Parental Characteristics and the Achievement Gap in Mathematics: Hierarchical Linear Modeling Analysis of Longitudinal Study of American Youth (LSAY)


  • Mohammad Shoraka University of Windsor
  • Robert Arnold University of Windsor
  • Eun Sook Kim University of South Florida
  • Geri Salinitri University of Windsor
  • Jeffrey Kromrey University of South Florida



Multilevel Modeling, Achievement Gap, Mathematics, Mots clés, modélisation à niveaux multiples, écart de rendement, mathématiques


One of the most salient problems in education is the achievement gap. The researchers investigated the effects of parental education and parental occupations in science, technology, engineering, mathematics, or medical professions (STEMM) on the achievement gap in mathematics. Because students were nested within schools, two-level Hierarchical Linear Modeling (HLM) was the main analysis. Findings were consistent with another Multilevel Modeling analysis, that is, parental characteristics such as parental education had a positive effect on student achievement (Teodorovic, 2012). Moreover, parental occupations in STEMM had a positive effect on student mathematics achievement.

L’écart de rendement constitue un des problèmes les plus importants en éducation. Les chercheurs ont étudié les effets de l’éducation et de la profession des parents en sciences, technologie, ingénierie, mathématiques et médecine (STIMM) sur l’écart de rendement en mathématiques. Puisque les élèves étaient emboités dans les écoles, l’analyse repose sur un modèle de régression linéaire hiérarchique à deux niveaux. Les résultats correspondent à ceux d’une autre analyse basée sur la modélisation à niveaux multiples qui indique que les caractéristiques des parents telles leur éducation a eu un effet positif sur le rendement des élèves (Teodorovic, 2012). De plus, quand la profession des parents est reliée à STIMM, le rendement en mathématiques de leurs enfants a eu meilleur.

Author Biographies

Mohammad Shoraka, University of Windsor

Mohammad Shoraka is a practitioner in education, currently teaching Calculus at Canadian B.C. Offshore School in Beijing, China. He has taught Intermediate Algebra at University of South Florida and Mathematics, Physics and Data Management at the Canadian Offshore Schools. Shoraka has a Master of Arts in Social Data Analysis and Master of Education in Educational Administration from the University of Windsor. His areas of research interests include Hierarchical Linear Modeling, Classic Test Theory, Program Evaluation and Mathematics Education. 

Robert Arnold, University of Windsor

Dr. Robert Arnold had worked for 13 years in applied research before becoming a university teacher. In applied settings he was much involved in program evaluation, and so was very pleased, in 1990, to become a member of the team evaluating Better Beginnings, Better Futures, a multi-site demonstration program aimed at improving the life chances of children from disadvantaged neighbourhoods. He has taught methods and statistics from the year he received his Ph.D. to the present, in courses from the undergraduate to the doctoral level. He has taught introductory sociology (several times), social psychology, and sociology of the contemporary family, which he has offered at Windsor for the past six years. He has co-authored academic papers in criminology and sociology of health as well as others in program evaluation. He consults regularly with students and faculty who have methodological questions.

Eun Sook Kim, University of South Florida

Dr. Eun Sook Kim is an Assistant Professor of Educational and Psychological Studies in University of South Florida. She has a broad interest in research methodology and psychometrics including structural equation modeling, multilevel modeling, and latent growth analysis. Her focal research interests include measurement invariance testing in multilevel and longitudinal data. She is interested in the behaviors of widely used statistical methods under various research settings and has been involved in research groups studying propensity score analysis, multilevel confirmatory factor analysis, Bayesian estimation, and ANOVA in collaboration with faculty and graduate students.

Geri Salinitri, University of Windsor

Dr. Geri Salinitri, PhD is the Associate Dean and Associate Professor with the Faculty of Education at the University of Windsor. She teaches in the area of Science Methodology, Guidance and Career Education, and Issues in Education. Her research interests include Mentoring, Social Learning, Social Justice, Teacher Education and At-Risk Youth. She has published in the areas of mentoring for 1st year university students, New Teacher Induction, learning outcomes and portfolio development in engineering cooperative education programs, and science pedagogy in collaboration with colleagues in diverse areas of education. Dr. Salinitri is currently part of a 7 year, 3.3 million dollar SSHRC partnership grant exploring the reciprocal learning of inquiry based science education between China and Canada.

Jeffrey Kromrey, University of South Florida

Jeffrey D. Kromrey is a Professor in the Department of Educational Measurement and Research at the University of South Florida. His specializations are applied statistics and data analysis. He primarily teaches courses in applied statistics. His work has been published in Communications in Statistics, Educational and Psychological Measurement, Multivariate Behavioral Research, Psychometrika, Journal of Educational Measurement and Educational Researcher. He is a former editor of the Journal of Experimental Education and Review of Educational Research. His research has been funded by the National Science Foundation, American Association of Medical Colleges, U.S. Department of Defense, and Association for Institutional Research.




How to Cite

Shoraka, M., Arnold, R., Kim, E. S., Salinitri, G., & Kromrey, J. (2016). Parental Characteristics and the Achievement Gap in Mathematics: Hierarchical Linear Modeling Analysis of Longitudinal Study of American Youth (LSAY). Alberta Journal of Educational Research, 61(3), 280–293.