2,219 research outputs found
A model-based approach to goodness-of-fit evaluation in item response theory
No full textThese authors congratulate Albert Maydeu-Olivares on his lucid and timely overview of goodness-of-fit assessment in IRT models, a field to which he himself has contributed considerably in the form of limited information statistics. In this commentary, Oberski and Vermunt focus on two aspects of model fit: (1) what causes there may be of misfit; and (2) what consequences misfit may have. They present their view on these topics in an integrated framework that is slightly different from that presented by Professor Maydeu-Olivares. They next provide a short illustration using an IRT analysis and, finally, draw some conclusions. This commentary expands on Maydeu-Olivares' overview of goodness-of-fit testing in item response theory by providing a model-based framework for GOF assessment. Oberski and Vermunt contend that many unresolved issues remain with the use of these measures, such as the problem of equivalent models, multiple testing, dealing with dependencies between parameters, and a shift of responsibility for deciding what may cause misfit and what is "of interest" toward the practitioner. These issues are, however, not absent in other goodness-of-fit assessment procedures, but merely less explicit. Therefore the model-based perspective appears to be a fruitful area of future research
Model-based approaches to synthesize microarray data: a unifying review using mixture of SEMs.
No full textSeveral statistical methods are nowadays available for the analysis of gene expression data recorded through microarray technology. In this article, we take a closer look at several Gaussian mixture models which have recently been proposed to model gene expression data. It can be shown that these are special cases of a more general model, called the mixture of structural equation models (mixture of SEMs), which has been developed in psychometrics. This model combines mixture modelling and SEMs by assuming that component-specific means and variances are subject to a SEM. The connection with SEM is useful for at least two reasons: (1) it shows the basic assumptions of existing methods more explicitly and (2) it helps in straightforward development of alternative mixture models for gene expression data with alternative mean/covariance structures. Different specifications of mixture of SEMs for clustering gene expression data are illustrated using two benchmark datasets
Power and type I error of local fit statistics in multilevel latent class analysis
Get PDFIn the social and behavioral sciences, variables are often categorical and people are often nested in groups. Models for such data, such as multilevel logistic regression or the multilevel latent class model, should account for not only the categorical nature of the variables, but also the nested structure of the persons. To assess whether the model accomplishes this goal adequately, local fit measures for multilevel categorical data were recently introduced by Nagelkerke, Oberski, and Vermunt (2015). The BVR-group evaluates the variable–group fit, and the BVR-pair evaluates the person–person fit within groups. In this article, we evaluate the performance of these 2 measures for the multilevel latent class model (Vermunt, 2003). An extensive simulation study indicates that whenever multilevel latent class modeling itself is viable, Type I error is controlled and power is adequate for both fit statistics. Thus, the BVR-group and BVR-pair are useful measures to locate important sources of misfit in multilevel latent class analysis
Importance and similarity in the evolving citation network of the International Journal of Research in Marketing.
Get PDFJ.K. Rowling: Her Life and Works
No full textBiographical information on author J.K. Rowling, a bibliography of her works, and a selection of her tweets
J.K. Rowling: Her Life and Works
No full textBiographical information on author J.K. Rowling, a bibliography of her works, and a selection of her tweets
Assessing the item response theory with covariate (IRT-C) procedure for ascertaining differential item functioning
No full textWe evaluate the item response theory with covariates (IRT-C) procedure for assessing differential item functioning (DIF) without preknowledge of anchor items (Tay, Newman, & Vermunt, 2011). This procedure begins with a fully constrained baseline model, and candidate items are tested for uniform and/or nonuniform DIF using the Wald statistic. Candidate items are selected in turn based on high unconditional bivariate residual (UBVR) values. This iterative process continues until no further DIF is detected or the Bayes information criterion (BIC) increases. We expanded on the procedure and examined the use of conditional bivariate residuals (CBVR) to flag for DIF; aside from the BIC, alternative stopping criteria were also considered. Simulation results showed that the IRT-C approach for assessing DIF performed well, with the use of CBVR yielding slightly better power and Type I error rates than UBVR. Additionally, using no information criterion yielded higher power than using the BIC, although Type I error rates were generally well controlled in both cases. Across the simulation conditions, the IRT-C procedure produced results similar to the Mantel-Haenszel and MIMIC procedures. Keywords: differential item functioning, item response theory, multiple covariates, simulatio
Relating latent class assignments to external variables: Standard errors for correct inference
No full textLatent class analysis is used in the political science literature in both substantive applications and as a tool to estimate measurement error. Many studies in the social and political sciences relate estimated class assignments from a latent class model to external variables. Although common, such a “three-step” procedure effectively ignores classification error in the class assignments; Vermunt (2010, “Latent class modeling with covariates: Two improved three-step approaches,” Political Analysis 18:450–69) showed that this leads to inconsistent parameter estimates and proposed a correction. Although this correction for bias is now implemented in standard software, inconsistency is not the only consequence of classification error. We demonstrate that the correction method introduces an additional source of variance in the estimates, so that standard errors and confidence intervals are overly optimistic when not taking this into account. We derive the asymptotic variance of the third-step estimates of interest, as well as several candidate-corrected sample estimators of the standard errors. These corrected standard error estimators are evaluated using a Monte Carlo study, and we provide practical advice to researchers as to which should be used so that valid inferences can be obtained when relating estimated class membership to external variables
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