In two subsequent experiments, the influence of mood on academic course evaluation is examined. By means of facial feedback, either a positive or a negative mood was induced while students were completing a course evaluation questionnaire during lectures. Results from both studies reveal that a positive mood leads to better ratings of different dimensions of lecture quality. While in Study 1 (N=109) mood was not directly controlled, Study 2 (N=64) replicates the findings of the prior study and reveals direct influences of positive and negative mood on academic course evaluation.
{"title":"Influences of Mood on Academic Course Evaluations.","authors":"Joerg Zumbach, J. Funke","doi":"10.7275/VBCZ-H361","DOIUrl":"https://doi.org/10.7275/VBCZ-H361","url":null,"abstract":"In two subsequent experiments, the influence of mood on academic course evaluation is examined. By means of facial feedback, either a positive or a negative mood was induced while students were completing a course evaluation questionnaire during lectures. Results from both studies reveal that a positive mood leads to better ratings of different dimensions of lecture quality. While in Study 1 (N=109) mood was not directly controlled, Study 2 (N=64) replicates the findings of the prior study and reveals direct influences of positive and negative mood on academic course evaluation.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83752412","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}
Copyright is retained by the authors' employer, the National Board of Medical Examiners, which grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.
{"title":"Editorial Changes and Item Performance: Implications for Calibration and Pretesting.","authors":"Heather Stoffel, M. Raymond, S. Bucak, S. Haist","doi":"10.7275/YN9J-QN49","DOIUrl":"https://doi.org/10.7275/YN9J-QN49","url":null,"abstract":"Copyright is retained by the authors' employer, the National Board of Medical Examiners, which grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78047711","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}
Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations of treatment effects is propensity score analysis. This method reduces a large number of pretreatment covariates to a single scalar function and allows researchers to compare subjects with similar probability to receive the treatment. This article provides an introduction to propensity score analysis and stratification, an example illustrating its use, and suggestions for using propensity score analysis in educational research.
{"title":"Educational Research with Real-World Data: Reducing Selection Bias with Propensity Scores.","authors":"J. Adelson","doi":"10.7275/4NR3-NK33","DOIUrl":"https://doi.org/10.7275/4NR3-NK33","url":null,"abstract":"Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations of treatment effects is propensity score analysis. This method reduces a large number of pretreatment covariates to a single scalar function and allows researchers to compare subjects with similar probability to receive the treatment. This article provides an introduction to propensity score analysis and stratification, an example illustrating its use, and suggestions for using propensity score analysis in educational research.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88020455","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}
Propensity score applications are often used to evaluate educational program impact. However, various options are available to estimate both propensity scores and construct comparison groups. This study used a student achievement dataset with commonly available covariates to compare different propensity scoring estimation methods (logistic regression, boosted regression, and Bayesian logistic regression) in combination with different methods for constructing comparison groups (nearest-neighbor matching, optimal matching, weighting) relative to balancing pre-existing differences and recovering a simulated treatment effect in small samples. Results indicated that applied researchers evaluating program impact should first consider use of standard logistic regression methods with nearest-neighbor or optimal matching or boosted regression in combination with propensity score weighting. Advantages and disadvantages of the methods are discussed.
{"title":"Comparing Propensity Score Methods in Balancing Covariates and Recovering Impact in Small Sample Educational Program Evaluations.","authors":"Clement A. Stone, Yun Tang","doi":"10.7275/QKQA-9K50","DOIUrl":"https://doi.org/10.7275/QKQA-9K50","url":null,"abstract":"Propensity score applications are often used to evaluate educational program impact. However, various options are available to estimate both propensity scores and construct comparison groups. This study used a student achievement dataset with commonly available covariates to compare different propensity scoring estimation methods (logistic regression, boosted regression, and Bayesian logistic regression) in combination with different methods for constructing comparison groups (nearest-neighbor matching, optimal matching, weighting) relative to balancing pre-existing differences and recovering a simulated treatment effect in small samples. Results indicated that applied researchers evaluating program impact should first consider use of standard logistic regression methods with nearest-neighbor or optimal matching or boosted regression in combination with propensity score weighting. Advantages and disadvantages of the methods are discussed.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88382067","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}
The semantics, or meaning, of validity is a fluid concept in educational and psychological testing. Contemporary controversies surrounding this concept appear to stem from the proper location of validity. Under one view, validity is a property of score-based inferences and entailed uses of test scores. This view is challenged by the instrument-based approach, which contends that tests themselves are either valid or invalid. These perspectives are contrasted by their ontological and epistemological emphases, as well as their breadth of validation focus. Ontologically, these positions diverge in their alliance with psychometric realism, or the position that attributes characterizing the aim of psychological and educational measurement exist in the actual world and that claims about their existence can be justified. Epistemologically, these positions deviate in the function of truth when accepting validity claims and inform distinct lines of inquiry in the validation process. Finally, validity under the instrument-based approach is restricted to a single proposition –namely, that observed score variation is caused by an underlying attribute. Though seemingly arbitrary, these distinct validity semantics may have a range of implications on assessment practices.
