ORCID Profile
0000-0002-5446-9758
Current Organisation
Australian Catholic University
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Publisher: SAGE Publications
Date: 14-04-2022
DOI: 10.1177/01466216221084209
Abstract: A computerized adaptive testing (CAT) solution for tests with multidimensional pairwise-comparison (MPC) items, aiming to measure career interest, value, and personality, is rare. This paper proposes new item selection and exposure control methods for CAT with dichotomous and polytomous MPC items and present simulation study results. The results show that the procedures are effective in selecting items and controlling within-person statement exposure with no loss of efficiency. Implications are discussed in two applications of the proposed CAT procedures: a work attitude test with dichotomous MPC items and a career interest assessment with polytomous MPC items.
Publisher: Informa UK Limited
Date: 08-2013
Publisher: Informa UK Limited
Date: 06-2023
Publisher: SAGE Publications
Date: 21-10-2021
Abstract: Ipsative tests with multidimensional forced-choice (MFC) items have been widely used to assess career interest, values, and personality to prevent response biases. Recently, there has been a surge of interest in developing item response theory models for MFC items. In reality, a statement in an MFC item may have different utilities for different groups, which is referred to as differential statement functioning (DSF). However, few studies have been investigated methods for detecting DSF owing to the challenges related to the features of ipsative tests. In this study, three methods were adapted for DSF assessment in MFC items: equal-mean-utility (EMU), all-other-statement (AOS), and constant-statement (CS). Simulation studies were conducted to evaluate the recovery of parameters and the performance of the proposed methods. Results showed that statement parameters and DSF parameters were well recovered for all the three methods when the test did not contain any DSF statement. When the test contained one or more DSF statements, only the CS method yielded accurate estimates. With respect to DSF assessment, both the EMU method using the bootstrap standard error and the AOS method performed appropriately so long as the test did not contain any DSF statement. The CS method performed well in cases where one or more DSF-free statements were chosen as an anchor. The longer the anchor statements, the higher the power of DSF detection.
Publisher: SAGE Publications
Date: 03-04-2018
Abstract: Many multilevel linear and item response theory models have been developed to account for multilevel data structures. However, most existing cognitive diagnostic models (CDMs) are unilevel in nature and become inapplicable when data have a multilevel structure. In this study, using the log-linear CDM as the item-level model, multilevel CDMs were developed based on the latent continuous variable approach and the multivariate Bernoulli distribution approach. In a series of simulations, the newly developed multilevel deterministic input, noisy, and gate (DINA) model was used as an ex le to evaluate the parameter recovery and consequences of ignoring the multilevel structures. The results indicated that all parameters in the new multilevel DINA were recovered fairly well by using the freeware Just Another Gibbs S ler (JAGS) and that ignoring multilevel structures by fitting the standard unilevel DINA model resulted in poor estimates for the student-level covariates and underestimated standard errors, as well as led to poor recovery for the latent attribute profiles for in iduals. An empirical ex le using the 2003 Trends in International Mathematics and Science Study eighth-grade mathematical test was provided.
Publisher: Wiley
Date: 26-03-2023
DOI: 10.1111/BMSP.12303
Abstract: The use of multidimensional forced‐choice (MFC) items to assess non‐cognitive traits such as personality, interests and values in psychological tests has a long history, because MFC items show strengths in preventing response bias. Recently, there has been a surge of interest in developing item response theory (IRT) models for MFC items. However, nearly all of the existing IRT models have been developed for MFC items with binary scores. Real tests use MFC items with more than two categories such items are more informative than their binary counterparts. This study developed a new IRT model for polytomous MFC items based on the cognitive model of choice, which describes the cognitive processes underlying humans' preferential choice behaviours. The new model is unique in its ability to account for the ipsative nature of polytomous MFC items, to assess in idual psychological differentiation in interests, values and emotions, and to compare the differentiation levels of latent traits between in iduals. Simulation studies were conducted to examine the parameter recovery of the new model with existing computer programs. The results showed that both statement parameters and person parameters were well recovered when the s le size was sufficient. The more complete the linking of the statements was, the more accurate the parameter estimation was. This paper provides an empirical ex le of a career interest test using four‐category MFC items. Although some aspects of the model (e.g., the nature of the person parameters) require additional validation, our approach appears promising.
Start Date: 2019
End Date: 2020
Funder: Faculty of Education, University of Hong Kong
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End Date: End date not available
Funder: Education University of Hong Kong
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