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School Mobility and Developmental Outcomes in Young Adulthood

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Abstract

School mobility has been shown to increase the risk of poor achievement, behavior problems, grade retention, and high school dropout. Using data over 25 years from the Chicago Longitudinal Study, we investigated the unique risk of school moves on a variety of young adult outcomes including educational attainment, occupational prestige, depression symptoms, and criminal arrests. We also investigated how the timing of school mobility, whether earlier or later in the academic career, may differentially predict these outcomes over and above associated risks. Results indicate that students who experience more school changes between kindergarten and 12th grade are less likely to complete high school on time, complete fewer years of school, attain lower levels of occupational prestige, experience more symptoms of depression, and are more likely to be arrested as adults. Furthermore, the number of school moves predicted outcomes above and beyond associated risks such as residential mobility and family poverty. When timing of school mobility was examined, results indicated more negative outcomes associated with moves later in the grade school career, particularly between 4th and 8th grades.
School Mobility and Developmental Outcomes in Young
Adulthood
Janette E. Herbers, Ph.D.,
Institute of Child Development, University of Minnesota
Arthur J. Reynolds, Ph.D., and
Institute of Child Development, University of Minnesota
Chin-Chih Chen, Ph.D.
School of Education, Virginia Commonwealth University
Abstract
School mobility has been shown to increase the risk of poor achievement, behavior problems,
grade retention, and high school drop-out. Using data over 25 years from the Chicago
Longitudinal Study, we investigated the unique risk of school moves on a variety of young adult
outcomes including educational attainment, occupational prestige, depression symptoms, and
criminal arrests. We also investigated how the timing of school mobility, whether earlier or later in
the academic career, may differentially predict these outcomes over and above associated risks.
Results indicate that students who experience more school changes between kindergarten and
twelfth grade are less likely to complete high school on time, complete fewer years of school,
attain lower levels of occupational prestige, are more likely to experience symptoms of depression,
and are more likely to be arrested as adults. Furthermore, the number of school moves predicted
above and beyond associated risks such as residential mobility and family poverty. When timing
of school mobility was examined, results indicated more negative outcomes associated with moves
later in the grade school career, particularly between fourth and eighth grade.
Investigations of school mobility have consistently demonstrated associations between the
number of times students change schools and a variety of negative developmental outcomes
(Gruman, Harachi, Abbott, Catalano, & Fleming, 2008; Heinlein & Shinn, 2000;
Mantzicopoulos & Knutson, 2001; Mehana & Reynolds, 2004; Pribesh & Downey, 1999;
Rumberger, 2003; Rumberger & Larson, 1998; South, Haynie, & Bose, 2007; Swanson &
Schneider, 1999; Temple & Reynolds, 1999). School mobility has been shown to increase
the risk of poor achievement, behavior problems, grade retention, and high school drop-out.
Because school mobility is a fairly common experience for many students, with
approximately 75%of students changing schools at least once between kindergarten and
eighth grade, it is important to understand how changing schools might impact students and
communities (Torre & Gwynne, 2009). Though studies examining school mobility have
increased over the past few decades, results can be difficult to interpret due to the
Correspondence concerning this article should be addressed to: Janette E. Herbers, Institute of Child Development, University of
Minnesota, 51 E. River Rd, Minneapolis, MN 55455. herbe064@umn.edu.
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Published in final edited form as:
Dev Psychopathol. 2013 May ; 25(2): 501–515. doi:10.1017/S0954579412001204.
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complexity of the problem, limitations of methodologies, and inconsistencies across studies
(Mehana & Reynolds, 2004; Reynolds, Chen, & Herbers, 2009). Furthermore, few
longitudinal studies have the information necessary to examine school mobility throughout
the school career in relation to adult outcomes. In the current study, we present findings
from the Chicago Longitudinal Study exploring the unique risk of school mobility on a
variety of young adult outcomes including educational attainment, occupational prestige,
depression symptoms, and criminal arrests. With 25 years of longitudinal data, we also
investigate how the timing of school mobility, whether earlier or later in the academic
career, may differentially predict these outcomes over and above associated risks.
School Mobility as a Risk Factor
Compared to other industrialized countries, the United States has one of the highest rates of
residential and school mobility (Long, 1992). According to the U.S. Census Bureau, one in
eight Americans changed residences between 2007 and 2008 (Census Bureau, 2009).
Roughly two thirds of residential moves necessitate school moves for children, meaning that
school mobility is a fairly common experience for many American children. Rumberger
(2003) reported that in one large study, 34% of fourth graders, 21% of eighth graders, and
10% of twelfth graders had changed schools at least once in the previous two years. More
recent studies have indicated that current rates may be even higher and increasing,
particularly among low income, minority students (National Research Council, 2010).
School mobility has been implicated as a risk factor for a variety of negative developmental
outcomes (Gruman et al., 2008; Mehana & Reynolds, 2004). Students who change schools
are more likely to demonstrate lower average academic achievement (Alexander et al., 1996;
Astone & McLanahan, 1994; Haveman, Wolfe, & Spaulding, 1991; Kerbow, 1996). They
are more likely to experience grade retention and more likely to drop out of school (Ou &
Reynolds, 2008; Reynolds, 1992; Rumberger & Larson, 1998; South et al., 2007; Temple &
Reynolds, 1999; Wood et al., 1993). Furthermore, students who change schools are at risk
for social problems and psychological difficulties including less social competence and low
self-esteem (Rumberger, 2003; South et al., 2007; Swanson & Schneider, 1999) as well as
truancy and suspension from school (Fantuzzo, Rouse, & LeBoeuf, 2009; Simpson &
Fowler, 1994) and other behavior problems (Leonard & Elias, 1993; Wood, Halfon,
Scarlata, Newacheck, & Nessim, 1993).
The link between school mobility and negative developmental outcomes has several likely
explanations. Most directly, changing schools outside of the normal structure of school
progression (i.e. not because the student’s current school does not provide the subsequent
grade, as in the progression from middle school to high school,) may present disruptions in
learning experiences as students are confronted with different curricula and different
expectations in new schools (Burkam, Lee, & Dwyer, 2009; Mehana & Reynolds, 2004).
Many schools differ in their climate and instructional environments, and adjusting to these
changes may interfere with student learning, particularly for students changing schools in
the middle of an academic year (Schwartz, Stiefel, & Chalico, 2009; Temple & Reynolds,
1999).
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Along with disruption in learning experiences, school mobility presents disruptions in social
relationships with peers, teachers, and other important adults. Theories of human and social
capital, such as those put forth by Coleman (1988), postulate that changing schools and
residences can negatively impact the community and social resources available to children
and their families by severing these ties. Indeed, researchers have found that high school
students who change schools are more likely to have smaller networks of friends and to
interact with peer groups who demonstrate lower achievement and less engagement in
school (South et al., 2007). This is consistent with the ecological model (Bronfenbrenner &
Morris, 1998) and school learning theories (Wang, Haertel, & Walberg, 1993) in which
changes in settings and instructional environments can be detrimental to student well-being.
Economic disadvantage and associated risks present another likely explanation for the link
between school mobility and poor outcomes. School mobility occurs more often among
students who also experience a variety of other potent risk factors, including poverty or low
socioeconomic status, homelessness, ethnic minority status, residing in low-income, single
parent homes, less parental involvement, residential instability, and placement in special
education (Fantuzzo et al., 2009; Heinlein & Shinn, 2000; Mehana & Reynolds, 2004;
Obradovic et al., 2009; Ou & Reynolds, 2008; Pribesh & Downey, 1999; Rumberger, 2003;
Rumberger & Larson, 1998; South et al., 2007). School mobility is much more common in
urban schools, which tend to serve higher rates of low-income, high risk students (National
Research Council, 2010; Temple & Reynolds, 1999). Students of such high risk
backgrounds are less likely to start school ready to learn and more likely to fall behind their
advantaged peers (Burchinal, Roberts, Zeisel, & Rowley, 2008). It is plausible, then, that
risks associated with school mobility actually arise due to these other related disadvantages
(Mantzicopoulos & Knutson, 2001). On the contrary, quite a few investigators have found
that school mobility predicts academic and other problems over and above substantial
effects of family risk, socioeconomic status, and pre-mobility achievement and adjustment
(Alexander et al., 1996; Lee & Burkham, 2002; Mehana & Reynolds, 2004; Pribesh &
Downey, 1999; Rumberger & Larson, 1998; Temple & Reynolds, 1999).
Several studies have addressed the potential for selection bias by including not only family
demographics but also school achievement and performance prior to mobility. In the
Beginning School Study, Alexander et al. (1996) found that about half of the observed
differences in fifth grade achievement test scores between mobile and nonmobile Baltimore
students were explained by covariates including prior achievement in first grade. The
significant difference between groups in math achievement, however, remained regardless
of model specification. Similarly, using data from the Chicago Longitudinal Study, Temple
and Reynolds (1999) reported nearly identical findings in seventh grade achievement
between mobile and nonmobile students. Even after accounting for family background and
achievement at the end of kindergarten, mobile students had significantly lower reading and
math achievement tests scores in seventh grade.
Previous studies also indicate that frequent school moves are most associated with adverse
outcomes including lower school achievement and higher rates of school dropout (Fantuzzo
et al., 2009; Mehana & Reynolds, 2004; Reynolds et al., 2009). In the Chicago study, for
example, youth with three or more moves had significantly lower school achievement and
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lower rates of dropout than was predicted from a model assuming a linear association
(Temple & Reynolds, 1999; Ou & Reynolds, 2008). Similarly, Hanushek et al. (2004)
reported in a Texas sample of elementary school students that multiple moves was most
associated with lower achievement growth. Because most longitudinal studies examining
mobility do not have data extending more than three years beyond the end of schooling
(National Research Council, 2010), the extent to which multiple and more frequent moves
are associated with adult outcomes in different domains has not yet been investigated.
