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Longitudinal Roles of Pre-College Contexts
In Low-Income Youths’ Postsecondary Persistence
Matthew A. Diemer & Cheng-Hsien Li
Michigan State University
Citation for this paper: Diemer, M.A. & Li, C. (in press). Longitudinal roles of pre-college contexts in low-income youths’ postsecondary persistence. Developmental Psychology.
*Author’s note. Correspondence regarding this paper should be directed to Matthew A. Diemer,
Department of Counseling, Educational Psychology and Special Education, 513D Erickson Hall,
College of Education, Michigan State University, East Lansing, MI 48824-1034; (517) 355-
6684; email: [email protected].
This paper was partially supported by a grant from the Michigan State University College of
Education In-House Grant program to the first author. The first author was also supported by the
Spencer Foundation/National Academy of Education Postdoctoral Fellowship while conducting
this research.
Thank you to James Fairweather and Barbara Schneider for their insightful comments on an
earlier version of this paper.
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Abstract
Low-income youth enroll at postsecondary institutions less frequently, drop out more
often, are less likely to return after dropping out, and are less likely to attain a postsecondary
degree than their more affluent peers. It is therefore important to understand how low-income
youth develop the capacity to persist in the postsecondary setting. This paper examines how
contextual supports contribute to low-income (and predominantly racial/ethnic minority) youths’
educational expectancies and postsecondary persistence. These questions are examined by
applying structural equation modeling to a longitudinal panel of youth surveyed three times over
a five year period, while controlling for academic achievement, age, and gender. The obtained
structural model suggests meditating “chains” by which parents and peers foster educational
expectancies and postsecondary persistence over time. This paper suggests that pre-collegiate
contexts and expectancies clearly matter in explaining how low-income youth progress through
intermediate checkpoints – postsecondary persistence – on the path to degree completion.
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Longitudinal Roles of Pre-College Contexts
in Low-Income Youths’ Postsecondary Persistence
Low-income youth encounter significant barriers to postsecondary enrollment, retention,
and degree completion (Terenzini, Cabrera, & Bernal, 2001). Low-income youth enroll at
postsecondary institutions less frequently, drop out more often, are less likely to return after
dropping out, and are less likely to attain a postsecondary degree than their more affluent peers
(Adelman, 1999; Bowen, Chingos, & McPherson, 2009). Low-income youth may find it difficult
to persist because they (and their families) do not understand the financial aspects of college
(Terenzini et al., 2001), lack awareness of and realistic information about college (Deil-Amen &
Rosenbaum, 2003), attend (on the aggregate) underfunded secondary schools that do not provide
as much academic preparation (Bowen et al., 2009), are more likely to be first-generation college
students (Adelman, 1999), and/or may experience class-based social marginalization in
postsecondary settings. That is, low-income youth are less likely to persist across multiple
“checkpoints” on the pathway to degree completion.
It is therefore important to understand how low-income youth develop the capacity to
persist over time in the postsecondary environment. This issue is underscored by longstanding
socioeconomic inequities in educational attainment and contemporary policy emphases on
educational attainment in the U.S. This study longitudinally examines factors that contribute to
the postsecondary persistence of low-income youth. These questions are examined over a span of
five years with a longitudinal panel of low-income youth, along with linked data from their
parents, as they transition across the checkpoints to degree completion.
Conceptualizing Postsecondary Persistence
4
Postsecondary persistence is conceptualized here as an intermediate step between
postsecondary enrollment and degree attainment, characterized by the continuous enrollment
most predictive of degree completion (Adelman, 1999). By contrast, dropping out or “stopping
out” (returning to college after dropping out) have a strong negative relationship to degree
attainment, particularly for low-income youth (Terenzini et al., 2001). The more intermediate
process of persistence is the focus, rather than degree attainment, to more clearly understand the
developmental processes by which low-income youth make progress toward their degrees.
Theoretical Framework
The expectancy-value model (Eccles & Wigfield, 1995; Eccles, Vida, & Barber, 2004)
broadly frames how contexts and cultural milieus affect domain-specific expectancies for future
success, which in turn affect educational/occupational choice and attainment. This study
examines a portion of this complex and multi-faceted model – how micro-level developmental
contexts shape youths’ educational expectancies and how youths’ expectancies predict
postsecondary persistence over time.
