Multiple Group IRT Modeling: Applications to Item Bias Analysis

Lehman, James Daniel; Muthén, Bengt

doi:10.2307/1164840

Cited by 4,562 publications

(5,946 citation statements)

References 0 publications

Supporting

Mentioning

5,837

Contrasting

Unclassified

Order By: Relevance

“…Then, we provide details on how estimation is performed with a popular SEM program, Mplus (L. Muthén & Muthén, 2001). …”

Section: Sem Of Ranking Datamentioning

confidence: 99%

“…We present a comprehensive nontechnical account of Thurstonian choice modeling that reviews, integrates, and expands on recent technical research on Thurstonian choice modeling. In addition, we embed Thurstonian models within a structural equation modeling (SEM) framework and show how these models can be estimated with a popular SEM package, Mplus (L. Muthén & Muthén, 2001). Technical accounts on a subset of Thurstonian ranking and paired-comparisons models within an SEM framework were given by MaydeuOlivares (1999MaydeuOlivares ( , 2001MaydeuOlivares ( , 2003b.…”

mentioning

confidence: 99%

“…In the second section (Thurstonian Choice Models for Rankings), we review three Thurstonian ranking models (Cases III and V and the unrestricted model) using a matrix formulation and discuss the restrictions that are imposed by these models on the thresholds and tetrachoric correlations that are estimated from the data. In the third section (SEM of Ranking Data), we describe how to estimate Thurstonian ranking models using Mplus (L. Muthén & Muthén, 2001). In the fourth section (A Ranking Application: Modeling Career Preferences Among Spanish Undergraduates), we analyze a ranking data set about career preferences of psychology undergraduate students using this software package.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Structural Equation Modeling of Paired-Comparison and Ranking Data.

Maydeu-Olivares

Böckenholt

2005

Psychological Methods

107

View full text Add to dashboard Cite

L. L. Thurstone's (1927) model provides a powerful framework for modeling individual differences in choice behavior. An overview of Thurstonian models for comparative data is provided, including the classical Case V and Case III models as well as more general choice models with unrestricted and factor-analytic covariance structures. A flow chart summarizes the model selection process. The authors show how to embed these models within a more familiar structural equation modeling (SEM) framework. The different special cases of Thurstone's model can be estimated with a popular SEM statistical package, including factor analysis models for paired comparisons and rankings. Only minor modifications are needed to accommodate both types of data. As a result, complex models for comparative judgments can be both estimated and tested efficiently.Keywords: Lisrel, preference data, choice models, random utility models, factor analysisThe methods of ranking and paired comparison play an essential role in the measurement of preferences, attitudes, and values. In a ranking task, respondents are presented with a set of alternatives (which, in the literature, are also referred to as options, stimuli, or items) and are asked to order them from most to least preferred. In a paired-comparison task, respondents are presented with pairs selected from the set of available alternatives and are instructed to select the more preferred alternative from each pair. The popularity of paired-comparison and ranking methods can be traced back to three reasons. First, by asking for a comparison of choice alternatives, these methods impose minimal constraints on the response behavior of a respondent. Especially when differences between choice alternatives are small, these methods are likely to provide more information about individual preferences than is obtainable by rating methods. Second, internal consistency checks are available that facilitate the identification of respondents who do not have well-defined preferences, values, or attitudes. If respondents can be shown to be consistent in their judgments, one can have much greater confidence in the obtained measurements and the predictive value of the derived scales in further applications. Third, paired-comparison and ranking data provide a rich source of information about the effects of individual differences and perceived similarity relationships among choice alternatives. This article presents two applications that illustrate this important feature.Because of their versatility, the methods of paired comparison and rankings are used in a wide range of studies.

show abstract

“…Then, we provide details on how estimation is performed with a popular SEM program, Mplus (L. Muthén & Muthén, 2001). …”

Section: Sem Of Ranking Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Structural Equation Modeling of Paired-Comparison and Ranking Data.