Further Challenges in the Study of School Mobility
The predictors and impacts of student mobility are complex. Not only is mobility much
more common among higher risk students, but there are a variety of reasons for different
instances of school mobility, and these reasons also differ depending on child and family
characteristics. Many students change schools because their families change residence
(Rumberger, 2003). In these instances, disruptions associated with the residential mobility
rather than the school changes, or both in combination, may account for risks to academic
and behavioral functioning. When students and families elect to change schools for reasons
of personal preference, however, the change may present favorable opportunities. Population
studies of impacts of mobility, which include non-minority, middle-class individuals who
are more likely to change schools due to improvements in lifestyle and financial
circumstances or better academic opportunities, often do not show negative effects.
However, studies that focus on high risk samples of predominantly low SES, ethnic minority
students have demonstrated robust evidence that the school changes themselves confer
additional risk (National Research Council, 2010). Among these families, school changes
are almost exclusively accounted for by reasons of safety or financial necessity and are
unlikely to result in placement in higher quality schools (Schafft, 2009). Perhaps because
single instances of school mobility can occur for such a variety of reasons, results of many
studies have indicated that frequent school mobility, often defined as three or more moves in
a specified time period, is much more predictive of negative outcomes than single moves
(Gruman et al., 2008; Heinlein & Shinn, 2000; Mehana & Reynolds, 2004; National
Research Council, 2010; Temple & Reynolds, 1999).
Children in the early elementary years have higher rates of school and residential mobility
than middle school children, and mobility rates during high school are lower still
(Rumberger, 2003; Schachter, 2004). In a cohort of mobile 6thgraders, 40% reported
changing schools for school-related reasons such as safety and dissatisfaction, 28% reported
changing schools due to a change in residence, and 30% reported changing schools for a
combination of school-related and residence-related reasons (Kerbow, 1996). In a high
school sample, the majority of mobile students reported moving because their families
changed residences, with fewer reporting a personal choice to move and very few indicating
that they changed schools because they were “asked to leave” (Rumberger, 2003;
Rumberger & Larson, 1998). With rates of school mobility and reasons for school changes
differing by age, it is important to consider whether school changes at different times in the
academic career differentially predict developmental outcomes.
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Some researchers have suggested that school mobility occurring in the early elementary
grades may be most detrimental to student well-being (Astone & McLanahan, 1994;
Heinlein & Shinn, 2000; Mehana & Reynolds, 2004). It is during these early years that
students are acquiring the academic building blocks and foundations for their future
learning. On the other hand, some researchers have found more negative outcomes for
students moving later in their school careers (Pribesh & Downey, 1999; Rumberger &
Larson, 1998; Swanson & Schneider, 1999). For older students, it may be more difficult to
catch up academically as curricula become more complex and vary more across schools.
Also, peer relationships increase in salience in middle school such that discontinuities in
these relationships due to mobility could have a greater impact on school engagement
(Dauber, et al., 1996; Rumberger, 2003; South et al., 2007). Furthermore, evidence for the
particular impact of frequent mobility suggests that mobility later in the school career may
show greater detriments as total school changes accumulate over time, requiring highly
mobile students to adapt repeatedly to disruptions and new academic and social
environments (Gruman et al., 2008; Mehana & Reynolds, 2004; Temple & Reynolds, 1999).
Given the complexity of issues related to school mobility, researchers must consider a
variety of factors when addressing the impact of school changes on student outcomes. In a
meta-analysis of school mobility studies, Mehana and Reynolds (2004) found substantial
differences in effect sizes for the impact of mobility on academic achievement depending on
the covariates and sample characteristics. Perhaps most importantly, studies of school
mobility must control for student socioeconomic status and family risk and adversity
(Heinlein & Shinn, 2000; Stoneman, Brody, Churchill, & Winn, 1999). In addition, studies
should consider residential mobility (Pribesh & Downey, 1999; Rumberger & Larson, 1998;
Swanson & Schneider, 1999), achievement and adjustment prior to mobility experiences
(Mantzicopoulos & Knutson, 2001; Reynolds et al., 2009; Rumberger, 2003), and the
cumulative effects of frequent changes throughout the school career and at different times
(Temple & Reynolds, 2000).
Less is known about impacts of school mobility beyond academic achievement, though
certainly lower achievement has been associated with risk for a variety of negative outcomes
in adulthood such as low attainment, less prestigious and lucrative jobs, poor mental health,
and more involvement in crime (Farrington, 2005; Heckman, 2006). Competence in
educational attainment, work, and appropriate conduct are important development tasks of
young adulthood that are influenced by developmental history and predictive of later well-
being (Masten et al., 2004; Schulenberg, Bryant, & O’Malley, 2004). With achievement as a
likely mediator, it is reasonable to expect that frequent school changes will also be
associated with negative outcomes beyond the school years. Which outcomes are most
affected and whether these impacts differ by mobility timing, however, has not been
explored extensively in the literature.
Previous studies on mobility in the Chicago Longitudinal Study have demonstrated
associations between risk factors and school mobility in the elementary grades (Reynolds &
Wolfe, 1999) and also that early school mobility mediated the effects of preschool
intervention and parent involvement on achievement in sixth grade, child abuse and neglect,
and high school completion (Reynolds, Mavrogenes, Bezruczko, & Hagemann, 1996;
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Reynolds & Robertson, 2003; Reynolds et al., 2004). Furthermore, school mobility from
first to sixth grades has been found to predict grade retention, achievement in first grade,
achievement in 7thgrade, and high school dropout (McCoy &Reynolds, 1999; Reynolds,
1992; Temple & Reynolds, 1999; Temple et al., 2000). Although these studies included a
comprehensive set of predictors of mobility, including pre-mobility achievement, they did
not include residential moves. In addition, the studies have emphasized mobility up to
middle school and have not included school mobility measured throughout the school career
(from kindergarten through twelfth grade). Finally, as with almost all previous studies in the
field, school achievement and dropout during K-12 education were the primary outcomes
assessed. Links to adult life-course outcomes such as educational attainment, economic
well-being, and social competence have not been investigated.
The Current Study
We are extending this work to explore the impacts of school mobility on outcomes of young
adulthood and to compare these impacts during the different academic and developmental
periods of the early elementary years, middle school years, and high school years. Two
major questions are addressed:
1. Are the number of school moves from kindergarten to twelfth grade associated with
indicators of adult well-being including educational attainment, occupational
prestige, depressive symptoms, and criminal arrests above and beyond other child
and family risk factors?
2. Do the links between the number of school moves and adult well-being vary by the
timing of the moves (by fourth grade, fourth to eighth grade, and during high
school)?
We examine 25 years of prospective longitudinal data from the Chicago Longitudinal Study
to address two broad hypotheses regarding the long-term unique impacts of school changes
and possible differences with respect to the timing of mobility. With yearly records of
school changes from kindergarten through twelfth grade as well as detailed information on
family background, socio-demographic risk, residential mobility, social/emotional maturity,
kindergarten academic achievement, child abuse and neglect, special education, grade
retention, and juvenile delinquency, we can apply a rigorous test of the effects of school
mobility throughout the elementary and high school years, controlling for associated risks.
We hypothesize that the number of school moves will predict reduced likelihood of on-time
graduation from high school, lower educational attainment, less occupational prestige, more
symptoms of depression, and greater likelihood of involvement in adult crime, above and
beyond the impact of socio-demographic risk, residential mobility, and early academic
achievement. We expect to find evidence of threshold effects, such that students with two or
more moves will show significantly greater impairment than those with one or fewer moves.
Furthermore, we hypothesize that school moves later in the academic career, such as during
middle school and high school when peer relationships are more salient and curricula more
complex, will have greater unique significance for young adult outcomes than school moves
early in elementary school.
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Method
Sample and Design
Data are from the Chicago Longitudinal Study (CLS, 2005), an on-going prospective
investigation of the life course development of 1,539 low-income minority children (93%
African American) born in 1979 or 1980 who grew up in high-poverty neighborhoods and
attended preschool or kindergarten programs in the Chicago Public Schools beginning in
1983. All participants attended kindergarten in 1985–1986. The original sample included the
entire cohort of 989 children who entered the Child-Parent Center (CPC) education program
in preschool and completed kindergarten in 20 centers, and 550 children who participated in
alternative kindergarten programs in Chicago schools without CPC preschool experience.
The se latter children attended 5 randomly selected schools (from 27) participating in all-day
kindergarten as part of a city-wide school intervention project. Although the entire sample
participated in early intervention services, it is generally representative of children at risk of
school failure in Chicago (Reynolds, 2000).
The study sample for this report are the 1,410 of the original 1,539 students who had at least
four years of active status in the Chicago Public Schools between kindergarten and twelfth
grade. Data comparisons showed no significant differences between the school mobility
sample of 1,410 and the original sample of 1,539 (see Table 1). Of these 1,410 students with
at least four years of active status, 1,316 (93.3%) were African American, 94 (6.7%) were
Hispanic, and 717 (50.1%) were female. 912 (64.7%) participated in some part of the CPC
program, either in preschool or as part of the follow-up intervention that took place between
first and third grade for some students. Data for the CLS participants have been regularly
collected from a wide variety of sources from birth up to early adulthood, including birth
records, Chicago Public School administrative data, data from the Illinois Board of Higher
Education, the Illinois Department of Child and Family Services, the Illinois Department of
Health and Human Services, Illinois Department of Public Health, Cook County Court and
Circuit Court, and the Illinois Department of Employment Security as well as data from
teacher, parent, and participant surveys in childhood and early adulthood.
Administrative school data collected every year includes standardized test scores in reading
and math as well as school unit numbers and district numbers. Parent surveys were
conducted when students were in second, fourth, and twelfth grade. Student surveys took
place each year during third through sixth grade and again in tenth grade. Teacher surveys
were conducted each year for kindergarten through seventh grade. School, neighborhood,
and census data were gathered during fifth grade, eighth grade, tenth grade, and twelfth
grade. Information from court records on delinquency and abuse/neglect were first obtained
when students were 19 years old, and employment data was first collected at ages 23–24. In
conjunction with on-going tracking efforts, the study has maintained high rates of sample
recovery for mobility, outcome, and explanatory variables.
Outcome Measures
Highest grade completed—Highest grade completed by participants is a continuous
variable which indicates the highest grade of educational completion in the secondary school
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system as well as GED completion and college attendance. The range of this variable is
seven to 17. GED completion is coded 12, and college attendance is coded according to the
credits earned by each participant. Every 30 credits earned by the participants add up to one
year of educational attainment. Data were collected from administrative school systems and
were supplemented with information from parent interviews.
