School Psychology
© 2019 American Psychological Association
2578-4218/19/$12.00
2019, Vol. 34, No. 4, 376 –385
http://dx.doi.org/10.1037/spq0000282
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Parent Involvement, Approaches to Learning, and Student Achievement:
Examining Longitudinal Mediation
Christopher James Anthony
Julia Ogg
Oklahoma State University
Northern Illinois University
Although there is evidence to suggest that parent involvement (PI) in children’s education positively
impacts their academic success, the mechanisms of this effect are less well studied. One potential
mechanism is a set of student-level motivational and behavioral factors labeled approaches to learning
(ATL). The purpose of the current study was to utilize rigorous longitudinal methodology to evaluate
whether ATL mediate the relationship between PI and student academic achievement. Using a large
sample drawn from the Early Childhood Longitudinal Study—Kindergarten Cohort (ECLS-K), three sets
of analyses were conducted focusing on three different types of PI (home-based involvement, schoolbased involvement, and home–school communication). Longitudinal mediation analyses indicated that
only school-based involvement and home–school communication predicted student reading achievement
and that this relationship was only mediated by ATL for school-based involvement. These findings
contribute to the literature base on PI and represent a methodological advance to addressing these
important mediational questions.
Impact and Implications
The current study utilized rigorous methodology to examine whether approaches to learning, a
variable including student motivation, persistence, and engagement, might help explain how parent
involvement affects student achievement. Results indicated that approaches to learning explained this
relationship partially for one type of parent involvement (school-based involvement). Additionally,
the findings shed light on inconsistencies in prior research and the study demonstrated the use of
appropriate methodology for answering these important questions.
Keywords: parent involvement, home-based involvement, school-based involvement, home–school communication, approaches to learning
Supplemental materials: http://dx.doi.org/10.1037/spq0000282.supp
ATL, which are learning-related attitudes and behaviors, represent an
important plausible mechanism by which PI might influence student
achievement. Indeed, there are theoretical models (Hoover-Dempsey
et al., 2005) and initial empirical evidence supporting this pattern (Xu,
Kushner Benson, Mudrey-Camino, & Steiner, 2010). Furthermore,
such a relationship might be especially important in kindergarten,
when the patterns that form the basis of later PI are first developed.
Previous research examining this important mediational question has
been cross-sectional and focused on older students (e.g., fifth graders).
As such, the goal of the present study was to expand on this research
to longitudinally examine whether ATL mediate the relationship
between kindergarten PI and later academic achievement.
The link between parent involvement (PI) and academic achievement is well studied, and meta-analyses have indicated small to
moderate correlations between these constructs (X. Fan & Chen,
2001; Jeynes, 2005). Yet the mechanisms by which PI may lead to
enhanced academic achievement are less well understood. Recently,
increasing scholarly attention (e.g., Anthony, DiPerna, & Amato,
2014) has focused on the role of a set of variables referred to as
approaches to learning (ATL) that have been linked with both PI (e.g.,
Fantuzzo, McWayne, Perry, & Childs, 2004; McWayne, Hampton,
Fantuzzo, Cohen, & Sekino, 2004) and academic achievement (e.g.,
Li-Grining, Votruba-Drzal, Maldonado-Carreño, & Haas, 2010).
Parent Involvement and Achievement
Christopher James Anthony, School of Teaching, Learning and Educational Sciences, Oklahoma State University; Julia Ogg, Department of
Psychology, Northern Illinois University.
Correspondence concerning this article should be addressed to Christopher James Anthony, who is now at School of Special Education, School
Psychology, and Early Childhood Studies, University of Florida, 2-173
Norman Hall, Gainesville, FL 32611. E-mail: canthony@coe.ufl.edu
Although the link between PI and academic achievement is well
established (Garbacz, Herman, Thompson, & Reinke, 2017;
Jeynes, 2005, 2012), the strength of the association depends on the
specific form of PI being investigated. Specifically, theoretical
models of PI have recognized the distinction between three types
of PI: home-based involvement (HBI), school-based involvement
376
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PARENT INVOLVEMENT, ATL, AND ACHIEVEMENT
(SBI), and home–school communication (HSC; Epstein, 1995;
Hoover-Dempsey et al., 2005). Empirical evidence supports this
distinction through factor analysis (Fantuzzo, Tighe, & Perry,
1999; Manz, Fantuzzo, & Power, 2004) and through findings
suggesting that each form of PI relates differently to outcomes
(Fantuzzo et al., 2004; Garbacz, McDowall, Schaughency, Sheridan, & Welch, 2015).
HBI includes reading or engaging in other academic activities
with children as well as more general intellectual activities such as
attending educational events or visiting sites in the community
(e.g., a zoo). Meta-analyses have indicated that HBI shows a small
to moderate positive association with academic achievement, including broad indicators such as GPA and subject-specific indicators (e.g., reading or math scores) of academic achievement in
school-age youth (X. Fan & Chen, 2001; Jeynes, 2005). SBI
includes activities in which a parent participates at school (e.g.,
PTA meetings, family nights). SBI has shown a small, positive
association with broad and subject-specific academic indicators in
meta-analyses (X. Fan & Chen, 2001; Jeynes, 2005). Finally, HSC
involves contact between a child’s parents and teacher or other
school personnel (e.g., in-person or via e-mail). This form of
involvement has been linked inconsistently to academic outcomes.
