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Matthew J Madison, Laine P Bradshaw
A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth...
September 27, 2018: Psychometrika
Keith A Markus
No abstract text is available yet for this article.
September 25, 2018: Psychometrika
Lili Yao, Shelby J Haberman, Mo Zhang
In best linear prediction (BLP), a true test score is predicted by observed item scores and by ancillary test data. If the use of BLP rather than a more direct estimate of a true score has disparate impact for different demographic groups, then a fairness issue arises. To improve population invariance but to preserve much of the efficiency of BLP, a modified approach, penalized best linear prediction, is proposed that weights both mean square error of prediction and a quadratic measure of subgroup biases. The proposed methodology is applied to three high-stakes writing assessments...
September 21, 2018: Psychometrika
Johan Koskinen, Peng Wang, Garry Robins, Philippa Pattison
We discuss measuring and detecting influential observations and outliers in the context of exponential family random graph (ERG) models for social networks. We focus on the level of the nodes of the network and consider those nodes whose removal would result in changes to the model as extreme or "central" with respect to the structural features that "matter". We construe removal in terms of two case-deletion strategies: the tie-variables of an actor are assumed to be unobserved, or the node is removed resulting in the induced subgraph...
September 18, 2018: Psychometrika
Maxwell R Hong, Ross Jacobucci
Research questions that address developmental processes are becoming more prevalent in psychology and other areas of social science. Growth models have become a popular tool to model multiple individuals measured over several time points. These types of models allow researchers to answer a wide variety of research questions, such as modeling inter- and intra-individual differences and variability in longitudinal process (Molenaar 2004). The recently published book, Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Grimm, Ram & Estabrook 2017), provides a solid foundation for both beginners and more advanced researchers interested in longitudinal data analysis by juxtaposing both the multilevel and structural equation modeling frameworks for several different models...
August 29, 2018: Psychometrika
Minjeong Jeon, Frank Rijmen, Sophia Rabe-Hesketh
We propose a class of confirmatory factor analysis models that include multiple sets of secondary or specific factors and a general factor. The general factor accounts for the common variance among manifest variables, whereas multiple sets of secondary factors account for the remaining source-specific dependency among subsets of manifest variables. A special case of the model is further proposed which constrains the specific factor loadings to be proportional to the general factor loadings. This proportional model substantially reduces the number of model parameters while preserving the essential structure of the general model...
August 17, 2018: Psychometrika
Joel B Greenhouse, Edward H Kennedy
No abstract text is available yet for this article.
August 13, 2018: Psychometrika
Peter F Halpin, Yoav Bergner
The social combination theory of group problem solving is used to extend existing psychometric models to collaborative settings. A model for pairwise group work is proposed, the implications of the model for assessment design are considered, and its estimation is addressed. The results are illustrated with an empirical example in which dyads work together on a twelfth-grade level mathematics assessment. In conclusion, attention is given to avenues of research that seem most fruitful for advancing current initiatives concerning the assessment of collaboration, teamwork, and related constructs...
August 9, 2018: Psychometrika
R Philip Chalmers
This paper proposes a model-based family of detection and quantification statistics to evaluate response bias in item bundles of any size. Compensatory (CDRF) and non-compensatory (NCDRF) response bias measures are proposed, along with their sample realizations and large-sample variability when models are fitted using multiple-group estimation. Based on the underlying connection to item response theory estimation methodology, it is argued that these new statistics provide a powerful and flexible approach to studying response bias for categorical response data over and above methods that have previously appeared in the literature...
September 2018: Psychometrika
Florian Böing-Messing, Joris Mulder
In comparing characteristics of independent populations, researchers frequently expect a certain structure of the population variances. These expectations can be formulated as hypotheses with equality and/or inequality constraints on the variances. In this article, we consider the Bayes factor for testing such (in)equality-constrained hypotheses on variances. Application of Bayes factors requires specification of a prior under every hypothesis to be tested. However, specifying subjective priors for variances based on prior information is a difficult task...
September 2018: Psychometrika
Zhengguo Gu, Wilco H M Emons, Klaas Sijtsma
Change scores obtained in pretest-posttest designs are important for evaluating treatment effectiveness and for assessing change of individual test scores in psychological research. However, over the years the use of change scores has raised much controversy. In this article, from a multilevel perspective, we provide a structured treatise on several persistent negative beliefs about change scores and show that these beliefs originated from the confounding of the effects of within-person change on change-score reliability and between-person change differences...
September 2018: Psychometrika
Klaas Sijtsma
No abstract text is available yet for this article.
September 2018: Psychometrika
Matthias von Davier, Claus Carstensen, Rolf Langeheine, Michael Eid
No abstract text is available yet for this article.
September 2018: Psychometrika
Pascal Jordan, Martin Spiess
Since Hooker, Finkelman and Schwartzman (Psychometrika 74(3): 419-442, 2009) it is known that person parameter estimates from multidimensional latent variable models can induce unfair classifications via paradoxical scoring effects. The open question as to whether there is a fair and at the same time multidimensional scoring scheme with adequate statistical properties is addressed in this paper. We develop a theorem on the existence of a fair, multidimensional classification scheme in the context of the classical linear factor analysis model and show how the computation of the scoring scheme can be embedded in the context of linear programming...
September 2018: Psychometrika
Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying
Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning...
September 2018: Psychometrika
Sun-Joo Cho, Sarah Brown-Schmidt, Woo-Yeol Lee
As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accounts for variability in lag effects across persons and items. The model is shown to be applicable to intensive binary time series eye-tracking data when researchers are interested in detecting experimental condition effects while controlling for previous responses...
September 2018: 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...
September 2018: 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...
September 2018: Psychometrika
Yuqi Gu, Jingchen Liu, Gongjun Xu, Zhiliang Ying
The recent surge of interests in cognitive assessment has led to the development of cognitive diagnosis models. Central to many such models is a specification of the Q-matrix, which relates items to latent attributes that have natural interpretations. In practice, the Q-matrix is usually constructed subjectively by the test designers. This could lead to misspecification, which could result in lack of fit of the underlying statistical model. To test possible misspecification of the Q-matrix, traditional goodness of fit tests, such as the Chi-square test and the likelihood ratio test, may not be applied straightforwardly due to the large number of possible response patterns...
July 11, 2018: Psychometrika
Ick Hoon Jin, Minjeong Jeon
Item response theory (IRT) is one of the most widely utilized tools for item response analysis; however, local item and person independence, which is a critical assumption for IRT, is often violated in real testing situations. In this article, we propose a new type of analytical approach for item response data that does not require standard local independence assumptions. By adapting a latent space joint modeling approach, our proposed model can estimate pairwise distances to represent the item and person dependence structures, from which item and person clusters in latent spaces can be identified...
July 9, 2018: Psychometrika
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