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Niels G Waller
The Schmid-Leiman (S-L; Psychometrika 22: 53-61, 1957) transformation is a popular method for conducting exploratory bifactor analysis that has been used in hundreds of studies of individual differences variables. To perform a two-level S-L transformation, it is generally believed that two separate factor analyses are required: a first-level analysis in which k obliquely rotated factors are extracted from an observed-variable correlation matrix, and a second-level analysis in which a general factor is extracted from the correlations of the first-level factors...
December 4, 2017: Psychometrika
(no author information available yet)
No abstract text is available yet for this article.
December 4, 2017: Psychometrika
Kai Wang
Previous studies have found some puzzling power anomalies related to testing the indirect effect of a mediator. The power for the indirect effect stagnates and even declines as the size of the indirect effect increases. Furthermore, the power for the indirect effect can be much higher than the power for the total effect in a model where there is no direct effect and therefore the indirect effect is of the same magnitude as the total effect. In the presence of direct effect, the power for the indirect effect is often much higher than the power for the direct effect even when these two effects are of the same magnitude...
November 27, 2017: Psychometrika
Edison M Choe, Jinming Zhang, Hua-Hua Chang
Item compromise persists in undermining the integrity of testing, even secure administrations of computerized adaptive testing (CAT) with sophisticated item exposure controls. In ongoing efforts to tackle this perennial security issue in CAT, a couple of recent studies investigated sequential procedures for detecting compromised items, in which a significant increase in the proportion of correct responses for each item in the pool is monitored in real time using moving averages. In addition to actual responses, response times are valuable information with tremendous potential to reveal items that may have been leaked...
November 22, 2017: Psychometrika
Peter W van Rijn, Usama S Ali
We propose a generalization of the speed-accuracy response model (SARM) introduced by Maris and van der Maas (Psychometrika 77:615-633, 2012). In these models, the scores that result from a scoring rule that incorporates both the speed and accuracy of item responses are modeled. Our generalization is similar to that of the one-parameter logistic (or Rasch) model to the two-parameter logistic (or Birnbaum) model in item response theory. An expectation-maximization (EM) algorithm for estimating model parameters and standard errors was developed...
November 21, 2017: Psychometrika
Haiyan Bai, Wei Pan
No abstract text is available yet for this article.
November 17, 2017: Psychometrika
Zsuzsa Bakk, Jouni Kuha
We consider models which combine latent class measurement models for categorical latent variables with structural regression models for the relationships between the latent classes and observed explanatory and response variables. We propose a two-step method of estimating such models. In its first step, the measurement model is estimated alone, and in the second step the parameters of this measurement model are held fixed when the structural model is estimated. Simulation studies and applied examples suggest that the two-step method is an attractive alternative to existing one-step and three-step methods...
November 17, 2017: Psychometrika
Chia-Yi Chiu, Yan Sun, Yanhong Bian
The focus of cognitive diagnosis (CD) is on evaluating an examinee's strengths and weaknesses in terms of cognitive skills learned and skills that need study. Current methods for fitting CD models (CDMs) work well for large-scale assessments, where the data of hundreds or thousands of examinees are available. However, the development of CD-based assessment tools that can be used in small-scale test settings, say, for monitoring the instruction and learning process at the classroom level has not kept up with the rapid pace at which research and development proceeded for large-scale assessments...
November 17, 2017: Psychometrika
Ting Wang, Carolin Strobl, Achim Zeileis, Edgar C Merkle
Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information...
November 17, 2017: Psychometrika
Eric F Lock, Nidhi Kohli, Maitreyee Bose
Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another...
November 17, 2017: Psychometrika
Pascal Jordan, Martin Spiess
In multidimensional item response models, paradoxical scoring effects can arise, wherein correct answers are penalized and incorrect answers are rewarded. For the most prominent class of IRT models, the class of linearly compensatory models, a general derivation of paradoxical scoring effects based on the geometry of item discrimination vectors is given, which furthermore corrects an error in an established theorem on paradoxical results. This approach highlights the very counterintuitive way in which item discrimination parameters (and also factor loadings) have to be interpreted in terms of their influence on the latent ability estimate...
October 13, 2017: Psychometrika
Niansheng Tang, Sy-Miin Chow, Joseph G Ibrahim, Hongtu Zhu
Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e...
October 13, 2017: Psychometrika
Sora Lee, Daniel M Bolt
While item complexity is often considered as an item feature in test development, it is much less frequently attended to in the psychometric modeling of test items. Prior work suggests that item complexity may manifest through asymmetry in item characteristics curves (ICCs; Samejima in Psychometrika 65:319-335, 2000). In the current paper, we study the potential for asymmetric IRT models to inform empirically about underlying item complexity, and thus the potential value of asymmetric models as tools for item validation...
September 25, 2017: Psychometrika
Francesco Bartolucci, Alessio Farcomeni, Luisa Scaccia
We propose a nonparametric item response theory model for dichotomously-scored items in a Bayesian framework. The model is based on a latent class (LC) formulation, and it is multidimensional, with dimensions corresponding to a partition of the items in homogenous groups that are specified on the basis of inequality constraints among the conditional success probabilities given the latent class. Moreover, an innovative system of prior distributions is proposed following the encompassing approach, in which the largest model is the unconstrained LC model...
September 12, 2017: Psychometrika
Monia Ranalli, Roberto Rocci
The literature on clustering for continuous data is rich and wide; differently, that one developed for categorical data is still limited. In some cases, the clustering problem is made more difficult by the presence of noise variables/dimensions that do not contain information about the clustering structure and could mask it. The aim of this paper is to propose a model for simultaneous clustering and dimensionality reduction of ordered categorical data able to detect the discriminative dimensions discarding the noise ones...
September 6, 2017: Psychometrika
Yang Liu, Ji Seung Yang
In most item response theory applications, model parameters need to be first calibrated from sample data. Latent variable (LV) scores calculated using estimated parameters are thus subject to sampling error inherited from the calibration stage. In this article, we propose a resampling-based method, namely bootstrap calibration (BC), to reduce the impact of the carryover sampling error on the interval estimates of LV scores. BC modifies the quantile of the plug-in posterior, i.e., the posterior distribution of the LV evaluated at the estimated model parameters, to better match the corresponding quantile of the true posterior, i...
September 6, 2017: Psychometrika
Yinghan Chen, Steven Andrew Culpepper, Yuguo Chen, Jeffrey Douglas
Cognitive diagnosis models are partially ordered latent class models and are used to classify students into skill mastery profiles. The deterministic inputs, noisy "and" gate model (DINA) is a popular psychometric model for cognitive diagnosis. Application of the DINA model requires content expert knowledge of a Q matrix, which maps the attributes or skills needed to master a collection of items. Misspecification of Q has been shown to yield biased diagnostic classifications. We propose a Bayesian framework for estimating the DINA Q matrix...
August 31, 2017: Psychometrika
Jean-Paul Fox, Joris Mulder, Sandip Sinharay
Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components...
August 29, 2017: Psychometrika
George Karabatsos
This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together...
August 25, 2017: Psychometrika
Xiang Liu, Zhuangzhuang Han, Matthew S Johnson
In educational and psychological measurement when short test forms are used, the asymptotic normality of the maximum likelihood estimator of the person parameter of item response models does not hold. As a result, hypothesis tests or confidence intervals of the person parameter based on the normal distribution are likely to be problematic. Inferences based on the exact distribution, on the other hand, do not suffer from this limitation. However, the computation involved for the exact distribution approach is often prohibitively expensive...
August 23, 2017: Psychometrika
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