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Sample size determination for interval estimation of the prevalence of a sensitive attribute under non‐randomized response models Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-02-27 Shi‐Fang Qiu, Jie Lei, Wai‐Yin Poon, Man‐Lai Tang, Ricky S. Wong, Ji‐Ran Tao
A sufficient number of participants should be included to adequately address the research interest in the surveys with sensitive questions. In this paper, sample size formulas/iterative algorithms are developed from the perspective of controlling the confidence interval width of the prevalence of a sensitive attribute under four non‐randomized response models: the crosswise model, parallel model, Poisson
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Assessment of fit of the time‐varying dynamic partial credit model using the posterior predictive model checking method Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-02-21 Sebastian Castro‐Alvarez, Sandip Sinharay, Laura F. Bringmann, Rob R. Meijer, Jorge N. Tendeiro
Several new models based on item response theory have recently been suggested to analyse intensive longitudinal data. One of these new models is the time‐varying dynamic partial credit model (TV‐DPCM; Castro‐Alvarez et al., Multivariate Behavioral Research, 2023, 1), which is a combination of the partial credit model and the time‐varying autoregressive model. The model allows the study of the psychometric
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When and how to use set-exploratory structural equation modelling to test structural models: A tutorial using the R package lavaan Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-02-15 Herb Marsh, Abdullah Alamer
Exploratory structural equation modelling (ESEM) is an alternative to the well-known method of confirmatory factor analysis (CFA). ESEM is mainly used to assess the quality of measurement models of common factors but can be efficiently extended to test structural models. However, ESEM may not be the best option in some model specifications, especially when structural models are involved, because the
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Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-02-12 Maoxin Zhang, Björn Andersson, Shaobo Jin
Different data types often occur in psychological and educational measurement such as computer-based assessments that record performance and process data (e.g., response times and the number of actions). Modelling such data requires specific models for each data type and accommodating complex dependencies between multiple variables. Generalized linear latent variable models are suitable for modelling
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Constructing tests for skill assessment with competence-based test development Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-02-02 Pasquale Anselmi, Jürgen Heller, Luca Stefanutti, Egidio Robusto
Competence-based test development is a recent and innovative method for the construction of tests that are as informative as possible about the competence state (the set of skills an individual has available) underlying the observed item responses. It finds application in different contexts, including the development of tests from scratch, and the improvement or shortening of existing tests. Given
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Identifiability and estimability of Bayesian linear and nonlinear crossed random effects models Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-01-24 Corissa T. Rohloff, Nidhi Kohli, Eric F. Lock
Crossed random effects models (CREMs) are particularly useful in longitudinal data applications because they allow researchers to account for the impact of dynamic group membership on individual outcomes. However, no research has determined what data conditions need to be met to sufficiently identify these models, especially the group effects, in a longitudinal context. This is a significant gap in
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Statistical inference for agreement between multiple raters on a binary scale Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-01-17 Sophie Vanbelle
Agreement studies often involve more than two raters or repeated measurements. In the presence of two raters, the proportion of agreement and of positive agreement are simple and popular agreement measures for binary scales. These measures were generalized to agreement studies involving more than two raters with statistical inference procedures proposed on an empirical basis. We present two alternatives
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A cluster differences unfolding method for large datasets of preference ratings on an interval scale: Minimizing the mean squared centred residuals Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2024-01-11 Rodrigo Macías, J. Fernando Vera, Willem J. Heiser
Clustering and spatial representation methods are often used in combination, to analyse preference ratings when a large number of individuals and/or object is involved. When analysed under an unfolding model, row-conditional linear transformations are usually most appropriate when the goal is to determine clusters of individuals with similar preferences. However, a significant problem with transformations
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Correcting for measurement error under meta-analysis of z-transformed correlations Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-12-28 Qian Zhang, Qi Wang
This study mainly concerns correction for measurement error using the meta-analysis of Fisher's z-transformed correlations. The disattenuation formula of Spearman (American Journal of Psychology, 15, 1904, 72) is used to correct for individual raw correlations in primary studies. The corrected raw correlations are then used to obtain the corrected z-transformed correlations. What remains little studied