On-time graduation—Another indicator of educational attainment is on-time high school
graduation. On-time high school graduation is a dichotomous variable which indicates
whether the participants completed secondary education on time (i.e., during or prior to
1998) based on the Chicago Public Schools administrative school system records.
Occupational prestige—The measure of occupational prestige was based on data from
the adult survey in age 22–24 and supplemented with administrative data from the state or
county. Self-reported information regarding current occupation and previous two positions
was coded using a 9-point scale based on well-known ratings of socioeconomic position, the
Barratt Simplified Measure of Social Status and the Nakao Treas Prestige Scores (Barratt,
2005; Davis, Smith, Hodge, Nakao, & Treas, 1991; Hollingshead, 1975)such that scores of
one correspond to generally unskilled job classifications including laborers, scores of five
correspond to moderate levels of job skill requiring postsecondary training, and scores of
nine correspond to high levels of job skills with advanced education and high earnings,
including lawyers and doctors. For participants who did not complete the adult survey,
occupational prestige was estimated based on other administrative data when possible using
the following guidelines: incarceration (1), 4 year degree (5), average annual income <
9.000 (2), high school dropout (2).
Adult arrest—Adult arrest is a dichotomous variable that measured adult arrest by age 26
through administrative reports of criminal records obtained from the county, state, and
federal level. All participants with any adult arrest by age 26 were coded one. Participants
were coded zero if they were not arrested by that point as an adult. Administrative county-
level arrest data were gathered from criminal court records in Cook County, Illinois. State-
level arrest data were obtained primarily through the Illinois Department of Corrections,
other mid-western states (Wisconsin, Iowa, and Minnesota), and the Department of
Corrections system from nationwide states. Federal-level records were collected from the
Federal Bureau of Prisons.
Felony arrest—In order to examine the impact of school mobility on the severity of adult
arrest, we also included adult felony arrest in the analysis. Adult felony arrest is a
dichotomous variable that indicates whether a participant had a felony arrest as an adult.
Individuals with any felony arrest are coded one; individuals with no felony arrest are coded
zero. Data were collected for this measure using the same methods described previously for
adult arrest.
Depression symptoms—Reported symptoms of depression were based on five items in
the adult survey at age 22–24. Participants responded to the five items modified from the
depression subscale of the Brief Symptom Inventory (Derogatis, 1975) indicating how often
in the past month they felt depressed, lonely, helpless, sad, or as if life wasn’t worth living.
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The reliability coefficient of these five items is .84. Composite scores of depression
symptoms were calculated as the number of depression symptoms participants reported
having experienced a few times a week or more in the past month, ranging from zero to five.
Mobility
School mobility—The school mobility variables representing the number of years in
which students changed schools from kindergarten to twelfth grade (school mobility k-12),
kindergarten to fourth grade (school mobility k-4), fourth to eighth grade (school mobility
4–8), and eighth to twelfth grade (school mobility 8–12) were used to examine the impact of
frequent school moves and timing of school mobility. The three time periods of k-4, 4–8,
and 8–12 were selected to represent developmentally salient intervals of middle childhood
(ages 5–10), early adolescence (ages 11–14), and high school or mid-to-late adolescence
(ages 15–18). For threshold analyses, we used dichotomized variables to indicate thresholds
of mobility, defined as two or more moves, three or more moves, and four or more moves
during the kindergarten to twelfth grade period. These counts were created by comparing the
school unit numbers of subsequent years for each student based on school administrative
system records. When school unit numbers between two grades were different, a move was
counted for that school year. When data were missing, indicating that students were not
enrolled or were inactive in Chicago Public Schools for one or more years, one move was
counted for the entire duration of missing years. Available information from school records
cannot account for more than one school move per year, nor can the information specify
whether the move occurred between school years or during the school year. For these
reasons, the school mobility measures are likely to be underestimates of school mobility for
some students. We consider this measure an index of mobility rather than a true count.
Students with less than four years of active status in the Chicago Public Schools were not
included in these analyses as they were judged to have too few years of school record data to
create valid school mobility scores. Since this cutoff choice was somewhat arbitrary, we also
tested our results with cutoffs of three and five years of active status and found no difference
in results. Because we were interested in non-structured school moves rather than moves
that occur naturally based on school structure, one move was subtracted between eighth and
ninth grade for each student who attended a traditional ninth to twelfth grade high school
(90% of students). For students who attended schools with structures other than kindergarten
through eighth grade prior to high school (24% of students), appropriate corrections were
made by subtracting one move between years based on their specific school structures.
Residential mobility—The residential mobility variable indicates the number of years in
which students had residential moves from kindergarten to twelfth grade. Data on residential
mobility was taken from the adult survey in ages 22–24 and parent survey in eleventh grade.
This count variable was created by using information from the item “how many times did
you move from kindergarten through age 18?” reported by the participants. Missing
information in this item was then supplemented with the item “how many times have you
and this child moved to another home since this child has been in kindergarten?” from the
parent survey. The distribution of residential moves was dramatically skewed right, thus the
variable was log-transformed to conform to normality assumptions for the purposes of data
imputation (see below). The log-transformed variable was used in all regression analyses.
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Control Variables
Early family risk—The index of early family risk was created based on a count of eight
different socio-demographic risk factors from birth to age three, including the following:
mother was less than 18 when child was born, mother did not complete high school, single
parent, four or more children in the household, family attendance in the public assistance
programs (i.e., AFDC), mother not employed, eligible for free lunch, and 60% or greater
poverty in school attendance area.
Prior academic achievement—Academic achievement prior to student mobility was
measured with word analysis subscale scores from the Iowa Tests of Basic Skills (Level 5
Form 7), which was administered to students in October of their kindergarten year. There
were 35 items in the word analysis subscale that assessed pre -literacy skills (e.g., letter-
sound correspondence and word attack skills). The norms were based on 1978, with high
internal consistency reliability of .87 (Hieronymus, Lindquist, & Hoover, 1982).
Social/emotional maturity—Child social/emotional maturity in first grade was measured
based on teacher response to the following six survey items, each rated on a five point scale:
came to my class ready to learn, completes work according to instructions, complies with
classroom rules, displays confidence in approaching learning tasks, participates in group
discussions, and works and plays well with others. The Cronbach’s alpha for the six items
was .79, and the sum of scores for all available participants ranged from 6 to 30.
Child abuse and neglect—The child abuse and neglect variable was created from child
protective service records and indicates whether each student ever experienced substantiated
child abuse or neglect between kindergarten and age 18. The data included petitions to the
juvenile court and referrals to the Child Protection Division of the Illinois Department of
Children and Family Services.
Grade retention—Grade retention was measured as a dichotomous variable that indicates
whether each student was ever retained between kindergarten and eighth grade based on
school records.
Special education—The special education variable was created from school records in
the Chicago Public Schools and indicates whether each student was ever enrolled in special
education placement between kindergarten and twelfth grade.
Juvenile Delinquency—Information on juvenile delinquency was obtained from official
court reports of petitions filed when participants were ages seven through 17. Petitions
indicated both formal and informal arrests into the juvenile justice system. Formal petitions
involved a juvenile court judge, whereas informal petitions often involved alternative social
services for children and families. Juvenile delinquency was coded as a dichotomous
variable, with a score of one indicating any formal or informal petition for arrest.
Descriptive statistics and correlations of all control variables and outcome variables with
school mobility measures are presented in Appendix A.
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Missing Data
Complete data were available for the following measures: school mobility k-4, school
mobility 4–8, school mobility 8–12, school mobility k-12, gender, ethnicity, CPC preschool
participation, CPC grade school participation, risk, child abuse/neglect, special education
placement, and juvenile delinquency. Rates of missing data for outcome variables were as
follows: 8% highest grade completed, 5% on-time graduation, 11% occupational prestige,
24% depression symptoms, 5% adult arrests, 5% adult felony arrests. Among control
variables, rates of missing data were 0.3% for kindergarten achievement, 3% for grade
retention, 15% for social/emotional maturity, and 17% for residential mobility. Social/
emotional maturity, residential mobility and depression symptoms had higher rates of
missingness because information from these variables was drawn exclusively from survey
data, for which there were lower levels of follow-up than administrative data sources.
Missing data were presumed to meet the assumptions of missing at random (MAR), which
means that missingness was related to other study variables that were included in imputation
procedures (Fitzmaurice, Laird, & Ware, 2004, ch. 14).
Missing data were imputed twenty times using PROC MI in SAS version 8.1 with the
recommended expectation-maximization (EM) algorithm and Markov chain Monte Carlo
(MCMC) method (Schafer & Graham, 2002). Analyses were run on each of the twenty
datasets with results combined according to Rubin’s rules (Rubin, 1987) using PROC
MIANALYZE. The pattern of significant findings did not differ between analyses based on
imputed data and results of the same analyses performed on the original, non-imputed data
using listwise deletion procedures.
Data Analysis
To test cumulative effects of school mobility between kindergarten and twelfth grade for
each of the six young adult outcome variables, we ran separate regression models with two
hierarchical steps. In the first step, outcomes were predicted by only the mobility variables,
school mobility k-12 and residential mobility. The second step included covariates gender,
ethnicity, CPC preschool, CPC grade school, early family risk, prior achievement, social/
emotional maturity, child abuse and neglect, grade retention, special education, and juvenile
delinquency to determine whether mobility predicted outcomes beyond its association with
these prior and concurrent risks. Linear regressions were run on the three continuous
outcomes of highest grade completed, occupational prestige, and depression symptoms.
Binary logistic regressions were run for the dichotomous outcomes of on-time graduation,
adult arrest, and felony arrest.
In order to test for differential impacts of school mobility based on timing, we ran similar
regression models for each of the outcomes, with mobility variables entered first and the
same covariates included in the second step. Based on extensive literature in support of
threshold effects and the distribution of observed school moves between k-4, 4–8, and 8–12
(presented in the results section and Table 2), we utilized dummy codes to reflect the
following: 1 school move k-4, 2 or more school moves k-4, 1 school move 4–8, 2 or more
school moves 4–8, and any school moves 8–12. These five dummy codes were added
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simultaneously with each other and with residential mobility in the first step of the
regression models.