For example, Fantuzzo et al. (2004) found that although preschoollevel HSC in the fall was positively correlated with spring receptive vocabulary scores, controlling for HBI rendered this association nonsignificant. In another study with a large, national sample
of fifth grade students, Xu et al. (2010) found that some aspects of
HSC (e.g., homework help) were negatively associated with reading assessment scores. One possibility for this inconsistency is that
HSC may concern both positive and negative child behaviors (i.e.,
HSC may increase in response to increased incidence of problematic behavior). Together, research suggests that at least two of the
three forms of PI (HBI and SBI) are positively related to academic
achievement, whereas this association is less clear with regard to
HSC, highlighting the importance of considering PI as multidimensional.
Parent Involvement and Approaches to Learning
PI has also been linked with aspects of ATL (W. Fan &
Williams, 2010; Fantuzzo et al., 2004; Xu et al., 2010). ATL refer
to a broad set of skills, attitudes, and behaviors, typically including
motivation, engagement, and other learning-related behaviors (e.g.,
study skills; DiPerna, Volpe, & Elliott, 2002, 2005). Although
many behaviors fall within this domain, ATL are considered to
broadly reflect the application of self-regulation or “the ability to
manage one’s behavior, emotions, and attention in voluntary and
adaptive ways” (Li-Grining et al., 2010, p. 1062) toward
academic-related goals. Regardless of which specific variables are
included, ATL have consistently been linked with academic
achievement (e.g., Anthony et al., 2014). Furthermore, drawing on
both ecological theory and social– cognitive theories, PI is linked
to ATL in several ways. From an ecological perspective, PI represents mesosystemic contexts (i.e., interactions between a child’s
home and school microsystems) hypothesized to impact studentlevel socioemotional, academic, and behavioral functioning (Bempechat & Shernoff, 2012; Bronfenbrenner & Ceci, 1994). Furthermore, PI has been hypothesized to communicate the value of
377
education from parents to children and may also model and scaffold ATL for children (Bempechat & Shernoff, 2012).
Empirical research has supported this theoretical connection.
Fantuzzo et al. (2004) found that all three forms of PI (SBI, HBI,
and HSC) measured in the fall were associated with three forms of
ATL measured in the spring of preschool: competence motivation,
attention/persistence, and attitudes toward learning. Specifically,
competence motivation was positively associated with all three
forms of PI, whereas HBI and SBI only were associated with
attention/persistence and attitudes toward learning in children enrolled in Head Start. HBI (.30 ⬍ r ⬍ .36) was more strongly
associated with all three ATL variables than SBI (.23 ⬍ r ⬍ .25)
or HSC (.12 ⬍ r ⬍ .23). In a national sample of fifth grade
students, Xu et al. (2010) found that SBI, parental education
expectations, homework frequency, and extracurricular activities
positively predicted students’ ATL. In the Xu et al. study, two
other forms of PI, TV rules and homework help, negatively predicted ATL. These finding indicate that although ATL is associated with all three forms of PI, different types of PI relate differently in terms of strength and direction of association to ATL.
Approaches to Learning and Achievement
The link between PI and ATL is particularly important because a
number of studies have documented a positive association between
ATL and academic achievement (Claessens, Duncan, & Engel, 2009;
DiPerna, Volpe, & Elliott, 2002; Duncan et al., 2007). ATL-related
constructs have been proposed to be one component of academic
competence (DiPerna & Elliott, 2002), which also consists of academic skills. This model of academic competence posits that both
academic skills and ATL (referred to as “academic enablers” by
DiPerna & Elliott, 2002) are integral aspects of students’ academic
development and school success. Several studies have supported this
conceptualization. For example, in two studies with kindergarten
through sixth grade students, DiPerna and colleagues (2002; DiPerna,
Volpe, & Elliott, 2005) found a moderate to strong association between ATL constructs, including engagement, motivation, and
achievement, in both reading and mathematics. Also, after combining
data from six large, longitudinal data sets, Duncan and colleagues
(2007) found that a variable that included ATL at school entry had a
small positive effect on later reading and math achievement. Such
findings are consistent with decades of research tying ATL-related
variables to academic achievement (e.g., Farrington et al., 2012;
Wang, Haertel, & Walberg, 1993).
Parent Involvement, Approaches to Learning,
and Achievement
Although PI may directly influence achievement (e.g., skill
building through homework), and much of the current literature
has examined this direct relationship, PI could also indirectly
influence achievement through the development of students’ ATL
(Bempechat & Shernoff, 2012). Several authors (Grolnick &
Slowiaczek, 1994; Xu et al., 2010) have explicitly proposed this
hypothesis, and theoretical models of PI have also emphasized this
possibility. Specifically, Hoover-Dempsey et al.’s (2005) model of
PI proposes that the effects of PI on achievement are mediated
through student attributes (i.e., academic self-efficacy, motivation,
self-regulation) consistent with ATL.
ANTHONY AND OGG
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378
In an empirical test of ATL as mediators, Grolnick and Slowiaczek (1994) examined whether the impact of PI (including HBI,
SBI, and HSC) on school performance was mediated through
children’s motivational resources (self-regulation, perceived competence, and control understanding) in a cross-sectional study of
middle-school students. Their findings indicated that students’
perceived competence and control understanding (i.e., their knowledge of how outcomes are linked to their behavior) both mediated
the relationship between HBI and HSC and students’ grades (averaged across curricular areas). Furthermore, using a national
cross-sectional sample of fifth graders from the Early Childhood
Longitudinal Study—Kindergarten Cohort of 1998 –1999 (ECLS-K;
Tourangeau, Nord, Lê, Sorongon, & Najarian, 2009), Xu et al. (2010)
found that ATL mediated the relationship between PI and reading
achievement. These findings point to the potential importance of ATL
as mediators of the impact of PI on student achievement.
