upon the desires of women currently seeking to adopt and the motivations of those who have adopted. This study contributes to the existing body of literature that has examined the characteristics of children in foster care, international adoption motivations and foster children’s attitudes towards adoption (Akin, 2011; Cooper, 2013; Diehl, 2011; Snowden, 2008;Wildeman &
Emanuel, 2014; Zhang, 2011). Prior research has failed to examine the demand for adopted children from the U.S. foster care system based upon the motives and desires of the prospective adoptive families.
Significance
In 2014, nearly a half million children were in the U.S. foster care system and approximately 25% of these children were eligible for and awaiting permanent adoption
(DHHS, 2015). Federal policies such as the 1994 Multiethnic Placement Act (MEPA)
10 and the 1997 Adoption and Safe Families Act (ASFA) were passed to decrease the number of children aging out of foster care by prohibiting racial discrimination in adoption and also reducing the amount of time children wait in foster care prior to being available for adoption (Allen & Bissell, 2004; Burrell & Cowen, 2004; Curtis &
Alexander, 1996; Parkinson, 2003). While these policies have made an impact on the
U.S. foster care system, the number of children awaiting adoption remains substantial
(108,746 in 2010 and 107,918 in 2014) (DHHS, 2015). Additionally, these policies have been criticized for not encouraging Black families to foster and adopt children, rather they have simply been a means to allow White families the opportunity and possibly preference to adopt Black children without considering the race of the child (Sargent,
2011). The large number of children aging out of foster care without being adopted is a social problem with a broad societal impact; children that age out or emancipate from the foster care system are at risk for mental health problems and substance abuse (Keller,
Salazar & Coutney, 2010; Smith, Leve & Chamberlain, 2011; Unrau, Seita & Putney,
2008). Coupled with their lack of emotional attachments and educational attainment, emancipated foster children have high levels of homelessness, incarcerations and victimizations (Keller, Salazar & Coutney, 2010; Smith, Leve & Chamberlain, 2011;
Unrau, Seita & Putney, 2008).
Background
Finding adoptive homes for the large number of children awaiting adoption is crucial. Children in foster care—especially those awaiting adoption—typically are older,
Black, come from large sibling groups, have significant behavioral and developmental needs and have been in continuous foster care for at least three years (DHHS, 2015;
11 Hegar, 2005; Howard, Livingston Smith & Ryan, 2004; Wildeman & Emanuel, 2014).
Scholars and policy makers have assumed that these child characteristics are not what typical adoptive parents desire. Potential adoptive families are assumed to be interested in healthy White infants because most adoptive parents are White, of high SES and are thus presumed to be seeking an adoptive child that would mirror a biological child (Fisher,
2003; Snowden, 2008). Accordingly, the common perception is that children in foster care do not match the preferences of potential adoptive families (Blackstone, Buck,
Hakim & Spiegel, 2008; Fisher, 2003; Zhang, 2011). Indeed, older Black children remain in foster care longer than White children (Barbell & Freundlich, 2001). Despite this often proffered reason for the low adoption rates of older Black children in foster care, it remains an open question as to whether potential adoptive parents are actually unwilling to adopt the available children in foster care.
Challenges of the Children Available for Adoption
The children awaiting adoption in foster care are predominately male and have been in foster care continuously for almost three years (DHHS, 2015). Additionally, they are being cared for by a non-relative foster family and are nearly 8 years of age (DHHS,
2015). These characteristics along with race, the number of siblings wanting to be adopted together, disabilities and behavior and mental health problems make the placement of children in the U.S. foster care system into adoptive homes challenging.
Race of Children
Black children, especially boys, are less likely to achieve permanency (permanent family living arrangement) after being placed into foster care (Akin, 2011; Kemp &
Bodonyl, 2000; Snowden, 2008). Further compounding the issue, the number of Black
12 children in foster care is substantial; Black children have 44% higher odds of being placed in foster care than White children, with 1 in 9 Black children entering foster care during some time in their childhood (Akin, 2011; Cooper, 2013; Kemp & Bodonyl, 2000;
Snowden, 2008; Wildeman & Emanuel, 2014). Figure 1 illustrates the percentage of children in each racial group awaiting adoption from foster care versus those who were adopted from foster care. White children made up a largest percentage of the adopted and waiting children, with a larger percentage adopted than awaiting adoption.
Figure 1. Racial Composition of Children in Foster Care Awaiting Adoption versus Children Adopted From Foster Care 60%
48% 50% 42% 40%
30% 23% 23% 22% 19% 20%
8% 8% 10% 4% 2% 0% Black Hispanic (any race) White Two or More Races Other
Awaiting Adopted
Data Source: Adapted from data retrieved from the AFCAR Report, DHHS, 2015
Once in foster care Black children are less likely to be reunified with their families and remain in foster care for a longer duration than non-Black children.
Literature suggests that the higher representation of Black children in foster care can be explained by an interaction of circumstances within the individual, family and community that are largely exacerbated by poverty (Knott & Donovan, 2010). By one
13 estimate, Black children are one-fifth as likely as White children to be adopted (Brooks,
James & Barth, 2002).
Moreover, research indicates that Black children remain in foster care longer and receive fewer services while in care than White children (Barbell & Freundlich, 2001).
Despite the greater placement rates for infants, Kemp & Bondonyl (2000), examining foster care in Washington State in 1995, found that Black infants declared free for adoption were not as easily placed as White infants. Prior studies have presumed that this lower likelihood of placement reflects the fact that the vast majority of prospective adoptive parents are White and are seeking children that are also White and young
(Brooks, James & Barth, 2002).
Furthermore, if prospective adoptive parents are to adopt outside of their race, they are thought to be more likely to select a child that is only slightly different than them in terms of racial makeup (Fisher, 2003; Zhang, 2011). White parents seek Russian,
Asian, Hispanic or biracial children over Black children. Indeed, transracial adoption rates show this pattern. While transracial adoptions have increased, Asian and Hispanic children have the highest rates of transracial adoption (Eschelbach Hansen & Simon,
2004; Zhang, 2011). Historically, transracial adoption rates have mirrored those of interracial marriage. Interracial marriage rates have increased since the 1970’s, however the increase varies drastically by race. Until recent years, the most common type of interracial marriage was a White male marrying an Asian woman (Fryer, 2007). Recent trends in transracial marriage are showing an increase in marriage among White and
Black Americans, thus racial boundaries are blurring (Qian, 2011). The blurring of racial divisions offers an opportunity for increased adoption of Black children in foster care.
14 With marriage trends foreshadowing the blurring of racial boundaries, race will take a less prominent role in adoption preference.
Critical race theory (CRT) offers a lens through which to understand the subordinate role that Black individuals play within our society and how our laws are utilized to reinforce the power differential (Crenshaw, Gotanda, Peller & Thomas, 1995;
Delgado & Stefancic, 2011). CRT grew out of the post-civil rights movement with the goal of understanding and articulating the role that race, power and law play within society. CRT argues that racism remains today, just in a different form than during the
1960’s (Crenshaw, Gotanda, Peller & Thomas, 1995; Sargent, 2011). Applying CRT to adoption, scholars believe that adoption in the U.S. is not a child-centered process but rather directed largely by White adoptive parents. The needs of the older, Black children are not viewed as important (Howe, 1995; Sargent 2011).
Roberts (1995) uses CRT to illustrate the historical plight of Black women and how they have been devalued throughout time, which has led to the over-representation of Black children in U.S. foster care. Historically, female slaves were valuable to their owners not only for the work that they produced but also for their ability to birth more slaves. Beatings by slave-owners were conducted in a calculated manner to not injury the growing fetus while the mother endured whippings. The children born to Black slaves were owned by the slave-owners and were often sold off to reinforce control. In the
1970’s, Black, poor women were the victims of forced sterilization. While today there are regulations to prohibit involuntary sterilization, health care providers still often strongly urge Black women to be sterilized to reduce what they view as excessive family sizes
(Roberts, 1995).
15 This racial power struggle continues to play out in current day. Government intervention into the lives of pregnant, poor, Black women is particularly punitive. Black women become the targets of governmental control since they are the least able, of all races, to conform to the White, middle-class vision of motherhood. Black women are more likely to be reported for perceived child neglect that their more affluent, White counterparts. As a result, Black mothers disproportionately lose custody of their children.
Their reliance on the welfare system, reinforces their subordination, as they are under the supervision/control of social workers. Black families are less likely to be a nuclear family so childrearing patterns are different. These patterns to do coincide with White, middle- class America so the government views Black women as neglectful. Along with the increased number of children in foster care, this fear-based system has also resulted in high infant mortality rates for poor Black women. Fear coupled with financial and other barriers deter Black women from receiving proper prenatal care (Roberts, 1995).
Age of Children
The number of older children available for adoption in foster care is substantial.
While national campaigns have been launched to market the children awaiting adoption, further efforts are needed to find additional families for aging children in care (Burrell
Cowan, 2004). In 2014, the average child adopted from foster care was six years of age, while nearly one third of the available children were over 10 years of age (DHHS, 2015).
Older children, specifically teenagers, have a low rate of adoption from foster care. In
2014, 12,249 children age 15 and above were seeking permanency (a permanent home) from foster care through adoption, while only 19% were actually adopted that year. The
16 largest percentage of children adopted in 2014 were adopted from the 1 to 3 age group, see Figure 2 (DHHS, 2015).
Figure 2. Age of Children in Foster Care Awaiting Adoption versus Children Adopted From Foster Care (DHHS, 2015) 12000 10000 8000 6000 4000 2000 0
Awaiting Adopted
Data Source: Adapted from data retrieved from AFCAR Report, DHHS, 2015
While Americans are seeking young White children, these children may have other issues making them unattractive to potential families. For instance, young children within the U.S. foster care system are likely to have more chronic health conditions largely due to prenatal drug exposure (Jee et al., 2006). If teenagers do find an adoptive home they are likely to have problems and are less likely to have a positive parent-child relationship (Brind, 2008). Briggs & Webb (2004) note, “not only do they [teenage adoptees] have to contend with the effects of difficult, disturbing, abusive and depriving birth families and the impact of repeated moves in care, but they have to undertake this whilst in the midst of the upheavals of the complex biopsycho-social changes of early adolescence (pg 2).”
17 Sibling Groups
While age is one component making it difficult to find permanent homes for children, large sibling groups pose an even more complex hurdle (Hegar, 2005; Hegar &
Rosenthal, 2011; Wulczyn & Zimmerman, 2005). It is estimated that 65-85% of children in the U.S. foster care system are from sibling groups (Hegar, 2005). Children in sibling groups have more favorable outcomes if they are allowed to remain in sibling groups when in foster care or adopted. However, when sibling groups must be separated, the grouping of multiple siblings in one placement promotes adjustment and fewer behavior problems (Hegar & Rosenthal, 2011).While the goal is to place such groups in a single home there are several obstacles to finding such an adoptive home. First, many siblings have not resided together before being placed in state care. Second, the children often come into care at different times and under various jurisdictions. Third, the diverse individual needs of the children may be difficult to meet in one family setting. Last, large sibling groups are physically difficult to place in one household setting with homes lacking the adequate space for multiple children (Hegar, 2005).
Disabilities of Children
While many of the children seeking adoption from foster care are older and are from large sibling groups, they are likely to also have disabilities. Children in foster care have a higher rate of disabilities than biological children, with 24-51% of adoptive children having special needs compared to 6-8% of biological children (Howard,
Livingston Smith & Ryan, 2004). The increase in parental alcohol and drug use is connected with the growing number of young children entering into foster care, as these children are often exposed to drugs and alcohol prenatally. Children exposed to alcohol and drugs in utero experience more medical and developmental difficulties creating
18 additional hurdles for permanency should they become involved in the foster care system
(Barbell & Freundlich, 2001; Brown & Rodger, 2009). Black children are the largest group of children testing positive for drug usage at birth (Hines, Lemon, Wyatt &
Merdinger, 2004) and are also over-represented in the foster care system.
Children in foster care experience many negative events that lead to long-term impacts on their health and well-being. Studies have found that mental health diagnoses are present in as many as 51-61% of foster children (Healey & Fisher, 2011; Scozzaro &
Janikowski, 2015). Additionally, many foster children have hormonal imbalances that heighten stressors and lessen their ability to cope (Healey & Fisher, 2011). Early intervention is critical to buffering the negative impacts of childhood abuse and neglect.
As children mature they become increasingly resistant to interventions. Without appropriate intervention foster children become a public health problem as they enter adulthood ill prepared and become an immense burden on society (Healey & Fisher,
2011). Children who have remained in foster care and are older have a higher likelihood of experiencing physical and mental health problems. Children in the age group of 5-12, commonly have diagnosed problems that require intervention(s) with many communities not having the necessary resources available to meet the children’s needs (Sullivan & van
Zyl, 2008). While disabilities decrease a child’s attractiveness to a potential adoptive family, children with disabilities are more likely to be adopted than children with mental health or behavioral issues (Akin, 2011; Snowden, 2008).
Behavioral and Mental Health Problems
The effects of abuse and neglect that caused the removal of the child from their birth family often manifests in emotional and behavioral problems that have
19 consequences on school performance and produce academic delays (Healey & Fisher,
2011). Children adopted from the child welfare system are significantly more likely to experience behavior problems when compared to children adopted internationally or as domestic infants (Howard, Livingston Smith & Ryan, 2004). Overall, children involved in the child welfare system have high behavioral and developmental needs that are present in children as young as toddlers and preschoolers, with few children receiving services for such issues (Stahmer et al., 2005). Further complicating the situation, the instability of foster care leads to additional poor developmental outcomes in the children making them less attractive to prospective families (Harden, 2004). School performance and behavioral problems are intensified due to frequent moves within the foster care system. With each school change comes different curriculum and expectations. Foster children are often placed in inappropriate classrooms and experience negative attitudes from teachers that further block their potential for successful educational attainment
(Vacca, 2008).
The vast majority of children in care have only one or two placements (different homes) while involved in the foster care system (Harden 2004). However, these placements often last less than two years, with the average wait in care until adoption in
2010 being 37 months (DHHS, 2011; Harden, 2004). With an average stay in foster care being over three years there is growing concern over the well-being and mental health of children that remain in foster care for such extended periods of time (Schofield & Beek,
2005). It is not clear whether children experience multiple placements due to poor developmental outcomes or if multiple placements cause the negative outcomes in the children (Harden, 2004). If older children and those with special needs are lucky enough
20 to get adopted, they are at increased risk of experiencing an adoption disruption
(unsuccessful adoption). Much of this risk is attributed to the past history of the child that contains multiple moves and increased traumas that are manifested as challenging behaviors (Wilson, 2004). Also, maltreated children have the additional challenge of a lack of trust towards caregivers and their need to control others. Maltreated children are not able to adjust to a new environment and adapt to a new reality without abuse and neglect present (Schofield & Beek, 2005).
Recent literature highlights issues related to foster care adoption. Adopted children often struggle with the concept of being adopted and must deal with the stigma, while at the same time dealing with the emotional loss of their biological families
(Samuels, 2009). Transracially adopted children have additional struggles due to their different physical appearance than their adoptive family and also the rejection by extended family members. Furthermore, transracially adopted children are often rejected by their racial group due to their Whiteness (Samuels, 2009). Openness with birth families has been found to mediate some of the issues associated with adoption. Crea &
Barth (2009) examined adoptions in California from 1997 to 2003 to study adoption openness between birth families and adoptive families. They found that continued contact had a positive relationship on both the birth families and adoptive families. However, children adopted from foster care were less likely to have an open relationship (Crea &
Barth, 2009). Behavioral problems, disabilities, large sibling groups, older children and nonwhite children are a reality when seeking to adopt from the U.S. foster care system.
21 Challenges Faced By Adoptive Parents
Infertility Issues
Prior literature suggests that the primary reason people adopt children is to cope with infertility (Bausch, 2006; Fisher, 2003; Zhang, 2011). Historically, in America childless couples are viewed as being materialistic or selfish and those with children are viewed as loving and hardworking (Gibson, 2009). Furthermore, the process of adoption is stigmatizing within the U.S. since having an adopted child is viewed as “not as good” as having a biological child and an adopted child is ultimately considered second-rate.
Children in foster care awaiting adoption have a host of issues that parents typically do not encounter with the same intensity with biological children; such as attachment issues, long-term negative effects of abuse and neglect, exposure to drugs and alcohol in utero and mental health issues (Akin, 2011; Barbell & Freundlich, 2001; Harden, 2004;
Howard, Livingston Smith & Ryan, 2004; Snowden, 2008). Adoption is seen as the final alternative to having a biological child since the advancement in medical procedures make biological children possible in cases of infertility (Fisher, 2003). The concept of kin and biological relation is important within many families in the U.S.; by adopting rather than having biological children the family bloodline has concluded (Bausch, 2006).
Adoption rates support that U.S. families do not consider adoption a primary source of family growth, with adoption rates declining since the 1970’s (Fisher, 2003).
Thus, the typical child in foster care is unattractive to an adoptive family that is seeking a healthy baby to address their infertility. Data shows that people are delaying childbearing with an increasing number of women seeking education and entrance into the labor market (Smock & Greenland, 2010). This delay or postponement of childbearing often leads to infertility issues once women are ready to have children with the majority of
22 married women seeking medical intervention for their infertility. Many older men and women who experienced infertility in their earlier years, express regret in later life that they did not have any biological children. Even those with stepchildren and adopted children still feel the void from not having a biological child (Bures, Koropeckyi-Cox &
Loree, 2009). Due to infertility issues, adoption has become more popular among a subgroup of women ages 40-44 that do not have biological children and have had unsuccessful infertility treatments (Smock & Greenland, 2010). While adoption has increased in recent years, the current trend is to adopt internationally rather than from foster care to fulfill the desire for an infant that would be similar to a biological child
(Zhang, 2011).
A large body of literature exists on the plight of infertile couples and the marital discord that exists due to the lack of conception of a biological child (Goldberg, 2009;
Jennings, 2010). As failed attempts at pregnancy increase, so does marital stress and psychological issues. When infertile couples seek adoption they are forced to re- conceptualize their definition of a child, since a biological child is what they had desired
(Goldberg, 2009). Often couples turn to religion to assist in the reconceptualization of becoming a parent and the acceptance of an adopted child (Jennings, 2010; Park &
Wonch Hill, 2014). While existing literature provides a link between infertility and adoption, if fails to show a correlation to foster care adoption.
Adoptive Parents’ Motivations
Yuanting & Lee (2011) examined the motives and preferences of individuals who adopted internationally through qualitative interviews with adoptive parents. They found that international adoptions were sought over domestic adoptions due to the lack of
23 availability of desirable children in the U.S., lengthy wait times, potential issues with birth parents, shortage of adoptable infants, prenatal drug abuse, adverse impacts of neighborhoods on childhood development and behavioral problems associated with older children available for adoption. Furthermore, internationally adopting a child of another race is seen as more socially acceptable, whereas adopting a Black or other minority child from the U.S. foster care system was less appealing. Sargent (2011) founds that White adoptive parents often perpetuate the myth that there are not any White children available in U.S. foster care as a justification to adopt internationally. When in reality, there are both White and Black children awaiting adoption in foster care, they are just viewed as damaged goods and rejected by potential adopters.
While much of the literature states that many individuals seek adoption due to infertility issues, conversely there is another body of work stating that individuals seek adoption for altruistic reasons (Fisher, 2003). This subgroup of potential adoptive parents, who may be willing to adopt children that do not mirror their biological children, is key to placing more children in adoptive homes that otherwise would remain in foster care.
The challenge lies in identifying which potential adoptive families are adopting with altruistic reasoning and if they are more flexible in the type of child they are willing to adopt.
Through the application of sociological theory, one can truly understand the role that societal influences and individual desires have on the motivations of potential adoptive parents. The family life course developmental framework illustrates how societal norms and expectations guide individual behaviors. For instance, a married couple in their 30’s should have children, as such infertile couples will seek out other
24 means of growing their families if they have not had children by this stage in their life course. Additionally, they will seek a child that would mirror a birth child in terms of race and age. Social exchange and rational choice theories add in the individual self- interests of potential adoptive parents. When adopting, parents educate themselves and must decide what child characteristics are most important to them and what mode of adoption they wish to undertake (international, foster care or private domestic).
25 CHAPTER 2: CONCEPTUAL & THEORETICAL ORIENTATIONS
Overview
There are a number of conceptual approaches and theories that can be utilized to examine the motivations of women currently seeking to adopt and those who have adopted. Through an examination of family theories and the meaning of parenthood, family life course developmental framework, social exchange and rational choice frameworks together offer an accurate view the roles played by society, the extended family and the individuals seeking to adopt. The family life course developmental theory is examined first to understand the roles that society and the extended family have in the adoption process. Social exchange and rational choice theories are then added to the discussion to show the impact that individuals have on their adoption process.
The family life course developmental framework and its approach to families can be utilized to examine the roles played by extended family and societal norms that enter into the adoption decision making process (Dannefer & Settersten, 2010; Duvall,
1971; Elder & Johnson, 2002; Hagestad, 2002; Settersten, 2002; White & Klein, 2008).
Family life course developmental framework is utilized to understand Specific Aims 2 and 3.
Social exchange and rational choice theories as developed by sociologists
(Coleman, 1990, 1988; Hechter, 1994; Nye, 1980; Turner, 2003), social psychologists
(Thibaut & Kelley, 1959) and family theorists (Sabatelli, 1994) add the role of the individual actors (women currently seeking to adopt) in their own decision making process. Social exchange and rational choice theories are utilized to understand Specific
Aims 1, 2 and 3.
26 Family Life Course Developmental Framework
The family life course development’s notion of sequencing of life event relates to
Specific Aims 2 and 3 that are explored more indepth in Chapter 3. Specific Aim 2, the preferred child-type of women currently seeking to adopt are explored and their level of flexibility gauged. It was hypothesized that White, older, more educated parents would be most flexible in the type of child they are seeking to adopt. Sequencing reinforces this hypothesis. Older, well-educated individuals would be most likely to want to adopt since one can assume that they have had a positive sequencing of life events since they have obtained education. Additionally, they are older and have worked through the life stages necessary to parent a difficult child, potentially a non-infant child. Specific Aim 3, the characteristics of families who have adopted are examined to understand their motives specific to their form of adoption (international, foster care or private domestic). It was hypothesized that the type of adoption selected would correlate with the motive for adoption; with adoptive parents experiencing infertility selecting to adopt an infant either through private adoption or internationally and those parents adopting for altruistic reasons being more willing to adopt from foster care. Related to sequencing, the infertile couple is unlikely to have already achieved the life stage of parenting and would be ill prepared to parent an older child, thus an infant adoption is most fitting.
In the family life course developmental framework, the sequencing of life events is more heavily regulated by societal norms than the timing of the event (Elder &
Johnson, 2002). This institutional approach to aging demonstrates the power that social policy and the institutions created through policy have in shaping the age-graded stages of the life course (Dannefer & Settersten, 2010). If a life event takes place out of sequence there are repercussions in later life for the individual and family (Elder &
27 Johnson, 2002; Hagestad 2002). Gunhild Hagestad (2002) in her study of families and aging noted that off-time events, particularly early events are more likely to cause personal crisis because individuals are not prepared for the event and also do not have the support systems. Due to medical advances childless couples have the option to adopt or undertake fertility treatments to grow their family (Fisher, 2003), but these events must happen within the prescribed time schedule. If a couple decides to adopt a child that is too old or too young to biologically be their birth child there are implications for the family unit since the family stage will be distrupted.
The family stage defines the social roles of the family members and their obligations to the family (Settersten, 2002). For example, if a young couple adopts a teenager there are different parental roles that they must assume for their older child than if they had an infant. These role changes not only impact the adoptive couple but also their extended family, friends, neighbors and other children in the household. Thus, when adopting a child, the sequencing and successful completion of life stages weighs heavily on the perceived ability to properly parent the child. As in the above example, a young married couple is not perceived by others to have to the tools necessary to effectively raise a teenager since they are too close in age to the child and have not progressed through the life stages necessary to have developed the skills to raise a teenager. This concept of sequencing is structurally embraced, as there are policies when adopting a child that limit one’s abililty to adopt based on parental age, either too young or too old.
With each forthcoming generation, the life course of individuals changes. Each cohort is shaped by life experiences which impact how the cohort develops and changes as they age (Dannefer & Settersten, 2010). Current life course trajectories are impacted
28 by the postponement of marriage and childbearing, increased cohabitation, higher divorce rates and advances in modern medicine (Liefbroer & Elzinga, 2012). These societal changes influence the timing or age at which individuals become concerned with family growth and possibly adoption.
The family life course developmental framework illustrates the role of the family in determining the life course of an individual and ultimately the acceptance or non- acceptance of adoption. Family life course developmental framework is comprised of three theoretical approaches: individual life span theory, family development theory and life course theory (Elder & Johnson, 2002; White & Klein, 2008). The individual life span theory is a psychological theory that focuses on the genetic development of the individual and tends to neglect history and the age-graded life course (Elder & Johnson,
2002). Bengston & Allen (1993) believe that all individuals universally develop regardless of time and place. This theory was largely criticized for not incorporating the social and historical contexts that impact individual development.
Within the family life course developmental framework, family development theory is concerned with the changes that occur within the family as they move through life stages and events together (Duvall, 1971; White & Klein, 2008). Evelyn Duvall
(1971) outlined the eight stages of family development that start with the newly married adult and end with retirement. The stages of family development theory are studied from both the structuralist and the interactionist perspectives. The structuralists focus on institutional impact on the family and they believe this can be studied by examining aggregate patterns. When two social institutions are out of sequence in their timing, social change will occur so that one institution will conform to the other. While the
29 interactionists believe that the actions of individual family members can dictate social roles and thus cause social change (White & Klein, 2008).
