Educational and Psychological Measurement最新文献_第7页

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":"83 3","pages":"433-472"},"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":"83 3","pages":"556-585"},"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

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":"83 3","pages":"520-555"},"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":"83 3","pages":"495-519"},"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

On Bank Assembly and Block Selection in Multidimensional Forced-Choice Adaptive Assessments. 关于多维强制选择适应性评估中的组库和组块选择。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 Epub Date: 2022-04-28 DOI: 10.1177/00131644221087986

Rodrigo S Kreitchmann, Miguel A Sorrel, Francisco J Abad

{"title":"On Bank Assembly and Block Selection in Multidimensional Forced-Choice Adaptive Assessments.","authors":"Rodrigo S Kreitchmann, Miguel A Sorrel, Francisco J Abad","doi":"10.1177/00131644221087986","DOIUrl":"10.1177/00131644221087986","url":null,"abstract":"Multidimensional forced-choice (FC) questionnaires have been consistently found to reduce the effects of socially desirable responding and faking in noncognitive assessments. Although FC has been considered problematic for providing ipsative scores under the classical test theory, item response theory (IRT) models enable the estimation of nonipsative scores from FC responses. However, while some authors indicate that blocks composed of opposite-keyed items are necessary to retrieve normative scores, others suggest that these blocks may be less robust to faking, thus impairing the assessment validity. Accordingly, this article presents a simulation study to investigate whether it is possible to retrieve normative scores using only positively keyed items in pairwise FC computerized adaptive testing (CAT). Specifically, a simulation study addressed the effect of (a) different bank assembly (with a randomly assembled bank, an optimally assembled bank, and blocks assembled on-the-fly considering every possible pair of items), and (b) block selection rules (i.e., T, and Bayesian D and A-rules) over the estimate accuracy and ipsativity and overlap rates. Moreover, different questionnaire lengths (30 and 60) and trait structures (independent or positively correlated) were studied, and a nonadaptive questionnaire was included as baseline in each condition. In general, very good trait estimates were retrieved, despite using only positively keyed items. Although the best trait accuracy and lowest ipsativity were found using the Bayesian A-rule with questionnaires assembled on-the-fly, the T-rule under this method led to the worst results. This points out to the importance of considering both aspects when designing FC CAT.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"83 2","pages":"294-321"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10823905","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

Investigating Confidence Intervals of Item Parameters When Some Item Parameters Take Priors in the 2PL and 3PL Models. 当某些项目参数在 2PL 和 3PL 模型中具有优先权时，调查项目参数的置信区间。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 Epub Date: 2022-05-16 DOI: 10.1177/00131644221096431

Insu Paek, Zhongtian Lin, Robert Philip Chalmers

{"title":"Investigating Confidence Intervals of Item Parameters When Some Item Parameters Take Priors in the 2PL and 3PL Models.","authors":"Insu Paek, Zhongtian Lin, Robert Philip Chalmers","doi":"10.1177/00131644221096431","DOIUrl":"10.1177/00131644221096431","url":null,"abstract":"To reduce the chance of Heywood cases or nonconvergence in estimating the 2PL or the 3PL model in the marginal maximum likelihood with the expectation-maximization (MML-EM) estimation method, priors for the item slope parameter in the 2PL model or for the pseudo-guessing parameter in the 3PL model can be used and the marginal maximum a posteriori (MMAP) and posterior standard error (PSE) are estimated. Confidence intervals (CIs) for these parameters and other parameters which did not take any priors were investigated with popular prior distributions, different error covariance estimation methods, test lengths, and sample sizes. A seemingly paradoxical result was that, when priors were taken, the conditions of the error covariance estimation methods known to be better in the literature (Louis or Oakes method in this study) did not yield the best results for the CI performance, while the conditions of the cross-product method for the error covariance estimation which has the tendency of upward bias in estimating the standard errors exhibited better CI performance. Other important findings for the CI performance are also discussed.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"83 2","pages":"375-400"},"PeriodicalIF":2.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10823906","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

Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching. 带有优势项目的多维强制选择 CAT：在不同可取性匹配条件下与最佳静态测试的实证比较。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 Epub Date: 2022-03-07 DOI: 10.1177/00131644221077637

Yin Lin, Anna Brown, Paul Williams

{"title":"Multidimensional Forced-Choice CAT With Dominance Items: An Empirical Comparison With Optimal Static Testing Under Different Desirability Matching.","authors":"Yin Lin, Anna Brown, Paul Williams","doi":"10.1177/00131644221077637","DOIUrl":"10.1177/00131644221077637","url":null,"abstract":"Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by simulations and lacking in empirical deployment. This empirical study trialed a FC CAT with dominance items described by the Thurstonian Item Response Theory model with research participants. This study investigated important practical issues such as the implications of adaptive item selection and social desirability balancing criteria on score distributions, measurement accuracy and participant perceptions. Moreover, nonadaptive but optimal tests of similar design were trialed alongside the CATs to provide a baseline for comparison, helping to quantify the return on investment when converting an otherwise-optimized static assessment into an adaptive one. Although the benefit of adaptive item selection in improving measurement precision was confirmed, results also indicated that at shorter test lengths CAT had no notable advantage compared with optimal static tests. Taking a holistic view incorporating both psychometric and operational considerations, implications for the design and deployment of FC assessments in research and practice are discussed.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"83 2","pages":"322-350"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10823904","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

Range Restriction Affects Factor Analysis: Normality, Estimation, Fit, Loadings, and Reliability. 范围限制对因子分析的影响：正态性、估计、拟合、载荷和可靠性。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 Epub Date: 2022-03-10 DOI: 10.1177/00131644221081867

Alicia Franco-Martínez, Jesús M Alvarado, Miguel A Sorrel

{"title":"Range Restriction Affects Factor Analysis: Normality, Estimation, Fit, Loadings, and Reliability.","authors":"Alicia Franco-Martínez, Jesús M Alvarado, Miguel A Sorrel","doi":"10.1177/00131644221081867","DOIUrl":"10.1177/00131644221081867","url":null,"abstract":"A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size ( <math><mrow><mi>N</mi></mrow> </math> = 200 and 500 cases), test size ( <math><mrow><mi>J</mi></mrow> </math> = 6, 12, 18, and 24 items), loading size ( <math><mrow><mi>L</mi></mrow> </math> = .50, .70, and .90), and restriction size (from <math><mrow><mi>R</mi></mrow> </math> = 1, .90, .80, and so on till .10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"83 2","pages":"262-293"},"PeriodicalIF":2.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10823909","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

Evaluating the Quality of Classification in Mixture Model Simulations. 评估混合模型模拟中的分类质量。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 Epub Date: 2022-04-29 DOI: 10.1177/00131644221093619

Yoona Jang, Sehee Hong

引用次数: 0

Summary Intervals for Model-Based Classification Accuracy and Consistency Indices. 基于模型的分类精度和一致性指标的汇总区间。

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2023-04-01 DOI: 10.1177/00131644221092347

Oscar Gonzalez

{"title":"Summary Intervals for Model-Based Classification Accuracy and Consistency Indices.","authors":"Oscar Gonzalez","doi":"10.1177/00131644221092347","DOIUrl":"https://doi.org/10.1177/00131644221092347","url":null,"abstract":"When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the linear factor model have been recently proposed, but parameter uncertainty of the CA and CC indices has not been investigated. This article demonstrates how to estimate percentile bootstrap confidence intervals and Bayesian credible intervals for CA and CC indices, which have the added benefit of incorporating the sampling variability of the parameters of the linear factor model to summary intervals. Results from a small simulation study suggest that percentile bootstrap confidence intervals have appropriate confidence interval coverage, although displaying a small negative bias. However, Bayesian credible intervals with diffused priors have poor interval coverage, but their coverage improves once empirical, weakly informative priors are used. The procedures are illustrated by estimating CA and CC indices from a measure used to identify individuals low on mindfulness for a hypothetical intervention, and R code is provided to facilitate the implementation of the procedures.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"83 2","pages":"240-261"},"PeriodicalIF":2.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10823910","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