Maydeu-Olivares

Böckenholt

2005

Psychological Methods

107

View full text Add to dashboard Cite

show abstract

“…A Wald test was performed to compare differences in mean on the distal outcome. The latent profile analysis was conducted with MPLUS statistical modelling software 8.0 (Muthén & Muthén, 2017). …”

Section: Methodsmentioning

confidence: 99%

Similarity in symptom patterns of posttraumatic stress among disaster-survivors: a three-step latent profile analysis

Bondjers

Willebrand

Arnberg

2018

European Journal of Psychotraumatology

View full text Add to dashboard Cite

Background: Individuals express symptoms of posttraumatic stress in various ways, noted for example in the many symptom combinations in the diagnostic manuals. Studies aiming to examine differences of symptom presentations by extracting latent classes or profiles indicate both the presence of subtypes with differing symptomatology and subtypes distinguished by severity levels. Few studies have examined subtype associations with long-term outcomes.Objective: The current study aimed to apply latent profile analysis on posttraumatic stress (PTS) in a highly homogenous sample of Swedish tourists exposed to the 2004 Southeast Asia tsunami and to examine if classes differed in their long-term outcome.Methods: An latent profile analysis was conducted using self-report data collected one year after the disaster from 1638 highly exposed survivors that endorsed ≥ 1 symptom of PTS. Associations were examined between the classes and predictors of PTS (loss of a relative or friend, subjective life threat) and levels of PTS at a three-year follow up.Results: The latent profile analysis indicated four classes: minimal, low, moderate, and severe symptoms. The classes were distinguished mainly by their levels of PTS. Loss of a relative or friend and subjective life threat were associated with a higher likelihood of belonging to any other class than the minimal class. The severity level of the classes at one year were predictive of PTS severity at the three-year follow-up.Conclusions: Homogeneous profiles of posttraumatic stress differing mainly in symptom severity were found in this sample of disaster survivors. Profile diversity may be related to sample variation and unmeasured confounders rather than reflect qualitatively different disorders.

show abstract

“…The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) provide reliable measures of the model fit and help to determine the number of classes (Akaike 1974;Bozdogan 1987;Magidson and Vermunt 2004;Muthén 1998Muthén -2004Schwarz 1978), and both measures are used in this analysis. The AIC is the goodness-of-fit statistic corrected for the complexity of the model by taking into account the number of parameters which were estimated (Field 2009).…”

Section: Latent Class Analysismentioning

confidence: 99%

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

Durrant

Maslovskaya

Smith

2017

Journal of Official Statistics

View full text Add to dashboard Cite

In recent years the use of paradata for nonresponse investigations has risen significantly. One key question is how useful paradata, including call record data and interviewer observations, from the current and previous waves of a longitudinal study, as well as previous wave survey information, are in predicting response outcomes in a longitudinal context. This article aims to address this question. Final response outcomes and sequence length (the number of calls/visits to a household) are modelled both separately and jointly for a longitudinal study. Being able to predict length of call sequence and response can help to improve both adaptive and responsive survey designs and to increase efficiency and effectiveness of call scheduling. The article also identifies the impact of different methodological specifications of the models, for example different specifications of the response outcomes. Latent class analysis is used as one of the approaches to summarise call outcomes in sequences. To assess and compare the models in their ability to predict, indicators derived from classification tables, ROC (Receiver Operating Characteristic) curves, discrimination and prediction are proposed in addition to the standard approach of using the pseudo R 2 value, which is not a sufficient indicator on its own. The study uses data from Understanding Society, a large-scale longitudinal survey in the UK. The findings indicate that basic models (including geographic, design and survey data from the previous wave), although commonly used in predicting and adjusting for nonresponse, do not predict the response outcome well. Conditioning on previous wave paradata, including call record data, interviewer observation data and indicators of change, improve the fit of the models slightly. A significant improvement can be observed when conditioning on the most recent call outcome, which may indicate that the nonresponse process predominantly depends on the most current circumstances of a sample unit.

show abstract

Multiple Group IRT Modeling: Applications to Item Bias Analysis

Cited by 4,562 publications

References 0 publications

Structural Equation Modeling of Paired-Comparison and Ranking Data.

Structural Equation Modeling of Paired-Comparison and Ranking Data.

Similarity in symptom patterns of posttraumatic stress among disaster-survivors: a three-step latent profile analysis

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

Contact Info

Product

Resources

About