Family development is regulated by society with family life events being defined by current cultural norms that vary by both cohort and period effect. Historical changes occur from one birth cohort to the next while historical events during the period shape lives (Elder & Johnson, 2002). Historically, married couples who were childless were viewed by their peers are being materialistic or selfish (Gibson, 2009). Today, while the stigma of childlessness have decreased, children are still part of the family developmental life course. Adoption has become more acceptable within society, however families have their own norms that regulate the family’s behaviors and ultimately the acceptance of an adopted child. Consequently, parents are likely to adopt a child that is only slightly different from themselves in terms of racial background (Fisher, 2003; Zhang, 2011).
One impact that life course theory examines is the life events of an individual and how early events shape the outcomes or trajectories of the individual in later life
(Settersten, 2002). Life course theory takes into account the timing of life events, cohort norms, period and age. While the focus in life course theory is on the individual, the family is incorporated into the analysis, as one individual’s action have implications for the entire family.
Social Exchange and Rational Choice Framework
While is it extreme to think that individuals value the social institution of the family more than their own wants and desires, societal expectations do impact potential adoptive parents’ decisions often resulting in individuals conforming to societal expectations of an acceptable family-type. The process of weighing the costs and benefits relates to Specific Aims 2 and 3, is further explored in Chapter 3. Specific Aim 2, the
30 preferred child-type of women currently seeking to adopt is explored and their level of flexibility gauged. It was hypothesized that women currently seeking to adopt will be moderately flexible in their desired child-type but unwilling to adopt a high needs child.
Specific Aim 3, the characteristics of families who have adopted are examined to understand their motives specific to their form of adoption (international, foster care and private domestic). It was hypothesized that the adoption type (international, foster care and private domestic) would correlate with the motive for adoption. Prospective adoptive parents that are seeking adoption due to infertility would lean towards domestic or international adoption, while those seeking to adopt for altruistic reasons would be more likely to adopt from foster care.
Potential adoptive families are limited by their human and social capital, which relates to Specific Aim 1, the attributes of women currently seeking to adopt are compared to women who do not want to adopt. It was hypothesized that White, more educated individuals are most likely to adopt. This group has a high amount of both financial and social capital that will assist them in their adoption process and provide them the opportunity to be more selective in their type of adoption and desired child-type.
Social exchange and rational choice theories are useful in the examination of personal motivations towards adoption of children from foster care. According to these utilitarian philosophies, individuals act in ways to promote their own self-interests and weigh the rewards and costs of a particular decision prior to acting. Personal values and self-interests guide the actor towards the outcome with the greatest benefits (Hechter,
1994). Applied to prospective adoptive families, exchange and rational choice theories provide an explanation for the individual decision making processes that a family must
31 go through when deciding to adopt a child and selecting which child is best for their family.
Turner (2003) in his analysis of classical exchange theory identifies his five basic assumptions of social exchange theory. These can be coupled with the rational choice framework to illustrate the decision making process that a potential adoptive family partakes in. First, individuals seek to maximize their profits in social situations (Hechter,
1994; Turner, 2003). According to Hechter (1994), individuals are motivated by the value assumption, which argues that actors are motivated to acquire goods that can be viewed as valuable and exchange them for innate goods. For potential adoptive parents, they are seeking what they perceive to be the best child for their family while making appropriate social connections in the foster care arena to connect them with this child. Theorists have contributed to this element of exchange theory by adding that in long-term social relationships, such as families, that individuals must examine both short- and long-term profits of a situation (White & Klein, 2008).
Families often opt for higher long-term profits rather than instant gratification, which is true in adoption. Families do not simply want to adopt any child, they desire a particular child-type; if they opt for instant gratification and select any child the result could be an adoption disruption and/or decreased long-term satisfaction with the adoption. This relates to Specific Aim 2, the preferred child-type of women currently seeking to adopt are explored and their level of flexibility gauged. It was hypothesized that women seeking to adopt would be moderately flexible, but unwilling to adopt high needs children which characterize children in foster care. Due to emotional and financial
32 costs associated with a high needs child, such a child is unlikely to be a good fit for most families and they will opt to look for a lower needs child available for adoption.
Secondly, people conduct an analysis of the costs and downfalls then carefully consider their moves in social situations (Nye, 1980; Turner, 2003). Nye (1980) in his study of adolescent runaways, noted that some situations may simultaneously be labelled as both positive and negative and be dependent upon the individual in question. For instance, schooling is typically viewed as a positive, as a means to move ahead in society.
However, for runaways, schooling can just be another venue within which they do not excel and thus contributes to them being labeled in a negative way. Potential adoptive families must make decisions that will impact their likelihood of being matched with a particular child; for example: what agency to use for their homestudy, whether they attend child matching events, what type of relationship are they willing to maintain with the child’s birth family, what unknowns they are willing to accept in a child and what child characteristics are unacceptable. Parents must decide what is best for their family by a careful examine of the pros and cons of each scenario. What is considered a positive for one family may be a negative for another. For example, while most families are seeking to adopt an infant or young child, some families may not want a child this young and may see this as a negative trait. Instead they may wish to adopt an older child which more is known about in terms of medical and mental health needs. Coleman (1988) argues that that while individuals are seeking to maximize their own profits in social situations, they prefer stable structures within society and make rational choices that promote and support the macro-level norms within society.
33 Third, individuals are not always educated on all of the potential alternative decisions that could be made, however, they are aware that other options are available
(Sabatelli, 1994; Thibaut & Kelley, 1959; Turner, 2003). Thibaut and Kelley (1959) termed this the comparison level and comparison level of alternatives, by which individuals rationally compare potential outcomes to select which maximizes their profit.
Within the comparison level analysis, individuals compare what they have to others in a similar situation. Then through the comparison level alternatives the individual adds other alternatives to the current situation (Sabatelli, 1994). Nye (1980) notes that many will remain in a negative situation awaiting a better opportunity to come along. This could be the case at both the individual level and also at the family level. For adoptive parents seeking a child from foster care, they are aware of other forms of adoption
(private/domestic infant and international) and while they may not be fully educated about each type, these other options guide their movements and serve as a basis for their decisions. This third assumption is related to Specific Aim 3, the characteristics of families who have adopted were examined to understand their motives specific to their form of adoption (international, foster care and private domestic). Through the homestudy process, which is necessary for any adoption, potential adoptive parents are educated on the different types of adoption. It was hypothesized that the motive for adoption will largely guide the type of adoption selected.
Forth, individuals while constrained by social structure and the need for cooperation must compete with one another to make a profit (Hechter, 1994; Ney, 1980;
Turner, 2003). Social structure requires that individuals cooperate and work together to reach a common goal. For example, a family must cooperate and assume roles to have a
34 fully functioning household. However, there are inequalities that exist within the cooperative relationships; for example, a woman’s role within the family is often devalued and she will assume a larger portion of the household duties and childrearing.
Although there is interdependence, the need to gain rewards or to make a profit is also present (Hechter, 1994). For potential adoptive parents, they need to work with other potential adoptive families to understand the foster care system but ultimately the families compete for potential children in child matchings. Potential families submit their homestudies and a committee must decide which family is the best match for the child available for adoption. Individuals want their homestudies to maximize their positive characteristics in an effort to make their family appear a better candidate for adoptive children.
Lastly, while individuals are seeking to maximize their profits, they are limited by the resources they have available to them (Coleman, 1990; Turner, 2003). All exchanges do not have to contain material goods; individuals have resources available to them in the form of human and social capital. James Coleman (1990) defines human capital as that which is gained by changing individuals to give them new capabilities, skills and the ability to act in new ways. Coleman (1990) defines social capital as gained through a network of relationships that allow for exchange of human and financial capital. Potential adoptive families are limited by items such as their socioeconomic status, their parenting experience, their location, the type of home they live in, their work situation and their access to medical care. However, a growth in their social capital can assist them in gaining other forms of resources (human and financial capital) to assist in their adoption process.
35 CHAPTER 3: RESEARCH QUESTIONS, CONCEPTUAL MODELS & HYPOTHESES
Introduction
This chapter first outlines the five research questions addressed and then provides three related conceptual models that illustrate the research questions that are answered by the study.
Research Questions
Research Question 1: Why do people adopt?
Research Question 1: Why do people adopt? supports Specific Aim 3, the characteristics of families who have adopted are examined to understand their motives specific to their form of adoption (international, foster care or private domestic). It was hypothesized that families will adopt largely for infertility reasons or if they have a close personal connection with adoption (individual is adopted or spouse is adopted)
(hypothesis 3). Hypothesis 3 draws upon the family life course developmental framework. Individuals seeking adoption due to infertility are less likely to have already achieved the life stage of parenting and thus would be ill prepared to parent an older child, thus an infant adoption would be more fitting (Hagestad, 2002; Settersten, 2002).
Bausch (2006), Fisher (2003) and Zhang (2011) suggest that the primary reason people adopt children is to cope with infertility. Fisher (2003) also found a subgroup of individuals who are seeking to adopt for altruistic reasons.
Research Question 2: Who is currently seeking to adopt?
Research Question 2: Who is currently seeking to adopt? is related to Specific
Aim 1, the attributes of women currently seeking to adopt are compared to women who do not want to adopt. It was hypothesized that women currently seeking to adopt would
36 likely be White and more educated (hypothesis 1). Hypothesis 1 draws upon social exchange and rational choice theories. Higher educated, White individuals are likely to have both high financial and social capital that will assist them throughout the adoption process and provide them the opportunity to be more selective in their adoption type and desired child-type (Coleman, 1988; Hechter, 1994; Nye 1980). Sargent (2011) argues that
Black families are not encouraged by the U.S. foster care system to foster or adopt, therefore those adopting are predominately White families.
Brooks, James & Barth (2002), Fisher (2003) and Snowden (2008), found that potential adoptive families are assumed to be interested in healthy, White infants because most adoptive parents are White and of high SES. The adopted child would then resemble a biological child. The desire for a White child draws upon Brooks, James &
Barth (2002) that stated an estimate that Black children are one-fifth as likely as White children to be adopted. Further support is found from the DHHS (2015) data that shows the largest percentage of children adopted from foster care in 2014 were from the 1 to 3 age group. Smock & Greenland (2010) found that a subpopulation of women age 40-44 that do not have biological children and who have infertility issues are now moving towards adoption.
Research Question 3: How flexible are women currently seeking to adopt in the type of child they are seeking to adopt?
Research Question 3: How flexible are women currently seeking to adopt in the type of child they are seeking to adopt? is related to Specific Aim 2, the preferred child- type of women currently seeking to adopt are explored and their level of flexibility gauged. It was hypothesized that women currently seeking to adopt would be moderately
37 flexible, but unwilling to adopt high needs children which characterize children in foster care (hypothesis 2). Social exchange and rational choice theories are suggested for hypothesis 2. Individuals seek to maximize their profits in social situations and weigh the costs and benefits of the situation (Coleman, 1988; Hechter, 1994; Nye, 1980). The commitment level required for a high needs child makes him/her unlikely to be a good fit for most families and they will opt to look for a lower needs child that is available for adoption.
Fisher (2003) and Zhang (2011) found that if prospective adoptive parents are to adopt outside of their race they are thought to be more likely to select a child that is only slightly different than them in terms of racial make-up. Hispanic and Asian children have the highest rates of transracial adoption (Eschelbach, Hansen & Simon, 2004; Zhang,
2011). Parents are semi-flexible when adopting from foster care but they are more likely to adopt a child with physical disabilities than one with mental health or behavioral issues
(Akin, 2011; Snowden, 2008).
Research Question 4: What role do parental demographics play in how flexible women currently seeking to adopt are in the type of child they are seeking to adopt?
Research Question 4: What role do parental demographics play in how flexible women currently seeking to adopt are in the type of child they are seeking to adopt? is a secondary research question. Research Question 4 applies to Specific Aim 2, the preferred child-type of women currently seeking to adopt is explored and their level of flexibility gauged. It was hypothesized that White, older, more educated parents will be most flexible in the type of child they are seeking to adopt (hypothesis 4). This hypothesis drew upon the family life course developmental framework. Individuals with
38 higher education are more likely to have had a positive sequencing of life events and be more secure. Thus these individuals are most likely to adopt and have the ability to be more flexible. Also, they are older and thus have already worked through the earlier life stages so they have the necessary life experiences to parent a difficult, older child
(Duvall, 1971; Hagestad 2002; Settersten 2002).
Eschelbach, Hansen & Simon (2004) and Zhang (2011) found that Hispanic and
Asian children have the highest rates of transracial adoption. If prospective adoptive parents are to adopt outside of their race they are thought to be more likely to select a child that is only slightly different than them in terms of racial make-up (Fisher, 2003;
Zhang 2011). Parents are semi-flexible when adopting from foster care but they are more likely to adopt a child with disabilities than one with mental health or behavioral issues
(Akin, 2011; Snowden, 2008).
Research Question 5: Why do adoptive parents select a specific form of adoption
(international adoption, foster care and private domestic)?
Research Question 5: Why do adoptive parents select a specific form of adoption type (international, foster care or private domestic)? it is a secondary research question and is linked to Specific Aim 3: the characteristics of families who have adopted are examined to understand their motives specific to their form of adoption (international, foster care and private domestic). It was hypothesized that the type of adoption selected would correlate with the motive for adoption. With adoptive parents experiencing infertility selecting to adopt an infant either through private adoption or internationally and those parents adopting for altruistic reasons being more willing to adopt from foster care (hypothesis 5).
39 Hypothesis 5 draws upon both the family life course developmental framework and social exchange and rational choice theories. Individuals seeking adoption due to infertility are less likely to have already achieved the life stage of parenting and thus would be ill prepared to parent an older child, thus an infant adoption would be more fitting (Hagestad 2002; Settersten 2002). Potential adoptive parents will partake in a reasoning process that outlines the pros and cons when deciding which type of adoption is right for them (Hechter, 1994; Nye, 1980). Parents that are adopting for infertility reasons will lean towards domestic or international adoption, while those who are adopting for altruistic reasons are more likely to adopt from foster care. Thus the motive for adoption will largely guide the type of adoption selected. According to Zhang (2011) the current trend is to adopt internationally rather than from foster care to fulfill the desire for an infant that would be similar to a biological child. According to Yaunting & Lee
(2011), international adoptions were sought over domestic adoptions due to the lack of availability of desirable children in the U.S., lengthy wait times, potential issues with birth parents, shortage of adoptable infants, prenatal drug abuse, adverse impacts of neighborhoods on childhood development and behavioral problems associated with older children available for adoption. Yaunting & Lee (2011) also found that adopting a child internationally of another race is seen as interesting whereas adopting a Black or other minority child from the U.S. foster care system was less appealing. Sargent (2011) takes the argument a step farther to say that potential adoptive parents purposely avoid adopting from foster care due to the unattractiveness of the children awaiting adoption and to justify their actions that perpetuate the myth that the U.S. foster care system only contains Black children.
40 Table 1. Overview of Research Questions, Aims, Hypotheses and Supporting Theories and Literature Research Aim Hypothesis Theoretical Supporting Literature Question Connection 1. Why do people Aim 3: The characteristics of Families will adopt largely for Family life course Bausch (2006); Dannefer & Settersten adopt? families who have adopted will be infertility reasons or if they have a developmental (2010); Duvall (1971); Elder & examined to understand their close personal connection with framework Johnson (2002); Fisher (2003); motives specific to their form of adoption Hagestad (2002); Settersten (2002); adoption. White & Klein (2008); Zhang (2011) 2. Who is currently Aim 1: The attributes of women Women currently seeking to adopt Social exchange and Brooks, James & Barth (2002); seeking to adopt? currently seeking to adopt will be will likely be White and more rational choice Coleman (1988); Coleman (1990); compared to those families who educated. DHHS (2015); Fisher (2003); Hechter do not want to adopt. (1994); Nye (1980); Sabatelli (1994); Sargent (2011); Smock & Greenland (2010); Snowden (2008); Thibaut & Kelley (1959); Turner (2003) 3. How flexible are Aim 2: The preferred child-type Women currently seeking to adopt Social exchange and Akin (2011); Coleman (1988); women currently of women currently seeking to will be moderately flexible, but rational choice Coleman (1990); Eschelbach, Hansen seeking to adopt in adopt will be explored and their unwilling to adopt high needs & Simon (2004); Fisher (2003); type of child they are level of flexibility gauged. children which characterize child in Hechter (1994); Nye (1980); Sabatelli seeking to adopt? foster care. (1994); Snowden (2008); Thibaut & Kelley (1959); Turner (2003); Zhang (2011) 4. What role do Aim 2: The preferred child-type White, older, more educated parents Family life course Akin (2011); Coleman (1988); parental of women currently seeking to will be most flexible in the type of developmental Coleman (1990); Dannefer & demographics play in adopt will be explored and their child they are seeking to adopt. framework Settersten (2010); Duvall (1971); Elder how flexible women level of flexibility gauged. & Johnson (2002); Eschelbach, seeking to adopt are Hansen & Simon (2004); Fisher in the type of child (2003); Hagestad (2002); Settersten they are seeking to (2002); Snowden (2008); White & adopt? Klein (2008); Zhang (2011) 5. Why do adoptive Aim 3: The characteristics of The type of adoption selected will Social exchange and Dannefer & Settersten (2010); Duvall parents select their families who have adopted will be correlate with the motive to rational choice; Family (1971); Elder & Johnson (2002); adoption type? examined to understand their adoption. With adoptive parents life course Hagestad (2002); Hechter (1994); Nye motives specific to their form of experiencing infertility selecting to developmental (1980); Sabatelli (1994); Sargent adoption. adopt an infant with through private framework (2011); Settersten (2002); Thibaut & adoption or internationally and those Kelley (1959); White & Klein (2008); parents adopting for altruistic Yaunting & Lee (2011); Zhang (2011) reasons being more likely to adopt from foster care. 41 Conceptual Models
The full conceptual model (Figure 3) contains variables from both The National
Family Grown Cycle VI (ICPSR, 2002) and The National Survey of Adoptive Families
(CDC, 2007-2008). The model is not a causal model, rather it is utilized to portray the linkages between the constructs and specific aims so that the research questions and hypotheses can be examined. The figure contains the hypotheses that are tested through multivariate analysis along with the associated dependent and independent variables.
Overall the study examines: who adopts, why they adopt, where they are adopting from, what type of child they are seeking to adopt and how flexible they are in the traits of a potential adoptive child.
The variables were color-coded based upon who they apply to. The variables contained in the light gray boxes are those related to the adoptive parents (either mothers currently seeking to adopt or experienced adoptive parents). These variables are parental demographics, their flexibility in the type of child they are seeking to adopt and their motivations leading them to adopt. The dark gray shapes contain variables related to the children in foster care: what makes foster care adoption attractive to adoptive parents and the demographics of the children in foster care. The white shapes were related to children available for adoption through sources other than foster care (international or domestic).
The five hypotheses are provided at the bottom of the model and also displayed within the model. The model corresponds to the three Specific Aims: (1) the attributes of women currently seeking to adopt were compared to women who are not seeking to adopt, (2) the preferred child-type of women currently seeking to adopt were explored and their level of flexibility gauged and (3) the characteristics of families who have
42 adopted were examined to understand their motives specific to their form of adoption
(international, foster care or private domestic).
Figure 3: Full Conceptual Model
Constructs operationalized by variables in: The National Family Growth Cycle VI (ICPSR, 2002) and The National Survey of Adoptive Families (CDC, 2007-2008)
Figure 4 contains the conceptual model that is examined with data from women currently seeking to adopt gathered from The National Family Growth Cycle IV (ICPSR,
43 2002). The variables in the model are maternal demographics (age, race, marital status, income, education and number of pregnancies), variables related to maternal flexibility in child characteristics (gender, race, age, disability status and number of children seeking to be adopted together) and the demographics of the children available for adoption (age, race, gender, disability status and number of children seeking to be adopted together).
These variables address two of the Specific Aims: (1) the attributes of women currently seeking to adopt were compared to women who are not currently seeking to adopt, and
(2) the preferred child-type of women currently seeking to adopt were explored and their level of flexibility gauged.
There were three hypotheses associated with the model. First, women currently seeking to adopt are likely to be White and more educated. This hypothesis draws upon social exchange and rational choice theories. White families with higher levels of education will likely possess greater human and social capital making them more likely to have the resources necessary to adopt (Coleman, 1980; Hechter, 1994). The individuals with the most capital will have the ability to be more selective in their type of adoption and their desired child-type, as they will have less financial constraints
(Hechter, 1994).
Second, women currently seeking to adopt are shown to be moderately flexible, but unwilling to adopt the high needs children which characterize children in foster care.
This hypothesis draws upon social exchange and rational choice theories. Families seeking to adopt undertake an analysis of the positive and negative factors and carefully consider the type of child they are seeking to adopt (Nye, 1980). Parents are seeking to adopt a child that will fit within their existing family structure and conform to the norms
44 within society, thus allowing them some flexibility but they will be unwilling to adopt a high needs child (Coleman, 1988).
Third, White, older, more women have been shown to be more flexible in the type of child they are seeking to adopt. This draws upon the family life course developmental framework. White & Klein (2008) state that older, well-educated individuals are more likely to have a positive sequencing of life events since they are more educated and older.
Furthermore, they have experienced more life stages and are at a point in their lives were they can accept a child with higher needs, specifically an older child since they would be the appropriate family stage to have a biological child that is not an infant (Elder &
Johnson, 2002; Hagestad, 2002).
45 Figure 4: Conceptual Model as Related to Women Currently Seeking to Adopt
Constructs operationalized by variables in: The National Family Growth Cycle VI (ICPRS, 2002)
Figure 5 contains the conceptual model that is examined with data from adoptive parents gathered from The National Survey of Adoptive Families (CDC, 2007-2008). The variables in the model are parental motivations towards adoption (infertility, expand family, wants siblings for an existing child, altruistic reasons or have a close connection with adoption), variables related to why they would adopt from foster care (shorter wait, cheaper, wanted older child, wanted special needs child, previously adopted from foster care or wanted to give a child a home) and other forms of adoption (international and
46 private domestic). These variables addressed Specific Aim: (3) the characteristics of families who have adopted were examined to understand their motives specific to their form of adoption (international, foster care and private domestic).
There are two hypotheses associated with the model. First, families adopt largely for infertility reasons or if they have a close personal connection with adoption (the individual or spouse is adopted). This hypothesis draws upon the family life course developmental framework. Historically, married couples without children have been viewed by society as being overly materialistic or selfish (Gibson, 2009), thus couples without children seek to grow their families to meet the cultural norms (Elder & Johnson,
2002). In more recent years, adoption has become more acceptable in society, however extended families still have their own level of acceptance. Thus, adoptive parents who have families who are comfortable with adoption will be more like to adopt themselves
(Elder & Johnson, 2002).
Second, the type of adoption correlates with the motivation for adoption. With adoptive parents experiencing infertility selecting to adopt an infant either through private or international adoption and those parents adopting for altruistic reasons being more willing to adopt from foster care. This hypothesis draws upon by social exchange and rational choice theories as well as the family life course developmental framework.
Parents adopting due to infertility will want an adoptive child that would mirror a biological child in terms of race and age. Related to life sequencing, they will seek an infant to achieve the infant stage of parenting and would be ill prepared to parent an older child (Hagestad, 2002). Adoptive parents partake in a weighing of their options to determine what child traits they are willing to accept (Nye, 1980). Infertile couples will
47 be less flexible and thus the children available for adoption from foster care will not fulfill their description of the ideal child. Those adopting for altruistic reasons would have different factors to consider in their positive and negative analysis and may feel that they can make the most impact on a child’s life by adopting through foster care.
Figure 5: Conceptual Model as Related to Adoptive Parents
Constructs operationalized by variables in: The National Survey of Adoptive Families (CDC, 2007-2008)
48 CHAPTER 4: RESEARCH DESIGN & METHODOLGY
Introduction
This chapter provides an overview of the research design and methodology, and examines the following elements: data sources, data management, analysis plan, descriptive and bivariate analysis, multivariate analysis and human subjects. The study utilizes secondary data analysis of two archival datasets that are available in the public domain. The data is quantitative in nature and is analyzed using multivariate methods.
Data Sources
Two national, random sample datasets were selected for this project to examine who is currently seeking to adopted, the level of maternal flexibility in adoption and who adopts from foster care. The National Survey of Family Growth Cycle VI (ICPSR,
2002) and The National Survey of Adoptive Parents (2007) were selected as the best sources due to their comprehensive operationalization of the study variables necessary to answer the research questions and they are both national random samples that are credible sources. Existing datasets containing variables related to adoption motivations are limited, especially those that are random, national samples. The National Survey of
Family Growth Cycle VI (ICPSR, 2002) asked respondents basic demographic questions as well as if they were currently seeking to adopt. Female respondents who reported that they were currently seeking to adopt were then asked specific questions related to what child traits they would prefer in an adoptive child, what traits they would be willing to accept and what traits they would not consider. The National Survey of Adoptive Parents
(2007) contains respondents who have adopted a child. Respondents were asked what motivated them towards adoption and why they chose their form of adoption. The dataset
49 contains respondents who have adopted from foster care, private domestic adoption and international adoption. Together these datasets lend themselves to a complex multivariate analysis that examines who adopts, why they adopt, what type of child they adopt and why they would select that child.
The National Survey of Family Growth Cycle VI (ICPSR, 2002) obtained from the Inter-University Consortium for Political and Social Research (ICPSR), at the
University of Michigan. Primary sampling units (121) were utilized for sampling with nearly every state being sampled and all large metropolitan areas included. The primary sampling units were metropolitan areas, counties or groups of cities. Within the primary sampling units, neighborhoods or adjacent blocks were selected as segments (secondary units). Addresses were compiled from each segment and randomly contacted. Persons between 15-44 years of age were interviewed. If multiple individuals within the household met the criteria, one person was randomly selected.
