The Multidimensional Random Coefficients Multinomial Logit Model

Adams, Raymond J.; Wilson, Mark; Wang, Wen-chung

doi:10.1177/0146621697211001

Cited by 730 publications

(597 citation statements)

References 32 publications

Supporting

Mentioning

560

Contrasting

Unclassified

Order By: Relevance

“…The multidimensional Rasch model used in this study is called the multidimensional random coefficients multinomial logit model (MRCMLM) [14]. Let person n's levels on the L latent traits (in this study L=4) be denoted as h T n ¼ ðh n1 ; .…”

Section: Resultsmentioning

confidence: 99%

“…More importantly, the definitions allow the specification of a range of multidimensional models by imposing linear constraints on the item parameters, such as multidimensional forms of the simple logistic model, the rating scale model, the partial credit model (to be used in this study), and the linear partial credit model. For details on how to manipulate a ij and b ij to form the above models and other customized models, the reader is referred to Adams et al [14] and Adams and Wilson [20].…”

Section: Resultsmentioning

confidence: 99%

“…Exploratory approaches enable researchers to get an impression of the number of latent traits and of which items are indicative of which latent traits. Although ''within-item'' multidimensional analysis (in which some items assess more than one latent trait simultaneously) is possible under the MRCMLM framework [14,45], it is not appropriate in this study due to the lack of theoretical within-item dimensionality for the WHOQOL-BREF.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Validating, Improving Reliability, and Estimating Correlation of the Four Subscales in the WHOQOL-BREF using Multidimensional Rasch Analysis

et al. 2006

Self Cite

View full text Add to dashboard Cite

Objective: This study examined the construct validity, and improved the test reliability and the estimation accuracy for the correlation between domains of the WHOQOL-BREF using multidimensional Rasch analysis. Method: A total of 13,083 adults were administered the 28-item WHOQOL-BREF Taiwan version, which consists of 4 subscales (domains). The multidimensional form of the partial credit model was used to examine the fit of the 4 subscales. For comparison, each subscale individually was also fitted to the unidimensional partial credit model. Standard item fit statistics and analysis of differential item functioning (DIF) were used to check model-data fit. Results: After excluding 2 overall items and deleting 7 DIF items, the remaining items of each subscale in the WHOQOL-BREF constituted a single construct. The test reliabilities and correlations between domains obtained from the multidimensional approach, (0.82-0.86) and (0.79-0.89), respectively, were much higher than those obtained from the unidimensional approach, (0.67-0.75) and (0.53-0.65), respectively. Conclusion: The 19-item WHOQOL-BREF measures more succinct latent traits than the original design. The multidimensional approach yields not only more accurate estimates for the correlation between domains but also substantially higher reliabilities, than the standard unidimensional approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Validating, Improving Reliability, and Estimating Correlation of the Four Subscales in the WHOQOL-BREF using Multidimensional Rasch Analysis

et al. 2006

Self Cite

View full text Add to dashboard Cite

show abstract

“…When (6) in fact holds, then estimation can be done using simple multivariate extensions of the 2PL model. Such extensions have been considered for Rasch models (Andersen, 1985;Embretson, 1991;Adams et al, 1997).…”

Section: The Longitudinal 2pl Modelmentioning

confidence: 99%

Modeling local dependence in longitudinal IRT models

Olsbjerg

2015

Behav Res

View full text Add to dashboard Cite

Measuring change in a latent variable over time is often done using the same instrument at several time points. This can lead to dependence between responses across time points for the same person yielding within person correlations that are stronger than what can be attributed to the latent variable. Ignoring this can lead to biased estimates of changes in the latent variable. In this paper we propose a method for modeling local dependence in the longitudinal 2PL model. It is based on the concept of item splitting, and makes it possible to correctly estimate change in the latent variable.

show abstract

“…In some situations, subjects are assessed at multiple occasions, perhaps with the same set or a related set of items. Several articles have described applications and developments of IRT modeling to such longitudinal situations in psychology [Adams et al, 1997a, Anderson, 1985, Embretson, 1991, 2000, Fischer and Pononcy, 1994.…”

Section: Introductionmentioning

confidence: 99%

Application of Item Response Theory Models for Intensive Longitudinal Data

Hedeker

Mermelstein

Flay

2006

Models for Intensive Longitudinal Data

View full text Add to dashboard Cite

SummaryItem Response Theory (IRT) models, also called latent trait models, have been extensively used in educational testing and psychological measurement. While an IRT model is essentially a mixed-effects regression model for longitudinal categorical data, use of IRT has been somewhat limited outside of education and psychology.In this chapter, we describe IRT models and some key developments in the IRT literature, and illustrate their application for intensive longitudinal data. In particular, we relate IRT models to mixed models and indicate how software for the latter can be used to estimate the IRT model parameters. Using Ecological Momentary Assessment (EMA) data from a study of adolescent smoking, we describe how IRT models can be used to address key questions in smoking research.

show abstract

The Multidimensional Random Coefficients Multinomial Logit Model

Cited by 730 publications

References 32 publications

Validating, Improving Reliability, and Estimating Correlation of the Four Subscales in the WHOQOL-BREF using Multidimensional Rasch Analysis

Validating, Improving Reliability, and Estimating Correlation of the Four Subscales in the WHOQOL-BREF using Multidimensional Rasch Analysis

Modeling local dependence in longitudinal IRT models

Application of Item Response Theory Models for Intensive Longitudinal Data

Contact Info

Product

Resources

About