Educational and Psychological Measurement最新文献_第4页

Using ROC Analysis to Refine Cut Scores Following a Standard Setting Process. 在标准制定过程中使用 ROC 分析法完善切分分数。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2024-09-24 DOI: 10.1177/00131644241278925

Dongwei Wang, Lisa A Keller

{"title":"Using ROC Analysis to Refine Cut Scores Following a Standard Setting Process.","authors":"Dongwei Wang, Lisa A Keller","doi":"10.1177/00131644241278925","DOIUrl":"10.1177/00131644241278925","url":null,"abstract":"In educational assessment, cut scores are often defined through standard setting by a group of subject matter experts. This study aims to investigate the impact of several factors on classification accuracy using the receiver operating characteristic (ROC) analysis to provide statistical and theoretical evidence when the cut score needs to be refined. Factors examined in the study include the sample distribution relative to the cut score, prevalence of the positive event, and cost ratio. Forty item responses were simulated for examinees of four sample distributions. In addition, the prevalence and cost ratio between false negatives and false positives were manipulated to examine their impacts on classification accuracy. The optimal cut score is identified using the Youden Index J. The results showed that the optimal cut score identified by the evaluation criterion tended to pull the cut score closer to the mode of the proficiency distribution. In addition, depending on the prevalence of the positive event and cost ratio, the optimal cut score shifts accordingly. With the item parameters used to simulate the data and the simulated sample distributions, it was found that when passing the exam is a low-prevalence event in the population, increasing the cut score operationally improves the classification; when passing the exam is a high-prevalence event, then cut score should be reduced to achieve optimality. As the cost ratio increases, the optimal cut score suggested by the evaluation criterion decreases. In three out of the four sample distributions examined in this study, increasing the cut score enhanced the classification, irrespective of the cost ratio when the prevalence in the population is 50%. This study provides statistical evidence when the cut score needs to be refined for policy reasons.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":" ","pages":"00131644241278925"},"PeriodicalIF":2.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650503","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 the Ordering Structure of Clustered Items Using Nonparametric Item Response Theory 利用非参数项目反应理论研究聚类项目的排序结构

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-09-06 DOI: 10.1177/00131644241274122

Letty Koopman, Johan Braeken

{"title":"Investigating the Ordering Structure of Clustered Items Using Nonparametric Item Response Theory","authors":"Letty Koopman, Johan Braeken","doi":"10.1177/00131644241274122","DOIUrl":"https://doi.org/10.1177/00131644241274122","url":null,"abstract":"Educational and psychological tests with an ordered item structure enable efficient test administration procedures and allow for intuitive score interpretation and monitoring. The effectiveness of the measurement instrument relies to a large extent on the validated strength of its ordering structure. We define three increasingly strict types of ordering for the ordering structure of a measurement instrument with clustered items: a weak and a strong invariant cluster ordering and a clustered invariant item ordering. Following a nonparametric item response theory (IRT) approach, we proposed a procedure to evaluate the ordering structure of a clustered item set along this three-fold continuum of order invariance. The basis of the procedure is (a) the local assessment of pairwise conditional expectations at both cluster and item level and (b) the global assessment of the number of Guttman errors through new generalizations of the H-coefficient for this item-cluster context. The procedure, readily implemented in R, is illustrated and applied to an empirical example. Suggestions for test practice, further methodological developments, and future research are discussed.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"108 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178007","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

Improving the Use of Parallel Analysis by Accounting for Sampling Variability of the Observed Correlation Matrix. 通过考虑观测相关矩阵的抽样变异性改进平行分析的使用。

IF 2.1 3区心理学

Educational and Psychological Measurement Pub Date : 2024-08-20 DOI: 10.1177/00131644241268073

Yan Xia, Xinchang Zhou

引用次数: 0

Added Value of Subscores for Tests With Polytomous Items 多项式项目测试的子分数附加值

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-08-07 DOI: 10.1177/00131644241268128

Kylie Gorney, Sandip Sinharay

引用次数: 0

Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets 利用随机数据集评估神经网络在心理学研究中的预测可靠性

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-07-25 DOI: 10.1177/00131644241262964

Yongtian Cheng, K. V. Petrides

{"title":"Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets","authors":"Yongtian Cheng, K. V. Petrides","doi":"10.1177/00131644241262964","DOIUrl":"https://doi.org/10.1177/00131644241262964","url":null,"abstract":"Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship between ordinal independent variables and continuous or binary-dependent variables) can provide an acceptable level of predictive performance from a psychologist’s perspective. Through a Monte Carlo simulation study, we found that this kind of erroneous conclusion is not likely to be drawn as long as the sample size is larger than 50 with continuous-dependent variables. However, when the dependent variable is binary, the minimum sample size is 500 when the criteria are balanced accuracy ≥ .6 or balanced accuracy ≥ .65, and the minimum sample size is 200 when the criterion is balanced accuracy ≥ .7 for a decision error less than .05. In the case where area under the curve (AUC) is used as a metric, a sample size of 100, 200, and 500 is necessary when the minimum acceptable performance level is set at AUC ≥ .7, AUC ≥ .65, and AUC ≥ .6, respectively. The results found by this study can be used for sample size planning for psychologists who wish to apply neural networks for a qualitatively reliable conclusion. Further directions and limitations of the study are also discussed.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"39 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772234","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