The interviews were 60 to 80 minutes in length and conducted by an interviewer using a computer-assisted self-interview (CASI). The study gathered information on family background, marriage, children, family planning, sexual relations, religion, income and insurance. Data were collected for both men and women, however the questions pertaining to adoption were only asked of women. Therefore, for the purpose of the present study, women’s interviews were analyzed to determine interest in adoption.
The dataset contained key demographic variables (age, marital status, race, income and education) of the focal women as well as data on number of past pregnancies and future plans to adopt. Additionally, the dataset provided the characteristics of the preferred adoptive child (age, gender, race and disabilities). Of the available 7,643 women
50 interviewed, 113 stated that they were currently seeking to adopt and were asked questions regarding their plans to adopt.
The National Survey of Adoptive Parents (2007) is a nationally representative survey sponsored by the Department of Health and Human Services (DHHS) and conducted under contract with the National Opinion Research Center at the University of
Chicago. Data was collected on 2,089 adoptive parents with children age 0 to 17, who adopted through the U.S. foster care system, domestic private adoption or internationally.
Adoptive parents were selected for the survey if they had a child who was identified as adopted in the 2007 National Survey of Children’s Health (NSCH). Interviews were collected via telephone interviews from April 2007 to July 2008.
Respondents were selected if they had a child between the ages of 0-17 years of age. Telephone numbers were randomly generated, with the goal of completing 1,700 interviews per state. If numbers for each state did not reach the 1,700 goal, additional numbers were drawn for that particular state until the goal was met. Data collected included demographic variables, pre-adoption experiences and intensions, relationship to others who have adopted and experiences throughout the adoption process. The dataset contained key data on why the families adopted (infertility, expand family, siblings for other child, altruistic, husband or wife was adopted, friends or family have adopted), what mode of adoption they selected (international, foster care or private domestic) and why they selected the mode of adoption (price, wait time, previous adoption, altruistic, wanted special needs).
51 Data Management
Data Structure: The National Survey of Family Growth Cycle VI (ICPSR, 2002)
The National Survey of Family Growth Cycle VI (ICPSR, 2002) contains 7,643 women’s interviews. Of those interviewed, 113 stated that they were currently seeking to adopt and were asked questions regarding their adoption plans. For comparison purposes, a 5% sample of 7,530 remaining women who were not seeking to adopt was selected.
The data analysis contains a total of 441 respondents, of which 328 are not currently seeking to adopt and 113 were currently seeking to adopt.
Sample Characteristics: The National Family Growth Cycle VI (ICPSR, 2002)
The basic analysis provided in this section was conducted prior to the substantial analysis and is offered as an overview of the datasets. Tables 2 through 9 display the sample characteristics for the 113 currently seeking to adopt.
At the time of the interview the female respondents were asked their current age as of their last birthday.
Table 2. Age of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Adopt Not Seeking to
(N=113) Adopt (N=328) Variable N % N % Age 17-24 years 15 13.3% 78 23.8% 25-29 years 17 15.0% 65 19.8% 30-34 years 25 22.1% 64 19.5% 35-39 years 27 23.9% 57 17.4% 40-44 years 29 25.7% 64 19.5% Data Source: The National Family Growth Cycle VI (2002)
Age was collected as a continuous variable with responses ranging from 17 to 44 years of age with the median age of 32 years with a mean of 31.8 years of age. Those seeking to
52 adopt had a median age of 34 years with a mean of 33.49 years of age. Those not currently seeking to adopt has a median age of 31 years with a mean of 31.22 years of age. Compared to the U.S. population in 2010, the mean age of the women in the sample is below the population average of 37.9 years of age (U.S. Census Bureau, 2011).
Interviewers recorded the respondent’s observed race. Data was collected as a categorical variable with three options: Black, White and Other.
Table 3. Race of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Not Seeking to
Adopt (N=113) Adopt (N=328) Variable N % N % Race Black 43 38.1% 74 22.6% White 53 46.9% 227 69.2% Other 17 15.0% 27 8.2% Data Source: The National Family Growth Cycle VI (2002)
Of the overall sample (N=441), 26.5% were recorded as Black and 63.5% as White. Of the 113 women seeking to adopt, 38.1% were reported as Black and 46.9% as White. Of the women who are not currently seeking to adopt, 22.6% were Black and 69.2% were
White. The racial breakdown of the sample is skewed compared to the U.S. population which is comprised of 72.4% White and 12.6% Black (U.S. Census Bureau, 2011).
Respondents were asked to select their current marital status from a card. Options included: married, not married but living together with a partner of the opposite sex, widowed, divorced, separated because you and your spouse are not getting along or never been married. For the purposes of data analysis, the variable was recoded to: married, divorced, separated or never married.
53 Table 4. Marital Status of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Not Seeking to
Adopt (N=113) Adopt (N=328) Variable N % N % Marital Status Married (includes 61 54.0% 146 44.5% widowed) Not Married but 5 4.4% 37 11.3% Cohabitating Widowed 0 0.0% 2 0.6% Divorced 13 11.5% 28 8.5% Separated 12 10.6% 13 4.0% Never been Married 22 19.5% 102 31.1% Data Source: The National Family Growth Cycle VI (2002) Of the overall sample (N=441), the majority were either married (46.9%) or never married (28.1%). Of those seeking to adopt (N=113), 54% were married and 19.5% were never married. Of those not seeking to adopt (N=328), 44.5% were married and 31.1% have never been married. According to the 2010 U.S. Census, 49.8% of women in the
U.S. are married, which is similar to the sample (U.S. Census Bureau, 2011).
Respondents were asked to select the category that best represents their total yearly income for their family in 2001, including income from all sources such as wages, salaries, Social Security or retirement benefits, help from relatives and so forth. They were told to enter the amount before taxes.
54 Table 5. Income of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Not Seeking to Adopt Adopt (N=328) (N=113) Variable N % N % Total Income (Annual) Under $25,000 41 37.2% 124 40.1% $25,000-$74,999 51 46.4% 150 48.5% $75,000 or more 18 16.4% 35 11.3% Data Source: The National Family Growth Cycle VI (2002) Of the 113 women seeking to adopt, 37.2% had an income below $25,000, 46.4% from
$25,000 to $74,999 and 16.4% above $75,000. Of the 328 women not seeking to adopt,
40.1% had an income below $25,000, 48.5% from $25,000 to $74,999 and 11.3% above
$75,000. According to the U.S. Census Bureau (2011), 25% of households have an income below $25,000, 43.3% have an income between $25,000 and $74,999 and 31.7% have an income of $75,000 or more. The study sample has more individuals in the lower income category and less in the above $75,000 category than in the U.S. population.
Respondents were asked what is the highest grade or year of school you have ever attended. The variable is a continuous variable ranging from no formal schooling to 19 years of school (7 or more years of college and/or grad school). Of the 441 respondents,
440 answered the question.
55 Table 6. Education Level of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Not Seeking to Adopt Adopt (N=328) (N=113) Variable N % N % Highest Education Level 11th grade or less 17 15.0% 63 15.0% High School Grad 36 31.9% 76 31.9% 1-3 years college 31 27.4% 102 27.4% 4 years+ college 29 25.7% 86 25.7% Data Source: The National Family Growth Cycle VI (2002)
Respondents provided answers ranging from 9th grade to 19 years of schools (7 or more years of college and/or grad school). The mean level of education for both groups were comparable (seeking to adopt=13.44 years of school and not seeking to adopt=13.4 years of school). For both groups, 85% of women had a high school education or above. The sample has slightly higher educational attainment compared to women across the U.S.
(82.7% had an education level of high school or above) (U.S. Census Bureau, 2011).
Respondents were asked how many times they have been pregnant in their life.
The variable is a continuous variable with responses ranging from 0-9.
56 Table 7. Number of Pregnancies of Potential Mothers Seeking to Adopt and Not Seeking to Adopt Seeking to Not Seeking to Adopt (N=113) Adopt (N=328) Variable N % N %
Number of Pregnancies None 30 26.8% 90 27.5% 1 21 18.8% 68 20.8% 2 23 20.5% 45 13.8% 3 12 10.7% 72 22.0% 4 11 9.8% 23 7.0% 5+ 15 13.4% 29 8.9% Data Source: The National Family Growth Cycle VI (2002) Overall women seeking to adopt had more pregnancies compared to those not seeking to adopt. The women seeking to adopt had a mean of 2.16 pregnancies. For the women not seeking to adopt, the mean number of pregnancies was lower at 1.93 pregnancies.
Respondents were asked the number of babies they have had born alive. The variable is a continuous variable with responses ranging from 0-22 babies born alive. For the purposes of the analysis, the number of babies born alive was recoded into a dichotomous (yes/no) variable, if the respondent has had a live birth(s).
57 Table 8. Have the Potential Mothers Seeking to Adopt and Not Seeking to Adopt Had a Live Birth(s) Seeking to Adopt Not Seeking to Adopt
(N=113) (N=328) Variable N % N % Has Had Live Birth(s) Yes 66 58.4% 208 63.4% No 47 41.6% 120 36.6% Data Source: The National Family Growth Cycle VI (2002)
Women seeking to adopt were less likely to have a live birth (58.4%) compared to those not seeking to adopt (63.4%). This is of importance since those seeking to adopt had more pregnancies than those not seeking to adopt, which implies more miscarriages were experienced among women who are seeking to adopt.
Respondents were asked, looking to the future, if it were possible, do you, yourself want to have a/another baby at some time. The variable is a dichotomous variable (yes/no).
Table 9. Potential Mothers Seeking to Adopt and Not Seeking to Adopt Wanting A/Another Baby Some Time in the Future Seeking to Not Seeking to Adopt Adopt (N=328) (N=113) Variable N % N % Wants A/Another Child Yes 86 76.8% 173 54.1% No 26 23.2% 147 45.9% Data Source: The National Family Growth Cycle VI (2002)
Of those seeking to adopt, most wanted a/another baby in the future (76.8%). Of those not seeking to adopt, slightly more than half reported wanting a/another baby in the future (54.1%).
58 Data Structure: National Survey of Adoptive Parents (CDC, 2007-2008)
The National Survey of Adoptive Parents (CDC, 2007-2008) contains 2,089 interviews of adoptive parents with children age 0 to 17, who adopted internationally, through the U.S. foster care system or via private domestic adoption.
Sample Characteristics: National Survey of Adoptive Parents (CDC, 2007-2008)
The basic analysis provided in this section was conducted prior to the substantial analysis and is offered as an overview of the datasets. Tables 10 through 12 display the sample characteristics for the 2,089 respondents who have adopted. The dataset contains individuals who have adopted internationally, through the U.S. foster care system and through private domestic adoption.
Table 10. Respondent's Type of Adoption (N=2,089) Variable N % International Adoption 545 26.1% Foster Care Adoption 763 36.5% Private Domestic Adoption 781 37.4% Data Source: National Survey of Adoptive Parents (CDC, 2007-2008) While the sample was fairly evenly divided, the majority of respondents adopted U.S. children either via private domestic adoption (37.4%) or through foster care (36.5%).
The reasons why individuals adopted were explored. The set of questions was prefaced with the statement “There are many reasons why people decide to adopt a child.
I am going to read a list of possible reasons why people sometimes choose to adopt. For each reason, please tell me whether or not this was one of the reasons why you or your spouse/partner chose adoption.” All of the questions had four answer options: (1) yes, (2) no, (6) don’t know and (7) refused.
59 Table 11. Why Respondents Choose to Adopt (N=2,089) International Foster Care Private Domestic Adoption Adoption Adoption (N=545) (N=763) (N=781) Variable N % N % N % Infertility 372 68.3% 337 44.2% 423 54.2% Expand Family 505 92.7% 497 65.1% 477 61.1% Wanted a Sibling for 157 28.8% 175 22.9% 135 17.3% Another Child Wanted to Adopt a 485 89.0% 633 83.0% 549 70.3% Child in Need Wanted to Help a Child 0 0.0% 8 1.0% 10 1.3% Avoid Foster Care Family Members have 218 40.0% 288 37.7% 271 34.7% Adopted Friends have Adopted 458 84.0% 484 63.4% 436 55.8% Respondent is Adopted 15 2.8% 33 4.3% 35 4.5% Spouse/Partner is 8 1.5% 21 2.8% 8 1.0% Adopted Respondent or Spouse 35 6.4% 68 8.9% 57 7.3% has Adopted Siblings Data Source: National Survey of Adoptive Parents (CDC, 2007-2008)
Wanting to adopt a child in need, wanting to expand their family, having friends who adopted, having infertility issues and having family members who have adopted were the top reasons why the respondents adopted. All three modes of adoption had the same top five reasons for adopting, but the ordering of their top two reasons fluctuated (wanting to expand their family and wanting to adopt a child in need).
The 763 respondents that adopted from foster care were asked what factors led to them seeking foster care as their route of adoption. The preface to the questions read
“Earlier you indicated that you adopted SC [child] through a foster care adoption. I am going to read a list of reasons to you for choosing this type of adoption. For each reason that I read to you please tell me whether or not this was one of your or your
60 spouse’s/partner’s reasons for choosing a foster care adoption.” The response categories for all of the following questions were: (1) yes, (2) no, (6) don’t know and (7) refused.
Table 12. Why Respondents That Adopted from Foster Care Chose this Mode of Adoption (N=763) Variable N % Shorter Wait Times 129 16.9% Cheaper than Other Forms of Adoption 195 25.6% Wanted an Older Child 60 7.9% Wanted a Special Needs Child 67 8.8% Previously Adopted Another Child from Foster Care 74 9.7% Wanted to Provide a Home for a Child in Need 66 8.7% Data Source: National Survey of Adoptive Parents (CDC, 2007-2008)
Most respondents selected foster care adoption due to it being cheaper than other forms of adoption or they felt that it had shorter waiting times compare to private domestic or international adoption.
Analysis Plan
The secondary data sources and research questions lend themselves to a complex multivariate analysis. The three specific analysis plans are explored through the analysis of the two national datasets: The National Survey of Family Growth Cycle VI (ICPSR,
2002) and the National Survey of Adoptive Parents (CDC, 2007-2008).
Data Analysis Plan Frame 1
Data analysis plan one relates to the conceptual model in Figure 4 (page 45) that illustrates child and maternal demographics. Demographics include: maternal age, race, marital status, total annual income, highest education level and number of pregnancies.
Descriptive, bivariate and multivariate analysis are used to examine the concepts.
61 Analysis Plan 1: Describe the characteristics of women currently seeking to adopt, comparing those seeking to adopt to those not seeking to adopt is accomplished through frequencies, t-tests, cross tabulations and a binary logistic regression model using data from The National Survey of Family Growth Cycle VI
(ICPSR, 2002).
The first step to examining analysis plan one is to conduct frequencies to examine the data. The frequencies display the percentage of respondents in each category.
Statistics selected to accompany the frequencies include the mean, median, standard deviation and range. Frequency distributions comparing mothers currently seeking to adopt to those not seeking to adopt are found in Chapter 4, Tables 2-9. The frequency tables display the respondent’s age, race, current marital status, total income (annual), highest education level, number of pregnancies, if the respondent has had a live birth(s) and if the respondent wants a/another child.
Descriptive and Bivariate Analysis
To examine the mean differences among groups, independent sample t-tests and cross tabulations are utilized as described below. An independent sample t-test is utilized to compare the means of a continuous variable between two groups; comparing the age, total income (annual), highest education level and number of pregnancies by mothers seeking to adopt and those not seeking to adopt (Chapter 5). The t-test generates descriptive statistics, a test of variance equality and the confidence interval. The Levene’s test for equality of variances, t-statistic, 2-tailed significance, standard error, 95% confidence interval of difference and degrees of freedom are examined for each variable.
The Levene’s test for equal variances assumed is utilized if the Levene’s test for equality
62 of variances is insignificance, meaning that the two groups have equal variance. If the
Levene’s test for equality of variances is significant, then the two groups do vary significantly and the equal variances not assumed values should be utilized (Hinton,
McMurray & Brownlow, 2014). The t-statistic is a ratio of the difference between the sample mean and the standard error of the mean. The t-statistic is relevant because the lower the standard error of the mean, the higher the t-statistic is and the lower the p value
(2-tailed significance). The 2-tailed significance is the computed p-value. If the p-value is less than .05 it can be concluded that the difference in means is statistically significant than zero. The standard error is an estimate of how much deviation or error is in the sampling mean compared to the population mean (Hinton, McMurray & Brownlow,
2014; Portney & Watkins, 2009). The confidence interval is the range in scores that occur between the set boundaries and the population mean should be contained within. The
95% confidence interval means that with 95% certainty the mean will fall between these upper and lower limits (Hinton, McMurray and Brownlow 2014; Portney & Watkins
2009). Degrees of freedom represent the number of scores that must be known in order to calculate the remaining scores based on the scores we already have (Hinton, McMurray and Brownlow 2014).
A cross tabulation is utilized to examine the association between two categorical variables (Hinton, McMurray & Brownlow, 2014). Cross tabulations are utilized for comparing race, current marital status, has had live births and wants a/another child by mothers seeking to adopt and those not seeking to adopt (Chapter 5). The cross tabulation statistics examined include the Pearson Chi-Square value, the 2-sided asymptotic significance, the Phi and the Cramer’s v. The Pearson Chi-Square test measures if the
63 discrepancies across the columns in the cross tabulation are due to chance (Hinton,
McMurray & Brownlow, 2014). The 2-sided asymptotic significance tests the significance of the relationship between the two variables. If it is significant at greater than .05 the two variables are in fact related. The strength of the relationship is found the symmetric measures nominal by nominal (Hinton, McMurray & Brownlow, 2014). The
Pearson’s is the most common Chi-Square test utilized. Alternatives to Person’s Chi-
Square include: Continuity Correction, Likelihood Ratio, Fisher’s Exact Test and Linear- by-Linear Association (Hinton, McMurray & Brownlow, 2014). The Continuity
Correction is utilized for 2x2 tables and is considered conservation and thus is often not utilized. The Likelihood Ratio is a good alternative when dealing with a small sample size. The Fisher’s Exact test can be utilized for 2x2 tables and is more conservative than the Pearson Chi-Square. The Linear-by-Linear Association would be examined if you are looking for a trend among ordinal categories (Hinton, McMurray & Brownlow,
2014). The Phi and Cramer’s V show the strength of the analysis. The Phi value should be utilized if the table is 2x2. If the table is larger than 2x2 the Cramer’s V should be utilized; a value greater than 0.3 is considered to be high.
Multivariate Analysis
Binary logistic regression is utilized to examine what variables predict the desire to adopt. This form of regression is appropriate since the dependent variable, currently seeking to adopt, is a yes/no response. The Omnibus Tests of Coefficients tables are examined to determine if each model predicts the dependent variable better when compared to the basic block (Hinton, McMurray & Brownlow, 2014). The Model
Summary details the amount of variation explained by the model. The Nagelkerke R
64 Squared and the Cox & Snell R Squared show the amount of variance in the dependent variable that is explained by the model (Hinton, McMurray & Brownlow, 2014). The
Classification Table shows the number of cases that are correctly predicted in the model.
You can compare this across models to see which has more predictive power (Hinton,
McMurray & Brownlow, 2014). The Variables in the Equation table displays the B, standard error, Wald, degrees of freedom and significance (Hinton, McMurray &
Brownlow, 2014). The B shows the direction and strength of the relationship between the independent and dependent variables (Sweet & Grace-Martin, 2012). The Exp(B) shows the log ratio and is interpreted in terms of times higher the odds of occurrence are for a one-unit increase in the independent variable (Sweet & Grace-Martin, 2012).
Data Analysis Plan Frame 2
Data analysis plan two relates to the conceptual model in Figure 4 (page 45) that illustrates the characteristics of children available for adoption. The traits that are explored to gauge adoptive mother’s flexibility are: child’s gender, child’s race, child’s age, the disabilities of the child and the number of children in the sibling group seeking to be adopted together. Descriptive, bivariate and multivariate analysis are utilized to examine the concepts.
Analysis Plan 2: Describe the preferred child-type of women currently seeking to adopt and gauge their level of flexibility in desired child-type is accomplished through frequencies, computations, t-tests, cross tabulations, correlations and a linear regression model using data from The National Survey of Family Growth
Cycle VI (ICPSR, 2002).
65 The first step in examining analysis plan 2 is to examine frequency distributions to examine the data. The frequencies display the percentage of respondents in each category. Frequency distributions are utilized to describe the characteristics that respondents considered desirable in an adoptive child (Table 15).
A computation is performed on the variables displayed in Table 16 (preference in child gender, age, race, disability and number of children in sibling group) to construct a flexibility scale. A value of 1 is assigned to those who select a preferred child trait and were not willing to accept a different child trait, a 2 is assigned those who selected a preferred child trait but were willing to accept a child with a different trait and a 3 is assigned to those who have no preference in the trait. The result is a variable for each child characteristic that ranges from 1-3, with 1 being ‘inflexible’, 2 ‘somewhat flexible’ and 3 ‘very flexible’. A Cronbach’s Alpha will be utilized to check for internal consistency between the five flexibility variables (child gender, age, race, disability and number of children in sibling group). An overall Cronbach’s Alpha value greater than .70 will show that together the variables create a reliable scale (Portney & Watkins 2009).
The five demographic variables will then be added together via compute statement to construct an overall flexibility scale ranging from 5-15, with 5 being the ‘most inflexible’ and 15 being the ‘most flexible’.
Descriptive and Bivariate Analysis
Examining the differences in means, independent sample t-tests, ANOVAs and correlations are utilized as described below. Independent sample t-tests (for 2 category independent variables) derived from ANOVAs (for categorical independent variables with more than two categories) and correlations (continuous independent variables) are
66 utilized to examine mother’s flexibility in child characteristics (gender, age, race, disability and number of children in a sibling group) by her demographics (age, race, current marital status, total income (annual), highest level of education, number of pregnancies and has live birth(s)).
Independent sample t-tests are utilized to see if there is a relationship between the flexibility (continuous dependent variable) and respondent demographics that are dichotomous in nature (race, has had live birth(s) and wants a/another child). The t-test generates descriptive statistics, a test of variance equality and the confidence interval.
The Levene’s test for equality of variances, t-statistic, 2-tailed significance, standard error, 95% confidence interval of difference and degrees of freedom are examined for each variable.
An analysis of variance (ANOVA) is utilized to see if there is a relationship between flexibility (continuous dependent variable) and maternal demographics that are categorical in nature and have more than two categories (current marital status). An
ANOVA will compare the means between the two variables being tested. The ANOVA output provides the following descriptive statistics: a frequency distribution, the mean, standard deviation, standard error and the 95% confidence interval. The SPSS ANOVA table displays the sum of squares, degrees of freedom, mean squared, f value and the significance levels. Each of these contain between group and within group statistics. The between group is the value for the factor and the within group is the error (Hinton,
McMurray & Brownlow, 2014). The sum of squares provides the variability of the scores
(Hinton, McMurray & Brownlow, 2014). The mean squares is the sum of squares divided
67 by the degrees of freedom which provides the variance (Hinton, McMurray & Brownlow,
2014).
Correlations are utilized to see if there is a relationship between the flexibility
(continuous dependent variable) and maternal demographics that are continuous in nature
(age, total income (annual), highest education level and number of pregnancies). The
Pearson Correlation statistic is the test statistics for correlations. A perfect correlation will have a value of 1. The two-tailed significance is the p value or strength of the prediction (Hinton, McMurray & Brownlow, 2014).
Multivariate Analysis
Linear regression models are utilized to explore if maternal demographic characteristics are predictors of their flexibility in child gender, race, age, disability status and number of children in a sibling group. Linear regression models are utilized since the dependent flexibility variables are continuous in nature. The R Squared is examined to determine the amount of variance that is explained in the dependent variable by the model. R Square is interpreted as a percentage (Hinton, McMurray & Brownlow, 2014).
The Adjusted R Squared is with an adjustment made for bias. The model summary also provides the standard error. The ANOVA table within the regression output tests the significance of the regression model as a whole. The degrees of freedom, the f-value and p-value appear within this table (Hinton, McMurray & Brownlow, 2014). The coefficients table provides us with the B value (value of the intercept and the slope of the regression line), the Standard Coefficients Beta (the impact that each individual variable makes on the model) and the t-value (if the intercept is statistically different than zero)
(Hinton, McMurray & Brownlow, 2014).
68 Data Analysis Plan Frame 3
Data analysis plan three relates to the conceptual model in Figure 5 (page 48) that relates to parents who have adopted. The conceptual model includes the motives for their adoption: infertility, wanting to expand their family, wanting siblings for an existing child, wanting to give a child a home (altruistic), wanting to help a child in foster care
(altruistic), having family members who adopted, having friends who have adopted, the respondent was adopted, the respondent’s spouse was adopted or the respondent or spouse has adopted siblings. These motivations are examined in relation to each possible form of adoption: international, private domestic and foster care. For the families that selected to adopt from foster care a more in-depth examination for why they selected their mode of adoption is conducted. Potential reasons are: shorter wait times, cheaper, wanted an older child, wanted a special needs child, previously adopted from foster care and wanted to provide a child in need with a home. Descriptive, bivariate and multivariate analysis are utilized to examine the concepts.
Analysis Plan 3: Describe the characteristics of families who have adopted and investigate their motives specific to this adoption type is accomplished through frequencies, cross tabulations and binary logistic regression models using data from the
National Survey of Adoptive Families (CDC, 2007-2008).
Frequency distributions are utilized to describe the respondent’s type of adoption
(Table 10), why the respondent chose to adopt (Table 11) and why respondent’s adopted from foster care (Table 12).
69 Descriptive and Bivariate Analysis
Cross tabulations are utilized to examine the means between groups since the goal is to examine the association between two categorical variables (Hinton, McMurray &
Brownlow, 2014). Cross tabulations are used for comparing the three adoption types
(international, foster care and private adoption) across the reasons why respondents stated they adopted (Chapter 7). The three adoption types were collected as one variable, they were recoded into three separate yes/no, dichotomous variables for the cross tabulations.
