British Journal of Mathematical & Statistical Psychology最新文献

Modelling non-linear psychological processes: Reviewing and evaluating non-parametric approaches and their applicability to intensive longitudinal data. 非线性心理过程建模：回顾和评估非参数方法及其对密集纵向数据的适用性。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-30 DOI: 10.1111/bmsp.12397

Jan I Failenschmid, Leonie V D E Vogelsmeier, Joris Mulder, Joran Jongerling

{"title":"Modelling non-linear psychological processes: Reviewing and evaluating non-parametric approaches and their applicability to intensive longitudinal data.","authors":"Jan I Failenschmid, Leonie V D E Vogelsmeier, Joris Mulder, Joran Jongerling","doi":"10.1111/bmsp.12397","DOIUrl":"https://doi.org/10.1111/bmsp.12397","url":null,"abstract":"Psychological concepts are increasingly understood as complex dynamic systems that change over time. To study these complex systems, researchers are increasingly gathering intensive longitudinal data (ILD), revealing non-linear phenomena such as asymptotic growth, mean-level switching, and regulatory oscillations. However, psychological researchers currently lack advanced statistical methods that are flexible enough to capture these non-linear processes accurately, which hinders theory development. While methods such as local polynomial regression, Gaussian processes and generalized additive models (GAMs) exist outside of psychology, they are rarely applied within the field because they have not yet been reviewed accessibly and evaluated within the context of ILD. To address this important gap, this article introduces these three methods for an applied psychological audience. We further conducted a simulation study, which demonstrates that all three methods infer non-linear processes that have been found in ILD more accurately than polynomial regression. Particularly, GAMs closely captured the underlying processes, performing almost as well as the data-generating parametric models. Finally, we illustrate how GAMs can be applied to explore idiographic processes and identify potential phenomena in ILD. This comprehensive analysis empowers psychological researchers to model non-linear processes accurately and select a method that aligns with their data and research goals.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A path signature perspective of process data feature extraction. 过程数据特征提取的路径签名视角。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-26 DOI: 10.1111/bmsp.12390

Xueying Tang, Jingchen Liu, Zhiliang Ying

引用次数: 0

A supervised learning approach to estimating IRT models in small samples. 一种估计小样本IRT模型的监督学习方法。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-15 DOI: 10.1111/bmsp.12396

Dmitry I Belov, Oliver Lüdtke, Esther Ulitzsch

{"title":"A supervised learning approach to estimating IRT models in small samples.","authors":"Dmitry I Belov, Oliver Lüdtke, Esther Ulitzsch","doi":"10.1111/bmsp.12396","DOIUrl":"https://doi.org/10.1111/bmsp.12396","url":null,"abstract":"Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unstable parameter estimates and large standard errors. To facilitate small-sample IRT estimation, we introduce a novel approach that does not rely on the likelihood. Our estimation approach derives features from response data and then maps the features to item parameters using a neural network (NN). We describe and evaluate our approach for the three-parameter logistic model; however, it is applicable to any model with an item characteristic curve. Three types of NNs are developed, supporting the obtainment of both point estimates and confidence intervals for IRT model parameters. The results of a simulation study demonstrate that these NNs perform better than Bayesian estimation using Markov chain Monte Carlo methods in terms of the quality of the point estimates and confidence intervals while also being much faster. These properties facilitate (1) pretesting items in a real-time testing environment, (2) pretesting more items and (3) pretesting items only in a secured environment to eradicate possible compromise of new items in online testing.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs. 一个新的非视觉程序筛选非平稳性的时间序列，从密集的纵向设计获得。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-04-25 DOI: 10.1111/bmsp.12394

Steffen Zitzmann, Christoph Lindner, Julian F Lohmann, Martin Hecht

引用次数: 0

A general diagnostic modelling framework for forced-choice assessments. 强迫选择评估的一般诊断模型框架。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-04-23 DOI: 10.1111/bmsp.12393

Pablo Nájera, Rodrigo S Kreitchmann, Scarlett Escudero, Francisco J Abad, Jimmy de la Torre, Miguel A Sorrel

