Applied Psychological Measurement最新文献

Distinguishing Between Models for Extreme and Midpoint Response Styles as Opposite Poles of a Single Dimension versus Two Separate Dimensions: A Simulation Study. 区分极端和中点响应风格模型作为单一维度与两个独立维度的对立极点：一项模拟研究。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-09-13 DOI: 10.1177/01466216251379471

Martijn Schoenmakers, Maria Bolsinova, Jesper Tijmstra

{"title":"Distinguishing Between Models for Extreme and Midpoint Response Styles as Opposite Poles of a Single Dimension versus Two Separate Dimensions: A Simulation Study.","authors":"Martijn Schoenmakers, Maria Bolsinova, Jesper Tijmstra","doi":"10.1177/01466216251379471","DOIUrl":"10.1177/01466216251379471","url":null,"abstract":"Extreme and midpoint response styles have frequently been found to decrease the validity of Likert-type questionnaire results. Different approaches for modelling extreme and midpoint responding have been proposed in the literature, with some advocating for a unidimensional conceptualization of the response styles as opposite poles, and others modelling them as separate dimensions. How these response styles are modelled influences the estimation complexity, parameter estimates, and detection of and correction for response styles in IRT models. For these reasons, we examine if it is possible to empirically distinguish between extreme and midpoint responding as two separate dimensions versus two opposite sides of a single dimension. The various conceptualizations are modelled using the multidimensional nominal response model, with the AIC and BIC being used to distinguish between the competing models in a simulation study and an empirical example. Results indicate good performance of both information criteria given sufficient sample size, test length, and response style strength. The BIC outperformed the AIC in cases where no response styles were present, while the AIC outperformed the BIC in cases where multiple response style dimensions were present. Implications of the results for practice are discussed.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251379471"},"PeriodicalIF":1.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12433433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

fcirt: An R Package for Forced Choice Models in Item Response Theory. 第一章：项目反应理论中强迫选择模型的R包。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-09-10 DOI: 10.1177/01466216251378771

Naidan Tu, Sean Joo, Philseok Lee, Stephen Stark

引用次数: 0

Automatic Generation of Rule-Based Raven-Like Matrices in R: The matRiks Package. 在R中自动生成基于规则的类乌鸦矩阵：矩阵包。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-09-02 DOI: 10.1177/01466216251374826

Andrea Brancaccio, Ottavia M Epifania, Pasquale Anselmi, Debora de Chiusole

引用次数: 0

CALMs: A Shiny Application for Comprehensive Analysis of Latent Means. CALMs：潜在均值综合分析的闪亮应用。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-08-31 DOI: 10.1177/01466216251371173

Kim Nimon, Julia Fulmore, Gregg Keiffer, Bryn Hammack-Brown

引用次数: 0

implicitMeasures: An R Package for Scoring the Implicit Association Test and the Single-Category Implicit Association Test. 内隐关联测验和单类别内隐关联测验的R量表。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-08-25 DOI: 10.1177/01466216251371532

Ottavia M Epifania, Pasquale Anselmi, Egidio Robusto

引用次数: 0

Using Deep Learning to Choose Optimal Smoothing Values for Equating. 利用深度学习选择最优的平滑值来求解方程。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-08-23 DOI: 10.1177/01466216251363244

Chunyan Liu, Zhongmin Cui

引用次数: 0

Combining Propensity Scores and Common Items for Test Score Equating. 结合倾向分数和测试分数相等的常见项目。

IF 1.2 4区心理学

Applied Psychological Measurement Pub Date : 2025-07-30 DOI: 10.1177/01466216251363240

Inga Laukaityte, Gabriel Wallin, Marie Wiberg

{"title":"Combining Propensity Scores and Common Items for Test Score Equating.","authors":"Inga Laukaityte, Gabriel Wallin, Marie Wiberg","doi":"10.1177/01466216251363240","DOIUrl":"10.1177/01466216251363240","url":null,"abstract":"Ensuring that test scores are fair and comparable across different test forms and different test groups is a significant statistical challenge in educational testing. Methods to achieve score comparability, a process known as test score equating, often rely on including common test items or assuming that test taker groups are similar in key characteristics. This study explores a novel approach that combines propensity scores, based on test takers' background covariates, with information from common items using kernel smoothing techniques for binary-scored test items. An empirical analysis using data from a high-stakes college admissions test evaluates the standard errors and differences in adjusted test scores. A simulation study examines the impact of factors such as the number of test takers, the number of common items, and the correlation between covariates and test scores on the method's performance. The findings demonstrate that integrating propensity scores with common item information reduces standard errors and bias more effectively than using either source alone. This suggests that balancing the groups on the test-takers' covariates enhance the fairness and accuracy of test score comparisons across different groups. The proposed method highlights the benefits of considering all the collected data to improve score comparability.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251363240"},"PeriodicalIF":1.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCorrSeqPerm: Searching for the Maximum Statistically Significant System of Linear Correlations and its Application in Work Psychology. 线性相关的最大统计显著性系统的搜索及其在工作心理学中的应用。

IF 1 4区心理学

Applied Psychological Measurement Pub Date : 2025-07-21 DOI: 10.1177/01466216251360562