Developmental Context of Mediation
One important aspect of studying associations between PI and
academic achievement is considering the developmental processes in
which effects are embedded. Students’ academic skills, as well as
their ATL (e.g., Mahatmya, Lohman, Matjasko, & Farb, 2012), develop throughout their academic careers. Furthermore, prior research
has found that that levels of PI also differ across grade level. For
example, a recent examination of patterns of the three forms of PI
(HBI, SBI, and HSC) in a large, multigrade, cross-sectional elementary school sample indicated that levels of SBI were roughly equal
across grades, whereas HBI and HSC were lower at the higher
elementary school grades (Garbacz et al., 2015). Thus, interrelationships between PI, ATL, and achievement occur within dynamic
developmental systems. As such, mediation may vary substantially
depending on when and for how long it is examined.
One particularly important period may be in kindergarten, when
a large majority of parents are interested in becoming involved
(McIntyre, Eckert, Fiese, DiGennaro, & Wildenger, 2007). As
further indication of the potential importance of examining PI in
kindergarten, research has shown that levels of PI in early elementary school are predictive of PI later in elementary school (Englund, Luckner, Whaley, & Egeland, 2004). Thus, interactions
between home and school during this period may set the stage for
ongoing interrelationships between PI, ATL, and achievement.
Such a possibility has not been previously examined, as prior
studies have focused on potential mediation in fifth grade (Xu et
al., 2010) and in middle school (Grolnick & Slowiaczek, 1994).
Current Study
The current study addresses several theoretical and methodological gaps in research on PI. A key limitation in research on PI is the
use of a wide variety of definitions for PI that are not aligned with
the constructs identified through theoretical work (i.e., SBI, HBI,
HSC), which may contribute to mixed findings (e.g., some studies
combine HSC and SBI). To address this gap, the current study
used a consistently empirically supported theoretical model of PI
by defining PI with three separate factors: HBI, SBI, and HSC
(Fantuzzo et al., 2004; Hoover-Dempsey et al., 2005).
Furthermore, much of the prior research on PI, in general, and
this mediational question, in particular, has been cross-sectional.
This makes it difficult to fully understand the dynamic relation-
ships between PI and other variables that occur across time,
especially mediational relationships. In fact, many methodologists
(e.g., Selig & Preacher, 2009) contend that only longitudinal
approaches can appropriately assess mediation, which necessarily
occurs over time. To address this shortcoming, the current study
builds on prior research by examining mediation that was explicitly longitudinal. This conceptualization also ties in with the importance of considering the dynamic developmental context in
which mediation occurs. As such, the current study incorporates
both methodological and conceptual strengths by considering mediation longitudinally as embedded within a particular developmental period in children’s school careers.
Research Questions and Hypotheses
The following broad research question guided the current study:
Do ATL mediate the relationship between kindergarten-level PI
and later academic achievement? This research question was divided in two subquestions framed according to our methodology:
1.
Does kindergarten-level PI (HBI, SBI, and HSC) exert a
total effect on later reading achievement? Based on prior
research demonstrating a positive association between
HBI and SBI (e.g., X. Fan & Chen, 2001), we hypothesize that both HBI and SBI will have a small total effect
on reading achievement. Given the lack of consistency
between HSC and achievement (e.g., Fantuzzo et al.,
2004), we hypothesize no significant total effect between
HSC and reading achievement.
2.
If a total effect is evident, does kindergarten-level PI
(HBI, SBI, and HSC) exert an indirect effect (indicating
mediation) on later reading achievement through ATL?
Based on prior research in this area, we hypothesize that
ATL will mediate the relationship between HBI and SBI
(Grolnick & Slowiaczek, 1994; Xu et al., 2010) and
reading achievement. Because we hypothesize no significant total effect between HSC and reading achievement,
we also hypothesize no significant indirect effect.
We also explored the individual components of the models used to
evaluate longitudinal mediation (i.e., PI on ATL and ATL and reading
achievement). Overall, we hypothesize that HBI and SBI will be
positively associated with ATL, whereas HSC will not be associated
with ATL. In addition, we anticipate a small to moderate positive
association between ATL and reading achievement based on prior
research in this area (e.g., Claessens et al., 2009; Duncan et al., 2007).
Method
Participants
Participants for this study were drawn from the ECLS-K, which
includes a nationally representative sample followed from kindergarten through the eighth grade. Based on data availability, data
were drawn from three waves for HBI (during the fall of the
kindergarten and spring of the first and third grade waves) and
HSC (during the spring of the kindergarten, first, and third grade
waves) analyses, and from four waves for SBI analyses (during
the spring of the kindergarten, first, third, and fifth grade waves).
PARENT INVOLVEMENT, ATL, AND ACHIEVEMENT
Because of the complex sampling design utilized by the ECLS-K,
sample weights are needed to ensure that statistical estimates are
representative of the target population. We identified and applied
the appropriate sampling weight for each set of analyses. Prior to
analysis, cases were dropped if they were missing data on the
weight variable. An additional 11 cases were missing all analytic
variables for the SBI data and were dropped. After dropping these
cases, the initial sample was 16,555 for both the HSC and HBI
samples, and 12,011 for the SBI sample (see Table 1).
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Measures
Parent involvement. PI was conceptualized as multidimensional and measured in three ways for the three different models in
this study. First, HBI was indicated by nine parent-rated items
utilizing a Likert scale ranging from 1 (not at all) to 4 (every day).
SBI was indicated by five parent-rated dichotomous (yes–no)
items. Finally, HSC was indicated by four teacher-rated dichotomous (yes–no) items. These sets of items have been used in
previous studies (e.g., Galindo & Sheldon, 2012) to indicate PI and
have been linked as expected with various outcomes (e.g., Xu et
al., 2010; Youn, Leon, & Lee, 2012).
Approaches to learning. ATL was assessed through a subscale of the social rating scale developed specifically for the
ECLS-K. This subscale included six items measuring teacher-rated
learning behaviors, including behaviors related to working independently, eagerness to learn, and student persistence. These items
Table 1
Weighted Demographic Characteristics of Participants for
Home-Based Involvement, School-Based Involvement, and
Home–School Communication Samples
Home-based School-based Home–school
involvement involvement communication
(n ⫽ 16,555) (n ⫽ 12,011) (n ⫽ 16,555)
Characteristic
Weighted %
Weighted %
Weighted %
Girls
Race/ethnicity
White, non-Hispanic
African American
Hispanic
Asian
Other
Region
Northeast
Midwest
South
West
Area
Central city
Urban-fringe/largetown
Small town and rural
School type
Public school
Private school
Disability status
Child with disability
Child without disability
49
49
49
58
16
19
3
5
58
16
19
3
5
58
16
19
3
5
18
23
37
22
18
23
37
22
18
23
37
22
37
42
37
42
37
42
21
21
21
85
15
85
13
85
15
16
84
16
84
16
84
Note. Percentages do not sum to 100 in some cases because of rounding.
Region, area, school type, and disability status reported for Wave 1
(kindergarten) in each model.
379
were rated on a Likert scale from 1 (never) to 4 (very often). This
scale has been used extensively in previous investigations using
the ECLS-K (e.g., Claessens et al., 2009; Li-Grining et al., 2010)
and has been found to relate as expected with academic achievement in both reading and mathematics (e.g., Anthony et al., 2014).
Student achievement. Student achievement was measured
through the directly administered reading tests developed using
item response theory (IRT) methodology for the ECLS-K. We
selected reading as the primary outcome measure for this study
because the ability to comprehend text is fundamentally important
for achievement across broad content domains throughout students’ educational careers (Ardoin & January, 2018).1 These assessments consisted of various questions focused on reading domains including vocabulary and passage comprehension. Reliability
of theta (an IRT-based internal consistency index) ranged from .91 to
.96 for these scores across waves. Furthermore, as validity evidence,
the patterns of reading score growth across the ECLS-K sample frame
were similar to those observed in similar longitudinal studies across
the same period (Najarian, Pollack, & Sorongon, 2009). Descriptive
information for student achievement by wave is shown in Table 2.
Covariates. Several covariates were included in all models,
including student gender, family socioeconomic status (SES; a
composite derived from measures of income, educational status,
and occupational status), and family structure. Gender and SES
were time invariant covariates, whereas family structure (parents’
reported marital status) was included as a time-varying covariate.
Descriptive statistics for covariates are displayed in Table 2.
Procedures
Parent data were collected primarily through telephone interviews
conducted by trained ECLS-K staff. Child data consisted of directly
administered assessment batteries also conducted by trained ECLS-K
staff. Finally, teacher data were collected through self-administered
questionnaires. Further details regarding data collection can be found
in the ECLS-K methodology reports (e.g., Tourangeau, Lê, & Nord,
2005). Institutional review board approval for analysis of these data as
an extant dataset was obtained through the first author’s institution.
Data Analysis
Missing data. There were varying levels of missing data
across samples, ranging from 0% to 31% (median ⫽ 15%) across
HBI variables, from 0% to 21% (median ⫽ 9%) across SBI
variables, and from 0% to 52% (median ⫽ 19%) across HSC
variables. Roughly 59%, 47%, and 87% of cases were missing at
least one data point across HBI, SBI, and HSC models, respectively. Because we used the mean and variance adjusted weighted
least squares estimator (WLSMV), which relies on pairwise deletion, we used multiple imputation prior to analyses to appropriately
address missing data. Based on the recommendation to generate
more imputed data sets than the number of incomplete cases
(White, Royston, & Wood, 2011), we generated 60, 50, and 90
imputed data sets for the HBI, SBI, and HSC models, respectively.
1
Although we have focused on reading in the current study, we also
recognize the importance of math skills in elementary school. Thus, we
have run parallel models for math. These results can be seen in Tables 1–2
in the online supplemental materials.
ANTHONY AND OGG
380
Table 2
Weighted Descriptive Statistics for Analysis Variables
Across Wave
Home-based
involvement
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Variable
Reading achievement
Wave 1
Wave 2
Wave 3
Wave 4
Wave 1 SES
Married parents
Wave 1
Wave 2
Wave 3
Wave 4
School-based
involvement
Home–school
communication
M
SD
M
SD
M
SD
35.20
77.22
126.27
—
⫺.03
9.98
23.83
28.12
—
.79
46.11
77.12
126.27
148.68
⫺.03
13.60
23.87
28.28
26.56
.79
46.25
77.15
126.20
—
⫺.04
13.79
23.85
28.11
—
.78
69
69
69
—
—
—
—
—
—
—
—
—
68
68
69
—
—
—
—
—
69
68
69
67
Note. Married parents reported as percentages. Reading achievement
scores are on a scale ranging from 0 to 212. Socioeconomic status (SES)
scores are standardized with a mean of 0 and standard deviation of 1.
Latent variable approach. Rather than including PI and ATL
variables in models as sum scores, we utilized a latent variable
modeling approach in which each construct (HBI, SBI, HSC, and
ATL) was indicated by several items. We favored this approach for
two reasons. First, latent variable approaches address measurement
error and disattenuate relationships between constructs (Cole &
Preacher, 2014). Next, latent variable approaches allow for explicitly testing measurement invariance, an important assumption of
all longitudinal models (Little, 2013). Because the availability of
PI measures differed across waves, we conducted three separate
mediation models for HBI, SBI, and HSC variables.
Analysis steps. Several steps based on Cole and Maxwell
(2003) and Little (2013) were conducted to test our hypotheses. All
models were estimated using Mplus Version 7 (Muthén & Muthén,
2017). Because of the categorical nature of the data, the WLSMV
Parent
Involvement
Kindergarten
Approaches
to Learning
Kindergarten
Reading
Kindergarten
arPI1
arATL1
arREAD1
Parent
Involvement
1st Grade
Approaches
to Learning
1st Grade
Reading
1st Grade
arPI2
arATL2
arREAD2
estimator was utilized for all analyses. Overall model fit was
evaluated relative to Hu and Bentler’s (1999) criteria. Specifically,
because chi-square values are sensitive to sample size (Little,
2013), we emphasized root mean squared error of approximation
(RMSEA) values less than .06 and comparative fit index (CFI)/
Tucker-Lewis index (TLI) values greater than .95 when interpreting overall model fit. Relative model fit was evaluated based on
the ⌬CFI ⬍ .01 criteria recommended by Cheung and Rensvold
(2002; i.e., if a more restrictive model did not result in the CFI
decreasing by .01 or more, the more restrictive model was retained). Because the default null model used to calculate CFI and
TLI is not appropriate for longitudinal models, a modified null
model was conducted and used to calculate all CFI and TLI
statistics (Little, 2013).
Step 1: Test measurement invariance. Following steps based
on Little (2013), a series of increasingly restrictive models were tested
to evaluate whether constructs were equivalent and measured equivalently across waves. First, a configural model was tested in which
loadings were freely estimated for constructs across time. Assuming
the configural model evidenced adequate fit, a metric invariance
model was tested in which factor loadings were fixed to be equivalent
across waves. If the metric model did not lead to unacceptable
degradation of model fit relative to the configural model, a scalar
model was tested in which all thresholds were fixed to be equivalent
across time. Once scalar measurement invariance was supported, the
constraints of this model were imposed on all subsequent models.
Step 2: Fit a saturated model. Once scalar invariance was
supported, we fit a saturated panel model in which variables were
regressed on all analytic variables from all previous waves. Statistical significance of paths from this model were evaluated to
help determine the final model for each analysis.
Step 3: Fit and test a final model. In the last step of the
analyses, a model including all paths consistent with longitudinal
partial mediation (i.e., those in Figure 1) and all statistically
significant paths from Step 2 was tested. Such a model was
considered a pruned and final model, assuming it did not lead to
Parent
Involvement
3rd Grade
Approaches
to Learning
3rd Grade
Reading
3rd Grade
arPI3
arATL3
arREAD3
Parent
Involvement
5th Grade
Approaches
to Learning
5th Grade
Reading
5th Grade
Figure 1. Example of longitudinal mediation model including paths estimated for all mediation models. The
a, b, and c paths are labeled consistent with standard mediation models (e.g., Baron & Kenny, 1986). ar ⫽
autoregressive; PI ⫽ Parent Involvement; ATL ⫽ Approaches to Learning; READ ⫽ Reading Achievement.
PARENT INVOLVEMENT, ATL, AND ACHIEVEMENT
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substantial degradation in model fit (as evaluated by ⌬CFI value
relative to the scalar invariance model). Once this model was
identified, total, direct, and indirect effects were calculated by
summing the products of different paths constituting different
types of effects. Direct effects were calculated by summing the
products of all paths by which PI in the first wave of the study
affected reading achievement in the final wave of the study without passing through ATL. Indirect effects represented the sum of
the products of all paths by which PI in the first wave affected
reading achievement in the final wave passing through ATL. For
example, the following equations would represent direct and indirect effects in Figure 1:
Direct effect ⫽ (c1 * arREAD3) ⫹ (arPI1 * c2) and
Indirect effect ⫽ (a1 * arATL2 * b2) ⫹ (a1 * b1 * arREAD3)
⫹ (arPI1 * a2 * b2).
Total effects represent the sum of all direct and indirect paths.
Statistical significance of these effects were evaluated using the
Monte Carlo method outlined by Preacher and Selig (2012). These
calculations were conducted with the monteCarloMed option from
the semTools package (semTools Contributors, 2016) in R Version
3.3.1 (R Core Team, 2016). If the generated 95% confidence
interval (CI) included zero, the effect was not considered statistically significant. To gauge magnitude of statistically significant
effects, direct, indirect, and total effects were recalculated with
fully standardized paths. These standardized effects were interpreted according to Cohen’s (1988) criterion for small (⬍ .10),
medium (.10 –.30), and large (⬎ .30) effects.
Results
Home-Based Involvement
The configural model fit the data adequately (see Table 3), and
neither the metric nor the scalar models led to unacceptable decrements in model fit (⌬CFI ⫽ ⬍.001 and .009 for these models,
381
respectively). Fit information from the final model did not indicate
substantial decrements in model fit (⌬CFI ⫽ .011) relative to the
scalar invariance model. Constrained unstandardized loadings
ranged from .40 to .58 (median ⫽ .54) for HBI indicators and from
.68 to .89 (median ⫽ .82; Table 4) across ATL indicators. Based
on parameter estimates from the final model, neither the total
effect of HBI on ATL (total effect ⫽ 0.005, 95% CI [⫺0.007,
0.016]) nor the direct effect of HBI on reading (direct effect ⫽
0.004, 95% CI [⫺0.011, 0.011]), nor the indirect effect of HBI on
reading through ATL (indirect effect ⫽ 0.001; 95% CI [⫺0.001,
0.002]) was statistically significant.
Standardized parameter estimates indicated that HBI did not have
a statistically significant effect on either ATL or reading in any wave.
Likewise, ATL did not have a statistically significant effect on HBI in
any wave. In contrast, ATL had a positive effect on reading both from
kindergarten to first grade (␤ ⫽ .16, p ⬍ .001) and third grade (␤ ⫽
.09, p ⬍ .001), and from first grade to third grade (␤ ⫽ .11, p ⬍ .001).
Reading scores in kindergarten had a negative effect on HBI in both
first grade (␤ ⫽ ⫺.09, p ⬍ .001) and third grade (␤ ⫽ ⫺.05, p ⫽
.047). Reading scores in first grade also had a negative effect on HBI
in third grade (␤ ⫽ ⫺.06, p ⫽ .022). Reading scores in kindergarten
had a positive effect on ATL in first grade (␤ ⫽ .18, p ⬍ .001), and
reading scores in first grade had a positive effect on ATL in third
grade (␤ ⫽ .13, p ⬍ .001).
School-Based Involvement
For the SBI models, the configural invariance model fit the data
adequately (see Table 3). Neither the metric nor the scalar models
led to substantial decrements in model fit (⌬CFI ⫽ .001 for both
models), and thus scalar invariance was imposed on all subsequent
models. Constrained unstandardized loadings ranged from .35 to
.59 (median ⫽ .47; Table 4) for SBI indicators and from .68 to .90
(median ⫽ .82) across ATL indicators. After fixing all nonsignificant paths from the full model to zero, a further two paths were
also nonsignificant and constrained to zero in the final model. The
final model did not lead to substantial decrements in model fit
Table 3
Model Fit Statistics
Model
Home-based involvement
Configural invariance
Metric invariance
Scalar invariance
Final model
School-based involvement
Configural invariance
Metric invariance
Scalar invariance
Final model
Home–school communication
Configural invariance
Metric invariance
Scalar invariance
Final model
␹2(df)
RMSEA
CFI
⌬CFI
TLI
3,797.45 (1158)
3,814.63 (1184)
4,867.75 (1270)
3,625.18 (1302)
.014
.014
.016
.013
.977
.977
.968
.979
—
⬍.001
.009
.011
.972
.972
.965
.978
1,928.52 (1132)
1,887.81 (1159)
2,054.06 (1222)
2,075.20 (1290)
.009
.008
.009
.008
.991
.992
.991
.991
—
.001
.001
⬍.001
.989
.990
.989
.990
3,205.95 (528)
2,902.37 (544)
2,966.77 (584)
2,891.69 (616)
.020
.019
.018
.017
.981
.984
.983
.984
—
.003
.001
.001
.975
.979
.980
.982
Note. All chi-square statistics were statistically significant. All statistics are averages over 60, 50, and 90
imputed data sets for home-based involvement, school-based involvement, and home–school communication
models respectively. df ⫽ degrees of freedom; RMSEA ⫽ root mean squared error of approximation; CFI ⫽
comparative fit index; TLI ⫽ Tucker-Lewis index.
ANTHONY AND OGG
382
Table 4
Unstandardized Factor Loadings for Final Measurement
Invariance Models
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Item
Home-based involvement
Tell stories?
Sing songs?
Help to do arts and crafts?
Involve in household chores?
Play games or do puzzles?
Talk about nature or do science projects?
Build something or play with construction toys?
Play a sport or exercise together?
Read books?
School-based involvement
Attended an open house or back-to-school night?
Attended a meeting of a PTA, PTO, or Parent–Teacher
Organization?
Attended a school or class event?
Volunteered at the school or served on a committee?
Participated in fundraising for your child’s school?
Home–school communication
Attended regularly scheduled conferences at your school?
Attended informal meetings that you initiated to talk
about the child’s progress?
Returned your telephone calls?
Initiated contact with you?
Approaches to learning
Keeps belongings organized?
Shows eagerness to learn new things?
Works independently?
Easily adapts to changes in routine?
Persists in completing tasks?
Pays attention well?
Loading
.58
.49
.40
.54
.54
.55
.55
.48
.50
.51
.35
.47
.59
.39
.85
.86
.90
.25
.69
.77
.89
.68
.86
.89
Note. Unstandardized loadings presented, as these are constrained to be
equal across time. Approaches to learning loadings presented from homebased involvement model, but loadings from other models were very
similar (all differences in loadings ⱕ.02). PTA ⫽ Parent-Teacher Association; PTO ⫽ Parent-Teacher Organization.
relative to the scalar model (⌬CFI ⬍ .001). Based on parameter
estimates from the final model, the total effect of SBI in kindergarten on reading achievement was statistically significant (total
effect ⫽ 0.11, 95% CI [0.09, 0.13], standardized effect ⫽ 0.26).
Next, the direct effect of SBI in kindergarten on reading in the fifth
grade was also statistically significant (direct effect ⫽ 0.10, 95%
CI [0.08, 0.12], standardized effect ⫽ .25), as was the indirect
effect of SBI on reading through ATL (indirect effect ⫽ 0.004,
95% CI [0.002, 0.007], standardized effect ⫽ 0.01).
Standardized parameter estimates indicated that SBI had a positive effect on ATL from kindergarten to first grade (␤ ⫽ .07, p ⫽
.042), and from first grade to third grade (␤ ⫽ .07, p ⫽ .011), but
not from third grade to fifth grade. SBI in kindergarten also had a
positive direct effect on reading in first grade (␤ ⫽ .06, p ⫽ .003),
third grade (␤ ⫽ .33, p ⬍ .001), and fifth grade (␤ ⫽ .22, p ⫽
.001). In contrast, SBI in first grade had a negative effect on
reading in third grade (␤ ⫽ ⫺.16, p ⫽ .021), and SBI in third grade
had a negative effect on reading in fifth grade (␤ ⫽ ⫺.10, p ⫽
.004). With regard to ATL, no statistically significant effects of
ATL on SBI emerged in the final model. ATL in kindergarten had
a positive effect on reading in first and third grades (␤ ⫽ .14, p ⬍
.001, and ␤ ⫽ .05, p ⫽ .020, respectively). Likewise, ATL had a
positive impact on reading from first grade to third grade (␤ ⫽ .12,
p ⬍ .001) and from third grade to fifth grade (␤ ⫽ .06, p ⬍ .001).