Studying Factorial Invariance With Nominal Items: A Note on a Latent Variable Modeling Procedure 用名义项目研究因子不变量：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 : 2024-06-24 DOI: 10.1177/00131644241256626

Tenko Raykov

引用次数: 0

A Note on Evaluation of Polytomous Item Locations With the Rating Scale Model and Testing Its Fit 用评分量表模型评估多项式项目位置并测试其拟合度的说明

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-06-24 DOI: 10.1177/00131644241259026

Tenko Raykov, Martin Pusic

引用次数: 0

Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices 利用机器学习加强对社会可取性偏见的检测：拟人指数的新应用

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-05-30 DOI: 10.1177/00131644241255109

Sanaz Nazari, Walter L. Leite, A. Corinne Huggins-Manley

{"title":"Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices","authors":"Sanaz Nazari, Walter L. Leite, A. Corinne Huggins-Manley","doi":"10.1177/00131644241255109","DOIUrl":"https://doi.org/10.1177/00131644241255109","url":null,"abstract":"Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"2018 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188132","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

Is Effort Moderated Scoring Robust to Multidimensional Rapid Guessing? 努力程度调节评分对多维快速猜测是否稳健？

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-04-28 DOI: 10.1177/00131644241246749

Joseph A. Rios, Jiayi Deng

{"title":"Is Effort Moderated Scoring Robust to Multidimensional Rapid Guessing?","authors":"Joseph A. Rios, Jiayi Deng","doi":"10.1177/00131644241246749","DOIUrl":"https://doi.org/10.1177/00131644241246749","url":null,"abstract":"To mitigate the potential damaging consequences of rapid guessing (RG), a form of noneffortful responding, researchers have proposed a number of scoring approaches. The present simulation study examines the robustness of the most popular of these approaches, the unidimensional effort-moderated (EM) scoring procedure, to multidimensional RG (i.e., RG that is linearly related to examinee ability). Specifically, EM scoring is compared with the Holman–Glas (HG) method, a multidimensional scoring approach, in terms of model fit distortion, ability parameter recovery, and omega reliability distortion. Test difficulty, the proportion of RG present within a sample, and the strength of association between ability and RG propensity were manipulated to create 80 total conditions. Overall, the results showed that EM scoring provided improved model fit compared with HG scoring when RG comprised 12% or less of all item responses. Furthermore, no significant differences in ability parameter recovery and omega reliability distortion were noted when comparing these two scoring approaches under moderate degrees of RG multidimensionality. These limited differences were largely due to the limited impact of RG on aggregated ability (bias ranged from 0.00 to 0.05 logits) and reliability (distortion was ≤ .005 units) estimates when as much as 40% of item responses in the sample data reflected RG behavior.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810537","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

Comparing Accuracy of Parallel Analysis and Fit Statistics for Estimating the Number of Factors With Ordered Categorical Data in Exploratory Factor Analysis 比较平行分析和拟合统计在探索性因子分析中估计有序分类数据的因子数的准确性

IF 2.7 3区心理学

Educational and Psychological Measurement Pub Date : 2024-04-17 DOI: 10.1177/00131644241240435

Hyunjung Lee, Heining Cham

{"title":"Comparing Accuracy of Parallel Analysis and Fit Statistics for Estimating the Number of Factors With Ordered Categorical Data in Exploratory Factor Analysis","authors":"Hyunjung Lee, Heining Cham","doi":"10.1177/00131644241240435","DOIUrl":"https://doi.org/10.1177/00131644241240435","url":null,"abstract":"Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is to compare the parallel analysis with the performance of fit indices that researchers have started using as another strategy for determining the optimal number of factors in EFA. The Monte Carlo simulation was conducted with ordered categorical items because there are mixed results in previous simulation studies, and ordered categorical items are common in behavioral science. The results of this study indicate that the parallel analysis and the root mean square error of approximation (RMSEA) performed well in most conditions, followed by the Tucker–Lewis index (TLI) and then by the comparative fit index (CFI). The robust corrections of CFI, TLI, and RMSEA performed better in detecting misfit underfactored models than the original fit indices. However, they did not produce satisfactory results in dichotomous data with a small sample size. Implications, limitations of this study, and future research directions are discussed.","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616292","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