610 results match your criteria British Journal of Mathematical and Statistical Psychology [Journal]


Using multidimensional item response theory to evaluate how response styles impact measurement.

Br J Math Stat Psychol 2019 Mar 28. Epub 2019 Mar 28.

Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA.

Multidimensional item response theory (MIRT) models for response style (e.g., Bolt, Lu, & Kim, 2014, Psychological Methods, 19, 528; Falk & Cai, 2016, Psychological Methods, 21, 328) provide flexibility in accommodating various response styles, but often present difficulty in isolating the effects of response style(s) from the intended substantive trait(s). Read More

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http://dx.doi.org/10.1111/bmsp.12169DOI Listing

Look-ahead content balancing method in variable-length computerized classification testing.

Br J Math Stat Psychol 2019 Mar 26. Epub 2019 Mar 26.

Educational Psychology & Research Methodology, College of Education, Purdue University, West Lafayette, Indiana, USA.

Content balancing is one of the most important issues in computerized classification testing. To adapt to variable-length forms, special treatments are needed to successfully control content constraints without knowledge of test length during the test. To this end, we propose the notions of 'look-ahead' and 'step size' to adaptively control content constraints in each item selection step. Read More

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http://dx.doi.org/10.1111/bmsp.12165DOI Listing

Assessing item-feature effects with item response tree models.

Authors:
Ulf Böckenholt

Br J Math Stat Psychol 2019 Mar 26. Epub 2019 Mar 26.

Kellogg School of Management, Northwestern University, Evanston, Illinois, USA.

Recent applications of item response tree models demonstrate that this model class is well suited to detect midpoint and extremity response style effects in both attitudinal and personality measurements. This paper proposes an extension of this approach that goes beyond measuring response styles and allows us to examine item-feature effects. In a reanalysis of three published data sets, it is shown that the proposed extension captures item-feature effects across affirmative and reverse-worded items in a psychological test. Read More

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http://dx.doi.org/10.1111/bmsp.12163DOI Listing

When is the Wilcoxon-Mann-Whitney procedure a test of location? Implications for effect-size measures.

Br J Math Stat Psychol 2019 Mar 21. Epub 2019 Mar 21.

Department of Mathematics and Statistics, American University, Washington, District of Columbia.

The Wilcoxon-Mann-Whitney procedure is invariant under monotone transformations but its use as a test of location or shift is said not to be so. It tests location only under the shift model, the assumption of parallel cumulative distribution functions (cdfs). We show that infinitely many monotone transformations of the measured variable produce parallel cdfs, so long as the original cdfs intersect nowhere or everywhere. Read More

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http://dx.doi.org/10.1111/bmsp.12162DOI Listing

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

Br J Math Stat Psychol 2019 Mar 18. Epub 2019 Mar 18.

ACTNext, Iowa City, Iowa, USA.

We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Read More

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http://dx.doi.org/10.1111/bmsp.12157DOI Listing

Individual, situational, and cultural correlates of acquiescent responding: Towards a unified conceptual framework.

Br J Math Stat Psychol 2019 Mar 9. Epub 2019 Mar 9.

Department of Survey Design and Methodology, GESIS - Leibniz Institute for the Social Sciences, Mannheim, Germany.

Acquiescence ('yea-saying') can seriously harm the validity of self-report questionnaire data. Towards a better understanding of why some individuals and groups acquiesce more strongly than others do, we developed a unified conceptual framework of acquiescent responding. Our framework posits that acquiescent responding is a joint function of respondent characteristics (e. Read More

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http://dx.doi.org/10.1111/bmsp.12164DOI Listing

Utilizing response times in cognitive diagnostic computerized adaptive testing under the higher-order deterministic input, noisy 'and' gate model.

Authors:
Hung-Yu Huang

Br J Math Stat Psychol 2019 Feb 22. Epub 2019 Feb 22.

Department of Psychology and Counseling, University of Taipei, Taiwan.