Results
Descriptive Statistics for Mobility
Percentages of students who changed schools between kindergarten and twelfth grade,
kindergarten to fourth grade, fourth to eighth grade, and eighth to twelfth grade are
presented in Table 2. Based on the index of yearly changes in school unit numbers, students
experienced between zero and eight unstructured school moves between kindergarten and
twelfth grade. More than half of the students (59%) experienced one or fewer unstructured
moves, and 95% of students experienced four or fewer unstructured moves between
kindergarten and twelfth grade. Between kindergarten and fourth grade, the number of
unstructured moves ranged from zero to four with nearly half of students (47%)
experiencing no moves, 34% experiencing one move, and the remaining 19% experiencing
two or more unstructured moves. Between fourth and eighth grade, number of unstructured
moves also ranged from zero to four. The majority of students (59%) did not experience any
unstructured moves, 26% experienced one unstructured move, and the remaining 15%
experienced two or more moves. In the high school years between eighth and twelfth grade,
number of moves ranged from zero to three. The vast majority of students (81%)
experienced no moves, 16% experienced one move, and only 3% experienced two or three
moves.
Also presented in Table 2 are rates of residential mobility between kindergarten and twelfth
grade based on available participant and parent report for 1,176 students (for whom survey
data were available). According to these reports, 18% of students did not change residence
at all between kindergarten and twelfth grade. Approximately half (52%) of students
changed residence two or fewer times, with 34% changing residences between three and five
times, 9% changing residences between six and nine times, and 5% changing residences
nine or more times.
Regression models for young adult outcomes
Results of the final linear and binary logistic models for cumulative school mobility k-12 are
presented in Table 3. Coefficients from the final models for mobility variables separated by
four time periods (k-4, 4–8, 8–12) are presented in Table 4.
Highest grade completed—In the first step of the model predicting highest grade
completed, both school mobility k-12 and residential moves emerged as significant
predictors (B = −.19, p < .01 and B = −.13, p < .05, respectively) and together accounted for
4% of the variance in highest grade completed. When all control variables were included in
the second step, however, school moves k-12 was not a significant predictor of highest grade
completed. Because some of these factors may mediate the effects of moving, this result
may be conservative. The final model predicted 23% of the variance in highest grade
completed (F = 30.3, p < .001). In the model with school moves measured during the three
different time periods, 2 or more moves4–8 and any moves 8–12 were significant predictors
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in the first step of the model (B = −.57, p < .01 and B = −.53, p < .01, respectively), and
remained significant in the final model with all control variables included (B = −..26, p < .05
and B = −.22, p < .05, respectively). These results indicate that on average, students who
experienced two or more unstructured school moves between fourth and eighth grade or any
unstructured school moves between eighth and twelfth grade ultimately completed about a
quarter of one year less of education than those who did not.
On-time graduation—School moves k-12 emerged as a significant predictor of on-time
graduation in both steps of the model, with residential moves only (OR = .71, CI = [.64, .77],
p < .01), and with all control variables included (OR = .88, CI = [.70, 1.11], p < .05). These
findings indicate that each additional move is associated with a 12 to 19 percent reduction in
the log-odds of on-time high school graduation (1.0 – 0.88 or 0.81) controlling for other
factors. The final model predicted 42% of the variance in on-time graduation. In the model
with mobility measured during different time periods, however, only 2 or more moves 4–8
emerged as a significant predictor of on-time graduation (OR = .56, CI =[.36, .89], p < .05),
indicating a threshold effect for multiple moves during the middle grades rather than a
consistent, continuous effect per individual move across all grades.
Occupational prestige—The pattern of results for the model predicting occupational
prestige was similar to that for highest grade completed, though residential moves was not a
significant predictor of occupational prestige even in the first step of the model. School
mobility k-12 was significant in the first (B = −.14, p < .01) but not the final step when all
control variables were included. The first step accounted for 3% of the variance (F = 19.0, p
< .001) while the final model accounted for 17% of the variance in occupational prestige (F
= 18.6, p < .001). In the mobility timing model, 2 or more moves 4–8 emerged as a
significant predictor of occupational prestige even when controlling for all other covariates
(B = −.33, p < .01). On average, students with two or more moves between fourth and eighth
grade had significantly less prestigious and lucrative jobs (about 1/3 of a point lower on an 8
point scale).
Adult arrest—School mobility k-12 significantly predicted adult arrest in the first (OR =
1.18, CI = [1.10, 1.28], p < .01) and final (OR = 1.15, CI = [1.03, 1.27], p < .01) steps of the
model. This finding indicates that each additional school move is associated with a 15
percent increase in the log-odds of adult arrest (controlling for other factors). Residential
moves did not emerge as a significant predictor. The first step predicted 3% of the variance,
and the final model predicted 40% of the variance in adult arrest. In the model considering
school mobility timing, none of the thresholds in separate time periods of school mobility
emerged as independently significant.
Felony arrest—The pattern of results was quite different when school moves k-12 and
residential moves predicted any felony arrest. School moves k-12 did not emerge as a
significant predictor of felony arrest even in the first step of the model. Residential moves
was significant in the first (OR = 1.76, CI = [1.40, 2.22], p < .01) and final step of the model
(OR = 1.64, CI = [1.25, 2.17], p < .01). The first step predicted 5% of the variance while the
final model predicted 44% of the variance in felony arrest. In the model that considered
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school mobility during different time periods, none of the thresholds in specific periods of
mobility emerged as significant predictors.
Depression symptoms—School moves k-12 and residential moves both emerged as
significant predictors of depression symptoms in both steps of the regression model (B = .04,
p < 05 and B = .10, p < .05, respectively, in the final model). The first step accounted for 2%
of the variance (F = 14.3, p < .001) while the final model with mobility and all control
variables accounted for 6% of the variance in depression symptoms (F = 6.73, p < .001). In
the model considering thresholds and timing of school mobility, 1 school move 4–8 and 2 or
more moves 4–8 both emerged as significant predictors in the final model (B = .18, p < .01
and B = .37, p < .01, respectively). These results indicate that any unstructured school
moves between fourth and eighth grade are associated with more depression symptoms in
adulthood, with a particularly large effect (37% of a point on a five point scale) for students
experiencing two or more unstructured moves during that time.
Additional Analyses
Based on evidence for threshold effects of school mobility in the literature (Mehana &
Reynolds, 2004; Ou & Reynolds, 2008; Reynolds et al., 2009; Temple & Reynolds, 1999)
and in our timing analyses, we also ran models for all outcomes using dummy coded
mobility predictors of1 move k-12, 2 moves k-12, 3 moves k-12, and 4 or more moves k-12.
In the models predicting on-time graduation, depression symptoms and including all
covariates, 4 or more moves k-12 emerged as significant (OR = .52 [.29, .91], p < .05 and B
= .24, p < .05, respectively). For adult arrest, 2 moves, 3 moves, and 4 or more moves were
all significant predictors, (ORs = 1.90 [1.24, 2.89], 1.69 [1.02, 2.81], and 1.91 [1.13, 3.24]
respectively, all ps <. 05), indicating a more linear effect of cumulative school moves for
this outcome. For highest grade completed, occupational prestige, and felony arrest,
however, effects did not emerge for thresholds of school mobility k-12, despite the
thresholds that were evident in grades 4–8 for highest grade completed and occupational
prestige. These results suggest that threshold analyses may function best when limited to
shorter time periods, and also that school mobility may show threshold effects for some
outcomes and more continuous, linear effects for others. Overall, there is clear evidence that
frequent school moves are more detrimental to student well-being than single moves.
We also investigated whether the inclusion of additional covariates altered the magnitude of
effects of the mobility measures. They did not, both in terms of effect size and the pattern of
overall findings. For example, the addition of a second pre-mobility achievement measure at
the beginning of kindergarten, child welfare services, and changes in family risk status had
little effect on the estimates for the number of moves, timing of moves, and threshold levels.
Results of these models are included in Appendix C.
Discussion
Results of the current study indicate that although school mobility is more likely to occur in
the presence of a variety of other risk factors, school mobility itself predicts unique variance
in several important outcomes of young adulthood when these associated risks are
controlled. In this sample, school mobility was significantly correlated with residential
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mobility, gender, ethnicity, early family risk, poor achievement in kindergarten, lower
social/emotional maturity in first grade, child abuse and neglect, special education
placement, grade retention, and juvenile delinquency. With these associated risks controlled,
a count of yearly school moves between kindergarten and twelfth grade predicted unique
variance in young adult outcomes including the number of depression symptoms at the age
of 23, having graduated high school on time, and having ever been arrested as an adult.
There were two young adult outcomes, highest grade completed and occupational prestige,
for which a count of school move years in kindergarten through twelfth grade did not
emerge as a significant unique predictor. When thresholds for school mobility during
different time periods of schooling were entered as separate independent variables, however,
school moves during particular times did significantly predict both educational attainment
and occupational prestige. These results are discussed in the next paragraph. Interestingly,
residential mobility emerged as a significant predictor for depression symptoms and felony
arrest even in the full model with juvenile delinquency and all other control variables, when
the count of school mobility k-12 was not a significant predictor. The finding that residential
mobility was only significant in the final models for depression symptoms and felony arrest,
and not in the final models for adult arrest or other outcomes, underscores the importance of
considering different types of mobility and suggests that for some outcomes, perhaps those
reflecting more severe disturbance, residential mobility may be a more potent factor than
school mobility.
Consistent with our hypotheses related to timing of school mobility, we found greater
detriments in young adult outcomes related to multiple school moves occurring later in the
school career, particularly in the middle school years between fourth and eighth grade.
School moves between fourth and eighth grade were most significant for predicting
outcomes of highest grade completed, depression symptoms, occupational prestige, and on-
time graduation. Thus while a count of school moves throughout kindergarten to twelfth
grade has predictive significance for some outcomes, the particular importance of school
changes during certain periods of time, particularly in middle school or later, may be
obscured when all moves are considered together.