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Mixtures of t factor analysers with censored responses and external covariates: An application to educational data from Peru Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-12-14 Wan-Lun Wang, Luis M. Castro, Huei-Jyun Li, Tsung-I Lin
Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of t $$ t $$ factor analysers (MtFA) have emerged as a powerful
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Using cross-validation methods to select time series models: Promises and pitfalls Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-12-07 Siwei Liu, Di Jody Zhou
Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross-validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However,
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The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-12-01 Sun-Joo Cho, Hao Wu, Matthew Naveiras
Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed
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On generating plausible values for multilevel modelling with large-scale-assessment data Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-11-13 Xiaying Zheng
Large-scale assessments (LSAs) routinely employ latent regressions to generate plausible values (PVs) for unbiased estimation of the relationship between examinees' background variables and performance. To handle the clustering effect common in LSA data, multilevel modelling is a popular choice. However, most LSAs use single-level conditioning methods, resulting in a mismatch between the imputation
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Correction to ‘a note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models’ Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-10-25
Liu, C. W., & Chalmers, R. P. (2021). A note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models. British Journal of Mathematical and Statistical Psychology, 74(1), 118–138. https://doi.org/10.1111/bmsp.12207 The acknowledgement of funding was included in error: the paper was received on 30 April 2020, while the mentioned grant commenced on 1 August 2020
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A correlated traits correlated (methods – 1) multitrait-multimethod model for augmented round-robin data Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-10-16 David Jendryczko, Fridtjof W. Nussbeck
We didactically derive a correlated traits correlated (methods – 1) [CTC(M – 1)] multitrait-multimethod (MTMM) model for dyadic round-robin data augmented by self-reports. The model is an extension of the CTC(M – 1) model for cross-classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round-robin designs
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A Gibbs-INLA algorithm for multidimensional graded response model analysis Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-29 Xiaofan Lin, Siliang Zhang, Yincai Tang, Xuan Li
In this paper, we propose a novel Gibbs-INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and
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A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-20 Tianyu Pan, Weining Shen, Clintin P. Davis-Stober, Guanyu Hu
We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set
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Replies to comments on "Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023). Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-15 Ke-Hai Yuan,Yongfei Fang
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Exploring examinees' responses to constructed response items with a supervised topic model Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-13 Seohyun Kim, Zhenqiu Lu, Allan S. Cohen
Textual data are increasingly common in test data as many assessments include constructed response (CR) items as indicators of participants' understanding. The development of techniques based on natural language processing has made it possible for researchers to rapidly analyse large sets of textual data. One family of statistical techniques for this purpose are probabilistic topic models. Topic modelling
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Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-06 Lu Ou, Michael D. Hunter, Zhaohua Lu, Cynthia A. Stifter, Sy-Miin Chow
Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables
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Using item scores and response times in person-fit assessment Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-09-05 Kylie Gorney, Sandip Sinharay, Xiang Liu
The use of joint models for item scores and response times is becoming increasingly popular in educational and psychological testing. In this paper, we propose two new person-fit statistics for such models in order to detect aberrant behaviour. The first statistic is computed by combining two existing person-fit statistics: one for the item scores, and one for the item response times. The second statistic
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Evaluating the performance of existing and novel equivalence tests for fit indices in structural equation modelling Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-07-13 Nataly Beribisky, Robert A. Cribbie
It has been suggested that equivalence testing (otherwise known as negligible effect testing) should be used to evaluate model fit within structural equation modelling (SEM). In this study, we propose novel variations of equivalence tests based on the popular root mean squared error of approximation and comparative fit index fit indices. Using Monte Carlo simulations, we compare the performance of
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K-Plus anticlustering: An improved k-means criterion for maximizing between-group similarity Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-07-11 Martin Papenberg
Anticlustering refers to the process of partitioning elements into disjoint groups with the goal of obtaining high between-group similarity and high within-group heterogeneity. Anticlustering thereby reverses the logic of its better known twin—cluster analysis—and is usually approached by maximizing instead of minimizing a clustering objective function. This paper presents k-plus, an extension of the
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Enhancing measurement validity in diverse populations: Modern approaches to evaluating differential item functioning Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-07-10 Daniel J. Bauer
When developing and evaluating psychometric measures, a key concern is to ensure that they accurately capture individual differences on the intended construct across the entire population of interest. Inaccurate assessments of individual differences can occur when responses to some items reflect not only the intended construct but also construct-irrelevant characteristics, like a person's race or sex
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Assessment of generalised Bayesian structural equation models for continuous and binary data Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-07-04 Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki
The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive p -values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17, 2012, 313), which involves formulating certain parameters (such as factor loadings)
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Testing indirect effect with a complete or incomplete dichotomous mediator Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-06-26 Fan Jia, Wei Wu, Po-Yi Chen
Past methodological research on mediation analysis mainly focused on situations where all variables were complete and continuous. When issues of categorical data occur combined with missing data, more methodological considerations are involved. Specifically, appropriate decisions need to be made on estimation methods of the indirect effects and on confidence intervals for testing the indirect effects
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Corrigendum Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-05-31
In the article by Beisemann (2022), there was a typographical error in Equation (36) in Appendix B on page 441. Equation (36) should read as: µ∂logP(xi|θi,ζ)∂θi=∑j=1M∂logCMPµ(xij;μij,νj)∂θi=∑j=1MxijαjμijV(μij,νj)−αjμij2V(μij,νj). The Equation can then be further simplified to ∂logP(xi|θi,ζ)∂θi=∑j=1Mxij−μijV(μij,νj)αjμij. The error only concerns an additional result provided in the appendix
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Variational Bayes inference for hidden Markov diagnostic classification models Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-05-30 Kazuhiro Yamaguchi, Alfonso J. Martinez
Diagnostic classification models (DCMs) can be used to track the cognitive learning states of students across multiple time points or over repeated measurements. This study developed an effective variational Bayes (VB) inference method for hidden Markov longitudinal general DCMs. The simulations performed in this study verified the validity of the proposed algorithm for satisfactorily recovering true
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A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-05-21 Auburn Jimenez, James Joseph Balamuta, Steven Andrew Culpepper
Cognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic
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Causality and prediction in structural equation modeling: A commentary by Yutaka Kano on: "Which method delivers greater signal-to-noise ratio: Structural equation modeling or regression analysis with weighted composites?" by Yuan and Fang. Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-05-11 Yutaka Kano
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A sequential Bayesian changepoint detection procedure for aberrant behaviours in computerized testing Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-05-10 Jing Lu, Chun Wang, Jiwei Zhang, Xue Wang
Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is essential to properly differentiate examinees' aberrant behaviours from solution behaviour to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to monitor the locations of changepoints for response
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Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023) Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-04-18 Florian Schuberth, Tamara Schamberger, Mikko Rönkkö, Yide Liu, Jörg Henseler
In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise
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A dual process item response theory model for polytomous multidimensional forced-choice items Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-03-26 Xuelan Qiu, Jimmy de la Torre
The use of multidimensional forced-choice (MFC) items to assess non-cognitive traits such as personality, interests and values in psychological tests has a long history, because MFC items show strengths in preventing response bias. Recently, there has been a surge of interest in developing item response theory (IRT) models for MFC items. However, nearly all of the existing IRT models have been developed
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Bayesian hierarchical response time modelling—A tutorial Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-02-22 Christoph Koenig, Benjamin Becker, Esther Ulitzsch
Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques
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A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-02-05 Fei Gu, Mike W.-L. Cheung
Principal component regression (PCR) is a popular technique in data analysis and machine learning. However, the technique has two limitations. First, the principal components (PCs) with the largest variances may not be relevant to the outcome variables. Second, the lack of standard error estimates for the unstandardized regression coefficients makes it hard to interpret the results. To address these
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Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-02-02 Josue E. Rodriguez, Donald R. Williams, Paul-Christian Bürkner
Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should
