Examining rating scales using Rasch and Mokken models for rater-mediated assessments

J Appl Meas. 2014;15(2):100-32.

Abstract

A variety of methods for evaluating the psychometric quality of rater-mediated assessments have been proposed, including rater effects based on latent trait models (e.g., Engelhard, 2013; Wolfe, 2009). Although information about rater effects contributes to the interpretation and use of rater-assigned scores, it is also important to consider ratings in terms of the structure of the rating scale on which scores are assigned. Further, concern with the validity of rater-assigned scores necessitates investigation of these quality control indices within student subgroups, such as gender, language, and race/ethnicity groups. Using a set of guidelines for evaluating the interpretation and use of rating scales adapted from Linacre (1999, 2004), this study demonstrates methods that can be used to examine rating scale functioning within and across student subgroups with indicators from Rasch measurement theory (Rasch, 1960) and Mokken scale analysis (Mokken, 1971). Specifically, this study illustrates indices of rating scale effectiveness based on Rasch models and models adapted from Mokken scaling, and considers whether the two approaches to evaluating the interpretation and use of rating scales lead to comparable conclusions within the context of a large-scale rater-mediated writing assessment. Major findings suggest that indices of rating scale effectiveness based on a parametric and nonparametric approach provide related, but slightly different, information about the structure of rating scales. Implications for research, theory, and practice are discussed.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Data Interpretation, Statistical
  • Educational Measurement / statistics & numerical data*
  • Female
  • Guidelines as Topic
  • Humans
  • Male
  • Models, Statistical*
  • Psychometrics / statistics & numerical data*
  • Reproducibility of Results
  • Statistics, Nonparametric
  • Surveys and Questionnaires*