The cross tabulation statistics being examined include the Pearson Chi-Square value, the
2-sided asymptotic significance, the Phi and the Cramer’s v.
Multivariate Analysis
Binary logistic regression is utilized to determine if the reasons for adoption are predictors of the specific adoption type. The mode of adoption variable is recoded into three separate dichotomous yes/no variables, one for each form of adoption. Binary logistic regression is appropriate due to the dependent variables being binary in nature.
The chi-square statistic will be examined to determine if each model predicts the dependent variable better when compared to the basic block (Hinton, McMurray &
Brownlow, 2014). The Cox & Snell R Squared shows the amount of variance in the dependent variable that is explained by the model (Hinton, McMurray & Brownlow,
2014). The Exp(B) shows the odds ratios and is interpreted in terms of the number of times higher the odds of occurrence are for a one-unit increase in the independent variable (Sweet & Grace-Martin, 2012).
70 Human Subjects
The IRB protocol was originally submitted to the Case Western Reserve
University Institutional Review Board on October 28, 2010. The protocol included a cover letter requesting the research be deemed exempt under DHHS regulation 45 CFR
46.101(b)(4) as the data analysis to be performed is on secondary, existing data that is publicly-available and de-identified. The protocol was signed by Rachel Hammel (Co-
Investigator), David Warner (Responsible Investigator) and Dale Dannefer (Department
Chair). On November 17, 2010 notification was received from the Case Western Reserve
University Institution Review Board that the IRB protocol 20101113 is exempt under 45
Code of Federal Regulations (CFR) part 46.010(b)(4), as the research involves “the collection or study of existing data, documents, records pathological specimens or diagnostics specimens, if these sources are publically available or if the information is recorded by the investigator in such a manner that subjects cannot be identified, directly or through identifiers linked to the subjects.” The IRB protocol application and approval are located in Appendix A (FWA number: FWA00004428; Case IRB registration number: IRB00000683).
On October 21, 2016, an IRB protocol was submitted to the Case Western
Reserve University Institutional Review Board to change the Responsible Investigator to
Gary Deimling. On October 24, 2016, IRB protocol IRB-2016-1720 (new number due to an update in the IRB’s electronic system) was deemed to not fit the definition of human subject research per 45 CFR 46.102 due to the datasets being publically available. The protocol no longer required an exempt status, further IRB review or IRB approval (see
Appendices for documentation).
71 CHAPTER 5: ANALYSIS PLAN 1 RESULTS
Chapter 5 contains the results of Analysis Plan 1 that address Aim 1, to describe the characteristics of women currently seeking to adopt, comparing those seeking to adopt to those not seeking to adopt. Analysis Plan 1 is examined utilizing data from the
National Survey of Family Growth Cycle VI (ICPSR, 2002). The total sample size is 441 women, of which 328 were not seeking to adopt and 113 were currently seeking to adopt.
Sample characteristics were discussed in Chapter 4. To summarize, the overall sample has a mean age of 31.8 years of age, they were mostly White, married or never married, had an annual income between $25,000 and $74,999, had educational attainment beyond high school and had been pregnant two times in the past. Overall those seeking to adopt, ideally wanted: a single child, under the age of 2, of either gender, any race, without any disabilities. Following the sample characteristics, a comparative analysis is conducted to establish similarities and difference among the groups.
Group Comparisons
Women currently seeking to adopt are compared to those not seeking to adopt by demographics (age, income, education level, race, current marital status) along with number of pregnancies, having had a live birth(s) and wanting a/another child.
Bivariate Results
Bivariate statistics (t-tests and cross tabulations) are utilized to compare women seeking to adopt to those not seeking to adopt by their demographic variables (Tables 13a
& b). Age, Race, Current Martial Status and Wants A/Another Child are all statistically significant. Women seeking to adopt are older than those not seeking to adopt. The mean age of those seeking to adopt is 33.49 years of age compared to 31.22
72 for those not seeking to adopt. Women not seeking to adopt are mostly White (69%), while slightly over half of those seeking to adopt are nonwhite (53%). There is a difference in marital status between those seeking to adopt and those not seeking to adopt. While the majority of both groups are married (seeking to adopt 54% and not seeking to adopt 45%), those not seeking to adopt have a significant number of respondents who have never been married (42%) compared to those seeking to adopt
(24%). Of the women seeking to adopt, 76% want a/another child, compared to only 53% of those not seeking to adopt.
Table 13a. Adoption Intentions by Maternal Age, Income, Education Level and Number of Pregnancies Overall Seeking to Not Seeking to p- (N=441) Adopt (N=113) Adopt (N=328) t-test value Mean SD Mean SD Mean SD Age 31.80 7.59 33.49 7.14 31.22 7.67 2.76 .01** Total Income 8.53 3.95 8.62 4.25 8.49 3.84 (Annual)* 0.29 .77 Highest Education 13.41 2.61 13.44 2.56 13.40 2.63 Level 0.16 .88 Number of 1.99 1.87 2.16 2.13 1.93 1.77 Pregnancies 1.13 .26 *In ordered categories: Income category 8 = $25,000-$29,999 annually, category 9 = $30,000-$34,999 annually Data source: The National Family Growth Cycle VI (ICPSR, 2002)
73 Table 13b. Adoption Intentions by Maternal Race, Marital Status, Has had a Live Birth and Wants A/Another Child Not Seeking Seeking to Adopt Chi- p- to Adopt Phi/V* (N=113) Square value (N=328) Race N % N % Nonwhite 60 37.3% 101 62.7% 18.038 .00* -.20 White 53 18.9% 227 81.1% Current Marital
Status Married 61 29.2% 148 70.8% Divorced 13 31.7% 28 68.3% Separated 12 48.0% 13 52.0% 16.385 .00* .19 Never Married 27 16.3% 139 83.7% Has Had Live Birth(s) Yes 66 24.1% 208 75.9% 0.896 .34 .05 No 47 28.1% 120 71.9% Wants A/Another Child Yes 86 33.2% 173 66.8% 17.842 .00* -.20 No 26 15.0% 147 85.0% *For strength, the Nominal by Nominal Phi was utilized for Race, Had had Live Births and Wants A/Another Child and the Cramer's V for Current Marital Status. Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Following the comparison data, the focus turns to predictive qualities to determine what variables predict the odds of a woman seeking to adopt.
Multivariate Results
Multivariate analysis is used to examine the odds of a woman seeking to adopt. A binary logistic model is selected since the dependent variable is dichotomous (yes/no).
The regression includes three models. The first model includes the demographic variables of race, age, highest level of education and total annual income. These key demographic variables are selected to be included in model one to see if together they have the strength to predict the odds of an individual seeking to adopt. The second model adds in four of the marital status variables: married, divorced and separated. Never married is held from
74 the model as the reference category. All of the martial status variables are include in the model because there is significance shown in marital status variables that was not anticipated. Being separated was not hypothesized to be a predictor of seeking to adopt.
The third model adds variables concerning births and birth intentions: has had live birth(s), number of pregnancies and wants a/another child. This model is selected as the third model since it deals with if the women already have children and want children in the future. The goal is to see if these child variables increase the predictive strength of the model over simply demographic variables (see Table 14).
Model 1 - race, age, highest level of education and total annual income
Model 1 contains the variables age, race, total annual income and highest level of education and is significance at .00. In reading the output, the Exp(B) shows the log ratio and is interpreted in terms of times higher the odds of occurrence are for a one-unit increase in the independent variable (Sweet & Grace-Martin, 2012). In model one, Age and Race are statistically significant. With each year increase in age the odds of seeking to adopt increases 1.04 times. The odds of seeking to adopt increases 2.83 times for nonwhites. The Cox & Snell R Squared show that the model explains .06 of the variance in a mother seeking to adopt. The model correctly predicts 72.8% of the cases.
Model 2 - race, age, highest level of education, total annual income and marital status variables
Model 2 adds the marital status variables of married, divorced and separated.
Race, Married and Separated are statistically significant. The odds of seeking to adopt are three times higher for nonwhites. Married respondents are 2.2 times more likely to want to adopt that unmarried respondents. Separated respondents are 3.65 times more
75 likely to wish to adopt than unseparated respondents. The model has a higher Omnibus
Test Coefficient than the previous model (chi-square 35.945 with a statistical significance of .00 compared to chi-square of 25.042 with a significance of .000). The Cox & Snell R
Squared shows that the model explains .084 of the variance in a mother seeking to adopt, which is higher than the previous model. The model correctly predicts 74.3% of the cases which is higher than model 1.
Model 2 is rerun collapsing marital status to married and divorced/separated in hope of gaining more significance through a larger n. The model’s prediction power is the same as with the split variables (74.3%).
Model 3- race, age, highest level of education, total annual income, marital status variables, has had live birth(s), number of pregnancies and wants a/another child
Model 3 adds in number of pregnancies, had had live birth(s) and wants a/another child. Age, Race, Married, Separated and Wants A/Another Child are all statistically significant. With each year increase in age there is a 1.09 times higher odds of that individual seeking to adopt. Nonwhite respondents have 3.59 times higher odds of seeking to adopt than White respondents. Married people have 2.66 times higher odds of seeking to adopt than never married people. Separated people have 4.86 times higher odds of seeking to adopt than never married people. Respondents wanting a/another child are 6.38 times more likely to seek to adopt a child when compared to those not wanting a/another child. Model 3 has a higher Omnibus Test Coefficient than the previous models
(chi-square of 83.39 with a statistical significance of .00 compared to chi-square 35.945 with a significance of .00). The Cox & Snell R Squared shows that the model explains
.19 of the variance in a mother seeking to adopt, which is higher than the previous model.
76 The model correctly predicts 79.4% of the cases which is higher than both models 1 and
2.
Model 3 is rerun collapsing marital status to married and divorced/separated in hope of gaining more significance through a larger n. The model’s prediction power is the lower than that of the model 3 with split marital status variables (77.9%).
Table 14. Odds of Seeking to Adopt (N=441) Model 1 Model 2 Model 3 Exp p- Exp p- Exp p- (B) S.E. value (B) S.E. value (B) S.E. value Age 1.04 0.02 .01* 1.03 0.02 .10 1.09 0.02 .00* Nonwhite 2.83 0.24 .00* 3.00 0.25 .00* 3.59 2.74 .00* Total Income (Annual) 1.03 0.03 .43 1.01 0.04 .81 1.00 0.04 .98 Highest Education Level 0.98 0.05 .64 0.99 0.05 .83 0.93 0.06 .18 Married 2.20 0.31 .01* 2.66 0.34 .00* Divorced 1.75 0.46 .22 2.01 0.50 .16 Separated 3.65 0.48 .01* 4.86 0.55 .00* Number of Pregnancies 1.09 0.08 .33 Has Had Live Birth(s) 0.58 0.34 .11 Wants A/Another Child 6.38 0.33 .00* Model 1: R2 = .06 (Cox & Snell R Squared), p-value = 0.00 Model 2: R2 = .08 (Cox & Snell R Squared), p-value = 0.00 Model 3: R2 = .19 (Cox & Snell R Squared), p-value = 0.00 *p < .05 **p < .01 Models 2 & 3: Never Married is the reference category Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Summary
In summary, through multivariate analysis it is determined that being older, nonwhite, married or separated and wanting a/another child are predictors of seeking to adopt. Predictive regression analysis shows that model 3 has the strongest predictive power (compared to models 1 and 2). Model 3 contains race, age, highest level of education, total annual income, marital status variables, has had live birth(s), number of
77 pregnancies and wants a/another child. Higher age, being nonwhite, being married, being separated and wanting a/another child are all significant predictors of seeking to adopt.
78 CHAPTER 6: ANALYSIS PLAN 2 RESULTS
Chapter 6 focuses on Analysis Plan 2 related to Aim 2, which is to describe the preferred child-type of women currently seeking to adopt and gauge their level of flexibility in desired child type. Aim 2 is examined utilizing data from the National
Survey of Family Growth Cycle VI (ICPSR, 2002). The total sample size for Aim 2 is
113 women who are seeking to adopt. Sample characteristics were discussed in Chapter
4.
Descriptive Results
Women who were interested in adoption (N=113) were asked about the characteristics they desired in a potential adoptive child (Table 15). To summarize the sample characteristics, in terms of preferred child gender, 38.2% did not have a gender preference for a future adopted child, while 37.1% preferred a girl and 24.7% preferred a boy. The majority (81.8% of those preferring a girl and 84.4% of those preferring a boy) would accept the other gender.
For preferred child age, 10.1% were open to a child of any age, 50.6% preferred a child younger than 2 years of age, 25.8% preferred a child between the ages of 2 and 5 years of age, 10.1% preferred a child between the ages of 6 and 12 years of age and 3.4% preferred a child age 13 years or older. Of those not preferring a particular age group,
80% would accept a child under the age of 2 years, 75% would accept a child between the ages of 2 and 5 years, 44.1% would accept a child between the ages of 6 and 12 years and 19.5% would accept a child age 13 years or above.
In terms of preferred race, 54.5% were open to a child of any race. Of those with a preference, 17% preferred a Black child, 14.8% preferred a White child and 13.6%
79 preferred a child of some other race. Of those not preferring a Black child, 70.8% said that would accept a Black child. Of those not preferring a White child, 77.8% would accept a White child. Of those not preferring a child of another race, 89.7% said that would accept a child of another race.
Examining preferred disability status, nearly a quarter (23.6%) of women did not have a preference on whether they would adopt a child with a disability. Of those with a preference, 49.4% preferred a child with no disability, 25.8% preferred a child with a mild disability and 1.1% preferred a child with a severe disability. Of those who did not prefer a child with no disability, 100% said they would accept a child with no disability. Of those not preferring a child with a mild disability, 81.8% said they would accept a child with a mild disability. Of those not preferring a child with a severe disability, 7.7% would accept a child with a severe disability.
In terms of the number of children in a sibling group, when asked how many children the women would like to adopt at one time, 14.6% did not have preference,
60.7% preferred a single child and 24.7% preferred a sibling group of 2 or more children.
Of those not preferring a single child, 100% would accept a single child. Of those not preferring a sibling group of 2 or more children, 44.4% said they would accept a sibling group.
80 Table 15. Desired Characteristics in an Adoptive Child (N=113) Variable N % Gender No Preference 34 38.2% Prefer Girl 33 37.1% Not Preferring Girl, But Accepting 18 81.8% Prefer Boy 22 24.7% Not Preferring Boy, But Accepting 27 84.4% Age No Preference 9 10.1% Prefer Younger than 2 45 50.6% Not Preferring Younger than 2, But Accepting 28 80.0% Prefer 2-5 Years 23 25.8% Not Preferring 2-5 Years, But Accepting 42 75.0% Prefer 6-12 Years 9 10.1% Not Preferring 6-12 Years, But Accepting 30 44.1% Prefer 13 Years or Older 3 3.4% Not Preferring 13+, But Accepting 15 19.5% Race No Preference 48 54.5% Prefer Black 15 17.0% Not Preferring Black, But Accepting 17 70.8% Prefer White 13 14.8% Not Preferring White, But Accepting 21 77.8% Prefer Some Other Race 12 13.6% Not Preferring Other, But Accepting 26 89.7% Disability No Preference 21 23.6% Prefer No Disability 44 49.4% Not Preferring No Disability, But Accepting 24 100.0% Prefer Mild Disability 23 25.8% Not Preferring Mild Disability, But Accepting 36 81.8% Prefer Severe Disability 1 1.1% Not Preferring Severe Disability, But Accepting 5 7.7% # Children in Sibling
Group No Preference 13 14.6% Prefer Single Child 54 60.7% Not Preferring Single Child, But Accepting 22 100.0% Prefer 2 or More Siblings 22 24.7% Not Preferring Sibling Group, But Accepting 24 44.4% Data Source: The National Family Growth Cycle VI (2002)
81 The aforementioned variables are computed in order to create a flexibility scale that offers the ability to determine what child characteristics future adoptive mothers are flexible on and those they are not.
Computations
A computation is utilized to create a flexibility scale that shows the respondent’s overall flexibility in adoptive child characteristics (child gender, age, race, disability and number of children in sibling group). The computation process is further described in
Chapter 4. Table 16 shows the frequency distribution for each of the computed child characteristic (child gender, age, race, disability and number of children in sibling group).
All of the variables have a minimum score of 1 (inflexible) and a maximum score of 3
(very flexible). Of the 113 women seeking to adopt, 87-89 answered each of the flexibility questions.
82 Table 16. Respondent Flexibility in Adoptive Child Characteristics (N=113) Variable N % Flexibility in Gender (n=88) Inflexible 9 10.2% Somewhat Flexible 45 51.1% Very Flexible 34 38.6% Flexibility in Age (n=89) Inflexible 65 73.0% Somewhat Flexible 15 16.9% Very Flexible 9 10.1% Flexibility in Race (n=87) Inflexible 11 12.6% Somewhat Flexible 28 32.2% Very Flexible 48 55.2% Flexibility in Disability (n=89) Inflexible 60 67.4% Somewhat Flexible 8 9.0% Very Flexible 21 23.6% Flexibility in # Children in Sibling Group (n=89) Inflexible 30 33.7% Somewhat Flexible 46 51.7% Very Flexible 13 14.6% Data Source: The National Family Growth Cycle VI (2002) For flexibility in gender, 88 respondent provided answers. Of those 88, the majority
(89.7%) have some flexibility in a potential adoptive child’s gender. Only 10.2% were only open to a single gender. Flexibility in age was provided by 89 respondents, with the majority being inflexible (73%), followed by some flexibility (16.9%) and very flexible
(10.1%). Flexibility in race had a sample size of 87, with most respondents being very flexible (55.2%) or somewhat flexible (32.2%). Flexibility in disability was provided by
89 respondents, the majority were inflexible (67.4%), followed by very flexible (23.6%) and somewhat flexible (9.0%). Flexibility in the number of children in a sibling group was answered by 89 respondents. The majority of respondents are somewhat flexible
83 (51.7%), followed by inflexible (33.7%) and very flexible (14.6%). A Cronbach’s Alpha of the five flexibility variables (child gender, age, race, disability and number of children in sibling group) shows that together they have questionable reliability as a scale (.634).
The result was not unexpected for such a measure.
The five child flexibility variables are then computed together to construct an overall flexibility scale ranging from 5-15, with 5 being the ‘most inflexible’ and 15 being the ‘most flexible’ (Table 17).
Table 17. Respondent's Overall Flexibility in Adoptive Child Characteristics (N=87) Variable N % 5 (Inflexible) 2 2.30% 6 3 3.40% 7 10 11.50% 8 23 26.40% 9 12 13.80% 10 10 11.50% 11 11 12.60% 12 5 5.70% 13 6 6.90% 14 3 3.40% 15 (Very Flexible) 2 2.30% Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Table 17 shows the frequency distribution of the respondent’s overall flexibility in an adoptive child’s demographic characteristics. The median of the distribution is 10, with the mean falling below at 9.44. The most common value was an 8 (26.4%).
Bivariate Results
The demographic variables are compared across the five computed child characteristics flexibility variables. T-tests, f-tests from ANOVAs and correlations are utilized to compare the five computed child characteristic (child gender, age, race,
84 disability and number of children in sibling group), as well as the overall computed flexibility score across the respondent’s demographic variables.
Flexibility by Characteristics of Women Seeking to Adopt
A correlation is utilized to examine the relationship between the various forms of flexibility and material demographics. Being married is significantly correlated with less flexibility in child age, while being separated is significantly correlated with flexibility in child age. Higher income levels are correlated with less flexibility in child age, disability status and are overall flexibility in child characteristics. A higher number of pregnancies is significantly correlated with increased flexibility in child race. Having had a live birth is significantly correlated with less flexibility in child gender. Wanting a/another child is significantly correlated with increased flexibility in overall child characteristics. See
Table 18.
85 Table 18. Correlations with Flexibility Variables and Characteristics of Women Seeking to Adopt (N=86-89) Flexibility in Flexibility in Flexibility in Flexibility in Flexibility in # Overall Gender Age Race Disability Children in Flexibility Sibling Group p- p- p- p- p- p- r r r r r r value value value value value value Age -0.09 0.39 -0.16 0.13 -0.07 0.50 -0.17 0.11 -0.02 0.88 -0.16 0.13 Nonwhite -0.21 0.05 0.08 0.46 -0.04 0.73 0.07 0.50 -0.11 0.29 -0.05 0.66 Married 0.14 0.19 -0.31** 0.00 -0.00 0.97 -0.20 0.07 0.13 0.21 -0.10 0.37 Divorced -0.17 0.12 0.05 0.66 -0.08 0.45 0.15 0.15 -0.05 0.67 -0.01 0.91 Separated -0.02 0.88 0.24* 0.02 0.15 0.18 0.16 0.13 0.03 0.77 0.18 0.09 Total Income (annual) 0.13 0.22 -0.41** 0.00 -0.10 0.38 -0.34** 0.00 0.12 0.26 -0.21* 0.05 Highest Education Level 0.18 0.10 -0.16 0.13 0.04 0.71 -0.11 0.31 0.14 0.20 0.02 0.88 Number of Pregnancies -0.21 0.06 0.15 0.17 0.26* 0.01 0.19 0.07 0.12 0.25 0.17 0.12 Has Had Live Birth(s) -0.26* 0.01 -0.03 0.80 0.07 0.55 0.08 0.45 0.01 0.96 -0.03 0.81 Want A/Another Child 0.20 0.06 0.18 0.10 0.19 0.08 0.14 0.19 0.18 0.09 0.28** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
86 Flexibility in Child Gender
Descriptive statistics (t-tests, ANOVAs and correlations) are utilized to compare flexibility in a potential adoptive child’s gender by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Table 19a, b & c). Race, Has Had Live Birth(s) and Wants A/Another
Child are all statistically significant. White respondents are more flexible in child gender when compared to nonwhite respondents. The mean flexibility value for White respondents is 2.42 which is in the moderately flexible range for child gender. Women who had not had a live birth are more flexible in child’s gender than those who have had a live birth in the past. The mean flexibility value for those who have not had a live birth is 2.46 which is in the moderately flexible range for child gender. Those wanting a/another child are more flexible in child’s gender than those not wanting a/another child.
The mean flexibility value for those wanting a/another child is 2.35 which is in the moderately flexible range for child gender.
87 Table 19a. Flexibility in Child Gender by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=88) Mean SD t-test p-value Power Race Nonwhite 2.16 0.60 1.95 .05* .42 White 2.42 0.66 Has Had Live Birth(s) Yes 2.13 0.68 2.52 .01* .54 No 2.46 0.55 Wants A/Another Child . Yes 2.35 0.65 -2.20 .04* -.62 No 2.00 0.53 Flexibility in gender ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 19b. Flexibility in Child Gender by Maternal Marital Status (N=87) Mean S.D. F-test p-value Current Marital Status Married 2.37 0.68 Divorced 2.00 0.77 1.01 .394 Separated 2.25 0.46 Never Married 2.26 0.54 Flexibility in gender ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
88 Table 19c. Correlations of Flexibility in Child Gender by Maternal Age, Income, Education Level and Number of Pregnancies (N=88) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level r p- r p- r p- r p- value value value value 1. Flexibility 2. Age -0.09 0.39 3. Total Income (annual) 0.13 0.22 0.04 0.66 4. Highest Education Level 0.18 0.10 0.19* 0.05 0.31** 0.00 5. Number of Pregnancies -0.21 0.06 0.11 0.27 0.01 0.94 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in gender ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Flexibility in Child Age
Descriptive statistics (t-tests, ANOVAs and correlations) are utilized to compare flexibility in a potential adoptive child’s age by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Table 20a, b & c). Wants A/Another Child, Current Martial Status and
Total Annual Income are all statistically significant. Wanting a/another child is associated with decreased flexibility in desired child’s age. While both waiting a/another child and not wanting a/another child were both at the lower end of flexibility, those wanting a/another child were slightly closer to indifference in terms of child’s age (1.43 out of a scale of 1-3). Separated individuals are the most flexible, followed by never married and divorced in terms of child age. Separated individuals are close to indifferent
89 in terms of child’s age (1.88 out of a scale of 1-3). Married respondents were the least flexible with a 1.17 out of 3. Income is highly correlated with flexibility in child’s age in a negative direction, meaning that women with lower income levels are more flexible in child’s age. Furthermore, income is significantly correlated with education in a positive direction, meaning that as education increases so does income.
Table 20a. Flexibility in Child Age by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=89) Mean SD t-test p-value Race Nonwhite 1.42 0.69 -0.74 0.46 White 1.32 0.64 Has Had Live
Birth(s) Yes 1.35 0.67 0.25 0.80 No 1.39 0.67 Wants A/Another
Child Yes 1.43 0.69 -2.05 0.05* No 1.13 0.50 Flexibility in age ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 20b. Flexibility in Child Age by Maternal Marital Status (N=89) Mean S.D. F-test p-value Current Marital Status Married 1.17 0.48 Divorced 1.45 0.82 4.07 0.01* Separated 1.88 0.83 Never Married 1.57 0.73 Flexibility in age ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
90 Table 20c. Correlations of Flexibility in Child Age by Maternal Age, Income, Education Level and Number of Pregnancies (N=89) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level p- p- p- p- r value r value r value r value 1. Flexibility 2. Age -0.16 0.13 3. Total Income (annual) -0.41** 0.00 0.04 0.66 4. Highest Education Level -0.16 0.13 0.19* 0.05 0.31** 0.00 5. Number of Pregnancies 0.15 0.17 0.11 0.27 0.01 0.93 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in age ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Flexibility in Child Race
Descriptive statistics (t-tests, ANOVAs and correlations) are used to compare flexibility in a potential adoptive child’s race by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Table 21a, b & c). Number of Pregnancies has a statistically significant correlation with flexibility in child race, with an increase in the number of pregnancies there is an increase in the flexibility in child’s race. Furthermore, highest level of education is negatively correlated with number of pregnancies, meaning that with additional education attainment, women are likely to have a lower number of pregnancies.