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Mixture-modelling-based Bayesian MH-RM algorithm for the multidimensional 4PLM Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-02-02 Shaoyang Guo, Yanlei Chen, Chanjin Zheng, Guiyu Li
Several recent works have tackled the estimation issue for the unidimensional four-parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time-consuming. In this paper, a mixture-modelling-based Bayesian MH-RM (MM-MH-RM) algorithm is proposed for the M4PLM to obtain
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Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-01-12 Maria T. Barendse, Yves Rosseel
Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML
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Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-01-10 Aisouda Hoshiyar, Henk A. L. Kiers, Jan Gertheiss
Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs
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Extending exploratory diagnostic classification models: Inferring the effect of covariates Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-01-05 Hulya Duygu Yigit, Steven Andrew Culpepper
Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates
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Effect sizes in ANCOVA and difference-in-differences designs Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2023-01-02 Larry V. Hedges, Elizabeth Tipton, Rrita Zejnullahi, Karina G. Diaz
It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and—in non-randomized designs—its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units
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Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites? Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-12-02 Ke-Hai Yuan, Yongfei Fang
Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression
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Empirical indistinguishability: From the knowledge structure to the skills Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-11-10 Andrea Spoto, Luca Stefanutti
Recent literature has pointed out that the basic local independence model (BLIM) when applied to some specific instances of knowledge structures presents identifiability issues. Furthermore, it has been shown that for such instances the model presents a stronger form of unidentifiability named empirical indistinguishability, which leads to the fact that the existence of certain knowledge states in
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A note on the use of rank-ordered logit models for ordered response categories Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-11-03 Timothy R. Johnson
Models for rankings have been shown to produce more efficient estimators than comparable models for first/top choices. The discussions and applications of these models typically only consider unordered alternatives. But these models can be usefully adapted to the case where a respondent ranks a set of ordered alternatives that are ordered response categories. This paper proposes eliciting a rank order
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Subtask analysis of process data through a predictive model Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-11-01 Zhi Wang, Xueying Tang, Jingchen Liu, Zhiliang Ying
Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally
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Two efficient selection methods for high-dimensional CD-CAT utilizing max-marginals factor from MAP query and ensemble learning approach Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-10-26 Fen Luo, Xiaoqing Wang, Yan Cai, Dongbo Tu
Computerized adaptive testing for cognitive diagnosis (CD-CAT) needs to be efficient and responsive in real time to meet practical applications' requirements. For high-dimensional data, the number of categories to be recognized in a test grows exponentially as the number of attributes increases, which can easily cause system reaction time to be too long such that it adversely affects the examinees
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A new goodness-of-fit measure for probit models: Surrogate R2 Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-10-17 Dungang Liu, Xiaorui Zhu, Brandon Greenwell, Zewei Lin
Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R2 used for linear models. Such a measure has long been sought to achieve ‘comparability’
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Penalization approaches in the conditional maximum likelihood and Rasch modelling context Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-09-14 Can Gürer, Clemens Draxler
Recent detection methods for Differential Item Functioning (DIF) include approaches like Rasch Trees, DIFlasso, GPCMlasso and Item Focussed Trees, all of which - in contrast to well established methods - can handle metric covariates inducing DIF. A new estimation method shall address their downsides by mainly aiming at combining three central virtues: the use of conditional likelihood for estimation
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Ordinal state-trait regression for intensive longitudinal data Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-09-08 Prince P. Osei, Philip T. Reiss
In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables
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Compromised item detection: A Bayesian change-point perspective Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-09-07 Yang Du, Susu Zhang, Hua-Hua Chang
Psychometric methods for accurate and timely detection of item compromise have been a long-standing topic. While Bayesian methods can incorporate prior knowledge or expert inputs as additional information for item compromise detection, they have not been employed in item compromise detection itself. The current study proposes a two-phase Bayesian change-point framework for both stationary and real-time