Educational Expectancies
Educational expectancies are beliefs about the likelihood of future educational success
and are key predictors of educational outcomes (e.g., Eccles & Wigfield, 1995; Furstenberg,
Cook, Eccles, Elder, & Sameroff, 1999). In this study, educational expectancies refer to youths’
beliefs about the likelihood of success in their postsecondary education. However, the role of
low-income youths’ educational expectancies in the more intermediate process of postsecondary
persistence has been subjected to little empirical scrutiny. This study examines whether
educational expectancies mediate the effects of pre-college contexts on postsecondary
persistence.
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Pre-College Contexts
Parents and peers may affect low-income youths’ educational expectancies and thereby
indirectly affect their postsecondary persistence. Parental expectations are theorized to affect
youths’ educational expectancies (Eccles & Wigfield, 1995). Similarly, discussions of youths’
future educational plans provide opportunities to encourage young people to aspire, apply to, and
enroll in college (Eccles et al., 2004) as well as to help youth understand the college application
and enrollment processes (Deil-Amen & Rosenbaum, 2003; Perna & Titus, 2006), which may
also raise youths’ expectancies for postsecondary success.
Parents with a greater sense of self-efficacy, confidence in managing life’s challenges
and daily hassles, may be better able to foster their children’s college-going. Efficacious parents
are particularly important in insulating children from the negative effects of poverty on
educational processes, such as by communicating high expectations and providing support
(Furstenberg et al., 1999). Whether more efficacious parents are better able to provide
postsecondary support and thereby affect youths’ educational expectancies will also be
examined.
Hypotheses
---Insert Figure 1 about here---
The conceptual model in Figure 1 depicts hypothesized relationships between latent
constructs. These constructs will be examined across three time points, spanning five years –
Time 1 (2002), Time 2 (2005), and Time 3 (2007). Academic achievement affects many of these
constructs and is therefore statistically controlled. Age (because study participants come from
heterogeneously-aged longitudinal panels) and gender (because of observed gender differences
constructs of interest) are modeled as observed covariates to also be controlled.
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Method
Sample
This study analyzed data from the Child Development Supplement (CDS-II: 2002) and
Transition to Adulthood (TA: 2005; TA: 2007) datasets, supplements to the Panel Study of
Income Dynamics (PSID). In 2002, CDS-II surveyed 2,907 children aged 5 to 18, along with
their primary care giver (generally, biological mother) and “other care giver” (generally,
biological father). Only those CDS-II participants who were at least 18 years old in 2005 or 2007
(760 and 1,115, respectively) were included in the subsequent TA: 2005 and TA: 2007 surveys.
This survey design differs in that an age-variant panel of youth were followed across CDS-II into
TA: 2005 and TA: 2007 (i.e., some participants were 13 years old and some were 17 at CDS-II).
This study examined a subsample of low-income participants old enough (older than 18)
to have made the postsecondary transition by the TA: 2005 or TA: 2007 survey. The subsample
therefore consisted of participants age 13 or older at CDS-II (2002) or who were over age 18 and
therefore included in either TA survey. In 2002, the subsample’s age ranged from 13 to 18.98,
(M = 15.80, SD = 1.64).
Low-income youth were selected by comparing five years of their parents’ averaged
household income to the U.S. Census poverty threshold for those same five years (Roosa, Deng,
Nair, & Burrell, 2005). Five years were examined because of the volatility of annual income,
particularly for low-income families. Participants whose averaged parental income equaled or
fell below 200% of the averaged threshold were selected. This more liberal criterion was used
because the poverty threshold misses many youth who experience poverty-related stressors and
because programs such as Head Start use 175% or 200% of the threshold to determine eligibility
7
(Roosa et al., 2005). The averaged household income for this subpopulation (M = $21,915 and
Mdn = $22,063) approached the averaged poverty threshold for those five years: $17,461.