Methods of cognitive diagnostic computerized adaptive testing (CD-CAT) under higher-order cognitive diagnosis models have been developed to simultaneously provide estimates of the attribute mastery statuses of examinees for formative assessment and estimates of a latent continuous trait for overall summative evaluation. In a typical CD-CAT environment, examinees are often subject to a time limit, and the examinees' response times (RTs) for specific test items can be routinely recorded by custom-made programs. Because examinees are individually administered tailored sets of test items from the item pool, they may experience different levels of speededness during testing and different levels of risk of running out of time. Read More

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http://dx.doi.org/10.1111/bmsp.12160DOI Listing
February 2019

Towards end-to-end likelihood-free inference with convolutional neural networks.

Br J Math Stat Psychol 2019 Feb 22. Epub 2019 Feb 22.

Heidelberg Collaboratory for Image Processing (HCI), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany.

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Read More

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http://dx.doi.org/10.1111/bmsp.12159DOI Listing
February 2019

Cognitive diagnosis models for multiple strategies.

Br J Math Stat Psychol 2019 05 12;72(2):370-392. Epub 2019 Feb 12.

Department of Educational Studies in Psychology, Research Methodology and Counseling, The University of Alabama, Tuscaloosa, Alabama, USA.

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' proficiency profiles. However, most CDMs assume that all students adopt the same strategy when approaching problems in an assessment, which may not be the case in practice. This study develops a generalized multiple-strategy CDM for dichotomous response data. Read More

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http://dx.doi.org/10.1111/bmsp.12155DOI Listing

A note on residual M-distances for identifying aberrant response patterns.

Br J Math Stat Psychol 2019 Feb 12. Epub 2019 Feb 12.

University of Giessen, Germany.

Although a statistical model might fit well to a large proportion of the individuals of a random sample, some individuals might give 'unusual' responses that are not well explained by the hypothesized model. If individual responses are given as continuous response vectors, M-distances can be used to produce real valued indicators of how well an individual's response vector corresponds to a covariance structure implied by a psychometric model. In this note, we focus on the so-called one-factor model. Read More

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http://dx.doi.org/10.1111/bmsp.12161DOI Listing
February 2019

IRTree models with ordinal and multidimensional decision nodes for response styles and trait-based rating responses.

Br J Math Stat Psychol 2019 Feb 12. Epub 2019 Feb 12.

University of Mannheim, Germany.

IRTree models decompose observed rating responses into sequences of theory-based decision nodes, and they provide a flexible framework for analysing trait-related judgements and response styles. However, most previous applications of IRTree models have been limited to binary decision nodes that reflect qualitatively distinct and unidimensional judgement processes. The present research extends the family of IRTree models for the analysis of response styles to ordinal judgement processes for polytomous decisions and to multidimensional parametrizations of decision nodes. Read More

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http://dx.doi.org/10.1111/bmsp.12158DOI Listing
February 2019

An empirical Q-matrix validation method for the sequential generalized DINA model.

Br J Math Stat Psychol 2019 Feb 5. Epub 2019 Feb 5.

Faculty of Education, University of Hong Kong, Hong Kong.

As a core component of most cognitive diagnosis models, the Q-matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q-matrix empirically because a misspecified Q-matrix could result in erroneous attribute estimation. Most existing Q-matrix validation procedures are developed for dichotomous responses. Read More

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http://dx.doi.org/10.1111/bmsp.12156DOI Listing
February 2019

Analysing multisource feedback with multilevel structural equation models: Pitfalls and recommendations from a simulation study.

Br J Math Stat Psychol 2019 05 29;72(2):294-315. Epub 2019 Jan 29.

Division of Methods and Evaluation, Department of Educational Science and Psychology, Freie Universität Berlin, Germany.

When multisource feedback instruments, for example, 360-degree feedback tools, are validated, multilevel structural equation models are the method of choice to quantify the amount of reliability as well as convergent and discriminant validity. A non-standard multilevel structural equation model that incorporates self-ratings (level-2 variables) and others' ratings from different additional perspectives (level-1 variables), for example, peers and subordinates, has recently been presented. In a Monte Carlo simulation study, we determine the minimal required sample sizes for this model. Read More

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http://dx.doi.org/10.1111/bmsp.12149DOI Listing

An improved stochastic EM algorithm for large-scale full-information item factor analysis.

Br J Math Stat Psychol 2018 Dec 3. Epub 2018 Dec 3.

Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD.

