A Fixed Effects Panel Data Model: Mathematics Achievement in the U.S.

Abstract

Statistical models that combine cross section and time series data offer analysis and interpretation advantages over separate cross section or time series data analyses (Mátyás & Severstre, 1996). Time series and cross section designs have not been commonplace in the research community until the last 25 years (Tieslau, 1999). In this study, a fixed effects panel data model is applied to the National Education Longitudinal Study of 1988 (NELS:88) data to determine if educational process variables, teacher emphasis, student self-concept, and socio-economic status can account for variance in student mathematical achievement. A model that includes seven independent variables accounted for 25% of the variance in student mathematical achievement test score. The study provides educational researchers with an applied model for panel data analysis.