These age and income-based selection criteria resulted in a subpopulation of 439
participants: 217 young men (49.4%) and 222 young women (50.6%). The racial/ethnic
demographics of the subpopulation were as follows: 288 (65.6%) identified as African
American, 84 (19.1%) as White, 51 (11.6%) as “Hispanic,” 4 (0.9%) as American
Indian/Alaskan Native, 2 (0.5%) as Asian/Pacific Islander, and 10 (2.3%) as “Other.” African
Americans comprised a larger proportion of the subpopulation because they are overrepresented
in the lower rungs of the U.S. income distribution and because the PSID oversampled lower-
income African American families.
Indicators of Latent Constructs
Detailed information about observed indicators used to operationalize latent constructs
and descriptive data are provided in Table 1.
---Insert Table 1 about here---
Parental Self-Efficacy
Participants’ primary care givers (generally mother) were surveyed at CDS-II. These
indicators measures how agentic and efficacious parents feel in managing life’s demands, a
domain-general measure of perceived self-efficacy.
Maternal Expectations
Participants’ primary care givers were surveyed about how far in school they expect their
child to go. Because only one indicator is available in CDS-II, it is modeled as an observed
indicator.
Contextual Postsecondary Support
8
CDS-II participants indicated how frequently they discussed their future plans with their
family and friends. Because these conversations presumably include discussion of youths’
postsecondary plans, these items are used to measure contextual support for college-going.
Educational Expectancies
Domain-specific expectancies for success on a future task are key in the expectancy-
value model (Eccles & Wigfield, 1995; Eccles et al., 2004). A CDS-II item surveys participants’
expectancies for success in college – the likelihood they will graduate from a 4-year institution.
The second indicator is a traditional measure of educational expectations, or how far in school
one thinks they will actually go – distinct from educational aspirations, how far in school one
would like to go. Because these items measure beliefs about future success in the postsecondary
domain, they are used here to measure educational expectancies.
Academic Achievement
Academic achievement was operationalized by the letter-word identification, passage
comprehension, and applied problems subscales of the Woodcock Johnson-Revised (WJ-R), a
measure of academic achievement.
Postsecondary Persistence
Two composite variables measured youths’ progress toward their degree at TA-05 and
TA-07. The syntax and coding rules used to create these composites can be obtained by
contacting the first author.
Enrollment Status
This composite measures participants’ progress toward and attainment of postsecondary
degrees, where higher scores indicate greater persistence. Very few participants (N = 7, 1.6%)
had attained at least an associate’s degree by TA-07. Participants over age 18 but enrolled in
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high school were classified as “legitimate skips” in the TA survey and as missing values in these
analyses.
Enrollment Continuity
This composite differentiates students who never enrolled in a postsecondary institution
from postsecondary dropouts, stopouts, and continuously enrolled students. High school
dropouts, those who received a GED, and high school graduates who never enrolled received the
lowest score (0) on this composite. Comparing those who have enrolled, college dropouts
(students who leave before graduating with stated intentions not to return) are the least likely to
attain a degree (coded a 1), stopouts (students who leave with stated intentions to return) are
somewhat more likely (coded a 2), and students who maintain continuous enrollment are the
most likely (Adelman, 1999). Continuously enrolled students and those who left an institution
only for compelling academic reasons, such as to pursue a major not offered at their current
institution, were coded a 3. Participants who persisted until attaining at least an associate’s
degree were coded a 4.
Results
No indicators were skewed or kurtotic enough to affect model fit or require
transformations, as depicted in Table 1 (Kline, 2005). Some indicators had a greater degree of
missing data (see Table 1), expected in a longitudinal survey spanning five years. Some
missingness may be due to the CDS/TA survey design – certain participants were not old enough
to make the postsecondary transition at Time 2 (TA-05) and should not have values for the
postsecondary persistence variables. However, missing values could not be accurately imputed
because these “legitimate skips” and non-responsive participants were not delineated in the TA
dataset.
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Analyses were therefore conducted under FIML (Full Information Maximum Likelihood)
conditions, which make use of all available data points in analyses, rather than deleting
information casewise or pairwise (Muthén & Muthén, 2010). FIML afforded inclusion of
participants who were only surveyed at one wave, rather than restricting analyses to a less
representative sample who were surveyed at all three waves. The WLSMV estimator was used
for analyses of these categorical and continuous variables.