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. Read More

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http://doi.wiley.com/10.1111/bmsp.12153
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http://dx.doi.org/10.1111/bmsp.12153DOI Listing
December 2018
15 Reads

Asymptotic bias of normal-distribution-based maximum likelihood estimates of moderation effects with data missing at random.

Br J Math Stat Psychol 2019 05 25;72(2):334-354. Epub 2018 Nov 25.

University of Notre Dame, Indiana, USA.

Moderation analysis is useful for addressing interesting research questions in social sciences and behavioural research. In practice, moderated multiple regression (MMR) models have been most widely used. However, missing data pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a non-linear function of the involved variables. Read More

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http://dx.doi.org/10.1111/bmsp.12151DOI Listing
May 2019
1 Read

Effect size, statistical power, and sample size for assessing interactions between categorical and continuous variables.

Authors:
Gwowen Shieh

Br J Math Stat Psychol 2019 02 23;72(1):136-154. Epub 2018 Nov 23.

Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan.

The reporting and interpretation of effect size estimates are widely advocated in many academic journals of psychology and related disciplines. However, such concern has not been adequately addressed for analyses involving interactions between categorical and continuous variables. For the purpose of improving current practice, this article presents fundamental features and theoretical developments for the variance of standardized slopes as a desirable standardized effect size measure for the degree of disparity between several slope coefficients. Read More

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http://doi.wiley.com/10.1111/bmsp.12147
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http://dx.doi.org/10.1111/bmsp.12147DOI Listing
February 2019
13 Reads

Robust regression: Testing global hypotheses about the slopes when there is multicollinearity or heteroscedasticity.

Authors:
Rand R Wilcox

Br J Math Stat Psychol 2019 05 23;72(2):355-369. Epub 2018 Nov 23.

Department of Psychology, University of Southern California, Los Angeles, California, USA.

A well-known concern regarding the usual linear regression model is multicollinearity. As the strength of the association among the independent variables increases, the squared standard error of regression estimators tends to increase, which can seriously impact power. This paper examines heteroscedastic methods for dealing with this issue when testing the hypothesis that all of the slope parameters are equal to zero via a robust ridge estimator that guards against outliers among the dependent variable. Read More

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http://dx.doi.org/10.1111/bmsp.12152DOI Listing
May 2019
1 Read

A caveat on the Savage-Dickey density ratio: The case of computing Bayes factors for regression parameters.

Authors:
Daniel W Heck

Br J Math Stat Psychol 2019 05 19;72(2):316-333. Epub 2018 Nov 19.

Statistical Modeling in Psychology, University of Mannheim, Germany.

The Savage-Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage-Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. Read More

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http://dx.doi.org/10.1111/bmsp.12150DOI Listing
May 2019
1 Read

Optimal designs for the generalized partial credit model.

Br J Math Stat Psychol 2019 05 19;72(2):271-293. Epub 2018 Nov 19.

Institute of Psychology, University of Münster, Germany.

Analysing ordinal data is becoming increasingly important in psychology, especially in the context of item response theory. The generalized partial credit model (GPCM) is probably the most widely used ordinal model and has found application in many large-scale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating persons' abilities with the GPCM for calibrated tests when item parameters are known from previous studies. Read More

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http://doi.wiley.com/10.1111/bmsp.12148
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http://dx.doi.org/10.1111/bmsp.12148DOI Listing
May 2019
22 Reads

Bayesian evaluation of informative hypotheses for multiple populations.

Br J Math Stat Psychol 2019 05 21;72(2):219-243. Epub 2018 Oct 21.

Department of Methodology and Statistics, Tilburg University, The Netherlands.

The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. Read More

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http://doi.wiley.com/10.1111/bmsp.12145
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http://dx.doi.org/10.1111/bmsp.12145DOI Listing
May 2019
9 Reads

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration.

Br J Math Stat Psychol 2019 05 21;72(2):244-270. Epub 2018 Oct 21.

Freie Universität Berlin, Germany.

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. Read More

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http://dx.doi.org/10.1111/bmsp.12146DOI Listing
May 2019
1 Read

A reinforcement learning approach to personalized learning recommendation systems.

Br J Math Stat Psychol 2019 02 12;72(1):108-135. Epub 2018 Sep 12.

Department of Statistics, Columbia University, New York, New York, USA.

