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A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200805
Chen‐Wei Liu; Robert Philip ChalmersUsing Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large‐sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de‐emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview

The problem of measurement bias in comparing selected subgroups Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200730
Jorge L. Mendoza; Seunghoo Lee; Dustin FifeEstimates of subgroup differences are routinely used as part of a comprehensive validation system, and these estimates serve a critical role, including evaluating adverse impact. Unfortunately, under direct range restriction, a selected mean ( ) is a biased estimator of the population mean as well as the selected true score mean . This is due partly to measurement bias. This bias, as we show, is a

Riemannian Newton and trust‐region algorithms for analytic rotation in exploratory factor analysis Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200726
Yang LiuIn exploratory factor analysis, latent factors and factor loadings are seldom interpretable until analytic rotation is performed. Typically, the rotation problem is solved by numerically searching for an element in the manifold of orthogonal or oblique rotation matrices such that the rotated factor loadings minimize a pre‐specified complexity function. The widely used gradient projection (GP) algorithm

Nested diagnostic classification models for multiple‐choice items Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200723
Ren Liu; Haiyan LiuThis study proposes and evaluates a diagnostic classification model framework for multiple‐choice items. Models in the proposed framework have a two‐level nested structure which allows for binary scoring (for correctness) and polytomous scoring (for distractors) at the same time. One advantage of these models is that they can provide distractor information while maintaining the statistical properties

Bayesian Gaussian distributional regression models for more efficient norm estimation Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200720
Lieke Voncken; Thomas Kneib; Casper J. Albers; Nikolaus Umlauf; Marieke E. TimmermanA test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution

An exploratory analysis of the latent structure of process data via action sequence autoencoders. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200522
Xueying Tang,Zhi Wang,Jingchen Liu,Zhiliang YingComputer simulations have become a popular tool for assessing complex skills such as problemsolving. Log files of computerbased items record the humancomputer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we

Doptimal design for the Rasch counts model with multiple binary predictors. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200514
Ulrike Graßhoff,Heinz Holling,Rainer SchwabeIn this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally Doptimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension

Inferences about which of J dependent groups has the largest robust measure of location. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200505
Rand R WilcoxRecently, a multiple comparisons procedure was derived with the goal of determining whether it is reasonable to make a decision about which of J independent groups has the largest robust measure of location. This was done by testing hypotheses aimed at comparing the group with the largest estimate to the remaining J  1 groups. It was demonstrated that for the goal of controlling the familywise error

Stopping rules for multi‐category computerized classification testing Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200402
Chun Wang; Ping Chen; Alan HuebnerComputerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non‐mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut‐off, and two common stopping criteria

Curiositydriven recommendation strategy for adaptive learning via deep reinforcement learning. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200221
Ruijian Han,Kani Chen,Chunxi TanThe design of recommendation strategies in the adaptive learning systems focuses on utilizing currently available information to provide learners with individualspecific learning instructions. As a critical motivate for human behaviours, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we propose a curiositydriven recommendation policy

Can we disregard the whole model? Omnibus noninferiority testing for R2 in multivariable linear regression and η ^ 2 in ANOVA. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200213
Harlan Campbell,Daniël LakensDetermining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology


A new quantile estimator with weights based on a subsampling approach. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200116
Gözde Navruz,A Fırat ÖzdemirQuantiles are widely used in both theoretical and applied statistics, and it is important to be able to deploy appropriate quantile estimators. To improve performance in the lower and upper quantiles, especially with small sample sizes, a new quantile estimator is introduced which is a weighted average of all order statistics. The new estimator, denoted NO, has desirable asymptotic properties. Moreover

Modelling monotonic effects of ordinal predictors in Bayesian regression models. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200113
PaulChristian Bürkner,Emmanuel CharpentierOrdinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which

Modelling inter‐individual differences in latent within‐person variation: The confirmatory factor level variability model Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200108
Steffen NestlerPsychological theories often produce hypotheses that pertain to individual differences in within‐person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between‐person differences in the mean level of a certain variable and the residual within‐person variance. Currently, these approaches

A latent topic model with Markov transition for process data Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200108
Haochen Xu; Guanhua Fang; Zhiliang YingWe propose a latent topic model with a Markov transition for process data, which consists of time‐stamped events recorded in a log file. Such data are becoming more widely available in computer‐based educational assessment with complex problem‐solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying

The danger of conflating level‐specific effects of control variables when primary interest lies in level‐2 effects Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191219
Jason D. Rights; Kristopher J. Preacher; David A. ColeIn the multilevel modelling literature, methodologists widely acknowledge that a level‐1 variable can have distinct within‐cluster and between‐cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level‐1 variables in a model is not problematic

Datadriven Qmatrix validation using a residualbased statistic in cognitive diagnostic assessment. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191125
Xiaofeng Yu,Ying ChengIn a cognitive diagnostic assessment (CDA), attributes refer to finegrained knowledge points or skills. The Qmatrix is a central component of CDA, which specifies the relationship between items and attributes. Oftentimes, attributes and Qmatrix are defined by subjectmatter experts, and assumed to be appropriate without any misspecifications. However, this assumption does not always hold in real

