Psychometrika

Strobl, Carolin; Kopf, Julia; Zeileis, Achim

2013 Psychometrika

doi: 10.1007/s11336-013-9388-3pmid: 24352514

A variety of statistical methods have been suggested for detecting differential item functioning (DIF) in the Rasch model. Most of these methods are designed for the comparison of pre-specified focal and reference groups, such as males and females. Latent class approaches, on the other hand, allow the detection of previously unknown groups exhibiting DIF. However, this approach provides no straightforward interpretation of the groups with respect to person characteristics. Here, we propose a new method for DIF detection based on model-based recursive partitioning that can be considered as a compromise between those two extremes. With this approach it is possible to detect groups of subjects exhibiting DIF, which are not pre-specified, but result from combinations of observed covariates. These groups are directly interpretable and can thus help generate hypotheses about the psychological sources of DIF. The statistical background and construction of the new method are introduced by means of an instructive example, and extensive simulation studies are presented to support and illustrate the statistical properties of the method, which is then applied to empirical data from a general knowledge quiz. A software implementation of the method is freely available in the R system for statistical computing.

Oravecz, Zita; Anders, Royce; Batchelder, William

2013 Psychometrika

doi: 10.1007/s11336-013-9379-4pmid: 24327065

Cultural Consensus Theory (CCT) models have been applied extensively across research domains in the social and behavioral sciences in order to explore shared knowledge and beliefs. CCT models operate on response data, in which the answer key is latent. The current paper develops methods to enhance the application of these models by developing the appropriate specifications for hierarchical Bayesian inference. A primary contribution is the methodology for integrating the use of covariates into CCT models. More specifically, both person- and item-related parameters are introduced as random effects that can respectively account for patterns of inter-individual and inter-item variability.

Jones, Jeff; Waller, Niels

2013 Psychometrika

doi: 10.1007/s11336-013-9380-ypmid: 24362970

Yuan and Chan (Psychometrika, 76, 670–690, 2011) recently showed how to compute the covariance matrix of standardized regression coefficients from covariances. In this paper, we describe a method for computing this covariance matrix from correlations. Next, we describe an asymptotic distribution-free (ADF; Browne in British Journal of Mathematical and Statistical Psychology, 37, 62–83, 1984) method for computing the covariance matrix of standardized regression coefficients. We show that the ADF method works well with nonnormal data in moderate-to-large samples using both simulated and real-data examples. R code (R Development Core Team, 2012) is available from the authors or through the Psychometrika online repository for supplementary materials.

Yuan, Ke-Hai; Tian, Yubin; Yanagihara, Hirokazu

2013 Psychometrika

doi: 10.1007/s11336-013-9386-5pmid: 24327067

Survey data typically contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. The most widely used statistic for evaluating the adequacy of a SEM model is T ML, a slight modification to the likelihood ratio statistic. Under normality assumption, T ML approximately follows a chi-square distribution when the number of observations (N) is large and the number of items or variables (p) is small. However, in practice, p can be rather large while N is always limited due to not having enough participants. Even with a relatively large N, empirical results show that T ML rejects the correct model too often when p is not too small. Various corrections to T ML have been proposed, but they are mostly heuristic. Following the principle of the Bartlett correction, this paper proposes an empirical approach to correct T ML so that the mean of the resulting statistic approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics follow the nominal chi-square distribution much more closely than previously proposed corrections to T ML, and they control type I errors reasonably well whenever N≥max(50,2p). The formulations of the empirically corrected statistics are further used to predict type I errors of T ML as reported in the literature, and they perform well.

Paek, Insu; Holland, Paul

2013 Psychometrika

doi: 10.1007/s11336-013-9394-5pmid: 24337958

When differential item functioning (DIF) is investigated, DIF classification is made using statistical test results and estimated DIF sizes in practice. One of the well-known DIF classifications is that of the Educational Testing Service (ETS) A (negligible DIF), B (medium DIF), and C (large DIF) rules. This article provides a clarifying note on (a) a sketch of the proof of the asymptotic normality of what is known as the Mantel–Haenszel (MH) delta, which provides the basis of a point and an interval null hypothesis test based on the MH delta, and (b) how to conduct an interval null hypothesis test using the MH delta, which is necessary for the C DIF classification.

Okubo, Tomoya; Mayekawa, Shin-ichi

2013 Psychometrika

doi: 10.1007/s11336-013-9392-7pmid: 24352515

A number of mathematical models for overcoming intransitive choice have been proposed and tested in the literature of decision theory. This article presents the development of a new stochastic choice model based on multidimensional scaling. This allows decision-makers to have multiple viewpoints, whereas current multidimensional scaling models are based on the assumption that a subject or group of subjects has only one viewpoint. The implication of our model is that subjects make an intransitive choice because they are able to shift their viewpoint. This paper also presents the maximum likelihood estimation of the proposed model, and reanalyzes Tversky’s gamble experiment data.

Liu, Jingchen; Ying, Zhiliang; Zhang, Stephanie

2013 Psychometrika

doi: 10.1007/s11336-013-9395-4pmid: 24327068

Computerized adaptive testing (CAT) is a sequential experiment design scheme that tailors the selection of experiments to each subject. Such a scheme measures subjects’ attributes (unknown parameters) more accurately than the regular prefixed design. In this paper, we consider CAT for diagnostic classification models, for which attribute estimation corresponds to a classification problem. After a review of existing methods, we propose an alternative criterion based on the asymptotic decay rate of the misclassification probabilities. The new criterion is then developed into new CAT algorithms, which are shown to achieve the asymptotically optimal misclassification rate. Simulation studies are conducted to compare the new approach with existing methods, demonstrating its effectiveness, even for moderate length tests.

Ligtvoet, Rudy

2013 Psychometrika

doi: 10.1007/s11336-013-9397-2pmid: 24337959

This note contains some remarks on Ligtvoet’s (Psychometrika, 77:479–494, 2012) treatment of the isotonic partial credit model. The Proposition relating to the observable property MWI is shown to be false.

DeSarbo, Wayne; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco

2013 Psychometrika

doi: 10.1007/s11336-013-9385-6pmid: 24327066

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).