---Insert Table 1 about here---
The sampling weights designed for the CDS and TA datasets could not be used. Although
MPlus is well-equipped to address weighting (Muthén & Muthén, 2010), weighted analyses
would not converge or resulted in inadmissible solutions. Unweighted analyses did not encounter
any of these problems. To address inflated Type I error risk resulting from unweighted analyses,
a more conservative statistical significance criterion (α = .01) was used.
Measurement Model
Model fit indices evaluate how well indicators loaded onto their specified latent construct
in the measurement model. Reviewing Table 2, the measurement model was a good fit to the
data.
---Insert Table 2 about here---
Inspecting Table 3, each indicator significantly loaded onto its specified latent construct.
This supports the operationalization of these latent constructs with these indicators, providing
construct validity evidence (Kline, 2005). Enrollment continuity had a very high standardized
loading (1.00) onto Time 3 postsecondary persistence. Loadings of this magnitude can occur
when factors are correlated in CFA, such as between the Time 2 and Time 3 postsecondary
persistence constructs (r = .87).
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---Insert Table 3 about here---
Structural Model
The relationships between latent constructs were then examined. The fit of this model is
depicted in Table 2 and relations among constructs in Figure 2. Standardized coefficients (β) are
effect size estimates, although SEM estimates are less inflated by error and generally smaller
(Kline, 2005). (Confidence intervals are not reported because they cannot be obtained with
MPlus when exogenous covariates are included.) Of the covariates, only gender (0 = female and
1 = male) predicted contextual support (β = -.20), suggesting male youth received less support.
(More information regarding non-significant paths from the covariates can be obtained from the
first author).
Four indirect relationships were also estimated. Time 1 expectancies indirectly affected
Time 3 persistence via Time 2 persistence (β = .38). Time 1 contextual support also indirectly
affected Time 3 persistence, via Time 1 expectancies Time 2 persistence, (β = .12). Time 1
maternal expectations indirectly affected Time 3 persistence, via Time 1 expectancies Time 2
persistence (β = .24). The indirect effect of Time 1 parental self-efficacy on Time 3 persistence
only approached significance (p = .03), via Time 1 maternal expectations Time 1 expectancies
Time 2 persistence (β = .06).
---Insert Figure 2 about here---
Alternative Model
An alternative model tested whether Time 1 maternal expectations and Time 1 contextual
support directly affect Time 2 persistence, rather than have an indirect or mediated effect via
Time 1 educational expectancies. This alternative model is otherwise identical to the model
depicted in Figure 2.
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Reviewing Table 2, the alternative model fit the data worse than the Figure 2 model. The
TLI and WRMR values did not meet cutoffs for good fit, suggesting that paths within this model
should not be interpreted and that the alternative model should be rejected in favor of the Figure
2 model. Considering the relations among constructs in both models, maternal expectations and
contextual support appear to indirectly affect persistence by fostering youths’ expectancies,
rather than directly affecting persistence.
Discussion
Given longstanding socioeconomic disparities in educational attainment, policy
initiatives emphasizing degree attainment for a greater number of U.S. citizens, and low-income
youths’ difficulty persisting across multiple “checkpoints” on the path to degree completion, it is
important to understand precursors to low-income youths’ postsecondary persistence. Pre-college
contexts affected youths’ expectancies, which directly predicted postsecondary persistence three
years later and indirectly affected persistence five years later (via Time 2 persistence). Maternal
expectations played a more important role in youths’ formation of expectancies than contextual
support from youths’ friends and family. In turn, educational expectancies were reasonably
strong predictors of youths’ postsecondary persistence three and five years later. The finding that
micro-level supports are important in educational processes converges with previous scholarship
(Deil-Amen & Rosenbaum, 2003; Perna & Titus, 2006), while advancing the literature by
suggesting the mediated chain of constructs that explain low-income youths’ postsecondary
persistence over time. The testing and rejection of an alternative model where Time 1 maternal
expectations and contextual support directly predicted Time 2 persistence, rather than were
mediated by Time 1 expectancies, further supports expectancies as mediators of contextual
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supports in youths’ postsecondary persistence. These findings also support and extend the
expectancy-value model by examining how it explains postsecondary persistence.
