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Improving the Measurement of Psychological Variables: Ideal Point Models Rock!

Published online by Cambridge University Press:  07 January 2015

Fritz Drasgow ,
Oleksandr S. Chernyshenko  and
Stephen Stark
Fritz Drasgow*
Affiliation:
University of Illinois at Urbana-Champaign
Oleksandr S. Chernyshenko
Affiliation:
Nanyang Technological University
Stephen Stark
Affiliation:
University of South Florida
*
E-mail: [email protected], Address: Department of Psychology, University of Illinois, 603 E. Daniel Street, Champaign, IL 61820
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Abstract

Although there is no doubt that Likert scaling suffices for straightforward scale development and use, it is important to appropriately model the response process for more complex measurement problems. In this response, we comment on the response process and four applications: assessment of dimensionality, computerized adaptive testing, differential item functioning, and individual differences in responding. In each case, we argue that correctly modeling the psychology of responding is critical.

Type
Response
Information
Industrial and Organizational Psychology , Volume 3 , Issue 4 , December 2010 , pp. 515 - 520
Copyright
Copyright © Society for Industrial and Organizational Psychology 2010 

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Footnotes

*

Department of Psychology, University of Illinois at Urbana-Champaign

References

Borman, W. C. (2010). Cognitive processes related to forced choice, ideal point responses: Drasgow, Chernyshenko, and Stark got it right! Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 504–506.Google Scholar
Brown, A., & Maydeu-Olivares, A. (2010). Issues that should not be overlooked in the dominance versus ideal point controversy. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 489–493.Google Scholar
Carter, N. T., Lake, C. J., & Zickar, M. J. (2010). Toward understanding the psychology of unfolding. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 511–514.Google Scholar
Chernyshenko, O. S., Stark, S., Drasgow, F., & Roberts, B. W. (2007). Constructing personality scales under the assumptions of an ideal point response process: Toward increasing the flexibility of personality measures. Psychological Assessment, 19, 88–106. Google Scholar
Credé, M. (2010). Two caveats for the use of ideal point items: Discrepancies and bivariate constructs. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 494–497.Google Scholar
Dalal, D. K., Withrow, S., Gibby, R. E., & Zickar, M. J. (2010). Six questions that practitioners (might) have about ideal point response process items. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 498–501.Google Scholar
Davison, M. L. (1977). On a metric, unidimensional unfolding model for attitudinal and developmental data. Psychometrika, 42, 523–548. Google Scholar
Drasgow, F., Levine, M. V., Tsien, S., Williams, B., & Mead, A. D. (1995). Fitting polytomous item response theory models to multiple-choice tests. Applied Psychological Measurement, 19, 143–165. Google Scholar
Joe, H., & Maydeu-Olivares, (2006). On the asymptotic distribution of Pearson's χ2 in cross-validation samples. Psychometrika, 71, 587–592. Google Scholar
Kantrowitz, T. M., & Tuzinski, K. A. (2010). The ideal point model in action: How the use of computer adaptive personality scales benefits organizations. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 507–510.Google Scholar
Landy, F. J., & Farr, J. L. (1980). A process model of performance rating. Psychological Bulletin, 87, 72–107. Google Scholar
Levine, M. V., & Drasgow, F. (1982). Appropriateness measurement: Review, critique and validating studies. British Journal of Mathematical and Statistical Psychology, 35, 42–56. Google Scholar
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22, 1–55. Google Scholar
Nye, C. D., Guo, J., & Drasgow, F. (2010). Infit, outfit, and misfit: How do you know when the model fits? Manuscript submitted for publication.Google Scholar
Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50–64. Google Scholar
Oswald, F. L., & Schell, K. L. (2010). Developing and scaling personality measures: Thurstone was right—but so far Likert was not wrong! Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 481–484.Google Scholar
Reise, S. P. (2010). Thurstone might have been right about attitudes, but Drasgow, Chernyshenko, and Stark fail to make the case for personality. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 485–488.Google Scholar
Rost, J. (1991). A logistic mixture distribution model for polychotomous item responses. British Journal of Mathematical and Statistical Psychology, 44, 75–92. Google Scholar
Spector, P. E., & Brannick, M. T. (2010). If Thurstone was right, what happens when we factor analyze Likert scales? Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 502–503.Google Scholar
Spector, P. E., Van Katwyk, P. T., Brannick, M. T., & Chen, P. Y. (1997). When two factors don't reflect two constructs: How item characteristics can produce artifactual factors. Journal of Management, 23, 659–677. Google Scholar
Stark, S., Chernyshenko, O. S., & Drasgow, F., & Williams, B. A. (2006). Item responding in personality assessment: Should ideal point methods be considered for scale development and scoring? Journal of Applied Psychology, 91, 25–39. Google Scholar
Stark, S., Chernyshenko, O. S., & Guenole, N. (in press). Can subject matter expert ratings of statement extremity be used to streamline the development of unidimensional pairwise preference scales? Organizational Research Methods.Google Scholar
Tay, L., Ali, U.S., Drasgow, F., & Williams, B. (in press). Fitting IRT models to dichotomous and polytomous data: Assessing the relative model-data fit of ideal point and dominance models.Google Scholar
Thurstone, L. L. (1928). Attitudes can be measured. The American Journal of Sociology, 33, 529–554. Google Scholar
Vermunt, J. K., & Magidson, J. (2005). Technical guide for Latent GOLD 4.0: Basic and advanced. Belmont, MA: Statistical Innovations. Google Scholar
Waples, C. J., Weyhrauch, W. S., Connell, A. R., & Culbertson, S. S. (2010). Questionable defeats and discounted victories for Likert rating scales. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 477–480.Google Scholar
Zinnes, J. L., & Griggs, R. A. (1974). Probabilistic, multidimensional unfolding analysis. Psychometrika, 39, 327–350. Google Scholar