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology and data science, personalized learning is becoming possible for anyone with a personal computer, supported by a data-driven recommendation system that automatically schedules the learning sequence. The engine of such a recommendation system is a recommendation strategy that, based on data from other learners and the performance of the current learner, recommends suitable learning materials to optimize certain learning outcomes. Read More

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http://dx.doi.org/10.1111/bmsp.12144DOI Listing
February 2019
2 Reads

Robust estimation of the hierarchical model for responses and response times.

Br J Math Stat Psychol 2019 02 27;72(1):83-107. Epub 2018 Jul 27.

Department of Rehabilitation Science, University of Dortmund, Germany.

Van der Linden's (2007, Psychometrika, 72, 287) hierarchical model for responses and response times in tests has numerous applications in psychological assessment. The success of these applications requires the parameters of the model to have been estimated without bias. The data used for model fitting, however, are often contaminated, for example, by rapid guesses or lapses of attention. Read More

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http://doi.wiley.com/10.1111/bmsp.12143
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http://dx.doi.org/10.1111/bmsp.12143DOI Listing
February 2019
7 Reads

On the assessment of procedural knowledge: From problem spaces to knowledge spaces.

Authors:
Luca Stefanutti

Br J Math Stat Psychol 2019 05 23;72(2):185-218. Epub 2018 Jul 23.

FISPPA Department, University of Padua, Italy.

By generalizing and completing the work initiated by Stefanutti and Albert (2003, Journal of Universal Computer Science, 9, 1455), this article provides the mathematical foundations of a theoretical approach whose primary goal is to construct a bridge between problem solving, as initially conceived by Newell and Simon (1972, Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.), and knowledge assessment (Doignon and Falmagne, 1985, International Journal of Man-Machine Studies, 23, 175; Doignon and Falmagne, 1999, Knowledge spaces. Read More

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http://dx.doi.org/10.1111/bmsp.12139DOI Listing
May 2019
23 Reads

A general Bayesian multilevel multidimensional IRT model for locally dependent data.

Authors:
Ken A Fujimoto

Br J Math Stat Psychol 2018 11 7;71(3):536-560. Epub 2018 Jun 7.

Loyola University Chicago, Illinois, USA.

Many item response theory (IRT) models take a multidimensional perspective to deal with sources that induce local item dependence (LID), with these models often making an orthogonal assumption about the dimensional structure of the data. One reason for this assumption is because of the indeterminacy issue in estimating the correlations among the dimensions in structures often specified to deal with sources of LID (e.g. Read More

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http://dx.doi.org/10.1111/bmsp.12133DOI Listing
November 2018
3 Reads

A diagnostic tree model for polytomous responses with multiple strategies.

Authors:
Wenchao Ma

Br J Math Stat Psychol 2019 02 23;72(1):61-82. Epub 2018 Apr 23.

The University of Alabama, Tuscaloosa, AL, USA.

Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students' problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students' partial credits and their strategies. Read More

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http://doi.wiley.com/10.1111/bmsp.12137
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http://dx.doi.org/10.1111/bmsp.12137DOI Listing
February 2019
4 Reads

A one-step method for modelling longitudinal data with differential equations.

Br J Math Stat Psychol 2019 02 6;72(1):38-60. Epub 2018 Apr 6.

Department of Mathematics, Texas State University, San Marcos, Texas, USA.

Differential equation models are frequently used to describe non-linear trajectories of longitudinal data. This study proposes a new approach to estimate the parameters in differential equation models. Instead of estimating derivatives from the observed data first and then fitting a differential equation to the derivatives, our new approach directly fits the analytic solution of a differential equation to the observed data, and therefore simplifies the procedure and avoids bias from derivative estimations. Read More

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http://dx.doi.org/10.1111/bmsp.12135DOI Listing
February 2019
8 Reads

Affinity propagation: An exemplar-based tool for clustering in psychological research.

Br J Math Stat Psychol 2019 02 6;72(1):155-182. Epub 2018 Apr 6.

Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, Florida, USA.