More efficacious parents were better-able to insulate their children from the adverse
effects of poverty and foster educational outcomes (Furstenberg et al, 1999). That is, parental
self-efficacy indirectly affected youths’ expectancies (mediated by Time 1 maternal
expectations) but its indirect effect on postsecondary persistence only approached significance.
This study also contributes to the literature by suggesting mediating mechanisms by which low-
income parents are able to foster youths’ expectancies.
Gender and age were statistically controlled, but had little effect on constructs in the
structural model. The gender covariate only significantly predicted contextual support,
suggesting that female participants discussed their future plans with their mother and friends
more often than male participants. The age covariate did not significantly predict any constructs,
suggesting that age heterogeneity in CDS/TA participants did not bias any measures.
Woodcock-Johnson scores generally correlate with other academic achievement
measures, but served as an imperfect control. Academic achievement did not predict educational
expectancies, diverging from the expectancy-value model (Eccles & Wigfield, 1995). Many
youth have inflated educational expectations, wherein the majority expect to attain at least a 4-
year degree, but few realize these ambitions (Schneider & Stevenson, 1999). These participants
may have similarly held inflated expectancies for future postsecondary success, irrespective of
their academic performance.
Academic achievement did not predict Time 2 or 3 persistence, although each path
approached significance (p = .03 and p = .06, respectively). Achievement did predict maternal
expectations, suggesting parents hold higher expectations for high-achieving youth. Because
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achievement plays an obviously important role in persistence, these non-significant findings
were unexpected – although it was important to control for achievement. Perhaps the time
elapsed between Time 1 and Times 2 and 3 (three and five years, respectively) dampened the
impact of Time 1 achievement on later persistence.
Limitations and Future Directions
It is difficult to examine all components of the multi-faceted expectancy-value model.
Cultural milieus and contexts are theoretically mediated by youths’ perceptions of external actors
and their self-schemata, but could not be examined in this study (Eccles & Wigfield, 1995).
Future studies could more closely examine how contexts affect youths’ expectancies, as well as
youths’ subjective valuing of postsecondary success.
Although racial/ethnic minorities also encounter barriers to persistence, this study’s focus
on socioeconomic disparities precluded closer examinations of racial/ethnic differences. This
subpopulation was predominantly comprised of youth of color (82.0%), mostly African
Americans (68.9%), which would provide insufficient sample sizes to separately examine these
constructs among low-income White youth and youth of color, a topic for future research.
Sampling weights could not be used in these analyses, presumably because this study
examined a subpopulation that the weights were not designed for. These unweighted analyses are
not nationally representative and run the risk of inflated Type I error – the latter addressed by
using a more conservative (.01) significance criterion.
These datasets do not include transcript data, which more accurately measure
postsecondary outcomes than self-reports (Goldrick-Rab, 2006) and would be preferable in
future studies. Participants in this study may have over-reported degrees they had attained or
15
under-reported dropout. Only seven participants (1.6%) reported attaining at least a 2-year
degree by TA-07, suggesting that over-reporting was not widespread.
Postsecondary GPA was not examined because these measures were too flawed (e.g.,
high missingness, non-respondents not delineated from participants who never attended a
postsecondary institution – precluding imputation). Future research should include postsecondary
GPA, which plays an obviously important role in persistence – although academic achievement,
which was statistically controlled, also plays an important role (Adelman, 1999). The latent
contextual support construct did not include parents’ financial support, which affects persistence
and should be examined in future research (Terenzini et al., 2001).
Summary and Conclusions
Because low-income youth are at increased risk for dropout and degree non-completion,
it is important to understand factors that contribute to their postsecondary persistence. This study
contributes to the literature by a) supporting and extending the expectancy-value model (Eccles
& Wigfield, 1995) by examining how it explains postsecondary persistence over time, b)
suggesting mediating mechanisms by which micro-level supports directly affect youths’
expectancies and indirectly affect postsecondary persistence over time, c) indicating how more
efficacious low-income parents form higher maternal expectations, engendering children’s
expectancies, d) more comprehensively and longitudinally measuring persistence than previous
scholarship, and e) applying SEM to panel longitudinal data and controlling for academic
achievement, age, and gender, obtaining more precise estimates of relations among constructs
(Kline, 2005). In sum, this study suggests that pre-college contexts and youths’ expectancies
clearly matter in explaining how low-income youth progress through intermediate checkpoints –
postsecondary persistence – on the path to degree completion.
