Educational and Psychological Measurement最新文献_第9页

On the Importance of Coefficient Alpha for Measurement Research: Loading Equality Is Not Necessary for Alpha's Utility as a Scale Reliability Index. 论系数 Alpha 在测量研究中的重要性：载荷相等并非阿尔法作为量表可靠性指标的必要条件

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2023-08-01 Epub Date: 2022-07-20 DOI: 10.1177/00131644221104972

Tenko Raykov, James C Anthony, Natalja Menold

引用次数: 0

A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning Within a Test. 贝叶斯一般模型可解释测试中特定操作学习的个体差异。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2023-08-01 Epub Date: 2022-09-19 DOI: 10.1177/00131644221109796

José H Lozano, Javier Revuelta

引用次数: 0

Awareness Is Bliss: How Acquiescence Affects Exploratory Factor Analysis. 意识是福:默许如何影响探索性因素分析。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 DOI: 10.1177/00131644221089857

E Damiano D'Urso, Jesper Tijmstra, Jeroen K Vermunt, Kim De Roover

{"title":"Awareness Is Bliss: How Acquiescence Affects Exploratory Factor Analysis.","authors":"E Damiano D'Urso, Jesper Tijmstra, Jeroen K Vermunt, Kim De Roover","doi":"10.1177/00131644221089857","DOIUrl":"https://doi.org/10.1177/00131644221089857","url":null,"abstract":"Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurements of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterward, rotational freedom is resolved to interpret these factors. This study assessed the effects of an acquiescence response style (ARS) on EFA for unidimensional and multidimensional (un)balanced scales. Specifically, we evaluated (a) whether ARS is captured as an additional factor, (b) the effect of different rotation approaches on the content and ARS factors recovery, and (c) the effect of extracting the additional ARS factor on the recovery of factor loadings. ARS was often captured as an additional factor in balanced scales when it was strong. For these scales, ignoring extracting this additional ARS factor, or rotating to simple structure when extracting it, harmed the recovery of the original MM by introducing bias in loadings and cross-loadings. These issues were avoided by using informed rotation approaches (i.e., target rotation), where (part of) the rotation target is specified according to a priori expectations on the MM. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of ARS when assessing the psychometric properties of balanced scales and use informed rotation approaches when suspecting that an additional factor is an ARS factor.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9846850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1

Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks. 使用人工神经网络对 2019 年 TIMSS 中的图形反应进行评分。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-05-23 DOI: 10.1177/00131644221098021

Matthias von Davier, Lillian Tyack, Lale Khorramdel

{"title":"Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks.","authors":"Matthias von Davier, Lillian Tyack, Lale Khorramdel","doi":"10.1177/00131644221098021","DOIUrl":"10.1177/00131644221098021","url":null,"abstract":"Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our results show that convolutional neural networks (CNNs) outperform feed-forward neural networks in both loss and accuracy. The CNN models classified up to 97.53% of the image responses into the appropriate scoring category, which is comparable to, if not more accurate, than typical human raters. These findings were further strengthened by the observation that the most accurate CNN models correctly classified some image responses that had been incorrectly scored by the human raters. As an additional innovation, we outline a method to select human-rated responses for the training sample based on an application of the expected response function derived from item response theory. This paper argues that CNN-based automated scoring of image responses is a highly accurate procedure that could potentially replace the workload and cost of second human raters for international large-scale assessments (ILSAs), while improving the validity and comparability of scoring complex constructed-response items.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9475856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Assessing Dimensionality of IRT Models Using Traditional and Revised Parallel Analyses. 使用传统和修订的平行分析法评估 IRT 模型的维度。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-07-21 DOI: 10.1177/00131644221111838

Wenjing Guo, Youn-Jeng Choi

{"title":"Assessing Dimensionality of IRT Models Using Traditional and Revised Parallel Analyses.","authors":"Wenjing Guo, Youn-Jeng Choi","doi":"10.1177/00131644221111838","DOIUrl":"10.1177/00131644221111838","url":null,"abstract":"Determining the number of dimensions is extremely important in applying item response theory (IRT) models to data. Traditional and revised parallel analyses have been proposed within the factor analysis framework, and both have shown some promise in assessing dimensionality. However, their performance in the IRT framework has not been systematically investigated. Therefore, we evaluated the accuracy of traditional and revised parallel analyses for determining the number of underlying dimensions in the IRT framework by conducting simulation studies. Six data generation factors were manipulated: number of observations, test length, type of generation models, number of dimensions, correlations between dimensions, and item discrimination. Results indicated that (a) when the generated IRT model is unidimensional, across all simulation conditions, traditional parallel analysis using principal component analysis and tetrachoric correlation performs best; (b) when the generated IRT model is multidimensional, traditional parallel analysis using principal component analysis and tetrachoric correlation yields the highest proportion of accurately identified underlying dimensions across all factors, except when the correlation between dimensions is 0.8 or the item discrimination is low; and (c) under a few combinations of simulated factors, none of the eight methods performed well (e.g., when the generation model is three-dimensional 3PL, the item discrimination is low, and the correlation between dimensions is 0.8).","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9475858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Changes in the Speed-Ability Relation Through Different Treatments of Rapid Guessing. 快速猜测的不同处理方式对速度-能力关系的影响。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-07-11 DOI: 10.1177/00131644221109490

