Practical Assessment, Research and Evaluation最新文献_第5页

Negatively-Worded Multiple Choice Questions: An Avoidable Threat to Validity. 消极措辞选择题:对效度的可避免威胁。

Practical Assessment, Research and Evaluation Pub Date : 2017-05-01 DOI: 10.7275/5VVY-8613

N. Chiavaroli

引用次数: 20

Constructing multiple-choice items to measure higher-order thinking 构建多项选择题来衡量高阶思维

Practical Assessment, Research and Evaluation Pub Date : 2017-05-01 DOI: 10.7275/CA7Y-MM27

Darina Scully

引用次数: 69

The Use of Reddit as an Inexpensive Source for High-Quality Data 使用Reddit作为高质量数据的廉价来源

Practical Assessment, Research and Evaluation Pub Date : 2017-01-01 DOI: 10.7275/SWGT-RJ52

Matthew R. Jamnik, D. J. Lane

{"title":"The Use of Reddit as an Inexpensive Source for High-Quality Data","authors":"Matthew R. Jamnik, D. J. Lane","doi":"10.7275/SWGT-RJ52","DOIUrl":"https://doi.org/10.7275/SWGT-RJ52","url":null,"abstract":"Today, researchers have the ability to conduct their investigations in a number of different manners, including both traditional testing using university subject pool participants and the more recent method of online recruitment. Although the use of internet participants is becoming more popular, this area of research is still very much in its infancy and needs further examination. Additionally, alternative web-based platforms need to be investigated because much of the literature has focused on using Amazon.com’s Mechanical Turk (MTurk). Therefore, the current study recruited an internet population using the website Reddit, and compared them to a traditional undergraduate sample to learn more about this web-based platform. The results demonstrated similarities and distinctions between the two samples. Furthermore, previous findings in the psychological well-being literature were replicated. As a whole, the participants recruited from Reddit provided high-quality data that were inexpensive and comparable to the responses gathered using undergraduate participants. We conclude that this website appears to be a promising tool for the field of psychological assessment, research, and evaluation.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90018097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

A note on using eigenvalues in dimensionality assessment 关于在维数评估中使用特征值的注释

Practical Assessment, Research and Evaluation Pub Date : 2017-01-01 DOI: 10.7275/E7GH-0785

Cengiz Zopluoglu, Ernest C Davenport

引用次数: 22

Investigating Causal DIF via Propensity Score Methods. 通过倾向评分方法调查因果DIF。

Practical Assessment, Research and Evaluation Pub Date : 2016-12-01 DOI: 10.7275/EWQZ-N963

Yan Liu, B. Zumbo, P. Gustafson, Yi Huang, Edward Kroc, Amery Wu

引用次数: 14

Partial Least Squares Structural Equation Modeling with R. 偏最小二乘结构方程建模。

Practical Assessment, Research and Evaluation Pub Date : 2016-09-01 DOI: 10.7275/D2FA-QV48

Hamdollah Ravand, Purya Baghaei

引用次数: 98

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests. 教育和认证考试中贝叶斯和逻辑回归子尺度概率的准确性。

Practical Assessment, Research and Evaluation Pub Date : 2016-07-01 DOI: 10.7275/Q7ZZ-D655

Lawrence M. Rudner

引用次数: 4

Regularization Methods for Fitting Linear Models with Small Sample Sizes: Fitting the Lasso Estimator Using R. 小样本量线性模型拟合的正则化方法:用R拟合Lasso估计量。

Practical Assessment, Research and Evaluation Pub Date : 2016-05-01 DOI: 10.7275/JR3D-CQ04

W. H. Finch, M. Finch

{"title":"Regularization Methods for Fitting Linear Models with Small Sample Sizes: Fitting the Lasso Estimator Using R.","authors":"W. H. Finch, M. Finch","doi":"10.7275/JR3D-CQ04","DOIUrl":"https://doi.org/10.7275/JR3D-CQ04","url":null,"abstract":"Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates exhibit very high variance and can therefore not be trusted, or because the statistical algorithm cannot converge on parameter estimates at all. There exist an alternative set of model estimation procedures, known collectively as regularization methods, which can be used in such circumstances, and which have been shown through simulation research to yield accurate parameter estimates. The purpose of this paper is to describe, for those unfamiliar with them, the most popular of these regularization methods, the lasso, and to demonstrate its use on an actual high dimensional dataset involving adults with autism, using the R software language. Results of analyses involving relating measures of executive functioning with a full scale intelligence test score are presented, and implications of using these models are discussed.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73574099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

A Comparison of Three Approaches to Correct for Direct and Indirect Range Restrictions: A Simulation Study. 三种直接和间接距离限制校正方法的比较:仿真研究。

Practical Assessment, Research and Evaluation Pub Date : 2016-03-01 DOI: 10.7275/X4EP-FV42

A. Pfaffel, Barbara Schober, C. Spiel

{"title":"A Comparison of Three Approaches to Correct for Direct and Indirect Range Restrictions: A Simulation Study.","authors":"A. Pfaffel, Barbara Schober, C. Spiel","doi":"10.7275/X4EP-FV42","DOIUrl":"https://doi.org/10.7275/X4EP-FV42","url":null,"abstract":"A common methodological problem in the evaluation of the predictive validity of selection methods, e.g. in educational and employment selection, is that the correlation between predictor and criterion is biased. Thorndike’s (1949) formulas are commonly used to correct for this biased correlation. An alternative approach is to view the selection mechanism as a missing data mechanism. The aim of this study was to compare Thorndike’s formulas for direct and indirect range restriction scenarios with two state-of-the-art approaches for handling missing data: full information maximum likelihood (FIML) and multiple imputation by chained equations (MICE). We conducted Monte-Carlo simulations to investigate the accuracy of the population correlation estimates in dependence of the selection ratio and the true population correlation in an experimental design. For a direct range restriction scenario, the three approaches are equally accurate. For an indirect range restriction scenario, the corrections using FIML and MICE are more precise than when using Thorndike’s formula. The higher the selection ratio and the true population correlation, the higher the precision of the population correlation estimates. Our findings indicate that both missing data approaches are alternative corrections to Thorndike’s formulas, especially in the case of indirect range restriction.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74165017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling. 效应大小的置信区间:应用自举重采样。

Practical Assessment, Research and Evaluation Pub Date : 2016-03-01 DOI: 10.7275/DZ3R-8N08

Erin S. Banjanovic, J. Osborne

引用次数: 70