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References
Adelman, C. (1999). Answers in the tool box: Academic intensity, attendance patterns,
and bachelor’s degree attainment. Washington, DC: Office of Educational Research and
Improvement, U.S. Department of Education.
Bowen, W.G., Chingos, M.M. & McPherson, M.S. (2009). Crossing the finish line:
Completing college at America’s public universities. Princeton, NJ: Princeton University Press.
Deil-Amen, R. & Rosenbaum, J.E. (2003). The social prerequisites of success: Can college
structure reduce the need for social know-how? Annals of the American Academy of Political and Social
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Eccles, J.S. & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents’
achievement task values and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21(3),
215-225.
Eccles, J.S., Vida, M.N. & Barber, B. (2004). The relation of early adolescents’ college
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Furstenberg, F.F., Cook, T.D., Eccles, J., Elder, G.H. & Sameroff, A. (1999). Managing
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Kline, R. B. (2005). Principles and practice of structural equation modeling:
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Muthén.
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Perna, L.W. & Titus, M.A. (2006). The relationship between parental involvement as
social capital and college enrollment: An examination of racial/ethnic group differences. The
Journal of Higher Education, 76(5), 485-518.
Roosa, M., Deng, S., Nair, R. & Burrell, G. (2005). Measures for studying poverty in
family and child research. Journal of Marriage and Family, 67(4), 971-988.
Schneider, B. & Stevenson, D. (1999). The ambitious generation: America’s teenagers,
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Terenzini, P.T., Cabrera, A.F. & Bernal, E. M. (2001). Swimming against the tide: The
poor in American higher education. New York, NY: College Board Publications.
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Table 1: Variables List & Descriptive Data
19
Latent Variables Variable and Description Possible Responses M SD Skew KurtMissing
%
Time 1 Parental Self-Efficacy
CDS-II-PCG-J10A: There is really no way I can solve some of the problems I have
1 = Strongly Disagree, 2 = Disagree, 3 = Agree, 4 = Strongly Agree.
(Reverse-coded items have been rekeyed.)
2.86 .87 -.35 -.59 8.9
CDS-II-PCG-J10B: Sometimes I feel that I’m being pushed around in life
2.87 .86 -.24 -.72 8.9
CDS-II-PCG-J10C: I have little control over the things that happen to me
3.03 .74 -.42 -.10 8.9
CDS-II-PCG-J10D: I often feel helpless in dealing with the problems of life 3.03 .74 -.39 -.18 8.9
Time 1Academic
Achievement
CDS-II-Child-Q24LWRAW: Letter Word Raw Score Continuous, 0-58 45.57 7.05 -1.17 1.68 18.9
CDS-II-Child-Q24PCRAW: Passage Comprehension Raw Score
Continuous, 0-43 26.49 5.29 -.59 1.48 19.8
CDS-II-Child-Q24APRAW: Applied Problems Raw Score
Continuous, 0-60 37.88 6.18 .15 .42 19.6
Time 1 Educational Expectancies
CDS-II-Child-L10: How far do you think you will actually go in school
1 = Leave high school before graduation, 2 = Graduate from high school, 3 = Graduate from vocational school, 4 = Graduate from 2-year
college, 5 = Attend 4-year college, 6 = Graduate from 4-year college, 7 = Get more than 4 years of
college.
4.56 1.82 -.30 -1.25 20.3
CDS-II-Child-J34D: You will graduate from a 4-year college
1 = No Chance, 2 = Some Chance, 3 = About 50-50, 4 = Pretty Likely, 5 = It Will Happen. 3.58 1.27 -.49 -.91 18.9
Time 1 Maternal Expectations
CDS-II-PCG-B2: How much schooling do you expect that child will really complete
1 = 11th Grade or less, 2 = Graduate from high school, 3 = Post-high school vocational training, 4 = Some
college, 5 = Graduate from 2 year college with associate’s, 6 = Graduate from 4 year college, 7 =
Master’s degree, 8 = MD, Law, Ph.D., or other doctoral degree.