Affinity propagation is a message-passing-based clustering procedure that has received widespread attention in domains such as biological science, physics, and computer science. However, its implementation in psychology and related areas of social science is comparatively scant. In this paper, we describe the basic principles of affinity propagation, its relationship to other clustering problems, and the types of data for which it can be used for cluster analysis. Read More

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http://dx.doi.org/10.1111/bmsp.12136DOI Listing
February 2019
6 Reads

A note on Type S/M errors in hypothesis testing.

Br J Math Stat Psychol 2019 02 23;72(1):1-17. Epub 2018 Mar 23.

Microsoft Corporation, Redmond, Washington, USA.

Motivated by the recent replication and reproducibility crisis, Gelman and Carlin (2014, Perspect. Psychol. Sci. Read More

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http://dx.doi.org/10.1111/bmsp.12132DOI Listing
February 2019
8 Reads

A note on monotonicity of item response functions for ordered polytomous item response theory models.

Br J Math Stat Psychol 2018 11 8;71(3):523-535. Epub 2018 Mar 8.

University of Illinois at Urbana-Champaign, Illinois, USA.

A monotone relationship between a true score (τ) and a latent trait level (θ) has been a key assumption for many psychometric applications. The monotonicity property in dichotomous response models is evident as a result of a transformation via a test characteristic curve. Monotonicity in polytomous models, in contrast, is not immediately obvious because item response functions are determined by a set of response category curves, which are conceivably non-monotonic in θ. Read More

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http://dx.doi.org/10.1111/bmsp.12131DOI Listing
November 2018
4 Reads

Information matrix estimation procedures for cognitive diagnostic models.

Br J Math Stat Psychol 2019 02 6;72(1):18-37. Epub 2018 Mar 6.

Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, China.

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Read More

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http://dx.doi.org/10.1111/bmsp.12134DOI Listing
February 2019
6 Reads

A penalized likelihood method for multi-group structural equation modelling.

Authors:
Po-Hsien Huang

Br J Math Stat Psychol 2018 11 3;71(3):499-522. Epub 2018 Mar 3.

Department of Psychology, National Cheng Kung University, Taiwan.

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. Read More

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http://dx.doi.org/10.1111/bmsp.12130DOI Listing
November 2018
7 Reads

Indistinguishability tests in the actor-partner interdependence model.

Br J Math Stat Psychol 2018 11 15;71(3):472-498. Epub 2018 Feb 15.

Clinical Psychological Science, Maastricht University, The Netherlands.

When considering dyadic data, one of the questions is whether the roles of the two dyad members can be considered equal. This question may be answered empirically using indistinguishability tests in the actor-partner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood based tests for equality in variance parameters; the choice between the structural equation modelling and multilevel modelling framework; and the use of sequential testing rather than one global test for a set of indistinguishability tests. Read More

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http://dx.doi.org/10.1111/bmsp.12129DOI Listing
November 2018
5 Reads

Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables.

Br J Math Stat Psychol 2018 05 11;71(2):387-413. Epub 2018 Jan 11.

School of Mathematics and Statistics, Nanjing University of Information Science and Technology, China.

The polychoric instrumental variable (PIV) approach is a recently proposed method to fit a confirmatory factor analysis model with ordinal data. In this paper, we first examine the small-sample properties of the specification tests for testing the validity of instrumental variables (IVs). Second, we investigate the effects of using different numbers of IVs. Read More

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http://dx.doi.org/10.1111/bmsp.12128DOI Listing
May 2018
7 Reads

On the solution multiplicity of the Fleishman method and its impact in simulation studies.

Br J Math Stat Psychol 2018 11 11;71(3):437-458. Epub 2018 Jan 11.

Department of ECPS, University of British Columbia, Vancouver, British Columbia, Canada.

The Fleishman third-order polynomial algorithm is one of the most-often used non-normal data-generating methods in Monte Carlo simulations. At the crux of the Fleishman method is the solution of a non-linear system of equations needed to obtain the constants to transform data from normality to non-normality. A rarely acknowledged fact in the literature is that the solution to this system is not unique, and it is currently unknown what influence the different types of solutions have on the computer-generated data. Read More

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https://onlinelibrary.wiley.com/doi/pdf/10.1111/bmsp.12126
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http://doi.wiley.com/10.1111/bmsp.12126
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http://dx.doi.org/10.1111/bmsp.12126DOI Listing
November 2018
13 Reads

Numerical approximation of the observed information matrix with Oakes' identity.

