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Accounting for individual differences in speed in the discretized signed residual time model Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201222
Jesper Tijmstra; Maria BolsinovaWith advances in computerized tests, it has become commonplace to register not just the accuracy of the responses provided to the items, but also the response time. The idea that for each response both response accuracy and response time are indicative of ability has explicitly been incorporated in the signed residual time (SRT) model (Maris & van der Maas, 2012, Psychometrika, 77, 615–633), which

A semiparametric approach for item response function estimation to detect item misfit Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201217
Carmen Köhler; Alexander Robitzsch; Katharina Fährmann; Matthias von Davier; Johannes HartigWhen scaling data using item response theory, valid statements based on the measurement model are only permissible if the model fits the data. Most item fit statistics used to assess the fit between observed item responses and the item responses predicted by the measurement model show significant weaknesses, such as the dependence of fit statistics on sample size and number of items. In order to assess

Notes on attribution functions Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201213
Xun Ge; Shou LinLet be the knowledge space derived from an attribution function σ on Q. Under an assumption for σ, this paper gives some necessary and sufficient conditions such that is discriminative. It also discusses the resolubility of σ when Q is an infinite set. More precisely, this paper proves that σ is not resoluble if Q is uncountable, and gives a necessary and sufficient condition such that σ is resoluble

Random effects and extended generalized partial credit models Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201210
David J. HessenIn this paper it is shown that under the random effects generalized partial credit model for the measurement of a single latent variable by a set of polytomously scored items, the joint marginal probability distribution of the item scores has a closed‐form expression in terms of item category location parameters, parameters that characterize the distribution of the latent variable in the subpopulation

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201124
Audrey J. Leroux; Christopher J. Cappelli; David R. J. FikisA three‐level piecewise growth model (3L‐PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L‐PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals

Balancing fit and parsimony to improve Q‐matrix validation Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201124
Pablo Nájera; Miguel A. Sorrel; Jimmy de la Torre; Francisco José AbadThe Q‐matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q‐matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q‐matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However

Accounting for auto‐dependency in binary dyadic time series data: A comparison of model‐ and permutation‐based approaches for testing pairwise associations Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201122
Nadja Bodner; Francis Tuerlinckx; Guy Bosmans; Eva CeulemansMany theories have been put forward on how people become synchronized or co‐regulate each other in daily interactions. These theories are often tested by observing a dyad and coding the presence of multiple target behaviours in small time intervals. The sequencing and co‐occurrence of the partners’ behaviours across time are then quantified by means of association measures (e.g., kappa coefficient

Relating latent class membership to external variables: An overview Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201116
Zsuzsa Bakk; Jouni KuhaIn this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund‐Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an overview of most recommended modeling options for models with covariates and larger models

Corrigendum Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201110
In Van Rijn, P. W., & Ali, U. S. (2017), the second affiliation for Usama S. Ali was omitted and it should read as given below: South Valley University, Qena, Egypt. The author’s second affiliation has been added to the online article.

Gaussian variational estimation for multidimensional item response theory Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201016
April E. Cho; Chun Wang; Xue Zhang; Gongjun XuMultidimensional item response theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying a functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that the likelihood involves intractable multidimensional

Causal graphical views of fixed effects and random effects models Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201015
Yongnam Kim; Peter M. SteinerDespite the long‐standing discussion on fixed effects (FE) and random effects (RE) models, how and under what conditions both methods can eliminate unmeasured confounding bias has not yet been widely understood in practice. Using a simple pretest–posttest design in a linear setting, this paper translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively

Maximum information per time unit designs for continuous online item calibration Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201012
Yinhong He; Ping Chen; Yong LiPrevious designs for online calibration have only considered examinees’ responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D‐optimal per

A close‐up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20201008
José E. ChacónThe misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in‐depth comparison of the two criteria, with the aim of better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data

Robust Bayesian growth curve modelling using conditional medians. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200914
Xin Tong,Tonghao Zhang,Jianhui ZhouGrowth curve models have been widely used to analyse longitudinal data in social and behavioural sciences. Although growth curve models with normality assumptions are relatively easy to estimate, practical data are rarely normal. Failing to account for non‐normal data may lead to unreliable model estimation and misleading statistical inference. In this work, we propose a robust approach for growth

Semiautomated Rasch analysis using inplusoutofquestionnaire log likelihood. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20200828
Feri Wijayanto,Karlien Mul,Perry Groot,Baziel G M van Engelen,Tom HeskesRasch analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch analysis is still considered to be a complex, labour‐intensive task, requiring human expertise and rather subjective judgements along the way. In this

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
ChenWei 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 trustregion 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 multiplechoice 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 D‐optimal 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 individual‐specific 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 curiosity‐driven 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 

Cognitive and psychometric modelling of responses and response times. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20170506
Dylan Molenaar,Ingmar Visser 
Pointbiserial correlation: Interval estimation, hypothesis testing, metaanalysis, and sample size determination. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190930
Douglas G BonettThe pointbiserial correlation is a commonly used measure of effect size in twogroup designs. New estimators of pointbiserial correlation are derived from different forms of a standardized mean difference. Pointbiserial correlations are defined for designs with either fixed or random group sample sizes and can accommodate unequal variances. Confidence intervals and standard errors for the pointbiserial

