PAPER: Examining Validity Evidence for Multidimensional Forced Choice Measures using Four Scoring Approaches
Bibliographic record
Abstract
Today, forced choice testing is perhaps the most widely explored approach to dealing with faking and other forms of response distortion in applied settings. This is due largely to advances in test construction and scoring over the last 15 years which have made it possible to obtain normative information from forced choice tests via classical test theory (CTT) (White & Young, 1998) and item response theory (IRT) methods (Brown & Maydeu-Olivares, 2011; de la Torre, Ponsoda, Leenen, & Hontangas, 2011; Stark, Chernyshenko, & Drasgow, 2005). For personality testing, in particular, multidimensional forced choice (MFC) applications are rapidly expanding. Our presentation will describe four MFC modeling approaches and research comparing convergent and criterion validities for MFC and Likert-type Big Five personality measures administered in Korea. The MFC Big Five measure was scored four ways: (1) a partially ipsative approach based on CTT (White & Young, 1998); (2) an analogous partially ipsative approach using an IRT graded response model (3) the Thurstonian MFC IRT approach (Brown & Maydeu-Olivares, 2011); and (4) the GGUM-RANK MFC IRT scoring approach (Authors, 2015). We found that all IRT-based scoring methods showed expected patterns of correlation with Likert-type measures, thus supporting the viability of these recently developed approaches. However, the much simpler CTT scoring method was also quite effective and may be adequate for many organizational and educational applications. In our presentation, we will elaborate on these issues and provide suggestions for future research. References Authors (2015). Paper title. Brown, A., & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71 , 460–502. de la Torre, J., Ponsoda, V., Leenen, I., & Hontangas, P. (2012, April). Examining the viability of recent models for forced-choice data. Presented at the Meeting of the American Educational Research Association, Vancouver, British Columbia, Canada. Stark, S., Chernyshenko, O. S., & Drasgow, F. (2005). An IRT approach to constructing and scoring pairwise preference items involving stimuli on different dimensions: The multiunidimensional pairwise preference model. Applied Psychological Measurement, 29 , 184 –201. White, L. A., & Young, M. C. (1998, August). Development and validation of the Assessment of Individual Motivation (AIM). Paper presented at the annual meeting of the American Psychological Association, San Francisco, CA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".