Katarzyna Stapor, Grzegorz Kończak, Damian Grabowski, Marta Żywiołek-Szeja, Agata Chudzicka-Czupała

{"title":"MCorrSeqPerm: Searching for the Maximum Statistically Significant System of Linear Correlations and its Application in Work Psychology.","authors":"Katarzyna Stapor, Grzegorz Kończak, Damian Grabowski, Marta Żywiołek-Szeja, Agata Chudzicka-Czupała","doi":"10.1177/01466216251360562","DOIUrl":"10.1177/01466216251360562","url":null,"abstract":"The paper addresses the problem of detecting a statistically significant subset of input considered relationships. The Pearson linear correlation coefficient calculated from a sample was used to determine the strength of a relationship. Simultaneous testing of the significance of many relationships is related to the issue of multiple hypothesis testing. In such a scenario, the probability of making a type I error without proper error control is, in practice, much higher than the assumed level of significance. The paper proposes an alternative approach: a new stepwise procedure (MCorrSeqPerm) allowing for finding the maximum statistically significant system of linear correlations keeping the error at the assumed level. The proposed procedure relies on a sequence of permutation tests. Its application in the analysis of relationships in the problem of examining stress experienced at work and job satisfaction was compared with Holm's classic method in detecting the number of significant correlations.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251360562"},"PeriodicalIF":1.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12279768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multidimensional Continuous Response Model for Measuring Unipolar Traits. 单极特质测量的多维连续响应模型。

IF 1 4区心理学

Applied Psychological Measurement Pub Date : 2025-07-15 DOI: 10.1177/01466216251360311

Pere J Ferrando, Fabia Morales-Vives, José M Casas, David Navarro-González

{"title":"A Multidimensional Continuous Response Model for Measuring Unipolar Traits.","authors":"Pere J Ferrando, Fabia Morales-Vives, José M Casas, David Navarro-González","doi":"10.1177/01466216251360311","DOIUrl":"10.1177/01466216251360311","url":null,"abstract":"Unipolar constructs are encountered in a variety of non-cognitive measurement scenarios that include clinical and forensic assessments, symptoms checklists, addictive behaviors, and irrational beliefs among others. Furthermore, Item Response Theory (IRT) models intended for fitting and scoring measures of unipolar constructs, particularly Log-Logistic models, are fully developed at present, but they are limited to unidimensional structures. This paper proposes a novel multidimensional log-logistic IRT model intended for double-bounded continuous response items that measure unipolar constructs. The chosen response format is a natural application, and is increasingly used, in the scenarios for which the model is intended. The proposed model is remarkably simple, has interesting properties and, at the structural level can be fitted by using linearizing transformations. Multidimensional item location and discrimination indices are developed, and procedures for fitting the model, scoring the respondents, and assessing conditional and marginal accuracy (including information curves) are proposed. Everything that is proposed has been implemented in fully available R program. The functioning of the model is illustrated by using an empirical example with the data of 371 undergraduate students who answered the Depression and Anxiety subscales of the Brief Symptom Inventory 18 and also the Rosenberg Self-Esteem Scale. The results show the usefulness of the new model to adequately interpret unipolar variables, particularly in terms of the conditional reliability of trait estimates and external validity.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251360311"},"PeriodicalIF":1.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study of Latent State-Trait Theory Framework in Piecewise Growth Models. 分段增长模型中潜在状态-特质理论框架研究。

IF 1 4区心理学

Applied Psychological Measurement Pub Date : 2025-07-15 DOI: 10.1177/01466216251360565

Ihnwhi Heo, Ren Liu, Haiyan Liu, Sarah Depaoli, Fan Jia

{"title":"A Study of Latent State-Trait Theory Framework in Piecewise Growth Models.","authors":"Ihnwhi Heo, Ren Liu, Haiyan Liu, Sarah Depaoli, Fan Jia","doi":"10.1177/01466216251360565","DOIUrl":"10.1177/01466216251360565","url":null,"abstract":"Latent state-trait (LST) theory provides a psychometric framework that facilitates the measurement of long-term trait change and short-term state variability in longitudinal data. While LST theory has guided the development and extension of linear latent growth models within its theoretical framework, the integration of piecewise growth models (PGMs) into the LST theory framework remains uninvestigated. PGMs are well suited for modeling nonlinear developmental processes comprised of distinct stages, which frequently arise in psychological and educational research. Their ability to capture phase-specific changes makes them a useful tool for applied and methodological researchers. This paper introduces a novel measurement approach that integrates PGMs into the framework of LST theory by presenting single-indicator piecewise growth models (SI-PGMs) and multiple-indicator piecewise growth models (MI-PGMs). We detail the model specifications for both SI-PGMs and MI-PGMs. For SI-PGMs, we define the reliability coefficient; for MI-PGMs, we define the consistency coefficient, occasion specificity coefficient, and reliability coefficient. We then conduct simulations to evaluate the models' performance in accurately recovering growth parameters and capturing true reliability. The simulation results indicated that SI-PGMs and MI-PGMs successfully recovered growth parameters and performed comparably in the absence of situational influences. However, MI-PGMs outperformed SI-PGMs when situational influences were present. We conclude by outlining directions for future research and providing Mplus syntax to support the dissemination of the models.","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251360565"},"PeriodicalIF":1.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0