Marginalized maximum a posteriori estimation for the fourparameter logistic model under a mixture modelling framework. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190925
Xiangbin Meng,Gongjun Xu,Jiwei Zhang,Jian TaoThe fourparameter logistic model (4PLM) has recently attracted much interest in various applications. Motivated by recent studies that reexpress the fourparameter model as a mixture model with two levels of latent variables, this paper develops a new expectationmaximization (EM) algorithm for marginalized maximum a posteriori estimation of the 4PLM parameters. The mixture modelling framework of

Combining diversity and dispersion criteria for anticlustering: A bicriterion approach. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190912
Michael J Brusco,J Dennis Cradit,Douglas SteinleyMost partitioning methods used in psychological research seek to produce homogeneous groups (i.e., groups with low intra‐group dissimilarity). However, there are also applications where the goal is to provide heterogeneous groups (i.e., groups with high intra‐group dissimilarity). Examples of these anticlustering contexts include construction of stimulus sets, formation of student groups, assignment

Confidence intervalbased sample size determination formulas and some mathematical properties for hierarchical data. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190907
Satoshi UsamiThe use of hierarchical data (also called multilevel data or clustered data) is common in behavioural and psychological research when data of lowerlevel units (e.g., students, clients, repeated measures) are nested within clusters or higherlevel units (e.g., classes, hospitals, individuals). Over the past 25 years we have seen great advances in methods for computing the sample sizes needed to obtain

The use of item scores and response times to detect examinees who may have benefited from item preknowledge. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190816
Sandip Sinharay,Matthew S JohnsonAccording to Wollack and Schoenig (2018, The Sage encyclopedia of educational research, measurement, and evaluation. Thousand Oaks, CA: Sage, 260), benefiting from item preknowledge is one of the three broad types of test fraud that occur in educational assessments. We use tools from constrained statistical inference to suggest a new statistic that is based on item scores and response times and can

Revisiting dispersion in count data item response theory models: The ConwayMaxwellPoisson counts model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190816
Boris Forthmann,Daniela Gühne,Philipp DoeblerCount data naturally arise in several areas of cognitive ability testing, such as processing speed, memory, verbal fluency, and divergent thinking. Contemporary count data item response theory models, however, are not flexible enough, especially to account for over and underdispersion at the same time. For example, the Rasch Poisson counts model (RPCM) assumes equidispersion (conditional mean and

A Latent Gaussian process model for analysing intensive longitudinal data. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190816
Yunxiao Chen,Siliang ZhangIntensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart‐phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under

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 non‐cognitive variables are often measured using rating or Likert‐type scales, which does not come without problems. Especially in low‐stakes 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

A mixture model for responses and response times with a higherorder ability structure to detect rapid guessing behaviour. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190806
Jing Lu,Chun Wang,Jiwei Zhang,Jian TaoMany educational and psychological assessments focus on multidimensional latent traits that often have a hierarchical structure to provide both overall‐level information and fine‐grained diagnostic information. A test will usually have either separate time limits for each subtest or an overall time limit for administrative convenience and test fairness. In order to complete the items within the allocated

The counterintuitive impact of responses and response times on parameter estimates in the drift diffusion model. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190721
Pascal JordanGiven a drift diffusion model with unknown drift and boundary parameters, we analyse the behaviour of maximum likelihood estimates with respect to changes of responses and response times. It is shown analytically that a single fast response time can dominate the estimation in that no matter how many correct answers a test taker provides, the estimate of the drift (ability) parameter decreases to zero

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.

Standard errors of twolevel scalability coefficients. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190623
Letty Koopman,Bonne J H Zijlstra,L Andries van der ArkFor the construction of tests and questionnaires that require multiple raters (e.g., a child behaviour checklist completed by both parents) a novel ordinal scaling technique is currently being further developed, called two‐level Mokken scale analysis. The technique uses within‐rater and between‐rater coefficients to assess the scalability of the test. These coefficients are generalizations of Mokken's

Back to the basics: Rethinking partial correlation network methodology. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190617
Donald R Williams,Philippe RastThe Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have

Testing two variances for superiority/noninferiority and equivalence: Using the exhaustion algorithm for sample size allocation with cost. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190612
JiinHuarng Guo,WeiMing LuhThe equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non‐inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation

Interval estimation for linear functions of medians in withinsubjects and mixed designs. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190507
Douglas G Bonett,Robert M PriceThe currently available distribution‐free confidence interval for a difference of medians in a within‐subjects design requires an unrealistic assumption of identical distribution shapes. A confidence interval for a general linear function of medians is proposed for within‐subjects designs that do not assume identical distribution shapes. The proposed method can be combined with a method for linear

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 kappa‐type 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 questionnaire‐based 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

Bayesian generalized structured component analysis. Br. J. Math. Stat. Psychol. (IF 2.388) Pub Date : 20190502
Ji Yeh Choi,Heungsun HwangGeneralized structured component analysis (GSCA) is a component‐based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well

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 JohnLikert‐type self‐report scales are frequently used in large‐scale educational assessment of social‐emotional skills. Self‐report 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 : 20190328
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 item‐feature effects. In a reanalysis of three published data sets, it is shown