Estimation of Response Styles Using the Multidimensional Nominal Response Model: A Tutorial and Comparison With Sum Scores
Author(s) -
Carl F. Falk,
Unhee Ju
Publication year - 2020
Publication title -
frontiers in psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.947
H-Index - 110
ISSN - 1664-1078
DOI - 10.3389/fpsyg.2020.00072
Subject(s) - item response theory , likert scale , context (archaeology) , computer science , contrast (vision) , psychology , interpretation (philosophy) , focus (optics) , machine learning , scale (ratio) , artificial intelligence , natural language processing , statistics , econometrics , psychometrics , mathematics , clinical psychology , paleontology , physics , quantum mechanics , optics , biology , programming language
Recent years have seen a dramatic increase in item response models for measuring response styles on Likert-type items. These model-based approaches stand in contrast to traditional sum-score-based methods where researchers count the number of times that participants selected certain response options. The multidimensional nominal response model (MNRM) offers a flexible model-based approach that may be intuitive to those familiar with sum score approaches. This paper presents a tutorial on the model along with code for estimating it using three different software packages: flexMIRT®, mirt , and M plus . We focus on specification and interpretation of response functions. In addition, we provide analytical details on how sum score to scale score conversion can be done with the MNRM. In the context of a real data example, three different scoring approaches are then compared. This example illustrates how sum-score-based approaches can sometimes yield scores that are confounded with substantive content. We expect that the current paper will facilitate further investigations as to whether different substantive conclusions are reached under alternative approaches to measuring response styles.
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