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

    1 OF 12

    A general Bayesian multilevel multidimensional IRT model for locally dependent data.
    Br J Math Stat Psychol 2018 Jun 7. 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

    A diagnostic tree model for polytomous responses with multiple strategies.
    Br J Math Stat Psychol 2018 Apr 23. 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

    A one-step method for modelling longitudinal data with differential equations.
    Br J Math Stat Psychol 2018 Apr 6. 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

    Affinity propagation: An exemplar-based tool for clustering in psychological research.
    Br J Math Stat Psychol 2018 Apr 6. 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

    A note on monotonicity of item response functions for ordered polytomous item response theory models.
    Br J Math Stat Psychol 2018 Mar 8. 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

    Information matrix estimation procedures for cognitive diagnostic models.
    Br J Math Stat Psychol 2018 Mar 6. 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

    A penalized likelihood method for multi-group structural equation modelling.
    Br J Math Stat Psychol 2018 Mar 3. 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

    Indistinguishability tests in the actor-partner interdependence model.
    Br J Math Stat Psychol 2018 Feb 15. 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

    Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables.
    Br J Math Stat Psychol 2018 May 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

    On the solution multiplicity of the Fleishman method and its impact in simulation studies.
    Br J Math Stat Psychol 2018 Jan 11. 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

    Numerical approximation of the observed information matrix with Oakes' identity.
    Br J Math Stat Psychol 2018 Jan 9. 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

    Extension of caution indices to mixed-format tests.
    Br J Math Stat Psychol 2018 May 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

    Mathematical transcription of the 'time-based resource sharing' theory of working memory.
    Br J Math Stat Psychol 2018 Feb 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

    A note on the expected value of the Rand index.
    Br J Math Stat Psychol 2018 May 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

    Approximations to the distribution of a test statistic in covariance structure analysis: A comprehensive study.
    Br J Math Stat Psychol 2018 May 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

    Two-Stage maximum likelihood estimation in the misspecified restricted latent class model.
    Br J Math Stat Psychol 2018 May 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

    A semi-parametric within-subject mixture approach to the analyses of responses and response times.
    Br J Math Stat Psychol 2018 May 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

    Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques.
    Br J Math Stat Psychol 2018 Feb 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

    Direction of dependence in measurement error models.
    Br J Math Stat Psychol 2018 Feb 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

    Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis.
    Br J Math Stat Psychol 2018 Feb 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

    Cognitive diagnosis modelling incorporating item response times.
    Br J Math Stat Psychol 2018 May 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

    Circular interpretation of regression coefficients.
    Br J Math Stat Psychol 2018 Feb 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

    Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses.
    Br J Math Stat Psychol 2018 May 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

    Sample size determination for a matched-pairs study with incomplete data using exact approach.
    Br J Math Stat Psychol 2018 Feb 30;71(1):60-74. Epub 2017 Jun 30.
    Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA.
    This research was motivated by a clinical trial design for a cognitive study. The pilot study was a matched-pairs design where some data are missing, specifically the missing data coming at the end of the study. Existing approaches to determine sample size are all based on asymptotic approaches (e. Read More

    Regression away from the mean: Theory and examples.
    Br J Math Stat Psychol 2018 Feb 30;71(1):186-203. Epub 2017 Jun 30.
    Department of Psychology, University of Potsdam, Germany.
    Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. Read More

    Improving precision of ability estimation: Getting more from response times.
    Br J Math Stat Psychol 2018 Feb 21;71(1):13-38. Epub 2017 Jun 21.
    Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.
    By considering information about response time (RT) in addition to response accuracy (RA), joint models for RA and RT such as the hierarchical model (van der Linden, 2007) can improve the precision with which ability is estimated over models that only consider RA. The hierarchical model, however, assumes that only the person's speed is informative of ability. This assumption of conditional independence between RT and ability given speed may be violated in practice, and ignores collateral information about ability that may be present in the residual RTs. Read More

    Standard errors and confidence intervals for correlations corrected for indirect range restriction: A simulation study comparing analytic and bootstrap methods.
    Br J Math Stat Psychol 2018 Feb 20;71(1):39-59. Epub 2017 Jun 20.
    National Institute for Testing and Evaluation, Jerusalem, Israel.
    A frequent topic of psychological research is the estimation of the correlation between two variables from a sample that underwent a selection process based on a third variable. Due to indirect range restriction, the sample correlation is a biased estimator of the population correlation, and a correction formula is used. In the past, bootstrap standard error and confidence intervals for the corrected correlations were examined with normal data. Read More