91 Table 21a. Flexibility in Child Race by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=87) Mean SD t-test p-value Race Nonwhite 2.40 0.72 0.34 0.73 White 2.50 0.71 Has Had Live Birth(s) Yes 2.47 0.75 -0.61 0.55 No 2.38 0.67 Wants A/Another
Child Yes 2.49 0.67 -1.80 0.75 No 2.13 0.83 Flexibility in race ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 21b. Flexibility in Child Race by Maternal Marital Status (N=86) Mean S.D. F-test p-value Current Marital Status Married 2.42 0.78 Divorced 2.27 0.65 0.74 0.53 Separated 2.75 0.46 Never Married 2.43 0.66 Flexibility in race ranges from 1 (inflexible) to 3 (very flexible) Source: The National Family Growth Cycle VI (ICPSR, 2002)
92 Table 21c. Correlations of Flexibility in Child Race and Maternal Age, Income, Education Level and Number of Pregnancies (N=87) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level p- p- p- p- r value r value r value r value 1. Flexibility 2. Age -0.74 0.50 3. Total Income (annual) -0.10 0.38 0.04 0.66 4. Highest Education Level 0.04 0.71 0.19* 0.05 0.31** 0.00 5. Number of Pregnancies 0.26* 0.01 0.11 0.27 0.01 0.94 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in race ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Flexibility in Child Disability
Descriptive statistics (t-tests, ANOVAs and correlations) are utilized to compare flexibility in a potential adoptive child’s disability status by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Table 22a, b & c). Total Annual Income is statistically associated with flexibility in child disability, there is increased flexibility in child disability status with lower levels of income. Total annual income is also correlated with education level, suggesting that with higher education levels individuals have a higher income.
93 Table 22a. Flexibility in Child Disability by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=89) Mean SD t-test p-value Race Nonwhite 1.62 0.91 -0.67 0.50 White 1.50 0.79 Has Had Live
Birth(s) Yes 1.63 0.89 -0.76 0.45 No 1.49 0.81 Wants A/Another
Child Yes 1.63 0.88 -1.53 0.14 No 1.31 0.70 Flexibility in disability ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 22b. Flexibility in Child Disability by Maternal Marital Status (N=88) Mean S.D. F-test p-value Current Marital Status Married 1.40 0.74 Divorced 1.91 1.04 1.91 0.14 Separated 2.00 0.93 Never Married 1.57 0.90 Flexibility in disability ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
94 Table 22c. Correlations of Flexibility in Child Disability by Maternal Age, Income, Education Level and Number of Pregnancies (N=89) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level p- p- p- p- r value r value r value r value 1. Flexibility 2. Age -0.17 0.11 3. Total Income (annual) -0.34** 0.00 0.04 0.66 4. Highest Education Level -0.11 0.31 .19* 0.05 .31** 0.00 5. Number of Pregnancies 0.19 0.07 0.11 0.27 0.01 0.94 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in disability ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Flexibility in Number of Children in Sibling Group
Descriptive statistics (t-tests, ANOVAs and correlations) are utilized to compare flexibility in the number of child in a sibling group by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Table 23a, b & c). There is not any statistical significance within the study variables.
95 Table 23a. Flexibility in Number of Child in Sibling Group by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=89) Mean SD t-test p-value Race Nonwhite 1.73 0.58 1.08 0.29 White 1.89 0.75 Has Had Live
Birth(s) Yes 1.81 0.67 -0.05 0.96 No 1.80 0.68 Wants A/Another
Child Yes 1.88 0.67 -1.71 0.09 No 1.56 0.63 Flexibility in number of children in sibling group ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 23b. Flexibility in Number of Children in Sibling Group by Maternal Marital Status (N=88) Mean S.D. F-test p-value Current Marital Status Married 1.89 0.70 Divorced 1.73 0.65 0.74 0.53 Separated 1.88 0.64 Never Married 1.65 0.65 Flexibility in number of children in sibling group ranges from 1 (inflexible) to 3 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
96 Table 23c. Correlations of Flexibility in Number of Children in Sibling Group by Maternal Age, Income, Education Level and Number of Pregnancies (N=89) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level
p- p- p- p- r value r value r value r value 1. Flexibility 2. Age -0.02 0.83 3. Total Income (annual) 0.12 0.26 0.04 0.66 4. Highest Education Level 0.14 0.20 0.19* 0.05 0.31** 0.00 5. Number of Pregnancies 0.12 0.25 0.11 0.27 0.01 0.94 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in number of children in sibling group ranges from 1 (inflexible) to 3 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Overall Flexibility
Descriptive statistics (t-tests, ANOVAs and correlations) are used to compare overall flexibility by the respondent’s race, has had a live birth(s), wants a/another child, marital status, age, income, education level and number of pregnancies (Tables 24a, b & c).
Values range from 5-15. Wants A/Another Child and Total Annual Income are statistically significant. If a woman wants a/another child, her overall flexibility in child characteristics increases. Those individuals wanting a/another child are close to indifferent in terms of child characteristics (9.76 out of a scale of 5-15) versus those not wanting a/another child (8.07 out of a scale of 5-15). In terms of total annual income, overall flexibility in child characteristics increases as income level decreases. Education
97 level is significantly correlated with income; with those with lower education levels having less income.
Table 24a. Flexibility in Child Characteristics by Maternal Race, Has had Live Birth(s) and Wants A/Another Child (N=87) Mean SD t-test p-value Race Nonwhite 9.33 2.24 0.44 0.66 White 9.55 2.35 Has Had Live Birth(s) Yes 9.38 2.38 0.24 0.81 No 9.50 2.18 Wants A/Another Child Yes 9.76 2.30 -3.45 0.00* No 8.07 1.58 Flexibility in child characteristics ranges from 5 (inflexible) to 15 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
Table 24b. Flexibility in Child Characteristics by Maternal Marital Status (N=86) Mean S.D. F-test p-value Current Marital Status Married 9.22 2.20 Divorced 9.36 2.50 1.02 0.39 Separated 10.75 1.98 Never Married 9.43 2.41 Flexibility in child characteristics ranges from 5 (inflexible) to 15 (very flexible) Data Source: The National Family Growth Cycle VI (ICPSR, 2002)
98 Table 24c. Correlations of Overall Flexibility in Child Characteristics and Maternal Age, Income, Education Level and Number of Pregnancies (N=87) Variable 1. Flexibility 2. Age 3. Total 4. Highest Income Education (annual) Level
p- p- p- p- r value r value r value r value 1. Flexibility 2. Age -0.16 0.13 3. Total Income (annual) -0.21* 0.05 0.04 0.66 4. Highest Education Level 0.02 0.88 0.19* 0.05 0.31** 0.00 5. Number of Pregnancies 0.17 0.12 0.11 0.27 0.01 0.94 -0.25** 0.01 Pearson Correlation with 2-tailed significance levels Flexibility in child characteristics ranges from 5 (inflexible) to 15 (very flexible) *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (ICPSR, 2002) Multivariate Results
Linear regression (OLS) models are utilized to examine the impact of respondent’s (women currently seeking to adopt) demographic characteristics on their flexibility in child gender, race, age, disability status and number of children in a sibling group (see Tables 25-30). OLS models are appropriates since the dependent flexibility variables were continuous measurements. For model one, in each OLS regression, the demographic variables (age, race, marital status, income and education level) are examined to see if they alone predict the type of flexibility. In model two, variables concerning current and future children (number of pregnancies, has had live birth(s) and wants a/another child) are added to see if they increase the predictive power of the model.
The sample sizes for each OLS table range from 87-89 respondents. There are 113
99 women in the sample that are considering adoption, however only 87-89 answered the specific flexibility questions.
Predictions of Flexibility in Child Gender
The first linear regression examines flexibility in child gender and includes two models. The first includes the demographic variables of age, race, current marital status
(married, divorced and separated), total annual income and highest level of education.
The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table 25).
100 Table 25. Flexibility in Child Gender by Maternal Demographic Characteristics (N=88) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.02 0.48 -0.16 0.16 -0.01 0.01 -0.11 0.33 Nonwhite -0.25 0.16 -0.20 0.11 -0.23 0.15 -0.18 0.15 Married -0.01 0.19 -0.01 0.96 0.41 0.19 0.03 0.83 Divorced -0.20 0.26 -0.10 0.43 -0.11 0.26 -0.05 0.68 Separated 0.06 0.27 0.03 0.83 0.09 0.27 0.04 0.74 Total Income (annual) 0.01 0.02 0.04 0.73 0.01 0.02 0.04 0.73 Highest Education Level 0.05 0.03 0.20 0.09 0.03 0.03 0.13 0.26 Number of Pregnancies -0.03 0.04 -0.11 0.41 Has Had Live Birth(s) -0.18 0.20 -0.14 0.29 Want A/Another Child 0.20 0.12 0.32 Model 1: Never Married is the reference category Flexibility in child gender ranges from 1 (inflexible) to 3 (very flexible) Model 1: R Squared =0.11, F-value = 1.41, p-value = 0.21 Model 2: R Squared =0.18, F-value = 1.60, p-value = 0.13 *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002) Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable flexibility in child gender and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. Model 1 explains 11.2% of the variance in the flexibility in child’s gender and is insignificant at .21 (R Squared =
.11, F-value = 1.41). None of the independent variables within the model are statistically significant on their own.
101 The model is rerun two ways in the hope of gaining greater statistical significance. First, by reducing marital status to just married (removing divorced and separated). Married is selected to remain in the models since it had the highest level of flexibility in the bivariate statistics. The reduced model explains less variance (10.3%) and is insignificant (.12). Second, by combining divorced and separated into one variable.
The combined divorced/separated model explains less variance than the original model
(10.5%) and is insignificant (.18).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .175 and an F-value of 1.591 which is insignificant (.13). Model 2 explains more variance than model one, increase from 11.2% to 17.5%, but remains statistically insignificant. Furthermore, all of the independent variables were also statistically insignificant.
The model is rerun two ways in an attempt to gain more statistical significance through a larger n. First, marital status is reduced to just married (removing divorced and separated). The reduced model explains less variance (17.1%) and is insignificant (.06).
Second, separated and divorced are combine into one variable. The combined divorced/separated model explained less variance (17.1%) and remained statistically insignificant (.119).
102 Predictions of Flexibility in Child Race
The second linear regression examines flexibility in child race and includes two models. The first includes the demographic variables of age, race, current marital status
(married, divorced and separated), total annual income and highest level of education.
The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table 26).
Table 26. Flexibility in Child Race by Maternal Demographic Characteristics (N=87) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.01 0.01 -0.11 0.36 -0.01 0.01 -0.12 0.32 Nonwhite -0.12 0.18 -0.08 0.52 -0.09 0.17 -0.07 0.59 Married 0.04 0.21 0.03 0.84 -0.05 0.21 -0.04 0.79 Divorced 0.00 0.30 0.00 1.00 -0.17 0.29 -0.07 0.56 Separated 0.34 0.31 0.14 0.26 0.07 0.30 0.03 0.81 Total Income (annual) -0.02 0.02 -0.11 0.38 -0.02 0.02 -0.14 0.26 Highest Education Level 0.03 0.03 0.11 0.36 0.05 0.03 0.19 0.12 Number of Pregnancies 0.12 0.05 0.36 0.01** Has Had Live Birth(s) -0.09 0.20 -0.06 0.65 Want A/Another Child 0.31 0.22 0.16 0.16 Model 1: Never Married is the reference category Flexibility in child race ranges from 1 (inflexible) to 3 (very flexible) Model 1: R Squared =0.05, F-value = 0.55, p-value = 0.80 Model 2: R Squared =0.16, F-value = 1.455, p-value = 0.18 *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002) Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable flexibility in child race and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. The overall model has an
103 R Squared value of .047 and an F-value of .548 which is insignificant at .795. The model explains 4.7% of the variance in the flexibility in child race. None of the independent variables are statistically significant.
The model is rerun two ways in the hope of gaining greater statistical significance through a larger n. First, the model is rerun reducing marital status to just separate
(removing married and divorced). Separated is selected to remain in the models since it had the highest level of flexibility in the bivariate statistics. The reduced model explains the same amount of variance (4.7%) and remains statistically insignificant (.569).
Second, separated and divorced are combined. The combined separated/divorced model explains less variance (3.6%) and remains statistically insignificant (.813).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .163 and an F-value of 1.446 which is statistically insignificant (.18). The model explains 16.3% of the variance in flexibility in child’s race. Number of pregnancies is the only statistically significant independent variable (.01), with a Beta (standardized partial regression coefficient) of .361. This suggests that with an increased number of prior pregnancies, the level of flexibility in child’s race increases.
The model two is rerun two ways in the hope of gaining more statistical significance through a larger n. First, marital status is reduced to just separate (removing married and divorced). The reduced model explains less of variance (16%) and remains
104 insignificant (.09). Second, separated and divorced are collapsed into one variable. The combined separated/divorced model explains less variance (15.8%) and remains statistically insignificant (0.02).
Predictions of Flexibility in Child Age
The third linear regression examines flexibility in child age and includes two models. The first includes the demographic variables of age, race, current marital status
(married, divorced and separated), total annual income and highest level of education.
The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table 27).
Table 27. Flexibility in Child Age by Maternal Demographic Characteristics (N=89) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.01 0.01 -0.14 0.19 -0.01 0.01 -0.13 0.22 Nonwhite -0.21 0.15 -0.16 0.16 -0.18 0.15 -0.14 0.21 Married -0.31 0.18 -0.23 0.08 -0.35 0.18 -0.26 0.05* Divorced 0.06 0.24 0.03 0.81 0.00 0.25 0.00 1.00 Separated 0.17 0.25 0.08 0.50 0.03 0.26 0.02 0.90 Total Income (annual) -0.06 0.02 -0.35 0.00** -0.06 0.02 -0.37 0.00** Highest Education Level 0.00 0.03 0.00 0.97 0.01 0.03 0.02 0.83 Number of Pregnancies 0.07 0.04 0.23 0.05* Has Had Live Birth(s) -0.19 0.16 -0.15 0.23 Want A/Another Child 0.17 0.18 0.10 0.34 Model 1: Never Married is the reference category Flexibility in child age ranges from 1 (inflexible) to 3 (very flexible) Model 1: R Squared =0.25, F-value = 3.83, p-value = 0.00* Model 2: R Squared =0.31, F-value = 1.935, p-value = 0.00** *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002)
105 Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable flexibility in child age and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. The overall model has an
R Squared value of .254 and an F-value of 3.832 which is statistically significant at .00.
Thus the model explains 25.4% of the variance in flexibility in child’s age. Total annual income is the only statistically significant independent variable (.00) with a Beta of -.346.
This suggests that as income decreases the level of flexibility in child’s age increases.
The model is rerun in two ways in the hope of gaining more statistical significance through a larger n. First, marital status is reduced to just separate (removing married and divorced). Separated is selected to remain in the model since it had the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance (21.1%) and remains significant (.00). Total annual income remains as the only significant independent variable (.00). Second, separated and divorced are combined into one variable. The combined separated/divorced variable explains less variance
(25.2%) and remains significant (.00). Total annual income remains as the only significant independent variable (.00).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .306 and an F-value
106 of 1.935 which is statistically significant (.00). The model explains 30.6% of the variance in flexibility in child’s age. Married, total annual income and number of pregnancies are all statistically significant. Married has a Beta of -.260 with a significant p-value of .050.
This suggests that married people are less flexible in child age compared to never married respondents. Total annual income has a Beta of -.367 and a significant p-value of .001.
This suggests that an increased level of flexibility is found in respondents with a lower level of income. Number of pregnancies has a Beta of .231 with a significant p-value of
.053. Thus individuals with more pregnancies are more flexible in child age.
Model 2 is rerun in two forms in the hope of gaining greater statistical significance through a larger n. First, marital status is reduced to just separate (removing married and divorced). Separated is selected to remain in the models since it has the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance (26.0%). Total annual income remains as the only significant independent variable (.00). Married, total annual income and number of pregnancies all lost their significance in the reduced model. Second, the model is run with divorced and separated combined. The combined divorced/separated model 2 explains the same amount of variance (30.6%).
Predictions of Flexibility in Child Disability
The fourth linear regression examines flexibility in child disability status and includes two models. The first includes the demographic variables of age, race, current marital status (married, divorced and separated), total annual income and highest level of education. The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table 28).
107 Table 28. Flexibility in Child Disability by Maternal Demographic Characteristics (N=89) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.02 0.01 -0.18 0.11 -0.02 0.01 -0.18 0.10 Nonwhite -0.17 0.20 -0.10 0.38 -0.16 0.19 -0.09 0.42 Married -0.01 0.23 0.00 0.98 -0.09 0.23 -0.05 0.70 Divorced 0.45 0.33 0.16 0.17 0.30 0.33 0.11 0.36 Separated 0.29 0.34 0.10 0.40 0.08 0.34 0.03 0.82 Total Income (annual) -0.07 0.02 -0.34 0.00** -0.07 0.02 -0.35 0.00** Highest Education Level 0.01 0.04 0.03 0.76 0.03 0.04 0.09 0.43 Number of Pregnancies 0.09 0.04 0.24 0.06 Has Had Live Birth(s) -0.03 0.21 -0.02 0.90 Want A/Another Child 0.20 0.24 0.09 0.40 Model 1: Never Married is the reference category Flexibility in child disability ranges from 1 (inflexible) to 3 (very flexible) Model 1: R Squared =0.18, F-value = 2.4, p-value = 0.03* Model 2: R Squared =0.23, F-value = 2.24, p-value = 0.02* *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002) Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable flexibility in child disability status and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. The overall model has an R Squared value of .175 and an F-value of 2.395 which is significant at
.028. The model explains 18% of the variance in flexibility in child’s disability status.
Total annual income is the only statistically significant independent variable (.00) with a
Beta of -.34. This suggests that as income decreases, the level of flexibility in child disability status increases.
108 Model 1 is rerun two ways in the hopes of gaining more statistical significance through a larger n. First, marital status is reduced to just separate (removing married and divorced). Separated is selected to remain in the models since it has the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance
(15%) and remains significant (.02). Total annual income remains as the only significant independent variable (.00). Secondly, the model is rerun combining separated and divorced into one variable. The combined separated/divorced model explains less variance than the original model (.173) and remains significant (.02) with income being the only statistically significant variable (.01).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .227 and an F-value of 2.238 which is statistically significant (.024). Model 2 explains more variance in the flexibility in child’s disability status than the previous model (increased from 18% to
23%). Total annual income remains the only statistically significant independent variable (.00) with a Beta of -.352. This suggests that as income level decreases, the level of flexibility in child disability status increases.
The model is rerun in two ways in the hope of obtaining greater statistical significance through a larger n. First, marital status is reduced to just separate (removing married and divorced). The reduced model explains less of the variance (21%) and remains significant (.01). Total annual income remains significant (.00). The number of
109 pregnancies also gains significance (.05) suggesting that with an increased number of pregnancies that individuals become more flexible in child’s disability status. Second, separated and divorced are combined into one variable. The combined separated/divorced model explains less variance than the original model 2 (.224) and is statistically insignificant (.17).
Predictions of Flexibility in Number of Children in Sibling Group
The fifth linear regression examines flexibility in the number of children in a sibling group and included two models. The first includes the demographic variables of age, race, current marital status (married, divorced and separated), total annual income and highest level of education. The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table 29).
110 Table 29. Flexibility in Number of Children in Sibling Group by Maternal Demographic Characteristics (N=89) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.01 0.01 -0.08 0.49 -0.01 0.01 -0.07 0.59 Nonwhite -0.12 0.17 -0.09 0.49 -0.09 0.17 -0.07 0.57 Married 0.16 0.20 0.12 0.41 0.11 0.20 0.08 0.58 Divorced 0.16 0.28 0.07 0.57 0.07 0.28 0.03 0.81 Separated 0.29 0.29 0.12 0.32 0.14 0.30 0.06 0.64 Total Income (annual) 0.01 0.02 0.06 0.62 0.01 0.02 0.06 0.65 Highest Education Level 0.04 0.03 0.15 0.20 0.05 0.03 0.18 0.15 Number of Pregnancies 0.05 0.04 0.15 0.27 Has Had Live Birth(s) 0.01 0.18 0.01 0.97 Want A/Another Child 0.29 0.21 0.17 0.17 Model 1: Never Married is the reference category Flexibility in number of children in sibling group ranges from 1 (inflexible) to 3 (very flexible) Model 1: R Squared =0.06, F-value = 0.71, p-value = 0.66 Model 2: R Squared =0.10, F-value = 0.86, p-value = 0.58 *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002) Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable flexibility in the number of children within a sibling group and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. The overall model has an R Squared value of .060 and an F-value of .714 which is insignificant at .66. Model 1 explains 6% of the variance in flexibility in the number of children in a sibling group. None of the independent variables are statistically significant in the model.
111 The model is rerun two ways in the hope of gaining greater statistical significance through a larger n. First, the model is rerun reducing marital status to just married
(removing divorced and separated). Married is selected to remain in the models since it has the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance (4.6%) and remains insignificant (.56). None of the independent variables are significant in the rerun model. Second, separated and divorced are combine into one variable. The combined separated/divorced model explains less variance than the original model (.058) and remains statistically insignificant (.660).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .102 and an F-value of .859 which is insignificant (.575). Model 2 explains 10% of the variance in the flexibility in the number of children in a sibling group. None of the independent variables are statistically significant.
Model two is rerun two ways in the hope of gaining statistical significance through a larger n. First, model 2 is rerun reducing marital status to just married
(removing divorced and separated). The reduced model explains a bit more of the variance (9.9%) but remains insignificant (.394). None of the independent variables were significant. Secondly, separated and divorced are combined into one variable. The combined separated/divorced model 2 explains less variance than the original model
(.101) and remains insignificant (.303).
112 Predictions of Overall Flexibility in Child Characteristics
The sixth linear regression examines overall flexibility and includes two models.
The first includes the demographic variables of age, race, current marital status (married, divorced and separated), total annual income and highest level of education. The second model adds number of pregnancies, has had live birth(s) and wants a/another child (Table
30).
Table 30. Overall Flexibility in Child Characteristics by Maternal Demographic Characteristics (N=87) Model 1 Model 2 p- p- B SE Beta value B SE Beta value Age -0.07 0.04 -0.21 0.08 -0.06 0.04 -0.19 0.10 Nonwhite -0.84 0.55 -0.18 0.13 -0.73 0.53 -0.16 0.17 Married -0.15 0.65 -0.03 0.82 -0.36 0.63 -0.08 0.57 Divorced 0.46 0.90 0.06 0.61 0.08 0.89 0.01 0.93 Separated 1.14 0.94 0.15 0.23 0.37 0.94 0.05 0.69 Total Income (annual) -0.13 0.07 -0.23 0.05* -0.14 0.06 -0.26 0.03* Highest Education Level 0.14 0.10 0.15 0.19 0.17 0.10 0.20 0.09 Number of Pregnancies 0.31 0.14 0.29 0.03* Has Had Live Birth(s) -0.53 0.60 -0.12 0.39 Want A/Another Child 1.25 0.67 0.21 0.07 Model 1: Never Married is the reference category Flexibility in child characteristics ranges from 5 (inflexible) to 15 (very flexible) Model 1: R Squared =0.13, F-value = 1.64, p-value = 0.14 Model 2: R Squared =0.23, F-value = 2.19, p-value = 0.03* *p < .05 **p < .01 Data Source: The National Family Growth Cycle VI (2002) Model 1 - age, race, current marital status (married, divorced and separated), total annual income and highest level of education
Model 1 contains the dependent computed variable overall flexibility in child characteristics and the following independent variables: age, race, marital status (married, divorced and separated), total annual income and highest level of education. The overall
113 model has an R Squared value of .129 and an F-value of 1.635 which is statistically insignificant at .138. Model 1 explains 12.9% of the variance in overall child flexibility.
Total annual income is the only statistically significant independent variable (.05) in the model with a Beta of -.234. This suggests that as income decreases, overall flexibility increases.
The model is rerun in two forms in the hope of gaining greater statistical significance. First, marital status is reduced to just separated (removing married and divorced). Separated is selected to remain in the models since it has the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance
(12.4%) and remains insignificant (.06). Total annual income remains the only significant independent variable (.04). Secondly, separated and divorced are combined into one variable. The combined separated/divorced model explains less variance than the original model (.125) and remains statistically insignificant (0.98).
Model 2 - age, race, current marital status (married, divorced and separated), total annual income, highest level of education, number of pregnancies, has had live birth(s) and wants a/another child
Model 2 adds in the variables number of pregnancies, has had live birth(s) and wants a/another child. The overall model has an R Squared value of .228 and an F-value of .2188 which is significant (.028). Model 2 explains 22.8% of the variance in overall child flexibility. Total annual income has a Beta of -.257 and a statistically significant p- value of .029. This suggests that as total annual income decreases, overall flexibility increases. Number of pregnancies has a Beta of .291 with a significant p-value of .030.
114 This suggests that as the number of pregnancies increases, the overall flexibility increases.
Model 2 is rerun two ways in the hope of gaining greater statistical significance through a larger n. First, marital status is reduced to just separated (removing married and divorced). Separated is selected to remain in the model since it has the highest level of flexibility in the bivariate statistics. The reduced model explains less of the variance
(22.3%) and remains significant (.01). Total annual income (.018) and number of pregnancies (.028) remain significant. Second, model two is rerun combining divorced and separated into one variable. The combined divorced/separated model 2 explains slightly less variance (.227) and remains statistically significant (.025). Total annual income (.023) and number of pregnancies (.027) remain significant.
