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Psychometrika

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https://www.readbyqxmd.com/read/27905058/regularized-latent-class-analysis-with-application-in-cognitive-diagnosis
#1
Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying
Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model...
November 30, 2016: Psychometrika
https://www.readbyqxmd.com/read/27905057/book-review
#2
Chun Wang, Jack Kostal
No abstract text is available yet for this article.
November 30, 2016: Psychometrika
https://www.readbyqxmd.com/read/27905056/principal-covariates-clusterwise-regression-pccr-accounting-for-multicollinearity-and-population-heterogeneity-in-hierarchically-organized-data
#3
Tom Frans Wilderjans, Eva Vande Gaer, Henk A L Kiers, Iven Van Mechelen, Eva Ceulemans
In the behavioral sciences, many research questions pertain to a regression problem in that one wants to predict a criterion on the basis of a number of predictors. Although in many cases, ordinary least squares regression will suffice, sometimes the prediction problem is more challenging, for three reasons: first, multiple highly collinear predictors can be available, making it difficult to grasp their mutual relations as well as their relations to the criterion. In that case, it may be very useful to reduce the predictors to a few summary variables, on which one regresses the criterion and which at the same time yields insight into the predictor structure...
November 30, 2016: Psychometrika
https://www.readbyqxmd.com/read/27905055/a-multimethod-latent-state-trait-model-for-structurally-different-and-interchangeable-methods
#4
Tobias Koch, Martin Schultze, Jana Holtmann, Christian Geiser, Michael Eid
A new multiple indicator multilevel latent state-trait (LST) model for the analysis of multitrait-multimethod-multioccasion (MTMM-MO) data is proposed. The LST-COM model combines current CFA-MTMM modeling approaches of interchangeable and structurally different methods and LST modeling approaches. The model enables researchers to specify construct and method factors on the level of time-stable (trait) as well as time-variable (occasion-specific) latent variables and analyze the convergent and discriminant validity among different rater groups across time...
November 30, 2016: Psychometrika
https://www.readbyqxmd.com/read/27900637/commentaries-on-the-ten-most-highly-cited-psychometrika-articles-from-1936-to-the-present
#5
(no author information available yet)
No abstract text is available yet for this article.
November 29, 2016: Psychometrika
https://www.readbyqxmd.com/read/27878414/a-creation-narrative-for-the-psychometric-society-and-psychometrika-in-the-beginning-there-was-paul-horst
#6
EDITORIAL
Willem Heiser, Lawrence Hubert
A review is provided for the creation of the Psychometric Society in 1935, and the establishment of its journal, Psychometrika, in 1936. This document is part of the 80th anniversary celebration for Psychometrika's founding, held during the annual meeting of the Psychometric Society in July of 2016 in Asheville, NC.
November 22, 2016: Psychometrika
https://www.readbyqxmd.com/read/27873150/commemorating-the-80th-anniversary-of-the-founding-of-psychometrika-introduction-by-the-guest-editors
#7
EDITORIAL
Willem Heiser, Lawrence Hubert
No abstract text is available yet for this article.
November 21, 2016: Psychometrika
https://www.readbyqxmd.com/read/27873149/erratum-to-quantifying-adventitious-error-in-a-covariance-structure-as-a-random-effect
#8
Hao Wu, Michael W Browne
No abstract text is available yet for this article.
November 21, 2016: Psychometrika
https://www.readbyqxmd.com/read/27848151/modeling-omitted-and-not-reached-items-in-irt-models
#9
Norman Rose, Matthias von Davier, Benjamin Nagengast
Item nonresponse is a common problem in educational and psychological assessments. The probability of unplanned missing responses due to omitted and not-reached items may stochastically depend on unobserved variables such as missing responses or latent variables. In such cases, missingness cannot be ignored and needs to be considered in the model. Specifically, multidimensional IRT models, latent regression models, and multiple-group IRT models have been suggested for handling nonignorable missing responses in latent trait models...
November 15, 2016: Psychometrika
https://www.readbyqxmd.com/read/27844271/monitoring-countries-in-a-changing-world-a-new-look-at-dif-in-international-surveys
#10
Robert J Zwitser, S Sjoerd F Glaser, Gunter Maris
This paper discusses the issue of differential item functioning (DIF) in international surveys. DIF is likely to occur in international surveys. What is needed is a statistical approach that takes DIF into account, while at the same time allowing for meaningful comparisons between countries. Some existing approaches are discussed and an alternative is provided. The core of this alternative approach is to define the construct as a large set of items, and to report in terms of summary statistics. Since the data are incomplete, measurement models are used to complete the incomplete data...
November 14, 2016: Psychometrika
https://www.readbyqxmd.com/read/27844270/book-review
#11
Ji An, Laura M Stapleton
No abstract text is available yet for this article.
