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John Ermisch – Sociological Methods & Research, 2025
Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event…
Descriptors: Attrition (Research Studies), Longitudinal Studies, Surveys, Statistical Analysis
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Cvetkovski, Stefan; Jorm, Anthony F.; Mackinnon, Andrew J. – Higher Education Research and Development, 2018
Studies of psychological distress (PD) in university students have shown that they have high prevalence rates. These findings have raised concerns that PD may be leading to poorer student outcomes, such as elevated dropout rates. The aim of this study was to examine the association of PD in undergraduate university students with the competing…
Descriptors: Stress Variables, Foreign Countries, Undergraduate Students, National Surveys
Liu, Haiyan; Zhang, Zhiyong; Grimm, Kevin J. – Grantee Submission, 2016
Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due…
Descriptors: Bayesian Statistics, Computation, Statistical Analysis, Growth Models
Anikin, Vasiliy A. – International Journal of Training and Development, 2017
What factors best explain the low incidence of skills training in a late industrial society like Russia? This research undertakes a multilevel analysis of the role of occupational structure in the probability of training. The explanatory power of occupation-specific determinants and skills polarization are evaluated, using a representative 2012…
Descriptors: Foreign Countries, Incidence, Skill Development, Probability
Kaplan, David; Chen, Jianshen – Society for Research on Educational Effectiveness, 2013
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
Descriptors: Bayesian Statistics, Models, Probability, Monte Carlo Methods
Timmons, Kristy; Pelletier, Janette – Early Child Development and Care, 2016
In this study, we explored the influence of kindergarten children's perspectives of school on their literacy and self-regulation outcomes. Children's early perspectives were captured in a three-question, finger-puppet interview. Responses to the interview questions were coded thematically as being academic and/or social in nature, and were…
Descriptors: Childhood Attitudes, Kindergarten, Longitudinal Studies, Puppetry
Rock, Donald A. – ETS Research Report Series, 2012
This paper provides a history of ETS's role in developing assessment instruments and psychometric procedures for measuring change in large-scale national assessments funded by the Longitudinal Studies branch of the National Center for Education Statistics. It documents the innovations developed during more than 30 years of working with…
Descriptors: Models, Educational Change, Longitudinal Studies, Educational Development
Loken, Eric – Multivariate Behavioral Research, 2004
Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks…
Descriptors: Probability, Personality, Infants, Bayesian Statistics