Tobias Deribo, Frank Goldhammer, Ulf Kroehne

{"title":"Changes in the Speed-Ability Relation Through Different Treatments of Rapid Guessing.","authors":"Tobias Deribo, Frank Goldhammer, Ulf Kroehne","doi":"10.1177/00131644221109490","DOIUrl":"10.1177/00131644221109490","url":null,"abstract":"As researchers in the social sciences, we are often interested in studying not directly observable constructs through assessments and questionnaires. But even in a well-designed and well-implemented study, rapid-guessing behavior may occur. Under rapid-guessing behavior, a task is skimmed shortly but not read and engaged with in-depth. Hence, a response given under rapid-guessing behavior does bias constructs and relations of interest. Bias also appears reasonable for latent speed estimates obtained under rapid-guessing behavior, as well as the identified relation between speed and ability. This bias seems especially problematic considering that the relation between speed and ability has been shown to be able to improve precision in ability estimation. For this reason, we investigate if and how responses and response times obtained under rapid-guessing behavior affect the identified speed-ability relation and the precision of ability estimates in a joint model of speed and ability. Therefore, the study presents an empirical application that highlights a specific methodological problem resulting from rapid-guessing behavior. Here, we could show that different (non-)treatments of rapid guessing can lead to different conclusions about the underlying speed-ability relation. Furthermore, different rapid-guessing treatments led to wildly different conclusions about gains in precision through joint modeling. The results show the importance of taking rapid guessing into account when the psychometric use of response times is of interest.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9846842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models. 样本大小和其他各种因素对二分法混合 IRT 模型估计的影响。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-05-19 DOI: 10.1177/00131644221094325

Sedat Sen, Allan S Cohen

{"title":"The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models.","authors":"Sedat Sen, Allan S Cohen","doi":"10.1177/00131644221094325","DOIUrl":"10.1177/00131644221094325","url":null,"abstract":"The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal). Effects were assessed using root mean square error (RMSE) and classification accuracy percentage computed between true parameters and estimated parameters. The results of this simulation study showed that more precise estimates of item parameters were obtained with larger sample sizes and longer test lengths. Recovery of item parameters decreased as the number of classes increased with the decrease in sample size. Recovery of classification accuracy for the conditions with two-class solutions was also better than that of three-class solutions. Results of both item parameter estimates and classification accuracy differed by model type. More complex models and models with larger class separations produced less accurate results. The effect of the mixture proportions also differentially affected RMSE and classification accuracy results. Groups of equal size produced more precise item parameter estimates, but the reverse was the case for classification accuracy results. Results suggested that dichotomous mixture IRT models required more than 2,000 examinees to be able to obtain stable results as even shorter tests required such large sample sizes for more precise estimates. This number increased as the number of latent classes, the degree of separation, and model complexity increased.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9475859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Small Sample Correction for Factor Score Regression. 因子得分回归的小样本校正。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-07-02 DOI: 10.1177/00131644221105505

Jasper Bogaert, Wen Wei Loh, Yves Rosseel

{"title":"A Small Sample Correction for Factor Score Regression.","authors":"Jasper Bogaert, Wen Wei Loh, Yves Rosseel","doi":"10.1177/00131644221105505","DOIUrl":"10.1177/00131644221105505","url":null,"abstract":"Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10349847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Evaluation of Polytomous Item Locations in Multicomponent Measuring Instruments: A Note on a Latent Variable Modeling Procedure. 评估多成分测量工具中的多项式项目位置：关于潜在变量建模程序的说明。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-03-02 DOI: 10.1177/00131644211072829

Tenko Raykov, Martin Pusic

引用次数: 0

Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models? 曲线下面积是否适合用于评估心理测量模型的拟合度？

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-06-01 Epub Date: 2022-05-24 DOI: 10.1177/00131644221098182

Yuting Han, Jihong Zhang, Zhehan Jiang, Dexin Shi

{"title":"Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models?","authors":"Yuting Han, Jihong Zhang, Zhehan Jiang, Dexin Shi","doi":"10.1177/00131644221098182","DOIUrl":"10.1177/00131644221098182","url":null,"abstract":"In the literature of modern psychometric modeling, mostly related to item response theory (IRT), the fit of model is evaluated through known indices, such as χ2, M2, and root mean square error of approximation (RMSEA) for absolute assessments as well as Akaike information criterion (AIC), consistent AIC (CAIC), and Bayesian information criterion (BIC) for relative comparisons. Recent developments show a merging trend of psychometric and machine learnings, yet there remains a gap in the model fit evaluation, specifically the use of the area under curve (AUC). This study focuses on the behaviors of AUC in fitting IRT models. Rounds of simulations were conducted to investigate AUC's appropriateness (e.g., power and Type I error rate) under various conditions. The results show that AUC possessed certain advantages under certain conditions such as high-dimensional structure with two-parameter logistic (2PL) and some three-parameter logistic (3PL) models, while disadvantages were also obvious when the true model is unidimensional. It cautions researchers about the dangers of using AUC solely in evaluating psychometric models.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10299668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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