Comparing Traditional and IRT Scoring of Forced-Choice Tests

• Published in: Applied psychological measurement Vol. 39; no. 8; pp. 598 - 612
• Main Authors: Hontangas, Pedro M., de la Torre, Jimmy, Ponsoda, Vicente, Leenen, Iwin, Morillo, Daniel, Abad, Francisco J.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2015
• Subjects: EAP; personality assessment; faking; traditional scoring; multi-unidimensional pairwise-preference; GGUM; forced choice; MUPP; ipsative data; unfolding model
• ISSN: 0146-6216; 1552-3497
• DOI: 10.1177/0146621615585851

This article explores how traditional scores obtained from different forced-choice (FC) formats relate to their true scores and item response theory (IRT) estimates. Three FC formats are considered from a block of items, and respondents are asked to (a) pick the item that describes them most (PICK), (b) choose the two items that describe them the most and the least (MOLE), or (c) rank all the items in the order of their descriptiveness of the respondents (RANK). The multi-unidimensional pairwise-preference (MUPP) model, which is extended to more than two items per block and different FC formats, is applied to obtain the responses to each item block. Traditional and IRT (i.e., expected a posteriori) scores are computed from each data set and compared. The aim is to clarify the conditions under which simpler traditional scoring procedures for FC formats may be used in place of the more appropriate IRT estimates for the purpose of inter-individual comparisons. Six independent variables are considered: response format, number of items per block, correlation between the dimensions, item discrimination level, and sign-heterogeneity and variability of item difficulty parameters. Results show that the RANK response format outperforms the other formats for both the IRT estimates and traditional scores, although it is only slightly better than the MOLE format. The highest correlations between true and traditional scores are found when the test has a large number of blocks, dimensions assessed are independent, items have high discrimination and highly dispersed location parameters, and the test contains blocks formed by positive and negative items.