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and itemlevel nonresponse. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191110
Esther Ulitzsch,Matthias von Davier,Steffi PohlIn lowstakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from lowstakes assessments

Deterministic blockmodelling of signed and twomode networks: A tutorial with software and psychological examples. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191108
Michael Brusco,Patrick Doreian,Douglas SteinleyDeterministic blockmodelling is a wellestablished clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering twomode networks). Even though some of the historical foundations for certain

Bayesian power equivalence in latent growth curve models. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191105
Angelika M Stefan,Timo von OertzenLongitudinal studies are the gold standard for research on timedependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013, Psychology and Aging, 28, 414) applied

Advances in modelling response styles and related phenomena. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20191101
Lale Khorramdel,Minjeong Jeon,Lihshing Leigh Wang 
Combining mixture distribution and multidimensional IRTree models for the measurement of extreme response styles. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190806
Lale Khorramdel,Matthias von Davier,Artur PokropekPersonality constructs, attitudes and other noncognitive variables are often measured using rating or Likerttype scales, which does not come without problems. Especially in lowstakes assessments, respondents may produce biased responses due to response styles (RS) that reduce the validity and comparability of the measurement. Detecting and correcting RS is not always straightforward because not

Evaluation on types of invariance in studying extreme response bias with an IRTree approach. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190710
Minjeong Jeon,Paul De BoeckIn recent years, item response tree (IRTree) approaches have received increasing attention in the response style literature for their ability to partial out response style latent variables as well as associated item parameters. When an IRTree approach is adopted to measure extreme response styles, directional and content invariance could be assumed at the latent variable and item parameter levels.

Hubert's multirater kappa revisited. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190506
Antonio Martín Andrés,María Álvarez HernándezThere is a frequent need to measure the degree of agreement among R observers who independently classify n subjects within K nominal or ordinal categories. The most popular methods are usually kappatype measurements. When R = 2, Cohen's kappa coefficient (weighted or not) is well known. When defined in the ordinal case while assuming quadratic weights, Cohen's kappa has the advantage of coinciding

Clustering preference data in the presence of responsestyle bias. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190502
Mariko Takagishi,Michel van de Velden,Hiroshi YadohisaPreference data, such as Likert scale data, are often obtained in questionnairebased surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select

Comparison of classical and modern methods for measuring and correcting for acquiescence. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190429
Ricardo Primi,Daniel Santos,Filip De Fruyt,Oliver P JohnLikerttype selfreport scales are frequently used in largescale educational assessment of socialemotional skills. Selfreport scales rely on the assumption that their items elicit information only about the trait they are supposed to measure. However, different response biases may threaten this assumption. Specifically, in children, the response style of acquiescence is an important source of systematic

Using multidimensional item response theory to evaluate how response styles impact measurement. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190329
Daniel J Adams,Daniel M Bolt,Sien Deng,Stevens S Smith,Timothy B BakerMultidimensional 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). In the presence of such measurement limitations

Assessing itemfeature effects with item response tree models. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190326
Ulf BöckenholtRecent 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 itemfeature effects. In a reanalysis of three published data sets, it is shown

Lookahead content balancing method in variablelength computerized classification testing. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190326
Xiao Li,Jinming Zhang,HuaHua ChangContent balancing is one of the most important issues in computerized classification testing. To adapt to variablelength 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 'lookahead' and 'step size' to adaptively control content constraints in each item selection step. The step

When is the WilcoxonMannWhitney procedure a test of location? Implications for effectsize measures. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190321
Scott Parker,Robert W Jernigan,Joshua M LanskyThe WilcoxonMannWhitney 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

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computeradaptive practice. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190318
Matthieu J S Brinkhuis,Gunter MarisWe 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

Individual, situational, and cultural correlates of acquiescent responding: Towards a unified conceptual framework. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190309
Clemens M Lechner,Melanie V Partsch,Daniel Danner,Beatrice RammstedtAcquiescence ('yeasaying') can seriously harm the validity of selfreport 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.g. age, education, values)

Utilizing response times in cognitive diagnostic computerized adaptive testing under the higherorder deterministic input, noisy 'and' gate model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190222
HungYu HuangMethods of cognitive diagnostic computerized adaptive testing (CDCAT) under higherorder 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 CDCAT environment, examinees are often subject to a time limit, and

Towards endtoend likelihoodfree inference with convolutional neural networks. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190222
Stefan T Radev,Ulf K Mertens,Andreas Voss,Ullrich KötheComplex simulatorbased models with nonstandard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, endtoend 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

IRTree models with ordinal and multidimensional decision nodes for response styles and traitbased rating responses. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190212
Thorsten Meiser,Hansjörg Plieninger,Mirka HenningerIRTree models decompose observed rating responses into sequences of theorybased decision nodes, and they provide a flexible framework for analysing traitrelated 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