Unstructured school changes during different periods of the academic career likely represent
different issues. In the early elementary years, moves are likely driven by the needs or
decisions of the parents and family rather than by child behavior. A young child may change
schools because her family is experiencing financial difficulties or dangerous and stressful
circumstances that require changes in residence either for the family as a whole or for the
child. On the other hand, a young child may change schools because her parents are
financially able to move to a better neighborhood or elect to send her to a private, magnet, or
other possibly higher quality school. Only in rare instances does a young child have to
change schools due to behavior problems resulting in expulsion. In the middle school and
high schools years, however, changes related to child behavior are much more likely.
Suspensions, expulsions, and issues related to truancy are more prevalent in older children
and tend to co-occur with a variety of risk factors including poverty and family adversity.
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While it is informative to investigate long-term outcomes and effects of differences in the
timing of mobility, it is important to consider that mobility may occur for different reasons
and with different correlates at different developmental periods and among different
populations. Findings presented here reflect circumstances for low SES, minority students, a
population that is much more likely to change schools and residence for reasons of necessity
rather than preference (Schafft, 2009). Unstructured school changes among mid and high
SES samples are more likely to be benign or even beneficial to student outcomes,
particularly because they are more likely to occur for positive reasons such as improvements
in family financial situation and access to higher quality schools.
In our data, the association between school mobility in kindergarten through fourth grade
and negative outcomes of young adulthood were found to be accounted for by other
variables such as family risk, child social/emotional maturity, kindergarten achievement, and
residential mobility. Young children may be more vulnerable to changes within the family
system and less impacted by a change of school, perhaps because curricula in the early
elementary years is more consistent across schools and because family relationships have
more salience than peer relationships when children are young. During middle school,
however, school mobility introduced a unique impact beyond its association with other risk
factors. This may occur because of the increasing importance of peer relationships for
school engagement and competent development in general. Changing schools between
fourth and eighth grade disrupts these developing peer relationships and requires adaptation
to new social situations in new schools, in addition to the academic challenges of potentially
changing curricula and discontinuous learning experiences. In the high school years, school
changes may reflect involvement in juvenile delinquency, or may support disengagement
from school and association with delinquent peers.
Changing schools frequently during students’ school careers has the potential to negatively
impact not only their academic achievement, as has been demonstrated in previous research
(Gruman et al., 2008; Heinlein & Shinn, 2000; Mehana & Reynolds, 2004; Mantzicopoulos
& Knutson, 2001; Pribesh & Downey, 1999; Rumberger & Larson, 1998; Rumberger, 2003;
South et al., 2007; Swanson & Schneider, 1999; Temple & Reynolds, 1999), but also to
extend beyond school to developmental outcomes of young adulthood. While results of
some studies have indicated particular importance of early mobility (Astone & McLanahan,
1994; Heinlein & Shinn, 2000; Mehana & Reynolds, 2004), our results indicate that school
changes occurring in middle school and high school relate more strongly to negative
outcomes in young adulthood. It is likely that disruptions caused by mobility at different
times have different consequences. Early in the school career, students are learning
fundamental academic skills. Later, however, they may encounter greater differences in
curricula across schools as academic concepts become more complex. Additionally,
negotiating peer relationships is a central developmental task of middle childhood and
adolescence (Parker, Rubin, Erath, Woislawowicz, & Buskirk, 2006), and school changes
that disrupt these relationships likely impact student school engagement, behavior, and
motivation to succeed academically.
Because mobility tends to occur in the context of other risk factors, the actual consequences
of changing schools has been difficult to demonstrate. However, it is becoming clear that
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efforts of policy-makers and schools to support academic achievement and positive
development of high-risk students should include attention to issues of school mobility.
Because school changes in the middle school and high school years appear particularly
detrimental, efforts can focus on encouraging school stability with students and families as
well as providing opportunities for social support and additional educational support for
students who do change schools. Furthermore, students who have experienced many school
changes may require more support and guidance beyond high school as they face
developmental tasks of young adulthood including seeking higher education, entering the
work force, and maintaining appropriate social conduct and psychological health.
Limitations
There are several important limitations to the study described above. First, the school
mobility measure was based on school records indicating a unit number for the school
attended each year. Moves were determined by a change in this unit number from one year
to the next. At most one move per year was possible for each student, and no information
was available regarding whether this move took place between school years or within one
school year. Thus the resulting mobility variable likely represents an underestimation of
actual school moves and should be considered an index of mobility rather than a true count
of school moves. Based on the data from school records alone, we cannot account for
differences in the timing (between or within years) or true number of moves, which could be
important considerations when investigating different types of moves and thresholds of
mobility effects. We also do not have the information necessary to determine the reason for
school moves, though we assume based on the existing literature that the majority of these
low-income, ethnic minority students are moving for reasons of necessity (National
Research Council, 2010; Schafft, 2009).
Second, our variable measuring residential mobility was based on retrospective self-report
and parent report spanning kindergarten through twelfth grade. Because this was only
measured one time in the adult interview, it was not possible to consider differences in
timing of residential mobility. Instead, we considered cumulative residential mobility as a
control variable when we investigated differences in timing of school mobility. It is possible
that a more accurate and more differentiated measure of residential mobility may have
accounted for a greater proportion of the variance in young adult outcomes. Retrospective
reports of other behaviors in the study (e.g., home environment, parent involvement),
however, have been found to be strongly predictive of concurrent reports. Furthermore,
though we were able to include residential and school mobility in the same models, we
could not identify situations in which students changed schools but not residences or
residences but not schools. Based on the extant literature, it seems likely that the most
detrimental mobility experiences would involve multiple school moves coupled with
homelessness or multiple residential moves (Fantuzzo et al., 2009; Pribesh & Downey,
1999).
Though our understanding of the important unique risks presented by school mobility has
improved, a great many questions remain regarding the developmental processes through
which mobility can take its toll. We believe that school mobility undermines academic
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achievement by disrupting learning experiences and affecting student motivation to succeed,
but we have little empirical evidence of the transactional processes between mobility and
achievement over time, or how cumulative experiences of mobility increase risk for negative
outcomes. Future research efforts should more specifically examine learning, achievement,
and socio-emotional factors as they mediate or transact with the effects of school mobility.
Furthermore, both school mobility and residential mobility are clearly important, but their
interplay is not well understood. Additional studies can investigate differences in types of
moves, detailing whether school and residential moves are happening in concert and whether
the moves present favorable conditions or result from necessity or family financial hardship.
It is likely that risk and family situation moderates the effect of mobility such that school
changes often carry unique risks, but particularly so in the presence of other influential risk
factors such as poverty.
Implications
Our findings highlight the detrimental impacts of unstructured school mobility on life -
course well-being, especially if school mobility is frequent. Identification and
implementation of a range of interventions, policies, and practices are warranted to reduce
mobility and its negative consequences. Many types of programs, services, and policies have
been developed to reduce rates of mobility or lessen its potentially negative consequences.
These include peer buddies and mentoring (Cornille, Bayer, & Smyth, 1983; Titus, 2007);
orientation and transition programs for new students (Cornille et al.); social skills training
(Durlak, 1997; Elias et al., 1985; Jason et al., 1993); whole school reforms such as Schools
of the 21stCentury (Zigler et al., 2006) and the School Development Program (Comer et al.,
1999), and preschool to third grade (PK-3) programs and practices (Reynolds, 2003;
Takanishi & Kauertz, 2008). Improvement in the general quality of schools through, for
example, enhanced professional development, small classes and parental involvement
(Popp, Stronge, & Hindman, 2003; Reynolds, 2000) also have been frequently
recommended. School district policies that encourage flexible attendance areas,
transportation for mobile students, and collaboration with housing and other service
agencies to maintain school stability also are more common (Kerbow, 1996).
Although a wide array of strategies are available to address school mobility, very few have
been empirically evaluated intensively and in longer-term follow-ups. Intensive and
comprehensive prevention programs, presumably one of the most desirable approaches, also
have been rarely investigated for their impact on mobility. There is growing evidence,
however, that four attributes of early childhood and related prevention programs are key to
their success: the promotion of continuity or consistency in learning, coherent organization
structures such as co-located or full-service schools, alignment of curriculum across grades,
and the availability of family support and community-based services. To the extent that
these and other attributes are present in children’s learning contexts and sustained over time,
the negative consequences of mobility can be reduced.
Acknowledgments
The study is funded by the National Institute of Child Health and Human Development (Grant No. R01
HD034294).