Br J Math Stat Psychol 2018 11 9;71(3):415-436. Epub 2018 Jan 9.

Department of Educational Psychology, University of Georgia, Athens, Georgia, USA.

An efficient and accurate numerical approximation methodology useful for obtaining the observed information matrix and subsequent asymptotic covariance matrix when fitting models with the EM algorithm is presented. The numerical approximation approach is compared to existing algorithms intended for the same purpose, and the computational benefits and accuracy of this new approach are highlighted. Instructive and real-world examples are included to demonstrate the methodology concretely, properties of the estimator are discussed in detail, and a Monte Carlo simulation study is included to investigate the behaviour of a multi-parameter item response theory model using three competing finite-difference algorithms. Read More

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http://dx.doi.org/10.1111/bmsp.12127DOI Listing
November 2018
5 Reads

Extension of caution indices to mixed-format tests.

Authors:
Sandip Sinharay

Br J Math Stat Psychol 2018 05 9;71(2):363-386. Epub 2018 Jan 9.

Educational Testing Service, Princeton, New Jersey, USA.

Tatsuoka suggested several extended caution indices and their standardized versions, and these have been used as person-fit statistics by various researchers. However, these indices are only defined for tests with dichotomous items. This paper extends two of the popular standardized extended caution indices for use with polytomous items and mixed-format tests. Read More

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http://dx.doi.org/10.1111/bmsp.12124DOI Listing
May 2018
4 Reads

Mathematical transcription of the 'time-based resource sharing' theory of working memory.

Br J Math Stat Psychol 2018 02 4;71(1):146-166. Epub 2017 Sep 4.

University of Nice Sophia Antipolis, France.

The time-based resource sharing (TBRS) model is a prominent model of working memory that is both predictive and simple. TBRS is a mainstream decay-based model and the most susceptible to competition with interference-based models. A connectionist implementation of TBRS, TBRS*, has recently been developed. Read More

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http://dx.doi.org/10.1111/bmsp.12112DOI Listing
February 2018
3 Reads

A note on the expected value of the Rand index.

Br J Math Stat Psychol 2018 05 20;71(2):287-299. Epub 2017 Nov 20.

Florida State University, Tallahassee, Florida, USA.

Two expectations of the adjusted Rand index (ARI) are compared. It is shown that the expectation derived by Morey and Agresti (1984, Educational and Psychological Measurement, 44, 33) under the multinomial distribution to approximate the exact expectation from the hypergeometric distribution (Hubert & Arabie, 1985, Journal of Classification, 2, 193) provides a poor approximation, and, in some cases, the difference between the two expectations can increase with the sample size. Proofs concerning the minimum and maximum difference between the two expectations are provided, and it is shown through simulation that the ARI can differ significantly depending on which expectation is used. Read More

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http://dx.doi.org/10.1111/bmsp.12116DOI Listing
May 2018
6 Reads

Approximations to the distribution of a test statistic in covariance structure analysis: A comprehensive study.

Authors:
Hao Wu

Br J Math Stat Psychol 2018 05 31;71(2):334-362. Epub 2017 Oct 31.

Boston College, Chestnut Hill, Massachusetts, USA.

In structural equation modelling (SEM), a robust adjustment to the test statistic or to its reference distribution is needed when its null distribution deviates from a χ distribution, which usually arises when data do not follow a multivariate normal distribution. Unfortunately, existing studies on this issue typically focus on only a few methods and neglect the majority of alternative methods in statistics. Existing simulation studies typically consider only non-normal distributions of data that either satisfy asymptotic robustness or lead to an asymptotic scaled χ distribution. Read More

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http://dx.doi.org/10.1111/bmsp.12123DOI Listing
May 2018
10 Reads

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model.

Authors:
Shiyu Wang

Br J Math Stat Psychol 2018 05 28;71(2):300-333. Epub 2017 Oct 28.

University of Georgia, Athens, Georgia, USA.

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. Read More

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http://dx.doi.org/10.1111/bmsp.12119DOI Listing
May 2018
12 Reads

A semi-parametric within-subject mixture approach to the analyses of responses and response times.

Br J Math Stat Psychol 2018 05 17;71(2):205-228. Epub 2017 Oct 17.

