A Mixture Rasch Model Analysis of Data from a Survey of Novice Teacher Core Competencies

Abstract

Although the Rasch model is used to measure latent traits like attitude or ability where there are multiple latent structures within the dataset it is best to use a technique called the Mixture Rasch Model (MRM) which is a combination of a Rasch model and a latent class analysis (LCA). This study used data from a survey for teachers, teacher candidates, and teacher education program faculty with a sample of 296 candidates, 648 graduates, and 501 program personnel. Survey items based on these competencies asked teacher candidates, graduates, and teacher education program faculty in one Western state how well the program attended prepared candidates for the teaching profession. The 40 items common to surveys of the three groups were submitted to mixture Rasch analysis to determine whether distinct patterns of item response were discernible. Analyses yielded two classes which brings the construct validity of the survey into question. Results showed that the Mixture Rasch Model is and can be useful to determine sub-groups for survey researchers. This research presents a demonstration of usefulness of the Mixture Rasch Model for the analysis of survey data.