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Appendix A
Descriptive statistics for non-imputed and imputed variables
n (observed) Non-imputed
M (SD) Imputed (N = 1410)
M (SD)
School mobility (k-12) 1410 1.6 (1.5) 1.6 (1.5)
Residential mobility 1176 3.1 (3.3) 3.1 (3.3)
Highest grade completed 1295 11.9 (1.6) 11.9 (1.6)
On-time graduation (%) 1340 39.5 39.5
Occupational prestige 1261 2.7 (1.5) 2.7 (1.4)
Any adult arrest (%) 1334 40.1 40.1
Any felony arrest (%) 1334 17.6 17.6
Depression symptoms 1068 0.7 (1.1) 0.7 (1.0)
Early family risk 1410 4.4 (1.7) 4.4 (1.7)
Kindergarten achievement 1405 63.5 (13.3) 63.5 (13.3)
Social/emotional maturity 1197 19.3 (5.6) 19.3 (5.6)
Child abuse/neglect (%) 1410 12.2 12.2
Grade retention (%) 1373 27.3 27.3
Special education (%) 1410 17.3 17.3
Juvenile delinquency (%) 1369 20.1 20.7
Appendix B
Bivariate correlations
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1. School mobility k-12 - .73** .75** .45** .36** −.07*.06*−.11** −.26** .22** −.16** −.15** .16** .25** .11** .13** −.19** .13** −.16** −.23** .14** .11**
2. School mobility k-4 - .23** .04 .32** −.04 .04 −.05 −.29** .14** −.09** −.11** .11** .12** .06*.02 −.08** .04 −.08** −.14** .07** .02
3. School mobility 4–8 - .17** .30** −.05*.01 −.13** −.14** .18** −.13** −.09** .13** .20** .07** .14** −.17** .16** −.15** −.19** .12** .12**
4. School mobility 8–12 - .01 −.04 .08** −.02 −.04 .10** −.09** −.10** .08** .19** .09** .14** −.14** .04 −.08** −.14** .09** .10**
5. Residential mobility - −.09** −.05 −.05 −.08** .14** −.02 −.02 .14** .02 .03 .11** −.12** .10** −.09** −.11** .08** .16**
6. Gender - .02 .04 .00 .03 .10** .26** .03 −.20** −.19** −.33** .22** −.06*.20** .22** −.48** −.40**
7. AA ethnicity - −.01 .01 .10** .05 .02 .03 .01 .03 .01 −.07*.03 −.05 −.03 .03 −.00
8. Any CPC preschool - .40** .00 .22** .16** −.09** −.12** −.11** −.10** .12** −.04 .10** .09** −.05 −.09**
9. Any CPC follow-on - −.02 .18** .10** −.04 −.15** −.07** −.02 .07** −.02 .05 .11** −.01 .00
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
10. Early family risk - −.15** −.10** .11** .14** .05 .12** −.23** .11** −.17** −.21** .09** .07*
11. K achievement - .45** −.05 −.35** −.26** −.12** .22** −.06*.17** .22** −.13** −.11**
12. Social/emotional - −.07*−.47** −.35** −.20** .33** −.16** .26** .32** −.24** −.20**
13. Child Abuse/Neglect - .09** .06*.17** −.12** .09** −.12** −.14** .08** .14**
14. Grade retention - .28** .16** −.28** .07** −.22** −.44** .15** .17**
15. Special education - .17** −.20** .10** −.20** −.16** .18** .19**
16. Juvenile delinquency - −.29** .15** −.29** −.30** .41** .44**
17. Highest grade - −.22** .46** .53** −.32** −.26**
18. Depression Symptoms - −.23** −.17** .18** .18**
19. Occupational Prestige - .46** −.32** −.37**
20. On-time graduation - −.32** −.31**
21. Adult Arrest - .56**
22. Felony Arrest -
*p < .05, two-tailed
**p < .01, two-tailed
Note. N = 1410.
Appendix C
Coefficients for linear regression and binary logistic regression models of mobility
predicting outcomes in the presence of additional control variables.
Linear Regression Binary Logistic Regression
Highest Grade Completed Depression Symptoms Occupational Prestige On-Time Graduation Adult Arrest Felony Arrest
B(SE) B(SE) B(SE) OR[CI] OR(CI) OR(CI)
School mobility k-12 −.04(.03) .04(.02)*−.03(.03) .90[0.81,.99]*1.13[1.02,1.25]*.95[.84,1.09]
Residential mobility −.10(.06) .10(.05)*−.05(.05) .87[.70,1.11] .94[.76,1.17] 1.66[1.25,2.19]**
Gender (female) .28(.09)** .03(.06) .22(.08)** 1.63[1.22,2.17]** .14[.11,.19]** .08[.04,.13]**
Ethnicity (AA) −.23(.16) .04(.11) −.10(.14) .87[.51,.1.49] 1.30[.75,2.26] .94[.52,1.11]
CPC preschool .14(.09) .01(.07) .07(.08) .93[.68,1.28] 1.07[.78,1.45] .76[.52,1.11]
CPC grade school −.07(.09) .02(.07) −.08(.08) 1.12[.82,1.52] 1.11[.82,1.51] 1.34[.91,1.96]
Risk Index −.12(.03)** .03(.02) −.07(.03)*.90[.81,.99]*1.00[.89,1.10] .98[.85,1.12]
K achievement .00(.00) .00(.00) .00(.00) 1.00[.99,1.01] 1.00[.98,1.01] 1.00[.99,1.01]
Social maturity .03(.01)** −.02(.01)** .01(.01)** 1.02[.99, 1.06] .98[.95,1.01] 1.01[.97,1.05]
Child Abuse/Neglect −.20(.18) .13(.13) .12(.16) 1.01[.53,1.95] 1.26[.70,2.29] 1.79[.89,3.59]
Grade retention −.34(.10)** −.08(.07) −.19(.09) .07[.04,.12]** .77[.54,1.09] 1.19[.80,1.78]
Special Education −.20(.11) .15(.08) −.31(.10) 1.17[.75,1.81] 1.31[.90,1.90] 1.45[.96,2.20]
Juvenile delinquency −.59(.11)** .25(.08)** −.62(.10)** .22[.14,.32]** 4.93[3.44,7.05]** 4.65[3.28,6.60]**
Any Welfare −.44(.14)** −.01(.10) −.36(.13)** .56[.33,.93]*.96[.59,1.54] 1.25[.71,2.23]
Risk Index (age 17) −.04(.03) .01(.02) −.03(.03) .90[.80,1.00] 1.13[1.01,1.26]*1.11[.97,1.28]
High Social maturity .71(.12)** −.28(.09)** .74(.11)** 1.93[1.30,2.88]** .60[.37,.95]*.24[.09,.61]
Model R2.25** .07** .20** .42** .40** .43**
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Note. N = 1410. SE = standard error, OR = odds ratio, CI = confidence interval.
*p < .05, two-tailed
**p < .01, two-tailed
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Table 1
Comparison of original sample and school mobility sample
Characteristic Original Sample
N= 1,539 Mobility Sample
N = 1,410
% Female 50.0 50.9
% African American 92.9 93.3
% CPC preschool participation 64.3 64.7
% CPC grade school participation 55.2 58.0
% Child abuse/neglect 11.5 12.2
Family Risk Index (age 0–3) 4.2 4.4
% Mother less than 18 at child’s birth 16.2 16.7
% Mother not complete high school 54.3 54.3
% Single parent 76.5 77.4
% 4 or more children in household 16.6 16.7
% Family in public assistance 62.8 63.0
% Mother not employed 66.3 66.2
% Eligible for free lunch 83.8 83.6
% High poverty in school area 76.0 76.1
Kindergarten readiness 47.4 47.3
ITBS word analysis in kindergarten 63.8 63.5
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Table 2
Percentages of students experiencing school moves and residential moves during specified time periods, based on school records and survey data
# of moves
School mobility N=1,410 Residential mobility
n=1,176
K – 12th K – 4th 4th – 8th 8th – 12th K – 12th
0 26.7 47.4 59.9 81.1 18.0
1 32.3 34.8 26.2 15.9 16.3
2 18.5 13.3 10.3 2.8 17.8
3 10.6 3.5 3.2 0.3 17.0
4 7.0 0.9 0.5 0.0 9.2
5 3.0 - - - 7.5
6 1.3 - - - 4.4
7 0.4 - - - 2.0
8 0.1 - - - 1.8
9–20 - - - - 4.4
20 or more - - - - 0.5
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Table 3
Final models for regressions predicting the six young adult outcomes with cumulative school mobility between kindergarten and twelfth grade
Linear Regression Binary Logistic Regression
Highest Grade Completed Occupational Prestige Depression Symptoms On-Time Graduation Adult Arrest Felony Arrest
B (SE) B (SE) B (SE) OR [CI] OR (CI) OR (CI)
Step One:
School mobility k-12 −.06 (.03) −.05 (.03) .04 (.02)*.88 [0.78,.98]*1.15 [1.03,1.27]*.98 [.86,1.11]
Residential mobility −.09 (.06) −.04 (.06) .10 (.05)*.87 [.70,1.11] .94 [.76,1.16] 1.65 [1.25,2.17]**
Step Two:
Gender (female) .30 (.09)** .25 (.08)** .02 (.06) 1.64 [1.24,2.17]** .15 [.11,.20]** .08 [.04,.13]**
Ethnicity (AA) −.30 (.16) −.18 (.15) .08 (.11) .84 [.50,.1.43] 1.34 [.78,2.31] 1.03 [.53,2.02]
CPC preschool .13 (.09) .06 (.08) .01 (.07) .92 [.67,1.26] 1.05 [.78,1.43] .77 [.52,1.12]
CPC grade school −.03 (.09) −.05 (.08) .02 (.07) 1.14 [.83,1.59] 1.11 [.82,1.50] 1.27 [.87,1.86]
Risk Index −.15 (.03)** −.09 (.02)** .04 (.02)*.83 [.77,.90]** 1.07 [.99,1.16]] 1.05 [.94,1.17]
K achievement .00 (.00) .00 (.00) .00 (.00) 1.00 [.99,1.01] 1.00 [.98,1.01] 1.00 [.98,1.01]
Social maturity .05 (.01)** .03 (.01)** −.03 (.01)** 1.05 [1.02, 1.08]** .97 [.94,.99]*.99 [.95,1.02]
Child Abuse/Neglect −.22 (.12) −.21 (.11) .13 (.09) .57 [.36,.90]*1.30 [.87,1.82] 2.28 [1.40,3.70]**
Grade retention −.34 (.10)** −.19 (.10) −.08 (.07) .07 [.04,.12]** .79 [.56,1.11] 1.19 [.79,1.79]
Special Education −.16 (.11) −.28 (.10) .13 (.08) 1.20 [.78,1.85] 1.26 [.87,1.82] 1.40 [.93,2.12]
Juvenile delinquency −.62 (.11)** −.65 (.10)** .25 (.08)** .21 [.14,.33]** 5.01 [3.51,7.15]** 4.81 [3.40,6.81]**
Model R2.23** .17** .06** .42** .40** .43**
Note. N = 1410. SE = standard error, OR = odds ratio, CI = confidence interval.