{"title":"A general diagnostic modelling framework for forced-choice assessments.","authors":"Pablo Nájera, Rodrigo S Kreitchmann, Scarlett Escudero, Francisco J Abad, Jimmy de la Torre, Miguel A Sorrel","doi":"10.1111/bmsp.12393","DOIUrl":"https://doi.org/10.1111/bmsp.12393","url":null,"abstract":"Diagnostic classification modelling (DCM) is a family of restricted latent class models often used in educational settings to assess students' strengths and weaknesses. Recently, there has been growing interest in applying DCM to noncognitive traits in fields such as clinical and organizational psychology, as well as personality profiling. To address common response biases in these assessments, such as social desirability, Huang (2023, Educational and Psychological Measurement, 83, 146) adopted the forced-choice (FC) item format within the DCM framework, developing the FC-DCM. This model assumes that examinees with no clear preference for any statements in an FC block will choose completely at random. Additionally, the unique parametrization of the FC-DCM poses challenges for integration with established DCM frameworks in the literature. In the present study, we enhance the capabilities of DCM by introducing a general diagnostic framework for FC assessments. We present an adaptation of the G-DINA model to accommodate FC responses. Simulation results show that the G-DINA model provides accurate classifications, item parameter estimates and attribute correlations, outperforming the FC-DCM in realistic scenarios where item discrimination varies. A real FC assessment example further illustrates the better model fit of the G-DINA. Practical recommendations for using the FC format in diagnostic assessments of noncognitive traits are provided.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144035912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Score-based tests for parameter instability in ordinal factor models. 有序因子模型中参数不稳定性的基于分数的检验。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-04-23 DOI: 10.1111/bmsp.12392

Franz Classe, Rudolf Debelak, Christoph Kern

引用次数: 0

Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models. 心理学研究中的因果区分：线性非高斯模型下基于独立性的方法。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-04-15 DOI: 10.1111/bmsp.12391

Dexin Shi, Bo Zhang, Wolfgang Wiedermann, Amanda J Fairchild

{"title":"Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models.","authors":"Dexin Shi, Bo Zhang, Wolfgang Wiedermann, Amanda J Fairchild","doi":"10.1111/bmsp.12391","DOIUrl":"https://doi.org/10.1111/bmsp.12391","url":null,"abstract":"Distinguishing cause from effect - that is, determining whether x causes y (x → y) or, alternatively, whether y causes x (y → x) - is a primary research goal in many psychological research areas. Despite its importance, determining causal direction with observational data remains a difficult task. In this study, we introduce an independence-based approach for causal discovery between two variables of interest under a linear non-Gaussian model framework. We propose a two-step algorithm based on distance correlations that provides empirical conclusions on the causal directionality of effects under realistic conditions typically seen in psychological studies, that is, in the presence of hidden confounders. The performance of the proposed algorithm is evaluated using Monte-Carlo simulations. Findings suggest that the algorithm can effectively detect the causal direction between two variables of interest, even in the presence of weak hidden confounders. Moreover, distance correlations provide useful insights into the magnitude of hidden confounding. We provide an empirical example to demonstrate the application of our proposed approach and discuss practical implications and future directions.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect sizes for experimental research. 实验研究的效应量。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-03-31 DOI: 10.1111/bmsp.12389

Larry V Hedges

引用次数: 0

Fusion of score-differencing and response similarity statistics for detecting examinees with item preknowledge. 分差统计与反应相似统计的融合在项目预知检测中的应用。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-03-25 DOI: 10.1111/bmsp.12388

Yongze Xu, Ruihang He, Meiwei Huang, Fang Luo

{"title":"Fusion of score-differencing and response similarity statistics for detecting examinees with item preknowledge.","authors":"Yongze Xu, Ruihang He, Meiwei Huang, Fang Luo","doi":"10.1111/bmsp.12388","DOIUrl":"https://doi.org/10.1111/bmsp.12388","url":null,"abstract":"Item preknowledge (IP) is a prevalent form of test fraud in educational assessment that can compromise test validity. Two common methods for detecting examinees with IP are score-differencing statistics and response similarity index (RSI). These statistics have different applications and respective advantages. In this paper, we propose a new method (Joint Survival Function Method, <math> <semantics><mrow><mtext>JSFM</mtext></mrow> <annotation>$$ mathrm{JSFM} $$</annotation></semantics> </math> ) to combine these two types of statistics to calculate a fusion statistic that tries to address the issue of distribution differences between the original indicators. By combining the advantages of the original indicators, the fusion statistic can more effectively detect examinees with IP. We fused two typical RSI and four typical score-differencing statistics using different methods and compared their performance. The results demonstrate that the proposed <math> <semantics><mrow><mtext>JSFM</mtext></mrow> <annotation>$$ mathrm{JSFM} $$</annotation></semantics> </math> exhibits strong cross-scenario stability and performs better than other fusion methods.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing calibration designs with uncertainty in abilities. 在能力不确定的情况下优化校准设计。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-03-10 DOI: 10.1111/bmsp.12387

Jonas Bjermo, Ellinor Fackle-Fornius, Frank Miller

引用次数: 0