Cognitive diagnosis models for multiple strategies. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190212
Wenchao Ma,Wenjing GuoCognitive 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 multiplestrategy CDM for dichotomous response data. The proposed model provides

A note on residual Mdistances for identifying aberrant response patterns. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190212
Christof Schuster,Dirk LubbeAlthough 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, Mdistances can be used to produce real valued indicators of how well an individual's response vector corresponds to a covariance

An empirical Qmatrix validation method for the sequential generalized DINA model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190205
Wenchao Ma,Jimmy de la TorreAs a core component of most cognitive diagnosis models, the Qmatrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Qmatrix empirically because a misspecified Qmatrix could result in erroneous attribute estimation. Most existing Qmatrix validation procedures are developed for dichotomous responses. However

Analysing multisource feedback with multilevel structural equation models: Pitfalls and recommendations from a simulation study. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190129
Jana Mahlke,Martin Schultze,Michael EidWhen multisource feedback instruments, for example, 360degree 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 nonstandard multilevel structural equation model that incorporates selfratings (level2 variables) and others' ratings from different additional perspectives

An improved stochastic EM algorithm for largescale fullinformation item factor analysis. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181203
Siliang Zhang,Yunxiao Chen,Yang LiuIn this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for largescale fullinformation item factor analysis. Innovations have been made on its implementation, including an adaptiverejectionbased Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic

Asymptotic bias of normaldistributionbased maximum likelihood estimates of moderation effects with data missing at random. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181125
Qian Zhang,KeHai Yuan,Lijuan WangModeration 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 nonlinear function of the involved variables. Normaldistributionbased

Effect size, statistical power, and sample size for assessing interactions between categorical and continuous variables. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181124
Gwowen ShiehThe 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

Robust regression: Testing global hypotheses about the slopes when there is multicollinearity or heteroscedasticity. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181123
Rand R WilcoxA wellknown 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

A caveat on the SavageDickey density ratio: The case of computing Bayes factors for regression parameters. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181119
Daniel W HeckThe SavageDickey 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 SavageDickey ratio only provides the correct Bayes factor if the prior distribution

Optimal designs for the generalized partial credit model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181119
PaulChristian Bürkner,Rainer Schwabe,Heinz HollingAnalysing 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 largescale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating persons' abilities with

Bayesian evaluation of informative hypotheses for multiple populations. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181021
Herbert Hoijtink,Xin Gu,Joris MulderThe 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

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181021
MarieAnn Sengewald,Peter M Steiner,Steffi PohlThe 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. But is it always necessary to use latent

A reinforcement learning approach to personalized learning recommendation systems. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20181003
Xueying Tang,Yunxiao Chen,Xiaoou Li,Jingchen Liu,Zhiliang YingPersonalized 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 datadriven recommendation system that automatically schedules the learning sequence. The

Robust estimation of the hierarchical model for responses and response times. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180728
Jochen Ranger,Anett Wolgast,JörgTobias KuhnVan 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. This distorts

On the assessment of procedural knowledge: From problem spaces to knowledge spaces. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180723
Luca StefanuttiBy 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: PrenticeHall.), and knowledge

A general Bayesian multilevel multidimensional IRT model for locally dependent data. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180609
Ken A FujimotoMany 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

A diagnostic tree model for polytomous responses with multiple strategies. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180425
Wenchao MaConstructedresponse items have been shown to be appropriate for cognitively diagnostic assessments because students' problemsolving 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 socalled twodigit scoring scheme into

A onestep method for modelling longitudinal data with differential equations. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180411
Yueqin Hu,Raymond TreinenDifferential equation models are frequently used to describe nonlinear 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

Affinity propagation: An exemplarbased tool for clustering in psychological research. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180411
Michael J Brusco,Douglas Steinley,Jordan Stevens,J Dennis CraditAffinity propagation is a messagepassingbased 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

A note on Type S/M errors in hypothesis testing. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180324
Jiannan Lu,Yixuan Qiu,Alex DengMotivated by the recent replication and reproducibility crisis, Gelman and Carlin (2014, Perspect. Psychol. Sci., 9, 641) advocated focusing on controlling for Type S/M errors, instead of the classic Type I/II errors, when conducting hypothesis testing. In this paper, we aim to fill several theoretical gaps in the methodology proposed by Gelman and Carlin (2014, Perspect. Psychol. Sci., 9, 641). In

A note on monotonicity of item response functions for ordered polytomous item response theory models. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180309
HyeonAh Kang,YaHui Su,HuaHua ChangA 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

Information matrix estimation procedures for cognitive diagnostic models. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180307
Yanlou Liu,Tao Xin,Björn Andersson,Wei TianTwo 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 sandwichtype 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

A penalized likelihood method for multigroup structural equation modelling. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180304
PoHsien HuangIn 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

Indistinguishability tests in the actorpartner interdependence model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20180216
Fien Gistelinck,Tom Loeys,Mieke Decuyper,Marieke DewitteWhen 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 actorpartner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood