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- All Subjects: Educational Psychology
Description
Children's academic experiences during first grade have substantial implications for their academic performance both concurrently and longitudinally. Using two complementary studies, this dissertation utilizing data from the National Institute of Child Development Study of Early Child Care and Youth Development helps create a better understanding of the importance of first-grade experiences for children's academic performance. The first study expands upon current literature by focusing on how children's academic experiences simultaneously influence children's academic performance through behavioral engagement. Specifically, study one examined the mediating role of first-grade behavioral engagement between first-grade academic experiences (i.e. parental involvement, positive peer interactions, student-teacher relationship, and instructional support) and second-grade academic performance. Using a panel model, results showed that behavioral engagement mediates relations between peer interactions and academic performance and relations between instructional support and academic performance. Implications for interventions focusing on children's positive peer interactions and teacher's high-quality instructional support in order to promote behavioral engagement during early elementary school are discussed.
The second study expands the current literature regarding instructional quality thresholds. Limited research has addressed the question of whether there is a minimum level of instructional quality that must be experienced in order to see significant changes in children's academic performance, and the limited research has focused primarily on preschoolers. The goal of study two was to determine if high-quality first-grade instructional support predicted children's first-, third-, and fifth-grade academic performance. Using piecewise regression analyses, results did not show evidence of a relation between first-grade instructional support quality and children's academic performance at any grade. Possible reasons for inconsistencies in findings from this study and previous research are discussed, including differences in sample characteristics and measurement tools. Because instructional quality remains at the forefront of discussions by educators and policy makers, the inconsistencies in research findings argue for further research that may clarify thresholds of instructional support quality that must be met in order for various subgroups of children to gain the skills needed for long-term academic success.
The second study expands the current literature regarding instructional quality thresholds. Limited research has addressed the question of whether there is a minimum level of instructional quality that must be experienced in order to see significant changes in children's academic performance, and the limited research has focused primarily on preschoolers. The goal of study two was to determine if high-quality first-grade instructional support predicted children's first-, third-, and fifth-grade academic performance. Using piecewise regression analyses, results did not show evidence of a relation between first-grade instructional support quality and children's academic performance at any grade. Possible reasons for inconsistencies in findings from this study and previous research are discussed, including differences in sample characteristics and measurement tools. Because instructional quality remains at the forefront of discussions by educators and policy makers, the inconsistencies in research findings argue for further research that may clarify thresholds of instructional support quality that must be met in order for various subgroups of children to gain the skills needed for long-term academic success.
ContributorsBryce, Crystal I (Author) / Bradley, Robert H (Thesis advisor) / Abry, Tashia (Committee member) / Swanson, Jodi (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
Description
Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.
ContributorsArcuria, Philip (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study examined the influence of childhood aggression, peer exclusion and associating with deviant peers on the development of antisocial behavior in early adolescence. To gain a stronger understanding of how these factors are associated with antisocial behavior and delinquency, multiple alternative pathways were examined based on additive, mediation and incidental models. A parallel process growth model was specified to assess whether early childhood aggression and peer exclusion (in 1st grade) and intra-individual increases in aggressive behaviors and exclusion through childhood (grades 1 to 6) are predictive of associating with deviant peers (in 7th grade) and antisocial behavior (in 8th grade). Based on a sample of 383 children (193 girls and 190 boys), results showed the strongest support for an additive effects model in which early childhood aggression, increases in aggression, increases in peer exclusion and associating with more deviant peers all predicted antisocial behavior. These findings have implications for how children's psychological adjustment is impacted by their behavioral propensities and peer relational context and the importance of examining developmental processes within and between children over time.
ContributorsEttekal, Idean (Author) / Ladd, Gary W (Thesis advisor) / Eggum, Natalie D (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2011
Description
The present study examined the relations between indices of parental involvement (parental aspirations, expectations, help with schoolwork, home learning and language materials) and children's academic achievement in a sample of 291 kindergarten-2nd grade children. Children's academic achievement was assessed with the Woodcock Johnson and parents reported on expectations, aspirations, help with schoolwork, home learning and language materials. Latent Growth Curve Models were used to test whether there was growth in the parent involvement variables and whether growth in the parent involvement variables predicted growth in academic achievement. The intercept for parental expectations was the only intercept to predict the intercept of academic achievement. Rates of growth in parental expectations, parental help with schoolwork, and home learning materials predicted rates of growth in academic achievement.
ContributorsSeeley, Bridget Granville (Author) / Valiente, Carlos (Thesis advisor) / Nakagawa, Kathryn (Thesis advisor) / Arzubiaga, Angela (Committee member) / Eggum, Natalie D (Committee member) / Arizona State University (Publisher)
Created2012
Description
Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the results of the analysis may be inaccurate. In the presence of clustered
ested data, hierarchical linear modeling or multilevel modeling (MLM; Raudenbush & Bryk, 2002) has the ability to predict outcomes for each level of analysis and across multiple levels (accounting for relationships between levels) providing a significant advantage over single-level analyses. When multilevel data contain missingness, multilevel multiple imputation (MLMI) techniques may be used to model both the missingness and the clustered nature of the data. With categorical multilevel data with missingness, categorical MLMI must be used. Two such routines for MLMI with continuous and categorical data were explored with missing at random (MAR) data: a formal Bayesian imputation and analysis routine in JAGS (R/JAGS) and a common MLM procedure of imputation via Bayesian estimation in BLImP with frequentist analysis of the multilevel model in Mplus (BLImP/Mplus). Manipulated variables included interclass correlations, number of clusters, and the rate of missingness. Results showed that with continuous data, R/JAGS returned more accurate parameter estimates than BLImP/Mplus for almost all parameters of interest across levels of the manipulated variables. Both R/JAGS and BLImP/Mplus encountered convergence issues and returned inaccurate parameter estimates when imputing and analyzing dichotomous data. Follow-up studies showed that JAGS and BLImP returned similar imputed datasets but the choice of analysis software for MLM impacted the recovery of accurate parameter estimates. Implications of these findings and recommendations for further research will be discussed.
ested data, hierarchical linear modeling or multilevel modeling (MLM; Raudenbush & Bryk, 2002) has the ability to predict outcomes for each level of analysis and across multiple levels (accounting for relationships between levels) providing a significant advantage over single-level analyses. When multilevel data contain missingness, multilevel multiple imputation (MLMI) techniques may be used to model both the missingness and the clustered nature of the data. With categorical multilevel data with missingness, categorical MLMI must be used. Two such routines for MLMI with continuous and categorical data were explored with missing at random (MAR) data: a formal Bayesian imputation and analysis routine in JAGS (R/JAGS) and a common MLM procedure of imputation via Bayesian estimation in BLImP with frequentist analysis of the multilevel model in Mplus (BLImP/Mplus). Manipulated variables included interclass correlations, number of clusters, and the rate of missingness. Results showed that with continuous data, R/JAGS returned more accurate parameter estimates than BLImP/Mplus for almost all parameters of interest across levels of the manipulated variables. Both R/JAGS and BLImP/Mplus encountered convergence issues and returned inaccurate parameter estimates when imputing and analyzing dichotomous data. Follow-up studies showed that JAGS and BLImP returned similar imputed datasets but the choice of analysis software for MLM impacted the recovery of accurate parameter estimates. Implications of these findings and recommendations for further research will be discussed.
ContributorsKunze, Katie L (Author) / Levy, Roy (Thesis advisor) / Enders, Craig K. (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2016