DIMTEST

Refinements of Stout’s Procedure for Assessing Latent Trait Unidimensionality. This article provides a detailed investigation of Stout’s statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. One finding was that DIMTEST may fail to perform as desired in the presence of guessing when coupled with many high-discriminating items. A revision of DIMTEST is proposed to overcome this limitation. Also, an automatic approach is devised to determine the size of the assessment subtests. Further, an adjustment is made on the estimated standard error of the statistic on which DIMTEST depends. These three refinements have led to an improved procedure that is shown in simulation studies to adhere closely to the nominal level of signficance while achieving considerably greater power. Finally, DIMTEST is validated on a selection of real data sets


References in zbMATH (referenced in 12 articles , 2 standard articles )

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  1. Steinley, Douglas L.; Brusco, M. J.: Using an iterative reallocation partitioning algorithm to verify test multidimensionality (2019)
  2. Price, Larry R.: Psychometric methods. Theory into practice (2016)
  3. Straat, J. Hendrik; van der Ark, L. Andries; Sijtsma, Klaas: Comparing optimization algorithms for item selection in Mokken scale analysis (2013)
  4. Zhang, Jinming: Conditional covariance theory and detect for polytomous items (2007)
  5. Bouwmeester, Samantha; Sijtsma, Klaas: Measuring the ability of transitive reasoning, using product and strategy information (2004)
  6. Habing, Brian; Roussos, Louis A.: On the need for negative local item dependence (2003)
  7. Stout, William: Psychometrics: from practice to theory and back. 15 years of nonparametric multidimensional IRT, DIF/test equity, and skills diagnostic assessment (2002)
  8. Ip, Edward Hak-Sing: Testing for local dependency in dichotomous and polytomous item response models (2001)
  9. Stout, William; Froelich, Amy Goodwin; Gao, Furong: Using resampling methods to produce an improved DIMTEST procedure (2001)
  10. Ip, Edward Hak-Sing: Adjusting for information inflation due to local dependency in moderately large item clusters (2000)
  11. Zhang, Jinming; Stout, William: Conditional covariance structure of generalized compensatory multidimensional items (1999)
  12. Stout, William: A nonparametric approach for assessing latent trait unidimensionality (1987)