*p < .05, two-tailed
**p < .01, two-tailed
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Table 4
Coefficients for mobility variables from final models for timing and threshold effects
Linear Regression Binary Logistic Regression
Highest Grade Completed Occupational Prestige Depression Symptoms On-Time Graduation Adult Arrest Felony Arrest
B (SE) B (SE) B (SE) OR [CI] OR [CI] OR [CI]
1 move (k-4) −.07 (.09) −.16 (.08) .09 (.06) .90 [.66,1.21] 1.13 [.83,1.53] .96 [.65,1.42]
2 or more moves (k-4) .10 (.12) .02 (.11) −.07 (.08) .89 [.59,1.36] 1.40 [.94,2.09] .65 [.39,1.09]
1 move (4–8) −.07 (.09) .02 (.09) .18 (.07)** .96 [.70,1.33] 1.22 [.89,1.68] 1.31 [.88,1.96]
2 or more moves (4–8) −.26 (.13)*−.33 (.12)** .37 (.09)** .56 [.36,.89]*1.30 [.86,1.98] .99 [.60,1.65]
Any moves (8–12) −.22 (.10)*−.05 (.09) .07 (.07) .82 [.57,1.19] 1.20 [.85,1.69] 1.15 [.76,1.75]
Residential mobility Controls −.11 (.06) −.03 (.06) .12 (.05)** .87 [.70,1.07] .94 [.76,1.17] 1.74 [1.31,2.32]**
Model R2.23** .18** .07** .42** .40** .44**
Note. N = 1410. SE = standard error, OR = odds ratio, CI = confidence interval.
*p < .05, two-tailed
**p < .01, two-tailed
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... With regards to the first point, a strong link has been found between pupil mobility and economic disadvantage (Machin et al., 2006;Strand & Demie, 2006;Herbers et al., 2013). Furthermore, in the UK, children with Special Educational Needs and Disabilities (SEND) have been found to be more mobile than children without SEND, and so have children who belong to a minority ethnic group or have a language other than English as their first language. ...
... A vast number of studies have explored the impact of pupil school mobility on academic progress and outcomes. Studies have generally found a negative correlation between achievement and mobility, and in particular with multi-mobility (Leckie, 2009;Herbers et al., 2013;Hutchings et al., 2013). However, findings are not School mobility and mobile pupils in England 3 unequivocal (Anderson and Leventhal, 2017) and several studies have also pointed out that the link between mobility and low achievement is not straightforward, as it is very difficult to isolate mobility from pre-existing and long-term factors associated with deprivation and low income, which are also strongly correlated with mobility (Pribesh & Downey, 1999;Dobson & Pooley, 2004;Bull & Gilbert, 2007;Cordes et al., 2019). ...
... However, in a related study (Strand & Demie, 2007) of secondary schools in the same LEA, a stronger negative impact of mobility was identified. Other studies have similarly found that the link between mobility and lower achievement increases with educational stage (Herbers et al., 2013;Anderson, 2017), illustrating the importance of investigating pupil school mobility over the full educational trajectory of children. ...
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A significant proportion of pupils move school during their school career for reasons other than standard structural moves between educational stages. Little is known about the underlying causes of these moves and the characteristics and experiences of mobile pupils are challenging to research. There is currently a large disconnect between the macro level of system structures, data and policy and the individual experiences and journeys of mobile pupils. This article brings together international literature around school mobility and mobile pupils, with analyses of the English National Pupil Database (NPD), tracking a cohort from age 5 to 16, to better understand when school moves occur and the characteristics of mobile pupils. Findings reveal a sizable underlying rate of moves in England of about 1.5–2% per term and identify differences in mobility related to disadvantage, school phase, ethnic group and SEND status. The predictive power of the data, however, is low, highlighting the need for more research, policy and practice in this area to better understand individual mobility circumstances. By bringing together the literature and the data, the article concludes with a discussion of what is known about school mobility and recommends further areas for research into the characteristics, experiences and outcomes of mobile school pupils.
... To illustrate, in the United States, school mobility in the past 2 years was estimated at 35% in 4th grade, 21% in 8th grade, and 9% in 12th grade (see Rumberger, 2015). Besides standing out as an uncommon phenomenon, mobility in late high school is also potentially particularly challenging academically and socially as it occurs at a time when curriculums are increasingly differentiated across schools, and when peer groups have had several years to crystallize (Herbers et al., 2013;Anderson et al., 2014). In addition, in late high school, students reach an age at which school is no longer compulsory. ...
... One study based on an analysis of United States data collected in the late 1980s and early 1990s found that mobility in late high school (i.e., in the 11th or 12th grade) was more likely to lead to negative outcomes, including dropout, than mobility earlier in secondary education (Swanson and Schneider, 1999). Similarly, in a Chicago-based cohort of low-income, minority children born between 1979 and 1980, Herbers et al. (2013) found that mobility between grades 4 and 12 was negatively associated with highest grade completed, but not mobility in earlier grades (i.e., prior to grade 4). Further research is needed to determine whether similar patterns hold in contemporary cohorts and in other national contexts, to gauge the current relevance of mobility in late high school as a signal for dropout vulnerability. ...
... Navigating these challenges might be particularly difficult in the middle of the school year, when academic routines and social habits are established, rather than at the beginning of the school year, when all students and teachers adapt to new, reshuffled groups and gradually settle into habits together. It might also be particularly difficult when mobility occurs toward the end of high school when peer groups have crystallized over several years and when curriculums generally become more specialized and differentiated (see Swanson and Schneider, 1999;Herbers et al., 2013). Mobility in later years vs. earlier grades also means that dropout can be contemplated as a feasible coping strategy, as schooling is no longer compulsory by the age of 16 in many jurisdictions, including in Quebec. ...
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Non-promotional school changes are fairly common, and although most mobile students successfully adjust to new peers, routines, and teachers, school mobility can sometimes indicate risk of disengagement and even dropout. To identify which mobile students are at risk and in need of support, it is important to differentiate when mobility may pose a threat and when it does not. The goal of this study was to examine the role of temporality in the relationship between non-promotional school changes and high school dropout, in a sample of N = 545 secondary school students (52% boys; Mage = 16.3 years) followed over a 6-month period. Participants were recruited in 12 socioeconomically disadvantaged public secondary schools with high dropout rates in Quebec (Canada). Logistic regression analyses (taking into account key potential confounding variables) revealed that non-promotional secondary school changes were associated with dropout, but only when they occurred during school years or in later secondary grades, and not when they occurred between school years (i.e., during the summer break) or in early secondary grades. These findings indicate that non-promotional school changes occurring at certain key time points are clear indicators of increased risk of high school dropout. Students who experience such changes would benefit from targeted support to help them integrate into their new school and cope with other problems often associated with mobility.
... School mobility and adjustment outside of the normal structure of school progression (such as progression from primary to high school) has been linked to many negative developmental outcomes during early adolescence (Herbers et al., 2013). Risk factors from school mobility in students aged 11-14 years include social and psychological difficulties (Herbers et al., 2013;South et al., 2007), lower academic grades (Crockett et al., 1989), and higher student disengagement (Langenkamp, 2014). ...
... School mobility and adjustment outside of the normal structure of school progression (such as progression from primary to high school) has been linked to many negative developmental outcomes during early adolescence (Herbers et al., 2013). Risk factors from school mobility in students aged 11-14 years include social and psychological difficulties (Herbers et al., 2013;South et al., 2007), lower academic grades (Crockett et al., 1989), and higher student disengagement (Langenkamp, 2014). These findings highlight the importance of providing support to early-adolescent school students who may be experiencing a period of adjustment as a result of school mobility. ...
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Growth in the international school sector continues, with significant expansion of the sector in Asia. Whilst substantial research has been conducted on the adjustment experience of tertiary-aged students, limited research attention has been given to school-aged students in international schools. The environment, conditions and challenges experienced by school-aged international students can differ considerably from those of tertiary-aged international students. This can be heightened during early-adolescence with adjustment from school mobility linked to many negative developmental outcomes. The present study investigates wellbeing, engagement and resilience of 178 early-adolescent international school students (aged 10-14) from an international school in Singapore that offers the International Baccalaureate Diploma and the national curriculum of England. Results reported a positive significant association between wellbeing, engagement and resilience constructs. The study also identified demographic and mobility characteristics that were associated with lower levels of wellbeing, behavioural engagement and resilience. Findings of the study highlight a potential cohort of early-adolescent international students who could benefit from additional support.
... It was found that school mobility had negative and medium effects on academic achievement. Even though it was the result obtained on the basis of only one meta-analysis (Mehana & Reynolds, 2004), it is known that there are many studies concluding that school mobility has negative effects on students' academic achievement (Herbers, Reynolds, & Chen, 2013). Besides, the longitudinal study conducted in 25 years by Herbers et al. (2013) indicated that individuals who experienced in nursery school or in K12 were more likely to fail to graduate from school on time, to fail to get a popular job and to have symptoms of depression. ...
... Even though it was the result obtained on the basis of only one meta-analysis (Mehana & Reynolds, 2004), it is known that there are many studies concluding that school mobility has negative effects on students' academic achievement (Herbers, Reynolds, & Chen, 2013). Besides, the longitudinal study conducted in 25 years by Herbers et al. (2013) indicated that individuals who experienced in nursery school or in K12 were more likely to fail to graduate from school on time, to fail to get a popular job and to have symptoms of depression. ...
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The study aimed to identify the factors and to demonstrate their effects on academic achievement in various publications that utilized meta-analyses. For this purpose, the meta-analyses publications on the Web of Science-All Database till 2018 were reviewed. In the study, the systematic review method was adopted. Following a related review, 169 meta-analyses were included in the scope of the study. The effects of 254 variables on academic achievement were investigated, and consequently, 427 effect sizes were found in total. Variables obtained from meta-analyses with the effect sizes between-.799 and 3.170 were examined in nine categories. The results revealed that the number of variables evaluated in the categories of psychological, socioeconomic , socio-demographic and individual characteristics, learning theories and teaching strategies, and family was bigger than other categories.
... 4 Hanushek and co-authors (2004) find in a Texas-based sample that moving homes and schools, independent of school quality, has a negative effect on both movers and their new peers, particularly for low-income students. In fact, whether as a result of foreclosure (Herbers et al., 2013), homelessness (Fantuzzo et al., 2012), natural disasters (Sacerdote, 2012), or the sale of their rental residence (Schwartz et al., 2017), students in these studies experience worse outcomes after moving. However, housing policies intended to ameliorate the neighborhood characteristics of program recipients have had mixed educational results (e.g., Sanbonmatsu et al., 2011;Schwartz, 2010), and the direction of their impact may depend on the age at which children move (e.g., Chetty et al., 2016). ...