Tilburg University, The Netherlands.

In item response theory, modelling the item response times in addition to the item responses may improve the detection of possible between- and within-subject differences in the process that resulted in the responses. For instance, if respondents rely on rapid guessing on some items but not on all, the joint distribution of the responses and response times will be a multivariate within-subject mixture distribution. Suitable parametric methods to detect these within-subject differences have been proposed. Read More

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http://dx.doi.org/10.1111/bmsp.12117DOI Listing
May 2018
14 Reads

Corrigendum.

Authors:

Br J Math Stat Psychol 2017 11;70(3):565

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http://dx.doi.org/10.1111/bmsp.12115DOI Listing
November 2017
4 Reads

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques.

Br J Math Stat Psychol 2018 02 12;71(1):96-116. Epub 2017 Sep 12.

Department of Psychology, University of Notre Dame, Indiana, USA.

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Read More

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http://dx.doi.org/10.1111/bmsp.12109DOI Listing
February 2018
10 Reads

Bias-corrected estimation of the Rudas-Clogg-Lindsay mixture index of fit.

Br J Math Stat Psychol 2018 11 12;71(3):459-471. Epub 2017 Sep 12.

University of Venice, Italy.

Rudas, Clogg, and Lindsay (1994, J. R Stat Soc. Ser. Read More

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http://dx.doi.org/10.1111/bmsp.12118DOI Listing
November 2018
7 Reads

Direction of dependence in measurement error models.

Br J Math Stat Psychol 2018 02 5;71(1):117-145. Epub 2017 Sep 5.

Department of Psychology, Michigan State University, East Lansing, Michigan, USA.

Methods to determine the direction of a regression line, that is, to determine the direction of dependence in reversible linear regression models (e.g., x→y vs. Read More

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http://dx.doi.org/10.1111/bmsp.12111DOI Listing
February 2018
7 Reads

Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis.

Br J Math Stat Psychol 2018 02 4;71(1):167-185. Epub 2017 Sep 4.

Department of Computer Science, College of Charleston, South Carolina, USA.

When bivariate normality is violated, the default confidence interval of the Pearson correlation can be inaccurate. Two new methods were developed based on the asymptotic sampling distribution of Fisher's z' under the general case where bivariate normality need not be assumed. In Monte Carlo simulations, the most successful of these methods relied on the (Vale & Maurelli, 1983, Psychometrika, 48, 465) family to approximate a distribution via the marginal skewness and kurtosis of the sample data. Read More

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http://dx.doi.org/10.1111/bmsp.12113DOI Listing
February 2018
30 Reads

Cognitive diagnosis modelling incorporating item response times.

Br J Math Stat Psychol 2018 05 5;71(2):262-286. Epub 2017 Sep 5.

Measurement, Statistics and Evaluation, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, USA.

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. Read More

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http://dx.doi.org/10.1111/bmsp.12114DOI Listing
May 2018
6 Reads

Circular interpretation of regression coefficients.

Br J Math Stat Psychol 2018 02 4;71(1):75-95. Epub 2017 Sep 4.

Department of Methodology and Statistics, Utrecht University, The Netherlands.

The interpretation of the effect of predictors in projected normal regression models is not straight-forward. The main aim of this paper is to make this interpretation easier such that these models can be employed more readily by social scientific researchers. We introduce three new measures: the slope at the inflection point (b ), average slope (AS) and slope at mean (SAM) that help us assess the marginal effect of a predictor in a Bayesian projected normal regression model. Read More

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http://dx.doi.org/10.1111/bmsp.12108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5811843PMC
February 2018
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Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses.

Br J Math Stat Psychol 2018 05 31;71(2):229-261. Epub 2017 Aug 31.

Department of Methodology and Statistics, Utrecht University, The Netherlands.

Informative hypotheses are increasingly being used in psychological sciences because they adequately capture researchers' theories and expectations. In the Bayesian framework, the evaluation of informative hypotheses often makes use of default Bayes factors such as the fractional Bayes factor. This paper approximates and adjusts the fractional Bayes factor such that it can be used to evaluate informative hypotheses in general statistical models. Read More

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http://dx.doi.org/10.1111/bmsp.12110DOI Listing
May 2018
8 Reads