3.89 1.92 .02 -1.70 11.6
Time 1 Contextual Postsecondary
Support
CDS-II-Child-H4B: Talk with your (mother/stepmother) about your plans for the future 1 = Never, 2 = Once or Twice, 3 = About Once a
Week, 4 = About 2 or 3 Days a Week, 5 = Almost Every Day, 6 = Every Day.
3.18 1.66 .38 -1.11 26.2
CDS-II-Child-H4H: Talk with your friends about your plans for the future 3.42 1.73 .06 -1.37 18.0
Time 2 Postsecondary
Persistence
TA05: (composite) Enrollment Status
1 = No diploma and no GED, 2 = GED, 3 = High school diploma, 4 = not enrolled, completed some college, 5 = currently enrolled, 2-year college, 6 =
currently enrolled, 4-year college, 7 = not enrolled, 2-year college graduate.
3.29 1.63 -.22 -1.41 50.3
TA05: (composite) Enrollment Continuity
0 = Never enrolled in a postsecondary institution, 1 = Dropout, 2 = Stopout, 3 = Continuous enrollment or left for compelling academic reasons, 4 = Attained at
least an associates
1.75 1.40 -.36 -1.74 62.2
20
Table 2: Model Fit Indices
Measurement Model Structural Model Alternative ModelModel Fit Index
CFI .99 .93 .91TLI .98 .90 .88
RMSEA .03 .06 .06WRMR .64 1.03 1.16
21
Table 3: Measurement Model: Factor Loadings for Latent Variables
Note. Standardized estimates represent the loading of an indicator on latent constructs; significant loadings are denoted in the second column with an asterisk. Values equal to or larger than 2.58 in the fourth column are significant at the .01 level.
Latent Variable and Indicators StandardizedEstimate SE Standardized
Estimate/SE
Time 1 Parental Self EfficacyCDS-II-PCG-J10A: There is really no way I can solve some of the problems I
have.63* .03 20.07
CDS-II-PCG-J10B: Sometimes I feel that I’m being pushed around in life .82* .02 35.92CDS-II-PCG-J10C: I have little control over the things that happen to me .72* .03 24.70CDS-II-PCG-J10D: I often feel helpless in dealing with the problems of life .80* .03 30.03
Time 1 Academic AchievementCDS-II-Child-Q24LWRAW: Letter Word Raw Score .86* .03 27.72CDS-II-Child-Q24PCRAW: Passage Comprehension Raw Score .90* .03 31.06CDS-II-Child-Q24APRAW: Applied Problems Raw Score .76* .04 20.39
Time 1 Educational ExpectanciesCDS-II-Child-L10: How far do you think you will actually go in school .85* .04 21.84CDS-II-Child-J34D: You will graduate from a 4-year college .90* .04 21.03
Time 1 Contextual Postsecondary SupportCDS-II-Child-H4B: Talk with your (mother/stepmother) about your plans for the future
.74* .12 5.97
CDS-II-Child-H4H: Talk with your friends about your plans for the future .50* .09 5.32Time 2 Postsecondary Persistence
TA05 Enrollment Status Composite .93* .04 24.30
TA05 Enrollment Continuity Composite .90* .03 27.61Time 3 Postsecondary Persistence
TA07 Enrollment Status Composite .92* .02 37.83TA07 Enrollment Continuity Composite 1.00* .03 36.33
22
Parents, Mental Health, Educational Attainment 23
Figure Captions
Figure 1: Conceptual Model
All latent constructs regressed on age and gender covariates; not depicted for clarity.
Figure 2: Structural Model
Note. N = 401. Standardized regression coefficients are noted for each path; coefficients
significant at p < .01 indicated with an asterisk (*). Only significant paths from the covariates are
depicted.
Parents, Mental Health, Educational Attainment 24
Parents, Mental Health, Educational Attainment 25