Using Standard Deviations of Student Measures to Predict Teacher Practices

• In Event: 26.081 - Poster Session 3
  In Poster Session: 26.081-4 - Division D Section 2 Poster Session: Statistical Methods

Abstract

Measures of variability (e.g., standard deviations) can be useful explanatory variables in cases where mean effects provide little predictive power. In this study, logistic regression models were employed with either means or standard deviations of student socio-economic status and math achievement from 2012 Programme for International Student Assessment (PISA) as independent variables. Eight teaching practices from the 2013 Teaching and Learning International Survey (TALIS) for a) all teachers and b) mathematics teachers served as dependent variables. Standard deviations were significant predictors in 11 of 16 models, accounting for an average of 54% of outcome variance, comparable to results from mean-predictor models. These results show how measures of variability can provide unique explanations compared to results based on the mean-effect approach.

Authors

• Bruce W Austin, Washington State University - Pullman

• Brian F. French, Washington State University

• Olusola Olalekan Adesope, Washington State University

• Chad M. Gotch, Washington State University