Reading scores in kindergarten had a positive effect on SBI in third
grade (␤ ⫽ .07, p ⫽ .026), but no other interval effect reached
statistical significance. Finally, reading scores had a positive effect
on ATL from kindergarten to first grade (␤ ⫽ .14, p ⬍ .001) and
from first grade to third grade (␤ ⫽ .06, p ⫽ .011), but not from
third grade to fifth grade.
Home–School Communication
Finally, analysis steps were repeated with the HSC model. First,
the configural model evidenced adequate model fit (see Table 3).
Neither the metric (⌬CFI ⫽ .003 relative to the configural model)
nor the scalar (⌬CFI ⫽ .001 relative to the metric model) models
led to substantial decrements in model fit, and thus scalar invariance was supported. Constrained unstandardized loadings ranged
from .25 to .90 (median ⫽ .86; Table 4) for HSC indicators and
from .69 to .90 (median ⫽ .84) across ATL indicators. The final
model fit the data well (see Table 3) and was associated with a low
⌬CFI value (.001 relative to the scalar model). Based on parameter
estimates from the final model, the total effect of HSC on reading
achievement was statistically significant (total effect ⫽ 0.103,
95% CI [0.084, 0.123], standardized effect ⫽ .213) as was the
direct effect of HSC on reading (direct effect ⫽ .102, 95% CI
[0.083, 0.120], standardized effect ⫽ .209). The indirect effect;
however, was not statistically significant (indirect effect ⫽ 0.002;
95% CI [⫺0.001, 0.004]).
With regard to parameter estimates in the final model, standardized estimates indicated that HSC did not have a statistically
significant effect on ATL in any wave. In contrast, HSC in kindergarten had a positive effect on reading in both first grade (␤ ⫽
.10, p ⬍ .001) and third grade (␤ ⫽ .21, p ⬍ .001), but HSC in first
grade had a negative effect on reading in the third grade
(␤ ⫽ ⫺.08, p ⫽ .005). The final model also indicated that ATL in
kindergarten had a negative effect on HSC in first grade (␤ ⫽ ⫺.11,
p ⫽ .006), but no effect from first grade to third grade. ATL had a
positive effect on reading both from kindergarten to first grade (␤ ⫽
.11, p ⬍ .001), and from first grade to third grade (␤ ⫽ .13, p ⬍ .001).
Finally, reading scores in kindergarten had a positive effect on HSC
in first grade (␤ ⫽ .19, p ⬍ .001), but not from first grade to third
grade. Reading also had a positive effect on ATL both from kindergarten to first grade (␤ ⫽ .18, p ⬍ .001) and from first grade to third
grade (␤ ⫽ .11, p ⬍ .001).2
Discussion
The current study examined whether ATL mediated the relationship between three forms of PI (HBI, SBI, HSC) and reading
achievement. Several major findings emerged from our analyses.
First, the finding that HBI was not associated with statistically
significant total or direct effects on children’s reading was unexpected. From an ecological perspective, HBI is unique relative to
2
As stated previously, we conducted equivalent analyses with the
ECLS-K mathematics test. These results indicated that although the general
mediational patterns found with reading held with math, the overall magnitude of these effects was slightly less than half as large as the effects
observed with reading. These results are available in Tables 1–2 in the
online supplemental materials.
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PARENT INVOLVEMENT, ATL, AND ACHIEVEMENT
SBI and HSC because it likely involves the least amount of
interaction between the home and school microsystems. As such,
the coordination between systems may represent a particularly
important way for parents to be involved in order to improve
school-based outcomes (e.g., achievement). This possibility could
have been compounded by the nature of the items used to indicate
HBI in this study. Specifically, items for this factor were not
focused on school-based academic material (e.g., homework completion), whereas previous investigations of HBI have included
some content related to such academic activities (Fantuzzo et al.,
2004). It is possible that these different types of HBI (broadly
cognitively stimulating vs. academic-focused) have differential
effects and should be considered separately.
Next, SBI and HSC significantly predicted children’s reading
achievement when examined longitudinally. Based on prior research (X. Fan & Chen, 2001; Fantuzzo et al., 2004), we anticipated that SBI would be a significant predictor of achievement.
Our findings corroborated this hypothesis using a rigorous, longitudinal design. It is striking that this longitudinal effect persisted
and was moderate across several years during children’s early
educational experiences, supporting the importance of these forms
of PI for student achievement. In contrast to SBI, we predicted the
HSC would not predict achievement, considering inconsistent
findings linking HSC to achievement (e.g., Fantuzzo et al., 2004).
This hypothesis was rejected by the overall model. Instead, our
findings indicated that HSC levels in kindergarten positively predicted reading achievement in third grade. Overall, these findings
highlight the importance of developing strong PI early in elementary school, as the current study showed that the academic benefits
last into later elementary school. Prior research has indicated a
majority of parents want to be involved in their child’s transition
to kindergarten; thus, school entry appears to be an excellent time
to establish home–school relationships (McIntyre et al., 2007).
Although not the emphasis of the study, the utilized longitudinal
design can potentially shed light on some reasons for the inconsistent
patterns found in the HSC literature. In the final HSC model, HSC in
kindergarten positively predicted reading in both first and third grade,
but HSC in first grade negatively predicted third grade reading.