November 14, 2016: Psychometrika
https://www.readbyqxmd.com/read/27844269/standard-errors-and-confidence-intervals-of-norm-statistics-for-educational-and-psychological-tests
#12
Hannah E M Oosterhuis, L Andries van der Ark, Klaas Sijtsma
Norm statistics allow for the interpretation of scores on psychological and educational tests, by relating the test score of an individual test taker to the test scores of individuals belonging to the same gender, age, or education groups, et cetera. Given the uncertainty due to sampling error, one would expect researchers to report standard errors for norm statistics. In practice, standard errors are seldom reported; they are either unavailable or derived under strong distributional assumptions that may not be realistic for test scores...
November 14, 2016: Psychometrika
https://www.readbyqxmd.com/read/27844268/predicting-rights-only-score-distributions-from-data-collected-under-formula-score-instructions
#13
Hongwen Guo
Under a formula score instruction (FSI), test takers omit items. If students are encouraged to answer every item (under a rights-only scoring instruction, ROI), the score distribution will be different. In this paper, we formulate a simple statistical model to predict the score ROI distribution using the FSI data. Estimation error is also provided. In addition, a preliminary investigation of the probability of guessing correctly on omitted items and its sensitivity is presented in the paper. Based on the data used in this paper, the probability of guessing correctly may be close or slightly greater than the chance score...
November 14, 2016: Psychometrika
https://www.readbyqxmd.com/read/27804079/generalized-sample-size-determination-formulas-for-investigating-contextual-effects-by-a-three-level-random-intercept-model
#14
Satoshi Usami
Behavioral and psychological researchers have shown strong interests in investigating contextual effects (i.e., the influences of combinations of individual- and group-level predictors on individual-level outcomes). The present research provides generalized formulas for determining the sample size needed in investigating contextual effects according to the desired level of statistical power as well as width of confidence interval. These formulas are derived within a three-level random intercept model that includes one predictor/contextual variable at each level to simultaneously cover various kinds of contextual effects that researchers can show interest...
November 1, 2016: Psychometrika
https://www.readbyqxmd.com/read/27796763/a-two-stage-approach-to-differentiating-normal-and-aberrant-behavior-in-computer-based-testing
#15
Chun Wang, Gongjun Xu, Zhuoran Shang
Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches...
October 28, 2016: Psychometrika
https://www.readbyqxmd.com/read/27770307/some-remarks-on-applications-of-tests-for-detecting-a-change-point-to-psychometric-problems
#16
Sandip Sinharay
Tests for a change point (e.g., Chen and Gupta, Parametric statistical change point analysis (2nd ed.). Birkhuser, Boston, 2012; Hawkins et al., J Qual Technol 35:355-366, 2003) have recently been brought into the spotlight for their potential uses in psychometrics. They have been successfully applied to detect an unusual change in the mean score of a sequence of administrations of an international language assessment (Lee and von Davier, Psychometrika 78:557-575, 2013) and to detect speededness of examinees (Shao et al...
October 21, 2016: Psychometrika
https://www.readbyqxmd.com/read/27743280/item-response-theory-observed-score-kernel-equating
#17
Björn Andersson, Marie Wiberg
Item response theory (IRT) observed-score kernel equating is introduced for the non-equivalent groups with anchor test equating design using either chain equating or post-stratification equating. The equating function is treated in a multivariate setting and the asymptotic covariance matrices of IRT observed-score kernel equating functions are derived. Equating is conducted using the two-parameter and three-parameter logistic models with simulated data and data from a standardized achievement test. The results show that IRT observed-score kernel equating offers small standard errors and low equating bias under most settings considered...
October 14, 2016: Psychometrika
https://www.readbyqxmd.com/read/27738958/considering-horn-s-parallel-analysis-from-a-random-matrix-theory-point-of-view
#18
Edoardo Saccenti, Marieke E Timmerman
Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation...
October 13, 2016: Psychometrika
https://www.readbyqxmd.com/read/27738957/extending-multivariate-distance-matrix-regression-with-an-effect-size-measure-and-the-asymptotic-null-distribution-of-the-test-statistic
#19
Daniel B McArtor, Gitta H Lubke, C S Bergeman
Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations...
October 13, 2016: Psychometrika
https://www.readbyqxmd.com/read/27738956/bayesian-approach-for-addressing-differential-covariate-measurement-error-in-propensity-score-methods
#20
Hwanhee Hong, Kara E Rudolph, Elizabeth A Stuart
Propensity score methods are an important tool to help reduce confounding in non-experimental studies and produce more accurate causal effect estimates. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error. Recent work has shown that ignoring such error could lead to bias in treatment effect estimates. In this paper, we consider an additional complication: that of differential measurement error across treatment groups, such as can occur if a covariate is measured differently in the treatment and control groups...
October 13, 2016: Psychometrika
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