    ANOVA and the variance homogeneity assumption: Exploring a better gatekeeper.
    Br J Math Stat Psychol 2018 Feb 1;71(1):1-12. Epub 2017 Jun 1.
    Quantitative Methods Program, Department of Psychology, York University, Toronto, Ontario, Canada.
    Valid use of the traditional independent samples ANOVA procedure requires that the population variances are equal. Previous research has investigated whether variance homogeneity tests, such as Levene's test, are satisfactory as gatekeepers for identifying when to use or not to use the ANOVA procedure. This research focuses on a novel homogeneity of variance test that incorporates an equivalence testing approach. Read More

    More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares.
    Br J Math Stat Psychol 2017 Nov 26;70(3):525-564. Epub 2017 May 26.
    Department of Psychology, University of Notre Dame, Indiana, USA.
    Data in psychology are often collected using Likert-type scales, and it has been shown that factor analysis of Likert-type data is better performed on the polychoric correlation matrix than on the product-moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. Read More

    Modelling individual response time effects between and within experimental speed conditions: A GLMM approach for speeded tests.
    Br J Math Stat Psychol 2017 May;70(2):238-256
    Goethe University, Frankfurt am Main, Germany.
    Completing test items under multiple speed conditions avoids the performance measure being confounded with individual differences in the speed-accuracy compromise, and offers insights into the response process, that is, how response time relates to the probability of a correct response. This relation is traditionally represented by two conceptually different functions: the speed-accuracy trade-off function (SATF) across conditions relating the condition average response time to the condition average of accuracy, and the conditional accuracy function (CAF) within a condition describing accuracy conditional on response time. Using a generalized linear mixed modelling approach, we propose an item response modelling framework that is suitable for item response and response time data from experimental speed conditions. Read More

    Analysing model fit of psychometric process models: An overview, a new test and an application to the diffusion model.
    Br J Math Stat Psychol 2017 May 3;70(2):209-224. Epub 2017 Feb 3.
    University of Münster, Germany.
    Cognitive psychometric models embed cognitive process models into a latent trait framework in order to allow for individual differences. Due to their close relationship to the response process the models allow for profound conclusions about the test takers. However, before such a model can be used its fit has to be checked carefully. Read More

    A comparison of item response models for accuracy and speed of item responses with applications to adaptive testing.
    Br J Math Stat Psychol 2017 May;70(2):317-345
    Educational Testing Service, Princeton, New Jersey, USA.
    We compare three modelling frameworks for accuracy and speed of item responses in the context of adaptive testing. The first framework is based on modelling scores that result from a scoring rule that incorporates both accuracy and speed. The second framework is the hierarchical modelling approach developed by van der Linden (2007, Psychometrika, 72, 287) in which a regular item response model is specified for accuracy and a log-normal model for speed. Read More

    A heteroscedastic generalized linear model with a non-normal speed factor for responses and response times.
    Br J Math Stat Psychol 2017 May 3;70(2):297-316. Epub 2017 Feb 3.
    University of Amsterdam, The Netherlands.
    In generalized linear modelling of responses and response times, the observed response time variables are commonly transformed to make their distribution approximately normal. A normal distribution for the transformed response times is desirable as it justifies the linearity and homoscedasticity assumptions in the underlying linear model. Past research has, however, shown that the transformed response times are not always normal. Read More

    Spontaneous and imposed speed of cognitive test responses.
    Br J Math Stat Psychol 2017 May 3;70(2):225-237. Epub 2017 Feb 3.
    University of Minnesota, Minneapolis, Minnesota, USA.
    Based on data from a cognitive test presented in a condition with time constraints per item and a condition without time constraints, the effect of speed on accuracy is investigated. First, if the effect of imposed speed on accuracy is negative it can be explained by the speed-accuracy trade-off, and if it can be captured through the corresponding latent variables, then measurement invariance applies between a condition with and a condition without time constraints. The results do show a negative effect and a lack of measurement invariance. Read More

    Non-ignorable missingness item response theory models for choice effects in examinee-selected items.
    Br J Math Stat Psychol 2017 Nov 8;70(3):499-524. Epub 2017 Apr 8.
    Department of Psychology, The Education University of Hong Kong, Hong Kong.
    Examinee-selected item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set, always yields incomplete data (i.e., when only the selected items are answered, data are missing for the others) that are likely non-ignorable in likelihood inference. Read More

    CDF-quantile distributions for modelling random variables on the unit interval.
    Br J Math Stat Psychol 2017 Nov 17;70(3):412-438. Epub 2017 Mar 17.
    The Australian National University, Canberra, Australian Capital Territory, Australia.
    This paper introduces a two-parameter family of distributions for modelling random variables on the (0,1) interval by applying the cumulative distribution function of one 'parent' distribution to the quantile function of another. Family members have explicit probability density functions, cumulative distribution functions and quantiles in a location parameter and a dispersion parameter. They capture a wide variety of shapes that the beta and Kumaraswamy distributions cannot. Read More