Because kindergarten levels of HSC are already accounted for by
virtue of the study design, this negative relationship represents the
effect of HSC in first grade on third grade reading controlling for the
effect of kindergarten levels of HSC. As such, the effect of HSC in
first grade represents the effect of changes in relative levels of HSC
over and above initial HSC levels in kindergarten. Thus, the final
model indicates that initial kindergarten levels of HSC are consistently positively predictive of reading, but increases in HSC from
kindergarten to first grade are negatively predictive of later reading.
This distinction could potentially shed light on why findings in
this domain have been conflicting (e.g., Izzo, Weissberg, Kasprow,
& Fendrich, 1999), as different studies may have captured different aspects of HSC (levels vs. changes in level). For example, in
a cross-sectional approach, high levels of measured HSC may be
related to (a) high levels of HSC regularly engaged in by parents
or (b) recent increases in HSC in response to some student difficulty. These different aspects of HSC could have markedly different effects, as were found in this study, and could help explain
previous conflicting results. Interestingly, the same pattern of
differential effects was also evident for SBI. Specifically, kindergarten levels of SBI positively predicted reading across all waves,
383
whereas later levels of SBI had negative effects on reading in later
waves. Together, these findings suggest an important distinction
between levels of HSC and SBI and changes in these variables.
Future research should examine these patterns in more detail.
Our finding for our main research question was that statistically
significant mediation of PI on students’ reading through student ATL
was present only for SBI. This finding stands in contrast to previous
findings (e.g., Xu et al., 2010) and was likely influenced by the
methodology used, as methodologists (e.g., Maxwell, Cole, & Mitchell, 2011) have shown that apparently strong mediational effects
estimated with cross-sectional models are often rendered null when
examined longitudinally. The general lack of statistically significant
mediation appears to be more related to the lack of a relationship
between PI and ATL (for both HSC and HBI) than because of the
relationship between ATL and reading (which was positive and statistically significant across all intervals and models). PI variables did
not exert any effects on ATL for any interval except the small effect
of SBI on ATL from kindergarten to first grade and from first grade
to third grade. The lack of significant mediation indicates that there
are other important mechanisms to explore to better understand the
relationship between PI and student achievement.
Despite the strengths of this study, there are several important
limitations. First, the ECLS-K data were collected starting in 1998 and
1999. It is possible that the dynamics of PI, ATL, and academic
achievement have changed since this time. For example, the increase
in the use of electronic communication since the time of data collection is significant, particularly in the domain of HSC (Thompson,
Mazer, & Flood Grady, 2015). Next, it is possible that mediation was
not found because the measure used for ATL was too broad. Researchers often separate constructs included under the ATL umbrella
(e.g., motivation, engagement, self-control), and it is possible that PI
has effects on some but not all ATL constructs. Likewise, this study
focused specifically on reading,3 whereas other subject-specific outcomes (e.g., science), as well as more global indicators of achievement, were not tested. Finally, although teachers have been shown to
be good reporters of HSC, it is possible that different patterns would
have emerged had parents also reported HSC. This possibility should
be explored in future research.
It is also important to note that although consideration of the
examined relationships in their developmental context was a
strength of the study, the results are specific to the measured
window. Thus, mediational patterns may vary substantially when
examined in other developmental windows, and future research
should continue to explore such a possibility with longitudinal
data. The transition to kindergarten is a particularly important time
to study, as prior research has indicated that parents are interested
in being involved in this transition (McIntyre et al., 2007); however, it is also important to understand other important transitions
such as the transitions to middle school and high school. Finally,
because variables were not measured in equivalent waves, PI
variables had to be examined separately rather than in an integrated model.
There are various directions for future research. First, future
research should continue to explore alternate mechanisms of the
relationship between PI and achievement to better understand
3
Supplemental mathematics models, available in Tables 1–2 in the
online supplemental materials, yielded roughly equivalent results.
ANTHONY AND OGG
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384
the effects of PI. Next, future research should continue to refine the
multidimensional conceptualization of PI used in this study and
explore other important distinctions such as the aforementioned
distinction between generic and academic HBI. Finally, the differential effects of levels versus changes in levels in SBI and HSC
offer a potential reason for inconsistent effects in the literature.
Other methodological approaches (e.g., latent state-trait models;
Prenoveau, 2016) decompose variance related to stable and
occasion-specific factors and might be profitably applied to more
specifically address this question in future research.
The current findings also have several implications for practice.
First, the overall positive total effects of kindergarten levels of SBI
and HSC on reading achievement were moderate, providing support for continuing to view these constructs as important targets for
educational interventions. Next, it is important to discuss the
distinction between initial levels and changes in levels of HSC
(and SBI). Practitioners should not assume that our results indicate
that increases in HSC and SBI in and of themselves negatively
impact student achievement. Rather, it is likely that the reason that
these relationships were found were related to some other variables
(e.g., some student difficulty) affecting both the increases in HSC
and SBI and reading achievement. Finally, our results indicated
that, in general, PI did not positively or negatively affect ATL.
These results would suggest that practitioners should consider
other potential factors affecting ATL when evaluating children’s
ATL in schools, including characteristics of classrooms (e.g.,
Urdan & Schoenfelder, 2006) and teachers (e.g., Roorda, Koomen,
Spilt, & Oort, 2011).
Ultimately, the results of the current study contribute to a growing
literature base examining PI, ATL, and student achievement. Although results did not indicate that ATL was a substantial mediator of
the effects of PI on academic achievement, observed relationships
between PI and academic achievement and between ATL and academic achievement underscore the continued importance of these
variables. Further examination of these relationships and exploration
of potential mediators of the effects of PI on academic achievement is
needed to better support students in schools and their families.
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Received April 5, 2018
Revision received July 23, 2018
Accepted July 30, 2018 䡲