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The biasing effects of selection and attrition on estimating the mean Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-08-07 Seunghoo Lee, Jorge Mendoza
Organizational and validation researchers often work with data that has been subjected to selection on the predictor and attrition on the criterion. These researchers often use the data observed under these conditions to estimate either the predictor or criterion's restricted population means. We show that the restricted means due to direct or indirect selection are a function of the population means
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CD-polytomous knowledge spaces and corresponding polytomous surmise systems Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-07-29 Bo Wang, Jinjin Li, Wen Sun
Heller (2021) generalized quasi-ordinal knowledge spaces to polytomous items. Inspired by this paper, we propose CD-polytomous knowledge space and its polytomous surmise system. A Galois connection is established between the collection K of all polytomous knowledge structures and the collection F1 of particular polytomous attribute functions. The closed elements of the Galois connection are CD-polytomous
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Item selection methods with exposure and time control for computerized classification test Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-07-15 Yingshi Huang, He Ren, Ping Chen
Computerized classification testing (CCT) commonly chooses items maximizing information at the cut score, which yields the most information for decision-making. However, a corollary problem is that all examinees will be given the same set of items, resulting in high test overlap rate and unbalanced item bank usage, which threatens test security. Moreover, another pivotal issue for CCT is time control
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Modelling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-07-10 Manqian Liao, Hong Jiao
Problem-solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy-oriented instructions that guide students to form problem-solving strategies are found to be more effective for low-achieving students than the skill-oriented instructions designed for enhancing their skill
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Flexible Bayesian modelling in dichotomous item response theory using mixtures of skewed item curves Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-07-05 Flávio B. Gonçalves, Juliane Venturelli S. L., Rosangela H. Loschi
Most item response theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a correct response and the latent traits of individuals taking a test. This assumption restricts the use of those models to the case in which all items behave symmetrically. On the other hand, asymmetric models proposed in the
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An explanatory mixture IRT model for careless and insufficient effort responding in self-report measures Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-06-22 Esther Ulitzsch, Seyma Nur Yildirim-Erbasli, Guher Gorgun, Okan Bulut
Careless and insufficient effort responding (C/IER) on self-report measures results in responses that do not reflect the trait to be measured, thereby posing a major threat to the quality of survey data. Reliable approaches for detecting C/IER aid in increasing the validity of inferences being made from survey data. First, once detected, C/IER can be taken into account in data analysis. Second, approaches
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A flexible approach to modelling over-, under- and equidispersed count data in IRT: The Two-Parameter Conway–Maxwell–Poisson Model Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-06-09 Marie Beisemann
Several psychometric tests and self-reports generate count data (e.g., divergent thinking tasks). The most prominent count data item response theory model, the Rasch Poisson Counts Model (RPCM), is limited in applicability by two restrictive assumptions: equal item discriminations and equidispersion (conditional mean equal to conditional variance). Violations of these assumptions lead to impaired reliability
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A flexible approach to modelling over-, under- and equidispersed count data in IRT: The Two-Parameter Conway-Maxwell-Poisson Model. Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-06-09 Marie Beisemann
Several psychometric tests and self-reports generate count data (e.g., divergent thinking tasks). The most prominent count data item response theory model, the Rasch Poisson Counts Model (RPCM), is limited in applicability by two restrictive assumptions: equal item discriminations and equidispersion (conditional mean equal to conditional variance). Violations of these assumptions lead to impaired reliability
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Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theory Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-06-06 Rudolf Debelak, Samuel Pawel, Carolin Strobl, Edgar C. Merkle
A family of score-based tests has been proposed in recent years for assessing the invariance of model parameters in several models of item response theory (IRT). These tests were originally developed in a maximum likelihood framework. This study discusses analogous tests for Bayesian maximum-a-posteriori estimates and multiple-group IRT models. We propose two families of statistical tests, which are
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Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theory. Br. J. Math. Stat. Psychol. (IF 2.6) Pub Date : 2022-06-06 Rudolf Debelak,Samuel Pawel,Carolin Strobl,Edgar C Merkle
A family of score-based tests has been proposed in recent years for assessing the invariance of model parameters in several models of item response theory (IRT). These tests were originally developed in a maximum likelihood framework. This study discusses analogous tests for Bayesian maximum-a-posteriori estimates and multiple-group IRT models. We propose two families of statistical tests, which are