... A common issue in AES, and specifically within interim alternative settings, is that students experience a disrupted education punctuated by movement between schools (Booker & Mitchell, 2011;Rumberger, Larson, Ream, & Palardy, 1999). Researchers have found that the characteristics of students who change schools tend to differ from those who do not, and that student mobility can have a negative effect on student learning (Herbers, Reynolds, & Chen, 2013;Raudenbush, Marshall, & Art, 2011), as well as increase the likelihood of misbehavior, delinquency, and school dropout (Gasper, DeLuca, & Estacion, 2010). Moreover, highly mobile students are roughly twice as likely to have reported delays in growth or development, to have a learning disorder, to have repeated a grade, or to have recurring behavioral problems (Gasper et al., 2010;Rumberger & Larson, 1998). ...
... The effects of such factors can be explained through proximal processes in microand mesocontexts in the bioecological model. Absenteeism (Ansari et al., 2020;Balfanz & Byrnes, 2012;Morrissey et al., 2014) and school mobility (Han, 2014;Herbers et al., 2013;Sandstrom & Huerta, 2013) have been found to have negative effects on academic achievement and education. However, the effects of some school-related factors on education, such as school quality and school resources, are mixed (Autor et al., 2016;Rumberger & Lim, 2008). ...
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Educational attainment is typically examined as a static status. As adult learners have become the new trend in higher education, the changes in educational attainment in adulthood warrant more attention. Using data from the Chicago Longitudinal study (CLS), an ongoing panel investigation of 1,539 children, predictors of educational growth trajectories in adulthood were investigated. Of the study sample (N = 1,418), 51.8% were women, 93.2% were Black, 6.8% were Hispanic, 83.4% were eligible for free lunch between birth and age 3. The average age of the study sample in June 2015 was 35.1, ranging from 34.4 to 36.6. Hierarchical linear modeling (HLM) was used to analyze the changes in educational attainment between ages 24 and 35. Findings indicate that mothers not completing high school by child's age 3 and days of absence at school were significantly associated with lower educational attainment at age 24. Classroom adjustment, student college expectations, 8th grade reading scores, and on-time high school graduation were significantly associated with higher educational attainment at age 24. Classroom adjustment, 8th grade reading score, and on-time high school graduation were significantly associated with a positive growth of education between ages 24 and 35. Findings suggest that improving academic achievement and socioemotional learning skills in elementary and middle school and promoting on-time high school graduation are likely to increase one's chances to continue pursuing higher education in adulthood for Black low-income children. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
... As consistent with the literature, youths' histories of school and residential mobility prior to leaving mainstream school was evident in our research. Student mobility, defined as changing schools for reasons other than grade promotion, has negative effects on a number of factors, including relationships with teachers, school connectedness, achievement, social capital, and peer group interactions (e.g., Herbers, Reynolds, & Chen, 2013;Scherrer, 2013), and for many of our participants, reflected shifts in foster care or family relationships (Panina-Beard, 2018;Vadeboncoeur et al., 2011). Differentiating between types of mobility-strategic mobility in pursuit of better schooling and reactive mobility given unexpected family and school changes-has highlighted various reasons for mobility. ...
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In previous ethnographic and qualitative research with students and teachers in alternative programs, we examined student-teacher relationships and, in particular, how these relationships mediated the process of re-engaging. Moral imagining—conceptualized from a meta-analysis of how these relationships were experienced and enacted—highlighted a quality of student-teacher relationships that enabled participants to create new social futures as a function of their relatedness. Inquiring into the possibilities that come into being as a result of present relationships, and the social futures to which they contribute, makes visible the ways these relationships may be more or less enabling and equitable. In this article, we elaborate a theoretical framework, by defining and relating additional concepts, and detail the empirical foundation with the inclusion of additional data. First, we describe our previous research with young people and teachers in alternative programs in the United States, Australia, and Canada. Second, we elaborate a theoretical framework for moral imagining and our current discussion. Third, we expand the empirical foundation for the concept of moral imagining in student-teacher relationships. In the fourth section, we conclude with implications from this developing framework and questions for future research.
... This same study found that younger children, compared to their older peers, were also more likely to switch schools (Mehana & Reynolds, 2004). Further, other studies have also found that ethnic minority students tend to have higher rates of school mobility compared to their peers (Alexander et al., 1996;Herbers et al., 2013). Alexander et al.'s (1996) study of mobility within the Baltimore City public schools also found that relatively wealthy, White students were more likely to move outside of their school district, whereas students of color in poverty were more likely to shift schools within the district. ...
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Public school pre-k programs are not only effective in promoting children’s school readiness but are also potentially useful for easing the transition from pre-k to kindergarten. One possible reason for this ease of transition is eliminating the need for children to change schools when starting kindergarten. However, little is known about whether children actually stay at the same school for kindergarten that they attended for pre-k and what predicts school mobility between pre-k and kindergarten. Using data from a large (N = 18,775) and ethnically diverse (34.7% Black, 54.9% Latino, 10.3% White/other) sample of predominantly low-income children attending public school pre-k in Miami, we describe the prevalence, nature, and predictors of school mobility between the pre-k and kindergarten years. We found that 20.7% of students who attended public-school pre-k switched schools in the transition from preschool to kindergarten. Logistic regression analyses indicated that, before accounting for school quality, Black and Latino children (compared to White children) had higher odds of switching schools, as did children receiving free or reduced-price lunch. After accounting for school quality, Black children and children receiving free or reduced-price lunch no longer had higher odds of switching schools. Children attending lower-performing schools (compared to higher-performing schools) in pre-k had higher odds of switching schools. We also describe pre- and post-move school quality for different groups of children. Implications of school mobility between public school pre-k and kindergarten are discussed.
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Using a multi-informant, longitudinal design, we investigated the independent and interactive associations between youth academic worries and parental involvement (parent-teacher relationship quality, academic socialization, schoolwork assistance) before the middle school transition as predictors of youth engagement and academic performance after the middle school transition among 100 youth (53% boys; Mage = 11.05 years; 57% White). We found that maternal academic socialization moderated the prospective association between youth academic worries and academic adjustment, such that youth who experienced more academic worries coupled with higher maternal academic socialization had higher academic performance and engagement; no association emerged for lower maternal academic socialization. Further, maternal schoolwork assistance before middle school directly predicted better academic performance during middle school. Findings highlight the importance of maternal schoolwork assistance in promoting youths' academic performance, as well as maternal academic socialization in promoting higher academic performance and engagement for youth experiencing more academic worries.
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Basic Concepts in Prevention. Prevention of Behavioral and Social Problems. Prevention of Learning Problems. Drug Prevention. Programs to Improve Physical Health. Injury Prevention. Child Maltreatment. Is Prevention Cost-Effective/ Importance of Policy. Current Status and Future Directions. Appendix A: Characteristics of Effective Skill Training Programs. Appendix B: Helpful Resources on Prevention. Indexes.
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Decades of research point to the need for a universal preschool education program in the U.S. to help give our nation's children a sound cognitive and social foundation on which to build future educational and life successes. In addition to enhanced school readiness and improved academic performance, participation in high quality preschool programs has been linked with reductions in grade retentions and school drop out rates, and cost savings associated with a diminished need for remedial educational services and justice services. This 2006 book brings together nationally renowned experts from the fields of psychology, education, economics and political science to present a compelling case for expanded access to preschool services. They describe the social, educational, and economic benefits for the nation as a whole that may result from the implementation of a universal preschool program in America, and provide guiding principles upon which such a system can best be founded
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The purpose of this article is to identify and estimate the influence of educational, psychological, and social factors on learning. Using evidence accumulated from 61 research experts, 91 meta-analyses, and 179 handbook chapters and narrative reviews, the data for analysis represent over 11,000 relationships. Three methods—content analyses, expert ratings, and results from meta-analyses—are used to quantify the importance and consistency of variables that influence learning. Regardless of which method is employed, there is moderate to substantial agreement on the categories exerting the greatest influence on school learning as well as those that have less influence. The results suggest an emergent knowledge base for school learning. Generally, proximal variables (e.g., psychological, instructional, and home environment) exert more influence than distal variables (e.g., demographic, policy, and organizational). The robustness and consistency of the findings suggest they can be used to inform educational policies and practices.
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A long-standing problem in the early childhood field is that there is no cohesive delivery system in place for preschool and child care services. Rather, we have a mix of fragmented services, some providing part-day preschool to four-year-olds, others providing all-day, year-round child care for children whose parents are working. Multiple funding streams support the programs, and a variety of institutional contexts exists – public schools, nonprofit and for-profit centers, churches, and community-based organizations – as well as licensed and unlicensed individual child care providers. Of significance is the general lack of quality that characterizes this nonsystem. Hence large numbers of preschool children attend programs that are of poor or mediocre quality, which has consequences for their healthy growth and development as well as their school readiness. Universal preschool has the potential to create a better and more equitable early care and education system. Many issues have to be addressed about the governance, structure, and scope of a proposed system. In this chapter we discuss our experiences with the development and implementation of a universal school-based program known as the School of the 21st Century (21C). In some communities in Kentucky and Connecticut, the program is referred to as the Family Resource Center. 21C is a comprehensive program that includes, in addition to other components, universally accessible child care for preschoolers.
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Americans in general have high rates of residential mobility, but American children are especially mobile compared to children in several other Western countries and Japan. This finding holds up under different ways of conceptualizing mobility and stability. The paper develops alternative explanations of the "excess" mobility of U.S. children and concludes that the most likely explanation is greater family disruption and childhood poverty in the United States. The paper identifies what is an average number of moves for children at successive ages and models the association of selected socioeconomic and other variables with different measures of mobility.
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Using longitudinal data from the Beginning School Study (BSS), the study reported here examined sources of influence on inner-city students' initial middle school placements in English and mathematics and continuity and change in placements through the end of middle school. The study found that test scores and prior retention in grade exert strong effects at the transition into middle school, but so do social background and parents' educational expectations, particularly for higher-level placements. Stability in middle school placements is strong, and neither social background nor educational expectations consistently influences eighth-grade course levels, net of sixth-grade placements and other predictors in the model. The authors conclude that educational trajectories are not grounded in academic considerations alone, but the contributions of nonacademic factors on track placements toward the end of middle school are obscured if the determinants of earlier placements are not included in the model.