International Education Journal

Educational Research Conference Workshop 2005 Special Issue

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Suppressor variables and multilevel mixture modelling


I Gusti Ngurah Darmawan 
School of Education, University of Adelaide 
igusti.darmawan@adelaide.edu.au
 

John P. Keeves 
School of Education, Flinders University 
john.keeves@flinders.edu.au

 

 

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Abstract

A major issue in educational research involves taking into consideration the multilevel nature of the data. Since the late 1980s, attempts have been made to model social science data that conform to a nested structure. Among other models, two-level structural equation modelling or two-level path modelling and hierarchical linear modelling are two of the techniques that are commonly employed in analysing multilevel data. Despite their advantages, the two-level path models do not include the estimation of cross-level interaction effects and hierarchical linear models are not designed to take into consideration the indirect effects. In addition, hierarchical linear models might also suffer from multicollinearity that exists among the predictor variables. This paper seeks to investigate other possible models, namely the use of latent constructs, indirect paths, random slopes and random intercepts in a hierarchical model. 

Multilevel data analysis, suppressor variables, multilevel mixture modelling, hierarchical linear modelling, two-level path modelling

 

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Darmawan, I.G.N. and Keeves, J.P. (2006) Suppressor variables and multilevel mixture modelling. International Education Journal, 7 (2), 160-173.
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