Summary
In summary, through comparative analysis it is determined that overall flexibility is seen most in those wanting a/another child or those with lower income levels. In terms of child gender, White respondents, those who have not had a live birth and those who want a/another child are most flexible. In relation to child age, those wanting a/another child, who have a marital status of separate and of lower income are most flexible. Those with a higher number of pregnancies are most flexible in child race. In terms of disability status, those with lower income are most flexible.
Four of the six linear regression models achieve statistical significance.
Flexibility in child race is explained at 16% through the inclusion of the demographic variables (age, race, marital status, income and education) and the family planning variables (number of pregnancies, had had a live birth(s) and wants a/another child). The
115 model suggests that with an increased number of pregnancies the level of flexibility in child’s race increases.
Flexibility in child age is explained at 31% through the inclusion of the demographic variables (age, race, marital status, income and education) and the family planning variables (number of pregnancies, had had a live birth(s) and wants a/another child). This suggests that respondents with lower income levels and higher numbers of pregnancies are more flexible in child age. Married respondents are less flexible in child’s age when compared to never married respondents.
Flexibility in child’s disability status is explained at 23% with the inclusion of of the demographic variables (age, race, marital status, income and education) and the family planning variables (number of pregnancies, had had a live birth(s) and wants a/another child). The model suggests that with lower income levels there is increased flexibility in child disability status.
The variance in overall child flexibility is explained at 23% through the inclusion of the demographic variables (age, race, marital status, income and education) and the family planning variables (number of pregnancies, had had a live birth(s) and wants a/another child). The model suggests that lower income levels and a higher number of pregnancies are significant predictors of overall child flexibility.
116 CHAPTER 7: ANALYSIS PLAN 3 RESULTS
Chapter 7 examines the results of Analysis Plan 3 that addresses Aim 3 to describe the characteristics of families who have adopted and investigate their motives specific to their form of adoption. Aim 3 is examined utilizing the data from the National
Survey of Adoptive Families (CDC, 2007-2008). The total sample size is 2,089. Sample characteristics were discussed in Chapter 4. To summarize, of the 2,089 respondents, 545 adopted internationally (26.1%), 763 adopted from foster care (36.5%) and 781 adopted via private domestic adoption (37.4%).
Infertility is cited as a reason for adoption in 68.3% international adoptions,
44.2% of the foster care adoptions and 54.2% of the private domestic adoptions.
Wanting to expand their family is stated as a reason for adoption by 92.7% of respondents adopting internationally, 65.1% of those adopting from foster care and
61.1% of those adopting via private domestic adoption. Wanting a sibling for an existing child is a reason for adoption in 28.8% of the international adoptions, 22.9% of the foster care adoptions and 17.3% of the private adoptions.
Wanting to adopt a child in need is stated as a reason for adoption in 89% of the international adoptions, 83% of the foster care adoptions and 70.3% of the private domestic adoptions. Wanting to help a child avoid foster care is a reason for adoption in 1% of the foster care adoptions, 1.3% of the private domestic adoptions and 0% of the international adoptions.
A close connection to adoption is often a motivation to adopt. Family members have adopted is provided as a motivation for adoption in 40% of the international
117 adoptions, 37.7% of foster care adoptions and 34.7% of the private domestic adoptions.
Friends have adopted is stated as a reason for adoption in 84% of the international adoptions, 63.4% of the foster care adoptions and 55.8% of the private domestic adoptions. The respondent is adopted is a reason for adoption in 2.8% of the international adoptions, 4.3% of the foster care adoptions and 4.5% of the private domestic adoptions. The respondent’s spouse/partner is adopted is noted as a reason for adoption in 1.5% of the international adoptions, 2.8% of the foster care adoptions and
1% of the private domestic adoptions. The respondent or the respondent’s spouse/partner has adopted siblings is provided as a motivation for adoption in 6.4% of the international adoptions, 8.9% of the foster care adoptions and 7.3% of the private domestic adoptions.
Of the 763 respondents that adopted from foster care, the following motivations for choosing to adopt from foster care were reported: 16.9% shorter wait times, 25.6% cheaper than other forms of adoption, 7.9% wanted an older child, 8.8% wanted a special needs child, 9.7% had previously adopted from foster care and 8.7% wanted to provide a home to a child in need.
Bivariate Results
Bivariate statistics (cross tabulations) are utilized to examine the respondent’s motivation for adoption across the three modes of adoption (international, foster care and private adoption). The adoption type variable is recoded into three separate dichotomous, yes/no variables that reflect each mode of adoption. The cross tabulation statistics to be examined include the Pearson Chi-Square value, the 2-sided asymptotic significance and the nominal by nominal phi value.
118 Motivation for Adoption by International Adoption
A cross tabulation is utilized to examine the respondent’s motivation for adoption by if the respondent adopted via international adoption (yes/no) (Table 31). Infertility,
Wanting to expand their family, Wanting a sibling for another child, Wanting to give a child a permanent family and Having friends that have adopted are all statistically significant indicators for international adoption. Those adopting internationally are more likely to suffer from infertility than those adopting via other modes. Respondents wanting to expand their family are more likely to adopt via international adoption that other forms of adoption. Those adopting internationally are more likely to want a sibling for another child than those adopting from other modes.
Wanting to give a child a permanent home is a significant reason for adoption regardless on mode of adoption, but is higher in those adopting internationally. Those adopting internationally have a higher percentage of friends who have adopted than those who have adopted from other avenues of adoption.
119 Table 31. International Adoption and Respondent's Reason for Adoption Adopted via Did Not Adopt Chi- p- Phi/V* International via Square Value Adoption International (N=545) Adoption (N=1544) N % N % Infertility Yes 372 68.6% 760 49.6% 58.67 0.00* 0.17 No 170 31.4% 773 50.4% Expand Family Yes 505 93.0% 974 63.2% 173.41 0.00* 0.29 No 38 7.0% 568 36.8% Wanted Sibling for an Existing Child Yes 157 55.7% 310 29.8% 65.15 0.00* 0.22 No 125 44.3% 731 70.2% Altruistic - Wanted to Give a Child a Permanent Family Yes 485 89.2% 1182 76.7% 39.15 0.00* 0.14 No 59 10.8% 360 23.3% Family Members have Adopted Yes 218 40.1% 559 36.4% 2.33 0.13 0.03 No 326 59.9% 977 63.6% Friends have Adopted Yes 458 84.0% 920 60.5% 100.19 0.00* 0.22 No 87 16.0% 601 39.5% Respondent is Adopted Yes 15 2.8% 68 4.4% 2.89 0.09 -0.04 No 530 97.2% 1475 95.6% Spouse is Adopted Yes 8 1.9% 29 3.0% 1.26 0.26 -0.03 No 405 98.1% 937 97.0% Respondent or Spouse has Adopted Siblings Yes 35 6.4% 125 8.1% 1.61 0.21 -0.03 No 510 93.6% 1418 91.9% *For strength, the Nominal by Nominal Phi was utilized for a 2x2 table Data Source: National Survey of Adoptive Parents (CDC, 2007-2008)
120 Motivation for Adoption by Foster Care Adoptions
A cross tabulation is utilized to examine the respondent’s motivation for adoption by if the respondent adopted via foster care adoption (yes/no) (Table 32). Infertility,
Wanting to expand their family, Wanting a sibling for another child, Wanting to give a child a permanent family and Respondent’s spouse being adopted are all statistically significant indicators for foster care adoption. Individuals adopting from foster care are less likely to be suffering from infertility than those adopting via other modes of adoption. While significant, wanting to expand their family, is reported less often as a motivation for foster care adoption compared to the other modes of adoption
(65.2% compared to 74.2%). Wanting a sibling for another child is not a reason why respondents selected to adopt from foster care. The majority of respondents adopted from foster care due to wanting to give a child a permanent family (83.2%). Respondent’s spouse being adopted is not noted as a reason for foster care adoption in most situations
(95.7%).
121 Table 32. Foster Care Adoption and Respondent's Reason for Adoption Adopted from Did Not Chi- p- Phi/V* Foster Care Adopt from Square Value (N=763) Foster Care (N=1326)
N % N % Infertility Yes 337 44.6% 795 60.3% 47.76 0.00* -0.15 No 419 55.4% 524 39.7% Expand Family Yes 497 65.2% 982 74.2% 19.01 0.00* -0.10 No 265 34.8% 341 25.8% Wanted Sibling for an Existing Child Yes 175 30.8% 292 38.7% 9.02 0.00* -0.08 No 394 69.2% 462 61.3% Altruistic - Wanted to Give a Child a Permanent Family Yes 633 83.2% 1034 78.0% 7.96 0.01* 0.06 No 128 16.8% 291 22.0% Family Members have Adopted Yes 288 37.9% 489 37.0% 0.15 0.70 0.01 No 472 62.1% 831 63.0% Friends have Adopted Yes 484 64.1% 894 68.2% 3.60 0.06 -0.04 No 271 35.9% 417 31.8% Respondent is Adopted Yes 33 4.3% 50 3.8% 0.40 0.53 0.01 No 729 95.7% 1276 96.2% Spouse is Adopted Yes 21 4.3% 16 1.8% 7.42 0.01* 0.07 No 470 95.7% 872 98.2% Respondent or Spouse has Adopted Siblings Yes 68 8.9% 92 6.9% 2.70 0.10 0.04 No 694 91.1% 1234 93.1% *For strength, the Nominal by Nominal Phi was utilized for a 2x2 table Data Source: National Survey of Adoptive Parents (CDC, 2007-2008)
122 Motivation for Adoption by Private Domestic Adoption
A cross tabulation is utilized to examine the respondent’s motivation for adoption by if the respondent adopted via private domestic adoption (yes/no) (Table 33). Wanting to expand their family, Wanting a sibling for another child, Wanting to give a child a permanent family and Having friends that adopted are all statistically significant indicators of domestic adoption. Those adopting via private domestic adoption are less likely to be adopting for family expansion as compared to the other forms of adoption
(61.2% versus 76.8%). Wanting a sibling for another child is less of a reason for adopting via private domestic adoption, with less people citing wanting a sibling for another child as a motivator (28.6% versus 39%). Wanting to give a child a permanent family is more of a motivator for other forms of adoption rather than private domestic. Having friends that have adopted is less of a motivator towards private domestic adoption than other forms (56.9% versus 72.5%).
123 Table 33. Private Domestic Adoption and Respondent's Reason for Adoption Adopted via Private Did Not Adopt via Chi- p- Phi/V* Domestic Adoption Private Domestic Square Value (N=781) Adoption (N=1308)
N % N % Infertility Yes 423 54.4% 709 54.6% 0.01 0.94 -0.00 No 354 45.6% 589 45.4% Expand Family Yes 477 61.2% 1002 76.8% 57.83 0.00* -0.17 No 303 38.8% 303 23.2% Wanted Sibling for an Existing Child Yes 135 28.6% 332 39.0% 14.41 0.00* -0.10 No 337 71.4% 519 61.0% Altruistic - Wanted to Give a Child a Permanent Family Yes 549 70.3% 1118 85.7% 71.96 0.00* -0.19 No 232 29.7% 187 14.3% Family Members have Adopted Yes 271 34.9% 506 38.8% 3.13 0.08 -0.04 No 505 65.1% 798 61.2% Friends have Adopted Yes 436 56.9% 942 72.5% 52.42 0.00* -0.16 No 330 43.1% 358 27.5% Respondent is Adopted Yes 35 4.5% 48 3.7% 0.84 0.36 0.02 No 746 95.5% 1259 96.3% Spouse is Adopted Yes 8 1.7% 29 3.2% 2.77 0.10 -0.05 No 467 98.3% 875 96.8% Respondent or Spouse has Adopted Siblings Yes 57 7.3% 103 7.9% 0.23 0.63 -0.01 No 724 92.7% 1204 92.1% *For strength, the Nominal by Nominal Phi was utilized for a 2x2 table Data Source: National Survey of Adoptive Parents (CDC, 2007-2008)
124 Multivariate Results
Binary logistic regression models are utilized to examine the predictors for each adoption type. The mode of adoption is recoded into three separate binary dichotomous yes/no variables, one for each form of adoption. The chi-square significance, the Cox &
Snell R Squared and the Exp(B) are reviewed to determine model predictive significance, variance explained and significant predictors of the dependent variable. For each binary logistic regression (international, foster care and private domestic), three models are utilized. Model 1 contains independent variables associated with wanting another child
(infertility, wanting to expand their family and wanting a sibling for an existing child).
These variables all deal with the individual/couple wanting a child. Model 2 adds in altruism to see if wanting to assist a child in need increases the likelihood of the adoption type. Model 3 adds in variables that show a close connection with adoption (have family or friends that have adopted or being close to others that have adopted) and shows the impact of others actions on the individual/couple seeking to adopt.
Predictions of Adopting via International Adoption
Model 1 – infertility, expand family and wanted siblings
Model 1 contains the following adoption motivation variables: infertility, wanting to expand their family and wanting a sibling for an existing child. Wanting to expand their family was a significant predictor of adopting internationally; with those wanting to expand their family being 6.34 times more likely to adopt internationally. Model 1 has a chi-square of 69.09 with a statistical significance of .00. The Cox & Snell R Squared shows that the model explains 8% of the variance in adopting internationally (see Table
34).
125 Model 2 – infertility, expand family, wanted siblings and altruism
Model 2 adds the variable wanting to give a child a home which is a measure of altruism. Wanting to expand their family was a significant predictor of adopting internationally; with those wanting to expand their family being 6.09 times more likely to adopt internationally. Model 2 has a chi-square of 70.26 with a statistical significance of
.00. The Cox & Snell R Squared shows that the model explains 8% of the variance in adopting internationally.
Model 3 – infertility, expand family, wanted siblings, altruism, having family members that adopted, friends who adopted, respondent adopted, spouse adopted, and respondent or spouse have adopted siblings
Model 3 adds the variables having family members that adopted, friends who adopted, respondent is adopted, spouse is adopted and respondent or spouse have adopted siblings. Wanting to expand their family and Having friends that adopted are significant predictors of adopting internationally. Those wanting to expand their family are 5.62 times more likely to adopt via international adoption. Those having friends who adopted are 2.09 times more likely to adopt via international adoption. Model 3 has a chi- square of 87.35 with a statistical significance of .00. The Cox & Snell R Squared shows that the model explains 10% of the variance in adopting internationally, which is an increase from models 1 and 2 (both explained 8% of the variance).
126 Table 34. Odds of Adopting via International Adoption (N=2,089) Model 1 Model 2 Model 3 Exp S.E. p-value Exp S.E. p-value Exp S.E. p-value (B) (B) (B) Infertility 1.13 0.18 0.50 1.15 0.18 0.44 1.15 0.18 0.44 Expand Family 6.34 0.32 0.00** 6.09 0.32 0.00** 5.62 0.32 0.00** Wanted Siblings 1.17 0.17 0.37 1.17 0.17 0.37 1.09 0.18 0.63 Altruistic - Give Child a Home 1.31 0.25 0.29 1.25 0.26 0.38 Family Members Adopted 0.84 0.17 0.31 Friends Adopted 2.09 0.21 0.00** Respondent is Adopted 0.85 0.51 0.74 Spouse is Adopted 0.32 0.78 0.15 Respondent or Spouse have Adopted Siblings 0.93 0.37 0.85 Model 1: R2 = .08 (Cox & Snell R Squared), p-value = 0.00** Model 2: R2 = .08 (Cox & Snell R Squared), p-value = 0.00** Model 3: R2 = .10 (Cox & Snell R Squared), p-value = 0.00** *p < .05 **p < .01 Data Source: National Survey of Adoptive Parents (CDC, 2007-2008) Predictions of Adopting via Foster Care
Model 1 – infertility, expand family and wanted siblings
Model 1 contains the following adoption motivation variables: infertility, wanting to expand their family and wanting a sibling for an existing child. Infertility and
Wanting siblings for an existing child are significant predictors of not adopting from foster care. Those with infertility are 0.64 times less likely to adopt from foster care.
Those wanting siblings for an existing child are 0.63 times less likely to adopt from foster care. Model 1 has a chi-square of 26.27 with a statistical significance of .00. The Cox &
Snell R Squared shows that the model explains 3% of the variance in adopting from foster care (see Table 35).
127 Model 2 – infertility, expand family, wanted siblings and altruism
Model 2 adds the variable wanting to give a child a home which is a measure of altruism. Infertility and Wanting siblings for an existing child are significant predictors of not adopting from foster care; Wanting to give a child a home is a significant predicator of adopting from foster care. Those with infertility are 0.65 times less likely to adopt from foster care. Those wanting siblings for an existing child are 0.62 times less likely to adopt from foster care. Those wanting to give a child a home are 1.60 times more likely to adopt from foster care. Model 2 has a chi-square of 31.58 with a statistical significance of .00. The Cox & Snell R Squared shows that the model explains
4% of the variance in adopting from foster care.
Model 3 – infertility, expand family, wanted siblings, altruism, having family members that adopted, friends who adopted, respondent adopted, spouse adopted, and respondent or spouse have adopted siblings
Model 3 adds the variables having family members that adopted, friends who adopted, respondent is adopted, spouse is adopted and respondent or spouse have adopted siblings. Infertility and Wanting siblings for an existing child are significant predictors of not adopting from foster care; Wanting to give a child a home is a significant predicator of adoption from foster care. Those with infertility are 0.64 times less likely to adopt from foster care. Those wanting siblings for an existing child are 0.60 times less likely to adopt from foster care. Those wanting to give a child a home are 1.66 times more likely to adopt from foster care. Model 3 has a chi-square of 39.69 with a statistical significance of .00. The Cox & Snell R Squared shows that the model explains 5% of the
128 variance in adopting from foster care, which is an increase from models 1 and 2 (3% and
4% respectively).
Table 35. Odds of Adopting via Foster Care (N=2,089) Model 1 Model 2 Model 3 Exp S.E. p- Exp S.E. p- Exp S.E. p- (B) value (B) value (B) value Infertility 0.64 0.16 0.00** 0.65 0.16 0.01* 0.64 0.16 0.01* Expand Family 1.05 0.19 0.79 0.97 0.19 0.89 0.94 0.20 0.75 Wanted Siblings 0.63 0.16 0.00** 0.62 0.16 0.00** 0.60 0.16 0.00** Altruistic - Give Child a Home 1.60 0.21 0.02* 1.66 0.21 0.02* Family Members Adopted 1.23 0.15 0.17 Friends Adopted 1.08 0.17 0.64 Respondent is Adopted 0.92 0.41 0.84 Spouse is Adopted 2.65 0.53 0.07 Respondent or Spouse have Adopted Siblings 1.27 0.30 0.43 Model 1: R2 = .03 (Cox & Snell R Squared), p-value = 0.00** Model 2: R2 = .04 (Cox & Snell R Squared), p-value = 0.00** Model 3: R2 = .05 (Cox & Snell R Squared), p-value = 0.00** *p < .05 **p < .01 Data Source: National Survey of Adoptive Parents (CDC, 2007-2008) Predictions of Adopting via Private Domestic Adoption
Model 1 – infertility, expand family and wanted siblings
Model 1 contains the following adoption motivation variables: infertility, wanting to expand their family and wanting a sibling for an existing child. Infertility and
Wanting siblings for an existing child are significant predictors of adopting from private domestic adoption. Wanting to expand their family was a significant predictor of not adopting via private domestic adoption. Those with infertility are 1.59 times more likely to adopt from private domestic adoption. Those wanting siblings for an existing child are 1.55 times more likely to adopt from private domestic adoption. Those wanting
129 to expand their family are 0.28 times less likely to adopt via private domestic adoption.
Model 1 has a chi-square of 38.12 with a statistical significance of .00. The Cox & Snell
R Squared shows that the model explains 5% of the variance in adopting from private domestic adoption (see Table 36).
Model 2 – infertility, expand family, wanted siblings and altruism
Model 2 adds the variable wanting to give a child a home which is a measure of altruism. Infertility and Wanting siblings for an existing child are significant predictors of adopting from private domestic adoption. Wanting to expand their family and Wanting to give a child a home are significant predictors of not adopting via private domestic adoption. Those with infertility are 1.53 times more likely to adopt from private domestic adoption. Those wanting siblings for an existing child are 1.56 times more likely to adopt from private domestic adoption. Those wanting to expand their family are 0.31 times less likely to adopt via private domestic adoption. Those wanting to give a child a home are 0.50 times less likely to adopt from private domestic adoption.
Model 2 has a chi-square of 49.34 with a statistical significance of .00. The Cox & Snell
R Squared shows that the model explains 6% of the variance in adopting from private domestic adoption.
Model 3 – infertility, expand family, wanted siblings, altruism, having family members that adopted, friends who adopted, respondent adopted, spouse adopted, and respondent or spouse have adopted siblings
Model 3 adds the variables having family members that adopted, friends who adopted, respondent is adopted, spouse is adopted and respondent or spouse have adopted siblings. Infertility and Wanting siblings for an existing child are significant predictors
130 of adopting from private domestic adoption. Wanting to expand their family, Wanting to give a child a home and Having friends who adopted are significant predictors of not adopting via private domestic adoption. Those with infertility are 1.58 times more likely to adopt from private domestic adoption. Those wanting siblings for an existing child are 1.74 times more likely to adopt from private domestic adoption. Those wanting to expand their family are 0.35 times less likely to adopt via private domestic adoption.
Those wanting to give a child a home are 0.50 times less likely to adopt from private domestic adoption. Those having friends who adopted are 0.51 times less like to adopt from private domestic adoption. Model 3 has a chi-square of 66.62 with a statistical significance of .00. The Cox & Snell R Squared shows that the model explains 8% of the variance in adopting from private domestic adoption, which is an increase from models 1 and 2 (5% and 6% respectively).
131 Table 36. Odds of Adopting via Private Domestic Adoption (N=2,089) Model 1 Model 2 Model 3 Exp S.E. p- Exp S.E. p- Exp S.E. (B) value (B) value (B) p-value Infertility 1.59 0.19 0.01* 1.53 0.19 0.02* 1.58 0.19 0.02* Expand Family 0.28 0.21 0.00** 0.31 0.22 0.00** 0.35 0.22 0.00** Wanted Siblings 1.55 0.19 0.02* 1.56 0.19 0.02* 1.74 0.19 0.00** Altruistic - Give Child a Home 0.50 0.21 0.00** 0.50 0.21 0.00** Family Members Adopted 0.90 0.17 0.55 Friends Adopted 0.51 0.18 0.00** Respondent is Adopted 1.23 0.45 0.64 Spouse is Adopted 0.69 0.62 0.55 Respondent or Spouse have Adopted Siblings 0.79 0.35 0.51 Model 1: R2 = .05 (Cox & Snell R Squared), p-value = 0.00** Model 2: R2 = .06 (Cox & Snell R Squared), p-value = 0.00** Model 3: R2 = .08 (Cox & Snell R Squared), p-value = 0.00** *p < .05 **p < .01
Data Source: National Survey of Adoptive Parents (CDC, 2007-2008) Summary
In summary, bivariate analysis is utilized to examine the reasons for adoption by the three adoption types. For international adoption, infertility, wanting to expand their family, wanting a sibling for an existing child, wanting to give a child a home and have friends who adopted are all significant adoption motivators. In relation to foster care adoption, infertility, wanting to expand their family, wanting a sibling for an existing child, wanting to give a child a home and having an adopted spouse are all significant reasons for adopting. For private domestic adoption, wanting to expand their family, wanting a sibling for an existing child, wanting to give a child a home and having friends who adopted are all significant motivators.
132 The binary logistic regression models are statistically significant and demonstrated why individuals have higher odds of adopting from one form of adoption over another. Examining international adoption: wanting to expand their family and having friends that have adopted are significant predictors of adopting via international adoption. Examining foster care adoption: wanting to give a child a home is a significant predictor of adopting from foster care. Infertility and wanting siblings for an existing child are significant predictors of not adopting from foster care. Examining private domestic adoption: infertility and wanting siblings for an existing child are significant predictors of adopting via private domestic adoption. Wanting to expand their family, wanting to give a child a home and having friends that adopted are significant predictors of not adopting from private domestic adoption.
133 CHAPTER 8: SUMMARY OF FINDINGS & DISCUSSION
This chapter provides a summary of the findings presented earlier in Chapters 5 through 7. The summary is organized by the original research questions and each finding is discussed in the context of the related literature and relevant theoretical and conceptual issues. Table 37 provides a summary of results by research question and specific aim.
Additionally, the contributions of this research to the Sociology of the Family are discussed. Finally, the limitations of this research are discussed along with suggestions for how future research may overcome these.
Who is currently seeking to adopt?
