Examining the Dimensionality and Monotonicity of an Attitude Dataset based on the Item Response Theory Models

In the current study, the factor structure of an attitude scale was analyzed by using the two different item response theory models that allow modeling non-monotonic item response curves. The current study utilized the two models to examine whether the two-factor solution of factor analysis may be caused by method effect, or by the failure of the analysis in describing and fitting the dataset because of the monotonicity assumption. This study was conducted on a dataset obtained from 355 undergraduate students who were studying at the Middle East Technical University. The data were obtained by carrying out the Attitude Scale Towards Foreign Languages as Medium of Instruction, which was developed by Kartal and Gulleroglu (2015). The fit of the scale items to the generalized graded unfolding model was examined based on the item response curves, item parameters, item fit statistics and fit graphics. For Mokken scaling, scalability coefficients were calculated, dimensionality analyzes were conducted by using the Automated Item Selection Procedure. The monotonicity assumption was investigated based on the rest-score group methods. The results of the current study revealed that items of the attitude scale fit to the unidimensional models that do not assume monotone increasing item response curves for all items, while the factor analysis suggested a two-factor solution for the data. Researchers are recommended to utilize statistical techniques that can identify any possible violation of the monotonicity assumption and model items having non-monotonic response curves to examine dimensionality of their data.