Careless Responding May Yield Spurious Factors in Unidimensionality Assessment: A Simulation Study.
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| Title: | Careless Responding May Yield Spurious Factors in Unidimensionality Assessment: A Simulation Study. |
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| Authors: | Abulela, Mohammed A. A. (AUTHOR), Nickodem, Kyle (AUTHOR), Rodriguez, Michael C. (AUTHOR) |
| Source: | Applied Measurement in Education. Jul-Dec2025, Vol. 38 Issue 3/4, p256-267. 12p. |
| Subjects: | Factor analysis, False positive error, Simulation methods & models, Data scrubbing, Response styles (Examinations), Psychometrics |
| Abstract: | Careless responding (CR) threatens validity evidence, yet its potential to yield spurious factors when testing unidimensionality remains underexplored. We simulated polytomous data for 500 participants, manipulating CR type (fixed, midpoint, random), prevalence (10%, 20%, 30%), and severity (25%, 50%, 75%) in a fully crossed 27-condition design with 500 replications each. Unidimensionality was assessed using parallel analysis (PA; principal axis factoring with polychoric correlations) and item factor analysis (IFA). For IFA, one- and two-factor models were fit and compared using descriptive indices and inferential tests. The outcome was Type I error, defined as falsely concluding multidimensionality in truly unidimensional data. PA falsely detected additional factors in 52% of conditions. Within IFA, BIC was most robust (21% Type I error). Two of four three-way interactions were significant: Method × Type × Prevalence and Type × Prevalence × Severity, explaining 23% and 20% of the variance in Type I error. Results underscore cleaning low-stakes data before assessing unidimensionality. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Careless responding (CR) threatens validity evidence, yet its potential to yield spurious factors when testing unidimensionality remains underexplored. We simulated polytomous data for 500 participants, manipulating CR type (fixed, midpoint, random), prevalence (10%, 20%, 30%), and severity (25%, 50%, 75%) in a fully crossed 27-condition design with 500 replications each. Unidimensionality was assessed using parallel analysis (PA; principal axis factoring with polychoric correlations) and item factor analysis (IFA). For IFA, one- and two-factor models were fit and compared using descriptive indices and inferential tests. The outcome was Type I error, defined as falsely concluding multidimensionality in truly unidimensional data. PA falsely detected additional factors in 52% of conditions. Within IFA, BIC was most robust (21% Type I error). Two of four three-way interactions were significant: Method × Type × Prevalence and Type × Prevalence × Severity, explaining 23% and 20% of the variance in Type I error. Results underscore cleaning low-stakes data before assessing unidimensionality. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 08957347 |
| DOI: | 10.1080/08957347.2026.2615196 |