To answer the research question, who is currently seeking to adopt, a binary logistic regression is utilized to determine the predictors (see Table 14, Chapter 5). It was hypothesized that women currently seeking to adopt are likely to be White and highly educated. The hypothesis is not supported by the analysis which demonstrates that the average woman currently seeking to adopt is older, nonwhite, married or separated and wants a/another child. The significant findings are: While age is a relatively weak predictor of seeking to adopt, it is significant and shows that with each year of age, a woman is 1.09 times more likely to be seeking to adopt; Nonwhite women are 3.59 times more likely to be seeking to adopt than White women; Married women are 2.66 times more likely to be seeking to adopt than never married women; Separated women are 4.86 times more likely to be seeking to adopt than never married women, and; Women wanting a/another child are 6.38 times more likely to be seeking to adopt. The respondent’s income level, level of education, being divorced, the number of previous
134 Table 37. Summary of Results by Research Question and Aim Research Question Aim Hypothesis Findings 1. Who is currently Aim 1: The attributes of women Women currently seeking to The average women seeking to adopt is older, nonwhite, seeking to? currently seeking to adopt will adopt will likely be White and married or separated and wants a/another child. Level of be compared to women who are more educated. education was not a predictor of seeking to adopt. not seeking to adopt 2. How flexible are Aim 2: The preferred child-type Women currently seeking to Respondents are very flexible in child race, moderately women currently seeking of women currently seeking to adopt will be moderately flexible in child gender and number of children in a to adopt in the type of adopt will be explored and their flexible, but unwilling to adopt sibling group, but inflexible in child age and disability child they are seeking to level of flexibility gauged. high needs children which status. adopt? characterize child in foster care. 3. Why do people adopt? Aim 3: The characteristics of Families will adopt largely for Overall 6 main reasons for wanting to adopt: 1) families who have adopted will infertility reasons or if they have infertility, 2) wanting to expand their family, 3) wanting be examined to understand their a close personal connection with a sibling for an existing child 4) wanting to adopt a child motives specific to their adoption in need, 5) friends had adopted, and 6) family members adoption type. had adopted. 4. What role do parental Aim 2: The preferred child-type White, older, more educated Respondent's race, age and education were not demographics play in how of women currently seeking to parents will be most flexible in significant predictors of flexibility in any of the child flexible women currently adopt will be explored and their the type of child they are seeking characteristics. A higher number of pregnancies seeking to adopt are in the level of flexibility gauged. to adopt. predicted overall flexibly and flexibility in child’s race. type of child they are Lower annual income was a predictor of overall seeking to adopt? flexibility and flexibility in child’s age and disability status. Being married was a predictor of flexibility in child’s age. 5. Why do adoptive Aim 3: The characteristics of The type of adoption selected International Adoption: wanting to expand their family parents select their families who have adopted will will correlate with the motive for and having friends who adopted were significant adoption type? be examined to understand their adoption. With adoptive parents predictors. motives specific to their experiencing infertility selecting Foster Care Adoption: wanting to give a child a home adoption type. to adopt an infant with through was a significant predictor. Those adopting due to private adoption or infertility and wanting a sibling for an existing child internationally and those parents were less likely to adopt from foster care. adopting for altruistic reasons Private Domestic Adoption: infertility and wanting a being more likely to adopt from sibling for an existing child were significant predictors. foster care. Those adopting due to wanting to expand their family, wanting to give a child a home or having friends who adopted were less likely to adopt from private domestic adoption. 135 pregnancies and if she has had a live birth are not significant predictors of seeking to adopt.
The finding that the average woman seeking to adopt is older is consistent with existing literature. According to Smock & Greenland (2010), there is a subset of older women who are seeking to adopt since many women are delaying motherhood as they are seeking higher education and success on the job front. This postponement of motherhood often leads to infertility or inability to have the desired number of children biologically; as such women are more willing to turn to adoption as they get older (Smock &
Greenland, 2010). Older women are likely to have already tried alternative methods to having their desired number of children. Adoption is likely not their first route, with infertility treatment options being scarce as they age, adoption becomes a more viable option (Akin, 2011; Barbell & Freundlich, 2001; Fisher, 2003; Harden 2004; Howard,
Livingston Smith & Ryan 2004; Snowden, 2008). This finding is consistent with social exchange and rational choice theories, which argue that individuals seek to maximize their profits in life and are limited by the resources available to them (Hechter, 1994).
Older women are likely to have more social connections established throughout their lifetime which will provide them with the opportunities or access to resources that will guide them through their adoption process. This makes adoption both more attractive and attainable to older women (Coleman, 1990; Hechter, 1994).
The data analysis shows that nonwhite women are more likely to be currently seeking adoption than White women. This finding is inconsistent with the existing literature that states that prospective adoptive families are White and have a high SES
(Brooks, James & Barth, 2002; Fisher, 2003; Snowden, 2008). If individuals are seeking
136 to adopt a child that would mirror a biological child, this offers hope that Black women seeking to adopt will adopt the Black children in foster care (Atkin, 2011; Copper, 2013;
Fisher, 2003; Kemp & Bodonyl; Snowden 2008; Wildeman & Emanuel, 2014). DHHS
(2015) data shows that Black children are not being adopted at the same rate as White children.
If Black women are seeking to adopt but are in fact not successfully adopting, this is consistent with the tenants of critical race theory. Our society is largely guided by color-blindness and as such people pretend or ignore race when making everyday decisions (Delgado & Stefancic, 2011). This colorblindness leads to further racism by simply ignoring the issues at hand. Black women are overlooked as potential mothers for the children in foster care due to their history of exploitation and subordination within society (Roberts, 1995; Sargent, 2011). Black women are deemed to be lesser mothers than their White, middle class counterparts (Roberts, 1995). White families are provided opportunity to adopt Black children through polices that ignore color, however, these same policies do not recruit Black families to adopt in the same manner, but rather ignore them and seek the perceived “better families” (Roberts, 1995; Sargent, 2011). Critical race theory calls for the end of colorblindness and the avoidance of race and argues for a sense of race consciousness where everyone is aware of the role that race plays rather than simply overlooking it (Delgado & Stefancic, 2011).
Married and separated women are significantly more likely to be seeking to adopt than never married women. The existing literature suggests that this is due to the expectation that married couples are supposed to have children. Historically, childless couples were perceived as materialistic or selfish (Gibson 2009). Throughout history this
137 stigma has decreased some but the fact still prevails that having children is seen as a large part of the married life course. As a result, families who have not fulfilled their needs to have the desired number of biological children may turn to adoption as an avenue to acquire that family composition (Hagestad, 2002).
A large body of literature exists on the plight of infertile couples and the marital discord that occurs as a result of their inability to conceive a biological child (Goldberg,
2009; Jennings, 2010). This could be an explanation for the finding that separated women are significantly more likely to be seeking to adopt compared to never married women.
The marital tension caused by infertility and the lack of children leads to a marital separation. In an effort to save their marriages, women turn to adoption to grow their families and repair their marriages.
The family developmental framework suggests that married or separated women would be seeking to adopt since it is a cultural norm to have children as part of family/married life (Duvall, 1971). Additionally, the timing of the event, having children, is critical. If a couple waits too long to have children there are repercussions that could last into later life for both the married couple as well as their family (Elder & Johnson,
2002; Hagestad, 2002). Families have defined stages and social roles that should occur during a specific time period. Infertility is a significant issue that leads to family instability and marital discord (Hagestad, 2002; Settersten, 2002).
How flexible are women currently seeking to adopt in the type of child they are seeking to adopt?
To answer the research question, how flexible are women currently seeking to adopt in the type of child they are seeking to adopt, computed flexibility scales are
138 created for each child trait (gender, age, race, disability status and the number of children in a sibling group) (see Table 16, Chapter 6). It was hypothesized that women currently seeking to adopt would have some flexibility in their desired child type, but would be unwilling to adopt the high needs children that are in the foster care system. The hypothesis is supported by the analysis which shows that respondents are: very flexible in child race; somewhat flexible in child gender and the number of children in a sibling group, and; inflexible in child age and disability status.
This finding provides a possible explanation for why the average woman seeking to adopt (older, nonwhite, married or separated and wants a/another child) is not adopting from foster care and is consistent with existing literature. Women seeking to adopt are inflexible in child age, with over half of the respondents wanting a child under the age of
2. In 2014, the average child adopted from foster care was six years of age (DHHS,
2015). Additionally, in 2014, 12,249 of the children seeking adoption from foster care were over the age of 15 (DHHS, 2015).
Women seeking to adopt were also less flexible in child disability status, with
49% preferring a child without a disability. The children available for adoption via U.S. foster care typically have significant behavioral and developmental needs (Hegar, 2005;
Howard, Livingston Smith & Ryan, 2004; Wildeman & Emanuel, 2014). Children in foster care have a higher rate of disabilities than most biological children, which 24-51% of adoptive children having special needs as compared to 6-8% of biological children
(Howard, Livingston Smith & Ryan, 2004). Prenatal drug and alcohol usage is significant among children in foster care which is found to lead to greater medical and developmental difficulties (Barbell & Freundlich, 2001; Brown & Roger, 2009). Roberts
139 (1995) argues that Black women are unfairly targeted by policies and regulations due to their values that differ from White women. This causes Black women to fear government oversight and as a result they do not seek prenatal medical care which increases the number of health issues with their children and also increases the mortality rate for Black infants.
Coleman (1988) through the lens of social exchange and rational choice theories notes that individuals will often conduct a pros and cons analysis when making decisions.
Prospective adoptive families want harmony in their lives, so they weigh the pros and cons of adopting a child with various characteristics. In addition to concerns of the well- being of their families within their own family unit, they must also be concerned with how their new addition will fit in with society and how their family will be viewed within their community (Coleman, 1988; Hechter, 1994). Through this pros and cons analysis potential adoptive parents decide upon a child with moderate to low risk characteristics.
The children in foster care are viewed as damaged goods and a less attractive option that adopting internationally (Howe, 1995; Sargent, 2011).
What role do parental demographics play in how flexible women currently seeking to adopt are in the type of child they are seeking to adopt?
To answer the question, what role to parental demographics play in how flexible women currently seeking to adopt are in the type of child they are seeking to adopt, a liner regression was utilized (see Table 30, Chapter 6). It was hypothesized that White, older, more educated parents will be the most flexible in the type of child they are seeking to adopt. The regression analysis showed that lower income and a higher number
140 of pregnancies were significant predictors of flexibility in child characteristics. The hypothesis was not supported.
The existing literature does not describe parental flexibility in adoption by parental demographics. The hypothesis was constructed based upon the concepts within the family developmental framework. As a result, the hypothesis may have taken too simplistic of a stance on who will be most flexible. While the family developmental framework could lead one to assume that individuals who are older and more educated would be more likely to have experienced positive sequencing of life events and to be more financially secure thus having the ability to parent a child with more challenging characteristics (Elder & Settersten, 2002; Hagestad, 2002). If one overlays social exchange and rational choice theories, these same older, more educated individuals understand the significance of adopting a high needs child. They are aware of the options available to them and want to maximize their long-term profits thus would seek the most ideal child available for adoption in an attempt to guarantee long-term satisfaction with the adoption (Hechter, 1994). Older, more educated individuals may be more concerned with how society will view their adopted child and their existing family, thus leading them down a path of conformity with the result being less flexibility in their desired child characteristics (Coleman, 1988; Roberts, 1995). The suggestions for future research discussed later in this chapter provides next steps for further exploring parental flexibility in adoption.
Why do people adopt?
To answer the research question, why do people adopt, a frequency distribution
(see Table 11, Chapter 4) and cross tabulations (see Tables 31-33, Chapter 7) are utilized.
141 It was hypothesized that families would adopt largely for infertility reasons or if they have a close personal connection with adoption (the respondent is adopted or spouse is adopted). The hypothesis is supported. Respondents provide six main reasons across the three modes of adoption (international, foster care and private domestic adoption) for wanting to adopt: 1) infertility, 2) wanting to expand their family; 3) wanting a sibling for an existing child; 4) wanting to adopt a child in need; 5) friends have adopted, and; 6) family members have adopted.
Existing literature suggests that infertility is a primary reason for adoption which is consistent with the study findings (Bausch, 2006; Fisher, 2003; Zhang, 2011). People are delaying childbearing due to an increase in women working and seeking higher education (Smock & Greenland, 2010). This delay or postponement of children often results in infertility issues when the couple decides to have children. Later in life, individuals regret that they did not have children and note step children do not fill that void (Bures, Koropeckyi-Cox & Loree, 2009). Later life infertility has opened the path for middle-aged couples who have not had children to turn to adoption (Smock &
Greenland, 2011).
The finding that infertility is the primary reason for adoption is consistent with the family development framework. Family developmental framework suggests that couples suffering from infertility are experiencing life events out of sequence by not having children within the socially defined timeframe. Thus societal pressure causes childless couples to have children either through infertility treatments or by adopting a child to re- establish the sequencing of their life events (Elder & Setterson, 2002).
142 Why do adoptive parents select their adoption type (foster care, private, international adoption)?
To answer the research question, why do adoptive parents select their adoption type, binary logistic regression was utilized (see Tables 34-36, Chapter 7). It was hypothesized that the adoption type selected will correlate with the motivation for adoption. Families adopting for infertility reasons would thus turn to private domestic adoption or international adoption to adopt an infant. Those families seeking to adopt for altruistic reasons would be more likely to adopt from foster care. The hypothesis is supported. Adoptive families turn to international adoption when seeking to expand their family and if they have friends who have adopted. Those adopting from foster care want to give a child a home. If adoptive families are dealing with infertility issues or want a sibling for an existing child, they are significantly less likely to adopt via foster care.
Those adopting from private domestic adopt due so largely due to infertility and wanting a sibling for an existing child. If adoptive parents want to expand their family, want to give a child a home or have friends who have adopted they are significantly less likely to adopt via private domestic adoption.
Existing literature does not draw connections between why people adopted from the various forms of adoption. Literature did find that international adoptions where more appealing than private domestic due to ease (Yaunting & Lee, 2011) and that international was preferred over foster care due to the ability to adopt an infant aboard
(Zhang, 2011). Additionally, critical race theories argue that White adopters see the children in U.S. foster care as damaged goods and often exaggerate and perpetuate the myth that only Black children are available for adoption via foster care. They use this
143 myth to distance themselves from the situation and to provide a socially acceptable reason for not adopting the local children in need (Howe, 1995; Sargent, 2011).
The findings are consistent with both the family developmental framework and social exchange and rational choice theories. Individuals seeking adoption due to infertility are less likely to have already achieved the life stage of parenting and would be ill prepared to parent an older child, thus an infant adoption would be more fitting
(Hagestad 2002; Settersten 2002). Potential adoptive parents will partake in a reasoning process that outlines the pros and cons when deciding which type of adoption is right for them (Hechter, 1994; Nye, 1980). Parents that are adopting for infertility reasons will lean towards domestic or international adoption, while those who are adopting for altruistic reasons are more likely to adopt from foster care. Thus the motive for adoption will largely guide the type of adoption selected.
Summary of Findings
Three of the five hypotheses of the project are supported via the analysis. Women currently seeking to adopt did have some flexibility in their desired child type, but are unwilling to adopt very high needs children. Respondents are flexible in child race, gender and number of children in a sibling group, but inflexible in child age and disability status. Families adopt largely for infertility reasons or if they have a close personal connection with adoption (individual is adopted or spouse is adopted). Their mode of adoption was predicted by their adoption motivations. Families adopting due to infertility adopted via private domestic adoption; families adopting for altruistic reasons adopted from foster care.
144 Two of the five hypotheses of the project are rejected. The data did not support that women currently seeking to adopt are likely to be White and highly educated.
Analysis found that typical adopters are older, nonwhite, married or separated and want a/another child. Additionally, it was also hypothesized that White, older, more educated women will be the most flexible in the type of child they are seeking to adopt. Race, age and education are not significant predictors of flexibility in any of the child characteristics. While all of the hypotheses were not supported the overall goals of the project were met. The analysis shows who is currently seeking to adopting, who is most flexible in their desired child characteristics, why people adopt and why they select their mode of adoption.
Implications for the Field of Family Sociology
The current research identifies several contributions to sociology of the family and the field of sociology more broadly. In terms of sociology of the family, several key concepts of family formation have been highlighted. On June 26, 2015, same-sex marriage became a normative variation to the family life course in the United States (De
Vogue & Diamond, June 27, 2015). The Supreme Court ruling illustrates social change that has allowed a once deviant family structure to become a current day normative variation (Klein, White & Martin, 2015). Same-sex marriage provides an opportunity for children waiting adoption to be linked with families who may be more flexible in their desired child traits and who are not necessarily looking to replace a birth child. The broader acceptance of a nontraditional family may also allow for greater approval of interracial adoption.
145 Klein, White & Martin (2015) suggest that the timing of family life events is becoming less important while the sequencing of events is becoming a more significant issue. If a family does not stay within positive sequencing, other areas of their lives will become unsynchronized as well, often resulting in marital discord and potentially marital disruption. Having children is supposed to come after getting married and the longer a couple stays childless the more likely it is that they will never have children. While timing is a less critical factor in how a family is viewed, it does have implications for infertility. Women pursuing higher education and success within their careers often delay motherhood which results in their inability to have their desired number of biological children due to age. Infertility opens up an avenue for adoption. Furthermore, if timing is less of an issue, adoptive parents may have the flexibility to adopt an older child rather than simply seeking a child under the age of two.
This research also has implications for the larger field of Sociology, specifically with regards to issues of disability, race and income. In terms of disability, Kelly-Moore,
Schumacher, Kahana & Kahana (2010) found in their research on perceived disabilities, that individuals do not view disability simply in terms of good or bad health but rather their attitude and social connections play heavily into their perceptions. Those with greater social capital are less likely to see disabilities. This has implications for issues related to adoption and offers hope for the children awaiting adoption, that individuals will not view them simply in terms of their disability status but will see other qualities that make them more desirable.
White, higher income couples are not seeking to adopt disabled children. These couples by virtue of their greater financial resources have greater options available to
146 them and are likely turning to expensive infertility treatments in hopes of producing their ideal child. While lower income and minority individuals may not have the financial ability to risk on expensive infertility treatments and as such are more willing to adopt.
However, current adoption trends to do show an increase in nonwhite adoptions. This leads one to question, are there structural inequalities present in the foster care system that creates roadblocks for lower income, nonwhite individuals who are seeking to adopt?
Fisher (2003) in his sociology of adoption notes that the majority of children in foster care are Black or Hispanic, come from low income households and have suffered abuse and neglect. Are low income, nonwhite individuals seen as the ones creating the problem
(i.e. producing the large number of children in foster care) rather than a potential solution
(i.e. becoming adoptive parents)?
Limitations
There are two limitations of the study. First, the study is limited due to it being secondary data and the researcher not having the ability to ask specific or additional questions. Second, the data was from two different datasets rather than being longitudinal in nature. A longitudinal study that follows the same subjects from the decision to adopt through adoption process would elicit greater results. It would allow one to determine if those seeking to adopt actually finalized an adoption and if individuals’ motivations and flexibility change throughout the adoption process.
Additionally, in the current study, the motivations related to adoption type were asked post-adoption, so the answers provided in hindsight may be different than if they were asked when they were actually going through the process.
147 Suggestions for Future Research
The current study broadens our knowledge of adoption motivations and preferences but does not offer all of the answers. Suggestions for future research include the following. First, a longitudinal study is needed to fully investigate the nuances of adoption and to show the decisions and challenges encountered through the process. We need to better understand what motivates individuals who are considering adoption to actually take that first step. There are likely hurdles that make potential adopters change their minds about their adoption type (domestic, foster or international) or even adoption all together.
Second, this study examined flexibility at the time when individuals said that they were interested in seeking to adopt. Does flexibility change once they actually begin the process of searching for a potential child? One could argue that as time goes on in the adoption waiting process, families could be more willing to accept some challenges that they would otherwise not have selected at the onset of their adoption journey. Higher needs children are more readily available rather than waiting years for what they deem to be the ideal child. Additionally, the current study did not allow women seeking to adopt to rank the potential adoptive child characteristics to their liking. It would be of importance to know what characteristics most guide their decisions. For example, would they chose to be more selective about gender than race? Another key point in terms of flexibility is, as certain characteristics become less stigmatizing within society and the family, such as disability status, is there room for increased adoptions of children with disabilities?
148 Third, more energy needs to be spent on determining the challenges in the adoption process outside of simply linking a potential family with a child in need of adoption. There are issues of parental birth rights versus a child’s right to permanency.
Furthermore, lifestyle and resources may limit the attractiveness of a potential adoptive family, thus when a social worker must select a best family how a family looks on paper could be a large indicator if they are matched with a child. Researchers need to examine if all potential adoptive parents are treated fairly and given the same odds at adopting from foster care. Specifically, if same-sex couples, nonwhites and lower income individuals are treated the same as White, middle- and upper-class couples. Additionally, adoption disruptions occur, thus adoption may not be as permanent as hoped. Research is needed to better understand the prevalence of adoption disruptions, why they occur and what ultimately happens to the children.
Fourth, the current study is comprised of two national samples; however, it does not provide the region of the country the respondents are from. There are likely differences in the willingness to adopt and flexibility in adoptive child characteristics across the various states. Additionally, the children available for adoption may vary by geographic location, providing a potential advantage or disadvantage to some potential adoptive parents simply based upon their locale.
Fifth, three main actors were not included in the current study: the partners of the women currently seeking to adopt, the social workers and the children. The partners of the women may have differing levels of flexibility and have different predictors of their flexibility. A national survey of social workers would be of great importance to better understand from their point of view the issues that are most challenging for children
149 seeking to be adopted. They also would be able to provide trends of who is adopting and their reason for doing so. The children need to be included to better understand the adoption process from their prospective and what resources could be beneficial to them.
This could include counseling prior to meeting potential adoptive families or an educational seminar to understand the characteristics of adoptive families and why they are seeking to adopt. Combined these elements will allow for greater education of all parties involved in the adoption process.
This study as well as further studies offers the opportunity to apply and refine the family developmental framework and social exchange and rational choice theories as well as add to the existing literature. Most of the theoretical textbooks deal with the theories from a macro prospective and do not drill down to the micro level of the family.
Furthermore, the publications to-date are mostly qualitative in nature and appear in social work journals. More quantitative analysis would be beneficial and with a multidisciplinary approach to better understand all of the factors that impact and predict adoption.
150 APPENDIX
A. Protection of Human Subjects (IRB)
B. Key variables National Survey of Family Growth Cycle VI
C. Key variables National Survey of Adoptive Families
D. Publications using data from the National Survey of Family Growth Cycle VI E. Codebook National Survey of Family Growth Cycle VI
F. Codebook National Survey of Adoptive Families
151 Appendix A: Protection of Human Subjects (IRB)
Initial IRB Approval
152 Initial IRB Application
153
154
155
156 157
158 IRB Protocol deemed not human subject research (10/2016)
159 Revised IRB Application (10/2016)
160 161 162 163 164 165 166
167 Appendix B. Descriptives of Study Variables National Survey of Family Growth Cycle IV Variable Question Percentage Mean (n=441) (SD) Seeking to SEEKADPT: (Not counting children 3.98 Adopt who have lived with you or children (1.748) who live with you now,/At this time,) are you (currently) seeking to adopt a child? Yes 25.6% No 74.4% Age AGE_A: First, I’d like to know your 31.80 (respondent) age and date of birth. How old are (7.591) you? ENTER age at last birthday in years. 17 years .2% 18 years 2.9% 19 years 2.0% 20 years 3.2% 21 years 3.4% 22 years 2.5% 23 years 3.6% 24 years 3.2% 25 years 4.1% 26 years 3.4% 27 years 3.4% 28 years 3.9% 29 years 3.9% 30 years 4.8% 31 years 4.3% 32 years 4.1% 33 years 2.9% 34 years 4.1% 35 years 2.9% 36 years 3.2% 37 years 4.5% 38 years 4.5% 39 years 3.9% 40 years 5.2% 41 years 4.5% 42 years 3.6% 43 years 4.3% 44 years 3.4% Race AC-5: Enter race of respondent by 1.83 (respondent) observation (.582) Black (1) 26.5%
168 White (2) 63.5% Other (3) 10% Current MARSTAT: Now I’d like to ask 3.02 Marital about your marital status. Please (2.199) Status look at Card 1. What is your current marital status? Married (1) 46.9% Not married but living together with 9.5% a partner of the opposite sex (2) Widowed (3) .5% Divorced (4) 9.3% Separated, because you and your 5.7% spouse are not getting along (5) Never been married (6) 28.1% Total TOTINC: Which Category 8.53 Income represents your total yearly (3.948) (annual) income/the total combined yearly income of your family in the year 2001, including income from all sources you just went through such as wages, salaries, Social Security or retirement benefits, help from relatives, and so forth? Please enter the amount before taxes. Under $5000 (1) 5.2% $5000-$7499 (2) 4.1% $7500-$9999 (3) 2.9% $10,000-$12,499 (4) 5.9% $12,500-$14,999 (5) 5.9% $15,000-$19,999 (6) 5.7% $20,000-$24,999 (7) 7.7% $25,000-$29,999 (8) 8.2% $30,000-$34,999 (9) 7.7% $35,000-$39,999 (10) 4.5% $40,000-$49,999 (11) 10.4% $50,000-$59,999 (12) 7.9% $60,000-$74,999 (13) 6.8% $75,000 or more (14) 12.0% Refused (98) 2.9% Don’t Know (99) 2.0% Highest HIGRADE: Please look at Card A_3. 13.41 Education What (is the highest grade or year of (2.610) Level (regular) school you have ever attended)/(grade or year of school are you in/were you in before vacation began)?
