Detecting Differential Item Functioning in Partial Credit Models by Penalization Techniques

• Applicant: Dr. Gunther Schauberger
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology ; General, Cognitive and Mathematical Psychology ; Statistics and Econometrics
• Term: from 2016 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 328195489

The proposed research project focuses on the detection of Differential Item Functioning (DIF) in item response data. The phenomenon of DIF is a very important issue in applied psychometrics or other social sciences, where item response data are used to measure a certain latent trait of interest. The construction of the respective questionnaires needs to be done with special diligence. Every item has to be constructed in a way that it only measures the latent trait of interest. In the case of dichotomous items, DIF occurs if the probability to answer a certain item positively differs between two persons with the same level of the respective latent trait. In most cases, one looks for DIF between two groups within the participants, e.g. male and female participants. For example in intelligence tests, the probability to answer an item correctly might differ between male and female participants with the same level of intelligence. Such items have to be detected and possibly be removed from the questionnaire because they might lead to biased estimates of the latent traits. Many statistical methods have been developed to detect DIF between two pre-specified groups, mostly using statistical tests. After all, there might be many different characteristics of the participants that potentially cause DIF, for example multi-categorical variables like ethnicity or continuous variables like age. Different variables should not be tested separately for DIF because automatically the issue of multiple testing will arise and possible correlations between the variables will not be regarded. For that purpose, Tutz and Schauberger (2015) proposed the method DIFlasso which is able to handle both categorical and continuous variables and to handle several variables simultaneously. It is a model-based approach which uses penalization techniques to identify DIF-items. The big advantage of the approach is that the practitioner can specify a number of possibly DIF-inducing variables (both continuous and categorical) and the method automatically detects the relevant items. However, the approach of Tutz and Schauberger (2015) is restricted to the detection of DIF in dichotomous items. The goal of the proposed project is, to extend the method to polytomous items within the general framework of partial credit models. For that purpose, a suitable parameterization of DIF-effects is necessary and a penalized estimation procedure that automatically detects DIF.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor James Sidanius, Ph.D.