British Journal of Mathematical & Statistical Psychology

Papers
(The TQCC of British Journal of Mathematical & Statistical Psychology is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Relating latent class membership to external variables: An overview65
A comparative evaluation of factor‐ and component‐based structural equation modelling approaches under (in)correct construct representations30
An explanatory mixture IRT model for careless and insufficient effort responding in self‐report measures21
Balancing fit and parsimony to improve Q‐matrix validation15
An overview of applied robust methods13
A psychometric model for respondent‐level anchoring on self‐report rating scale instruments11
Fisher transformation based confidence intervals of correlations in fixed‐ and random‐effects meta‐analysis10
The evidence interval and the Bayesian evidence value: On a unified theory for Bayesian hypothesis testing and interval estimation10
Which method delivers greater signal‐to‐noise ratio: Structural equation modelling or regression analysis with weighted composites?9
Two‐way ANOVA: Inferences about interactions based on robust measures of effect size8
A flexible approach to modelling over‐, under‐ and equidispersed count data in IRT: The Two‐Parameter Conway–Maxwell–Poisson Model7
Effect sizes in ANCOVA and difference‐in‐differences designs6
Shrinkage estimation of the three‐parameter logistic model6
Accounting for auto‐dependency in binary dyadic time series data: A comparison of model‐ and permutation‐based approaches for testing pairwise associations6
When and how to use set‐exploratory structural equation modelling to test structural models: A tutorial using the R package lavaan6
Factor copula models for mixed data6
A new goodness‐of‐fit measure for probit models: Surrogate R25
CD‐polytomous knowledge spaces and corresponding polytomous surmise systems5
Treating random effects as observed versus latent predictors: The bias–variance tradeoff in small samples5
Subtask analysis of process data through a predictive model5
Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)4
Empirical underidentification in estimating random utility models: The role of choice sets and standardizations4
Reliability coefficients for multiple group item response theory models4
A Gibbs sampler for the multidimensional four‐parameter logistic item response model via a data augmentation scheme4
Enhancing measurement validity in diverse populations: Modern approaches to evaluating differential item functioning4
On the Q statistic with constant weights for standardized mean difference4
Notes on attribution functions4
A semiparametric approach for item response function estimation to detect item misfit3
Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random3
Using item scores and response times in person‐fit assessment3
Penalization approaches in the conditional maximum likelihood and Rasch modelling context3
Treatment effects on count outcomes with non‐normal covariates3
Bayesian explanatory additive IRT models3
Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment3
Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm3
3
An item response tree model with not‐all‐distinct end nodes for non‐response modelling3
Modelling multilevel nonlinear treatment‐by‐covariate interactions in cluster randomized controlled trials using a generalized additive mixed model3
Computerized adaptive testing for testlet‐based innovative items3
The diamond ratio: A visual indicator of the extent of heterogeneity in meta‐analysis3
Statistical inference for agreement between multiple raters on a binary scale3
A sequential exploratory diagnostic model using a Pólya‐gamma data augmentation strategy3
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