    Rank-based permutation approaches for non-parametric factorial designs.
    Br J Math Stat Psychol 2017 Nov 15;70(3):368-390. Epub 2017 Mar 15.
    Institute of Statistics, Ulm University, Germany.
    Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. Read More

    Order-constrained linear optimization.
    Br J Math Stat Psychol 2017 Nov 27;70(3):391-411. Epub 2017 Feb 27.
    Department of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA.
    Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. Read More

    Person-specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels.
    Br J Math Stat Psychol 2017 Nov 22;70(3):480-498. Epub 2017 Feb 22.
    Human Development and Family Studies, Department of Human Ecology, University of California, Davis, California, USA.
    This paper compares the multilevel modelling (MLM) approach and the person-specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i. Read More

    The assessment of knowledge and learning in competence spaces: The gain-loss model for dependent skills.
    Br J Math Stat Psychol 2017 Nov 17;70(3):457-479. Epub 2017 Feb 17.
    Department FISPPA, University of Padua, Italy.
    The gain-loss model (GaLoM) is a formal model for assessing knowledge and learning. In its original formulation, the GaLoM assumes independence among the skills. Such an assumption is not reasonable in several domains, in which some preliminary knowledge is the foundation for other knowledge. Read More

    Analysis of categorical moderators in mixed-effects meta-analysis: Consequences of using pooled versus separate estimates of the residual between-studies variances.
    Br J Math Stat Psychol 2017 Nov 6;70(3):439-456. Epub 2017 Feb 6.
    Department of Basic Psychology & Methodology, Faculty of Psychology, University of Murcia, Spain.
    Subgroup analyses allow us to examine the influence of a categorical moderator on the effect size in meta-analysis. We conducted a simulation study using a dichotomous moderator, and compared the impact of pooled versus separate estimates of the residual between-studies variance on the statistical performance of the Q and Q tests for subgroup analyses assuming a mixed-effects model. Our results suggested that similar performance can be expected as long as there are at least 20 studies and these are approximately balanced across categories. Read More

    Population models and simulation methods: The case of the Spearman rank correlation.
    Br J Math Stat Psychol 2017 Nov 31;70(3):347-367. Epub 2017 Jan 31.
    University of British Columbia, Vancouver, British Columbia, Canada.
    The purpose of this paper is to highlight the importance of a population model in guiding the design and interpretation of simulation studies used to investigate the Spearman rank correlation. The Spearman rank correlation has been known for over a hundred years to applied researchers and methodologists alike and is one of the most widely used non-parametric statistics. Still, certain misconceptions can be found, either explicitly or implicitly, in the published literature because a population definition for this statistic is rarely discussed within the social and behavioural sciences. Read More

    Developing new online calibration methods for multidimensional computerized adaptive testing.
    Br J Math Stat Psychol 2017 Feb;70(1):81-117
    University of Illinois at Urbana-Champaign, Illinois, USA.
    Multidimensional computerized adaptive testing (MCAT) has received increasing attention over the past few years in educational measurement. Like all other formats of CAT, item replenishment is an essential part of MCAT for its item bank maintenance and management, which governs retiring overexposed or obsolete items over time and replacing them with new ones. Moreover, calibration precision of the new items will directly affect the estimation accuracy of examinees' ability vectors. Read More

    Meta-CART: A tool to identify interactions between moderators in meta-analysis.
    Br J Math Stat Psychol 2017 Feb;70(1):118-136
    Mathematical Institute, Leiden University, The Netherlands.
    In the framework of meta-analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta-regression has difficulties in identifying interactions between them. To overcome this problem, meta-CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta-analysis to test the significance of moderator effects. Read More

    Gaussian model-based partitioning using iterated local search.
    Br J Math Stat Psychol 2017 Feb;70(1):1-24
    Florida State University, Tallahassee, Florida, USA.
    The emergence of Gaussian model-based partitioning as a viable alternative to K-means clustering fosters a need for discrete optimization methods that can be efficiently implemented using model-based criteria. A variety of alternative partitioning criteria have been proposed for more general data conditions that permit elliptical clusters, different spatial orientations for the clusters, and unequal cluster sizes. Unfortunately, many of these partitioning criteria are computationally demanding, which makes the multiple-restart (multistart) approach commonly used for K-means partitioning less effective as a heuristic solution strategy. Read More

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