169 No formal schooling (0) 0.0% 1st grade (1) 0.0% 2nd grade (2) 0.0% 3rd grade (3) 0.0% 4th grade (4) 0.0% 5th grade (5) 0.0% 6th grade (6) 0.0% 7th grade (7) 0.0% 8th grade (8) 0.0% 9th grade (9) 10.9% 10th grade (10) 1.6% 11th grade (11) 5.7% 12th grade (12) 25.4% 1 year of college or less (13) 11.1% 2 years of college (14) 12.9% 3 years of college (15) 6.1% 4 years of college/grad school (16) 14.5% 5 years of college/grad school (17) 4.3% 6 years of college/grad school (18) 3.6% 7 or more years of college and/or 3.6% grade school (19) Don’t Know (99) .2% Number of NUMPREGS: (Including this 1.99 Pregnancies pregnancy,) how many times have (1.866) you been pregnant in your life? None 27.2% 1 Pregnancy 20.0% 2 Pregnancies 15.4% 3 Pregnancies 19.0% 4 Pregnancies 7.7% 5 Pregnancies 5.7% 6 Pregnancies 1.1% 7 Pregnancies 2.0% 8 Pregnancies .7% 9 Pregnancies .5% Refused (98) .5% Wants RWANT: (Looking to the future, 2.60 A/Another do/If it were possible would) you, (1.962) Child yourself, want to have (a/another) baby at some time (after this pregnancy is over/in the future)? Yes 58.7% No 39.2% Don’t Known (9) 2.0%
170 Number of NUMBABES: Number of babies 1.36 Babes Born form alive to respondent (1.682) Alive 0 37.9% 1 22.4% 2 19.7% 3 13.4% 4 3.9% 5 2.0% 6 0.2% 8 0.2% 22 0.2% Has Had a LIVEBIRTHS: Number of babies .6213 Live Birth(s) born alive to respondent recoded into (.4851) yes/no by data collector Yes 37.9% No 62.1% Gender (of CHOSESEX: (Asked if respondent 2.13 Child not seeking to adopt a child she (.786) Respondent knows) If you could choose exactly Prefers to the child you want, would you prefer Adopt) to adopt a boy or a girl? (n=113) Boy (1) 19.5% Girl (2) 29.2% Indifferent (3) 30.1% Missing data 21.2% Accept a TYPESEXF: (Asked if respondent 1.73 Girl? (n=22) said she preferred a boy) Would you (1.579) accept a girl? Yes (1) 81.8% No (5) 18.2% Accept a TYPESEXM: (Asked if respondent 1.63 Boy? (n=32) said she preferred a girl) Would you (1.476) accept a boy? Yes (1) 84.4% No (5) 15.6% Race (of CHOSRACE: (Ask if respondent not 3.06 Child seeking to adopt a child she knows) (1.178) Respondent If you could choose exactly the child Prefers to you wanted, would you prefer to Adopt) adopt a black child, a white child, or (n=113) a child of some other race? Black (1) 13.3% White (2) 11.5% Some other race (3) 10.6%
171 Indifferent (4) 42.5% Missing data 22.1% Accept a TYPRACBK: (Ask if respondent 2.17 Black Child? said she preferred something other (1.857) (n=24) than black) Would you accept a black child? Yes (1) 70.8% No (5) 29.2% Accept a TYPACWH: (Ask if respondent said 1.89 White she preferred something other than (1.695) Child? white) Would you accept a white (n=27) child? Yes (1) 77.8% No (5) 22.2% Accept a TYPRACOT: (Ask if respondent 1.41 Child of said she preferred something other (1.24) Some Other than “other race”) Would you accept Race? a child of some other race, neither (n=29) black nor white? Yes (1) 89.7% No (5) 10.3% Age (of CHOSEAGE: (Ask if respondent is 1.97 Child not seeing to adopt a child she (1.292) Respondent knows)(If you could choose exactly Prefers to the child you wanted), Would you Adopt prefer to adopt a child younger than )(n=113) 2 years, a child 2 to 5 years old, a child 6 to 12 years old, or a child 13 years old or older? A child younger than 2 years (1) 39.8% A child 2-5 years old (2) 20.4% A child 6-12 years old (3) 8.0% A child 13 years old or older (4) 2.7% Indifferent (5) 8.0% Missing data 21.2% Accept a TYPAGE2M: (Ask if respondent 1.8 Child said she preferred something other (1.623) Younger than “younger than 2”) Would you than 2? accept a child younger than 2 years? (n=35) Yes (1) 80.0% No (5) 20.0% Accept a TYPAGE5M: (Ask if respondent 2.0 Child 2 to 5 said she preferred something other (1.748) Years? than “2-5 years”) Would you accept (n=56) a child 2 to 5 years old?
172 Yes (1) 75.0% No (5) 25.0% Accept a TYPAG12M: (Ask if respondent 3.24 Child 6 to 12 said she preferred something other (2.001) Years? than “6-12 years”) Would you accept (n=68) a child 6 to 12 years old? Yes (1) 44.1% No (5) 55.9% Accept a TYPAG13M: (Ask if respondent 4.22 Child 13 or said she preferred something other (1.595) Older? than “13 or older”) Would you (n=77) accept a child 13 years old or older? Yes (1) 19.5% No (5) 80.5% Disability CHOSDISB: (Ask of respondent not 1.99 (Status of seeking to adopt a child she knows) (1.211) Child (If you could choose exactly the Respondent child you wanted), Would you prefer Prefers to to adopt a child with no disability, a Adopt) child with a mild disability, or a (n=113) child with a severe disability? A child with no disability (1) 38.9% A child with a mild disability (2) 20.4% A child with a severe disability (3) 0.9% Indifferent (4) 18.6% Missing data 21.2% Accept a TYPDISBN: (Ask if respondent said 1.00 Child With she preferred something other than (0.00) No “no disability”) Would you accept a Disability? child with no disability? (n=24) Yes (1) 100% No (5) 0.0% Accept a TYPDISBM: (Ask if respondent said 1.73 Child With she preferred something other than (1.561) Mild “mild disability”) Would you accept Disability? a child with a mild disability? (n=44) Yes (1) 81.8% No (5) 18.2% Accept a TYPDISBS: (Ask if respondent said 4.69 Child With she preferred something other than (1.074) Severe “severe disability”) Would you Disability? accept a child with a severe (n=65) disability? Yes (1) 7.7%
173 No (5) 92.3% # of CHOSENUM: (Ask if respondent is 1.54 Children in not seeking to adopt a child she (.739) Sibling knows) (If you could choose exactly Group the child you wanted), Would you (n=113) prefer to adopt a single child or 2 or more brothers and sisters at once? A single child (1) 47.8% 2 or more brothers and sisters at once 19.5% (2) Indifferent (3) 11.5% Missing data 21.2% Accept a TYPNUM1M: (Ask is respondent 1.00 Single said she preferred 2 or more sibs at (0.00) Child? once) Would you accept a single (n=22) child? Yes (1) 100% No (5) 0.0% Accept 2 or TYPNUM2M: (Ask is respondent 3.22 More said she preferred a single child) (2.006) Siblings? Would you accept 2 or more brothers (n=54) and sisters at once? Yes (1) 44.4% No (5) 55.6%
174 Appendix C. Descriptives of Study Variables National Survey of Adoptive Parents Variable Question Percentage Mean (n=2089) (SD) Infertility C12AR: [My Spouse/partner and I 0.58 were/I was] unable to have a (0.671) biological child. No (0) 45.1% Yes (1) 54.2% Don’t Know (6) 0.6% Refused (7) 0.0% Expand C12B: [My spouse/partner and I/I+] 0.72 Family wanted to expand [our/my] family. (0.520) No (0) 29.0% Yes (1) 70.8% Don’t Know (6) 0.1% Refused (7) 0.1% Wanted a C12CR: [My spouse/partner and I/I] 0.36 Sibling for wanted a sibling for another child. (0.526) Another Child No (0) 41.0% Yes (1) 22.4% Don’t Know (6) 0.1% Wanted to C12FR: [My spouse/partner and I/I] 0.81 Adopt a Child wanted to adopt a child in need of a (0.205) in Need permanent family. No (0) 20.1% Yes (1) 79.8% Don’t Know (6) 0.1% Refused (7) 0.0% Wanted to C12GA4: Other reason: to help a 1.00 Help a Child child avoid foster care (0.00) Avoid Foster Care Yes (1) 0.9% Missing data 99.1% Family C13_N: Do any of your [or your 0.4 Members spouse’s/partner’s] relatives have (0.607) have Adopted adopted children? No (0) 62.4% Yes (1) 37.2% Don’t Know (6) 0.4% Friends have C14: Do any of your [or your 0.73 Adopted spouse’s/partner’s] friends or close (0.732)
175 acquaintances have adopted children? No (0) 32.9% Yes (1) 66..0% Don’t Know (6) 1.1% Refused (7) 0.0% Respondent is C15_N: Were you adopted as a 0.04 Adopted child? (0.235) No (0) 96.0% Yes (1) 4.0% Don’t Know (6) 0.0% Spouse/Partn C15B: Was your [spouse/partner] 0.03 er is Adopted adopted as a child? (0.228) No (0) 64.2% Yes (1) 1.8% Don’t Know (6) 0.0% Missing data 33.9% Respondent C16_N: Do you [or your 0.08 or Spouse has spouse/partner] have siblings who (0.296) Adopted were adopted? Siblings No (0) 92.3% Yes (1) 7.7% Don’t Know (6) 0.0% Respondent’s ADOPT_TYPE: Type of Adoption 2.11 Type of (0.789) Adoption International Adoption (1) 26.1% Foster Care Adoption (2) 36.5% Private Domestic Adoption (3) 37.4% Shorter Wait C24A: Foster Care Adoption – 0.4 Times (Foster Thought [I/we] would get a child (0.858) Care) sooner. (n=763) No (0) 36.4% Yes (1) 16.9% Don’t Know (6) 0.5% Refused (7) 0.3% Missing data 45.9% Cheaper Than C24BR: Foster Care Adoption – 0.74 Other Forms Adopting from U.S. foster care was (1.079) of Adoption less costly than adopting (Foster Care) internationally or privately. (n=763) No (0) 19.1% Yes (1) 25.6%
176 Don’t Know (6) 1.3% Refused (7) 0.1% Missing data 53.9% Wanted Older C24C: Foster Care Adoption – 0.23 Child (Foster Wanted an older child. (0.688) Care)(n=763) No (0) 37.0% Yes (1) 7.9% Don’t Know (6) 0.3% Refused (7) 0.1% Missing data 54.8% Wanted a C24DR: Foster Care Adoption – 0.25 Special Wanted a child with special needs. (0.696) Needs Child (Foster Care (n=763) No (0) 36.0% Yes (1) 8.8% Don’t Know (6) 0.3% Refused (7) 0.1% Missing data 54.8% Previously C24ER: Foster Care Adoption – 0.25 Adopted Previously adopted another child (0.630) Another through the foster care system. Child from Foster Care (Foster Care) (n=763) No (0) 35.4% Yes (1) 9.7% Don’t Know (6) 0.1% Refused (7) 0.1% Missing data 54.7% Wanted to C24FA_1: Foster Care Adoption – 1.00 Provide a Other: Wanted to help/provide a (0.00) Home For a home for a child in need. Child in Need (Foster Care) (n=763) No (0) 0.0% Yes (1) 8.7% Missing data 91.3%
177 Appendix D. Publications using data from the National Survey of Family Growth Cycle VI List of Publications, As of 11/24/15 (118) 2014 Guzzo, Karen Benjamin, Hayford, Sarah R. Fertility and the stability of cohabiting unions: Variation by intendedness. Journal of Family Issues. 35, (4), 547-576. Full Text Options: DOI Worldcat Google Scholar PubMed Central Export Options: RIS EndNote XML
2014 Lansky, Amy, Finlayson, Teresa, Johnson, Christopher, Holtzman, Deborah, Wejnert, Cyprian, Mitsch, Andrew, Gust, Deborah, Chen, Robert, Mizuno, Yuko, Crepaz, Nicole . Estimating the number of persons who inject drugs in the United States by meta-analysis to calculate national rates of HIV and Hepatitis C virus infections. PLoS One. 9, (5), e97596 Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2014 Brewster, Karin L., Tillman, Kathryn H., Jokinen-Gordon, Hanna . Demographic characteristics of lesbian parents in the United States. Population Research and Policy Review. 33, (4), 503-526. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2013 Leichliter, Jami S., Haderxhanaj, Laura T., Chesson, Harrell W., Aral, Sevgi O. Temporal trends in sexual behavior among men who have sex with men in the United States, 2002 to 2006-10. Journal of Acquired Immune Deficiency Syndromes. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2013 Cavazos-Rehg, Patricia A., Krauss, Melissa J., Spitznagel, Edward L., Schootman, Mario, Cottler, Linda B., Bierut, Laura J. Characteristics of sexually active teenage girls who would be pleased with becoming pregnant. Maternal and Child Health Journal. 17, 470- 476. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2013 Carlson, M.J., Vanorman, A.G., Pilkauskas, N.V. . Examining the antecedents of U.S. nonmarital fatherhood. Demography. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Rutman, S., Taualii, M., Ned, D., Tetrick, C. Reproductive health and sexual violence among urban American Indian and Alaska Native young women: Select findings from the
178 National Survey of Family Growth (2002). Maternal and Child Health Journal. 16, (2 Sup), 347-352. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Hall, K.S., Moreau, C., Trussell, J. Young women's perceived health and lifetime sexual experience: Results from the National Survey of Family Growth. Journal of Sexual Medicine. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Joyner, K., Peters, H.E., Hynes, K., Sikora, A., Taber, J.R., Rendall, M.S. . The quality of male fertility data in major U.S. surveys. Demography. 49, (1), 101-124. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Brewster, Karin L., Tillman, Kathryn H. Sexual orientation and substance use among adolescents and young adults. American Journal of Public Health. 102, (6), 1168-1176. Full Text Options: Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Hall, K.S., Moreau, C., Trussell J. Determinants of and disparities in reproductive health service use among adolescent and young adult women in the United States, 2002-2008. American Journal of Public Health. 102, (2), 359-367. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Kraft, J.M., Kulkarni, A., Hsia,J., Jamieson, D.J., Warner, L. Sex education and adolescent sexual behavior: Do community characteristics matter?. Contraception. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Hall, K.S., Moreau, C., Trussell, J. Lower use of sexual and reproductive health services among women with frequent religious participation, regardless of sexual experience. Journal of Women's Health. 21, (7), 739-747. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Niederberger, Craig . Re: men who seek infertility care may not represent the general U.S. population: Data from the National Survey of Family Growth. Journal of Urology. 188, (2), 559-560.
179 Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Centers for Disease Control and Prevention . Sexual experience and contraceptive use among female teens - United States, 1995, 2002, and 2006-2010. Morbidity and Mortality Weekly Report. 61, (17), 297-301. Full Text Options: Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Lang, K., Nuevo-Chiquero, A. Trends in self-reported spontaneous abortions: 1970-2000. Demography. 49, 989-1009. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Clear, Emily R., Williams, Corrine M., Crosby, Richard A. Female perceptions of male versus female intendedness at the time of teenage pregnancy. Maternal and Child Health Journal. 16, (9), 1862-1869. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Hall, K.S., Moreau, C., Trussell, J. Associations between sexual and reproductive health communication and health service use among U.S. adolescent women. Perspectives on Sexual and Reproductive Health. 44, (1), 6-12. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Stidham-Hall, K., Moreau, C., Trussell, J. Patterns and correlates of parental and formal sexual and reproductive health communication for adolescent women in the United States, 2002-2008. Journal of Adolescent Health. 50, (4), 410-413. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Hotaling, James M., Davenport, Michael T., Eisenberg, Michael L., VenDenEeden, Stephen K., Walsh, Thomas J. Men who seek infertility care may not represent the general U.S. population: Data from the National Survey of Family Growth. Urology. 79, (1), 123-127. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Henderson, J.T., Saraiya, M., Martinez, G., Harper, C.C., Sawaya, G.F. . Changes to cervical cancer prevention guidelines: Effects on screening among U.S. women ages 15-29. Preventive Medicine. 56, (1), 25-29.
180 Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Littlejohn, Krystale E. Hormonal contraceptive use and discontinuation because of dissatisfaction: Differences by race and education. Demography. 49, (4), 1433-1452. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2012 Lau, M., Lin, H., Flores, G. Clusters of markers identify high and low prevalence of adolescent pregnancy in the US. Journal of Pediatric and Adolescent Gynecology. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Taylor, E.M., Behets, F.M., Schoenbach, V.J., Miller, W.C., Doherty, I.A., Adimora, A.A. . Coparenting and sexual partner concurrency among White, Black, and Hispanic men in the United States. Sexually Transmitted Diseases. 38, (4), 293-298. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Adimora, A.A., Schoenbach, V.J., Taylor, E.M., Khan, M.R., Schwartz, R.J. . Concurrent partnerships, nonmonogamous partners, and substance use among women in the United States. American Journal of Public Health. 101, (1), 128-136. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Lindberg, Laura D., Orr, M. Neighborhood-level influences on young men's sexual and reproductive health. American Journal of Public Health. 101, (2), 271-274. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Gartrell, Nanette K., Bos, Henny M.W., Goldberg, Naomi G. Adolescents of the U.S. National Longitudinal Lesbian Family Study: Sexual orientation, sexual behavior, and sexual risk exposure. Archives of Sexual Behavior. 40, (6), 1199-1209. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Phillips, Ghasi, Brett, Kate, Mendola, Pauline . Previous breastfeeding practices and duration of exclusive breastfeeding in the United States. Maternal and Child Health Journal. 15, (8), 1210-1216. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
181
2011 Edwards, Lisa M., Haglund, Kristin, Fehring, Richard J., Pruszynski, Jessica . Religiosity and sexual risk behaviors among Latina adolescents: Trends from 1995 to 2008. Journal of Women's Health. 20, (6), 871-877. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Magnusson, Brianna M., Masho, Sabo W., Lapane, Kate L. Adolescent and sexual history factors influencing reproductive control among women aged 18-44. Sexual Health. 8, (1), 95-101. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Sherrod, Roy A., DeCoster Jamie . Male infertility: An exploratory comparison of African American and white men. Journal of Cultural Diversity. 18, (1), 29-35. Full Text Options: Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Fu, V.K., Wolfinger, N.H. . Broken boundaries or broken marriages? Racial intermarriage and divorce in the United States. Social Science Quarterly. 92, (4), 1096-1117. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 van Gelder, M.M., Reefhuis, J., Herron, A.M., Williams, M.L., Roeleveld, N. Reproductive health characteristics of marijuana and cocaine users: Results from the 2002 National Survey of Family Growth . Perspectives on Sexual and Reproductive Health. 43, (3), 164-172. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Jeffries, W.L. . The number of recent sex partners among bisexual men in the United States. Perspectives on Sexual and Reproductive Health. 43, (3), 151-157. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Axinn, William G., Link, Cynthia F., Groves, Robert M. Responsive survey design, demographic data collection, and models of demographic behavior. Demography. 48, (3), 1127-1149. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011
182 Guzzo, Karen B., Hayford, Sarah . Fertility following an unintended first birth. Demography. 48, (4), 1493-1516. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 McCabe, J., Brewster, K.L., Tillman, K.H. . Patterns and correlates of same-sex sexual activity among U.S. teenagers and young adults . Perspectives on Sexual and Reproductive Health. 43, (3), 142-150. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2011 Sassler, Sharon, Joyner, Kara . Social exchange and the progression of sexual relationships in emerging adulthood. Social Forces. 90, (1), 223-245. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Wildsmith, E., Guzzo, K.B., Hayford, S.R. . Repeat unintended, unwanted and seriously mistimed childbearing in the United States. Perspectives on Sexual and Reproductive Health. 42, (1), 14-22. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Anderson, J.E., Warner, L., Jamieson, D.J., Kissin, D.M., Nangia, A.K., Macaluso, M. Contraceptive sterilization use among married men in the United States: Results from the male sample of the National Survey of Family Growth. Contraception. 83, (3), 230-235. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Isley, M., Edelman, A., Kaneshiro, B., Nichols, M., Jensen, J. Sex education and contraceptive use at coital debut in the United States: Results from cycle 6 of the National Survey of Family Growth. Contraception. 82, (3), 236-242. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Chandra, Anjani, Stephen, Elizabeth Hervey . Infertility service use among U.S. women: 1995 and 2002. Fertility and Sterility. 93, (3), 725-736. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Marcell, A.V., Bell, D.L., Lindberg, L.D., Takruri, A. Prevalence of sexually transmitted infection/human immunodeficiency virus counseling services received by teen males, 1995-
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2002. Journal of Adolescent Health. 46, (6), 553-559. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Robert, A.C., Sonenstein, F.L. . Adolescents' reports of communication with their parents about sexually transmitted diseases and birth control: 1988, 1995, and 2002. Journal of Adolescent Health. 46, (6), 532-537. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Borrero, Sonya, Moore, Charity G., Qin, Li, Schwarz, Eleanor B., Akers, Aletha, Creinin, Mitchell D., Ibrahim, Said A. Unintended pregnancy influences racial disparity in tubal sterilization rates. Journal of General Internal Medicine. 25, (2), 122-128. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Eisenberg, M.L., Shindel, A.W., Smith, J.F., Breyer, B.N., Lipshultz, L.I. . Socioeconomic, anthropomorphic, and demographic predictors of adult sexual activity in the United States: Data from the national survey of family growth. Journal of Sexual Medicine. 7, (1 pt. 1), 50-58. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Castillo-Guajardo, D., Garcia-Ramos, G. Estimates of sexual partnership dynamics: Extending negative and positive gaps to status lengths. Journal of Epidemiology and Community Health. 64, 672-677. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Riskind, R.G., Patterson, C.J. . Parenting intentions and desires among childless lesbian, gay, and heterosexual individuals. Journal of Family Psychology. 24, (1), 78-81. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Goodwin, Paula Y., Mosher, William D., Chandra, Anjani . Marriage and Cohabitation in the United States: A Statistical Portrait Based on Cycle 6 (2002) of the National Survey of Family Growth. Vital and Health Statistics, Series 28. Number 28, Hyattsville, MD: . Full Text Options: PDF Export Options: RIS EndNote XML
2010
184 Postlethwaite, Debbie, Armstrong, Mary Anne, Hung, Yun-Yi, Shaber, Ruth . Pregnancy outcomes by pregnancy intention in a managed care setting. Maternal and Child Health Journal. 14, (2), 227-234. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Whitaker, A.K., Dude, A.M., Neustadt, A., Gilliam, M.L. . Correlates of use of long-acting reversible methods of contraception among adolescent and young adult women. Contraception. 81, (4), 299-303. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Haglund, K.A., Fehring, R.J. . The association of religiosity, sexual education, and parental factors with risky sexual behaviors among adolescents and young adults. Journal of Religion and Health. 49, (4), 460-472. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Bauer, G.R., Jairam, J.A., Baidoobonso, S.M. . Sexual health, risk behaviors, and substance use in heterosexual-identified women with female sex partners: 2002 US National Survey of Family Growth. Sexually Transmitted Diseases. 37, (9), 531-537. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Upson, K., Reed, S.D., Prager, S.W., Schiff, M.A. . Factors associated with contraceptive nonuse among US women ages 35-44 years at risk of unwanted pregnancy. Contraception. 81, (5), 427-434. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Trepka, M.J., Kim, S. Prevalence of human immunodeficiency virus testing and high-risk human immunodeficiency virus behavior among 18 to 22 year-old students and nonstudents: Results of the National Survey of Family Growth. Sexually Transmitted Diseases. 37, (10), 653-659. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Borrero, S., Moore, C., Creinin, M., Ibrahim, S. Low rates of vasectomy among minorities: A result of differential receipt of counseling?. American Journal of Men's Health. 4, (3), 243- 249. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
185 2010 Stockman, J.K., Campbell, J.C., Celentano, D.D. . Sexual violence and HIV risk behaviors among a nationally representative sample of heterosexual American women: The importance of sexual coercion. Journal of Acquired Immune Deficiency Syndromes. 53, (1), 136-143. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Aral, Sevgi O., Leichliter, Jami S. Non-monogamy: Risk factor for STI transmission and acquisition and determinant of STI spread in populations. Sexually Transmitted Infections. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Liddon, Nicole, Leichliter, Jami S., Habel, Melissa A., Aral, Sevgi, O. Divorce and sexual risk among U.S. women: Findings from the National Survey of Family Growth. Journal of Women's Health. 19, (11), 1-5. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Jeffries, William L., IV . HIV testing among among bisexual men in the United States. AIDS Education and Prevention. 22, (4), 356-370. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Reinhold, S. Reassessing the link between premarital cohabitation and marital instability. Demography. 47, (3), 719-733. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Eisenberg, Michael L., Lipshultz, Larry I. Estimating the number of vasectomies performed annually in the United States: Data from the National Survey of Family Growth. Journal of Urology. 184, (5), 2068-2072. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010 Leichliter, Jami S., Chesson, Harrell W., Sternberg, Maya, Aral, Sevgi O. The concentration of sexual behaviours in the USA: A closer examination of subpopulations. Sexually Transmitted Infections. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
2010
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2006 Anderson, John E., Mosher, William D., Chandra, Anjani . Measuring HIV Risk in the U.S. Population Aged 15-44: Results from Cycle 6 of the National Survey of Family Growth. Advance Data from Vital and Health Statistics. 377, Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics. Export Options: RIS EndNote XML
193 2005 National Center for Health Statistics . Health, United States, 2005: With Chartbook on Trends in the Health of Americans. Hyattsville, MD: United States Department of Health and Human Services, National Center for Health Statistics. Full Text Options: PDF Export Options: RIS EndNote XML
2004 Mosher, William D., Martinez, Gladys M., Chandra, Anjani, Abma, Joyce, Willson, Stephanie . Use of Contraception and Use of Family Planning Services in the United States: 1982-2002. Advance Data from Vital and Health Statistics. 350, Hyattsville, MD: United States Department of Health and Human Services, National Center for Health Statistics. Full Text Options: PDF Export Options: RIS EndNote XML
2004 Abma, Joyce C., Martinez, Gladys M., Mosher, William D., Dawson, B.S. . Teenagers in the United States: Sexual Activity, Contraceptive Use, and Childbearing, 2002. Data from the National Survey of Family Growth. Vital and Health Statistics, Series 23, Number 24. (PHS) 2005-1976, Hyattsville, MD: United States Department of Health and Human Services, National Center for Health Statistics. Full Text Options: PDF Export Options: RIS EndNote XML
2003 Harawa, Nina T., Greenland, Sander, Cochran, Susan D., Cunningham, William E., Visscher, Barbara . Do differences in relationship and partner attributes explain disparities in sexually transmitted disease among young white and black women?. Journal of Adolescent Health. 32, (3), 187-191. Full Text Options: DOI Worldcat Google Scholar Export Options: RIS EndNote XML
194 Appendix E. Codebook National Survey of Family Growth Cycle VI
Codebook is too large to attach. Full code book can be retrieved from: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4157
Appendix F. Codebook National Survey of Adoptive Families
Codebook is too large to attach. Full code book can be retrieved from: http://www.cdc.gov/nchs/slaits/nsap.htm
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