{"title":"Validity Semantics in Educational and Psychological Assessment","authors":"John D. Hathcoat","doi":"10.7275/AY6P-XW09","DOIUrl":"https://doi.org/10.7275/AY6P-XW09","url":null,"abstract":"The semantics, or meaning, of validity is a fluid concept in educational and psychological testing. Contemporary controversies surrounding this concept appear to stem from the proper location of validity. Under one view, validity is a property of score-based inferences and entailed uses of test scores. This view is challenged by the instrument-based approach, which contends that tests themselves are either valid or invalid. These perspectives are contrasted by their ontological and epistemological emphases, as well as their breadth of validation focus. Ontologically, these positions diverge in their alliance with psychometric realism, or the position that attributes characterizing the aim of psychological and educational measurement exist in the actual world and that claims about their existence can be justified. Epistemologically, these positions deviate in the function of truth when accepting validity claims and inform distinct lines of inquiry in the validation process. Finally, validity under the instrument-based approach is restricted to a single proposition –namely, that observed score variation is caused by an underlying attribute. Though seemingly arbitrary, these distinct validity semantics may have a range of implications on assessment practices.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81425754","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}
Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.
{"title":"Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations","authors":"Matthew Courtney","doi":"10.7275/9CF5-2M72","DOIUrl":"https://doi.org/10.7275/9CF5-2M72","url":null,"abstract":"Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83839030","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}
Forest plots provide an effective means of presenting a wealth of information in a single graphic. Whether used to illustrate multiple results in a single study or the cumulative knowledge of an entire field, forest plots have become an accepted and generally understood way of presenting many estimates simultaneously. This article explores advanced uses of the forest plot with the intent of highlighting the flexibility of Excel in generating both simple and complex forest plots. A step-by-step tutorial is included with specific directions for generating a stratified forest plot and general suggestions for modifying the forest plot to meet the user’s specific needs.
{"title":"Forest Plots in Excel: Moving beyond a Clump of Trees to a Forest of Visual Information.","authors":"James H Derzon, Aaron A. Alford","doi":"10.7275/96VM-5C74","DOIUrl":"https://doi.org/10.7275/96VM-5C74","url":null,"abstract":"Forest plots provide an effective means of presenting a wealth of information in a single graphic. Whether used to illustrate multiple results in a single study or the cumulative knowledge of an entire field, forest plots have become an accepted and generally understood way of presenting many estimates simultaneously. This article explores advanced uses of the forest plot with the intent of highlighting the flexibility of Excel in generating both simple and complex forest plots. A step-by-step tutorial is included with specific directions for generating a stratified forest plot and general suggestions for modifying the forest plot to meet the user’s specific needs.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88904843","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}
Amy S. Beavers, J. Lounsbury, J. Richards, S. Huck, Gary J. Skolits, S. L. Esquivel
Recommended Citation Beavers, Amy S.; Lounsbury, John W.; Richards, Jennifer K.; Huck, Schuyler W.; Skolits, Gary J.; and Esquivel, Shelley L. (2013) "Practical Considerations for Using Exploratory Factor Analysis in Educational Research," Practical Assessment, Research, and Evaluation: Vol. 18 , Article 6. DOI: https://doi.org/10.7275/qv2q-rk76 Available at: https://scholarworks.umass.edu/pare/vol18/iss1/6
《海狸》，艾米·S.;约翰·w·朗斯伯里;理查兹，詹妮弗·k;哈克，斯凯勒w;Gary J. Skolits;谢莉·l·埃斯奎维尔(2013)“在教育研究中使用探索性因素分析的实际考虑”，《实践评估、研究与评价》，第18卷，第6条。DOI: https://doi.org/10.7275/qv2q-rk76可在:https://scholarworks.umass.edu/pare/vol18/iss1/6
{"title":"Practical Considerations for Using Exploratory Factor Analysis in Educational Research.","authors":"Amy S. Beavers, J. Lounsbury, J. Richards, S. Huck, Gary J. Skolits, S. L. Esquivel","doi":"10.7275/QV2Q-RK76","DOIUrl":"https://doi.org/10.7275/QV2Q-RK76","url":null,"abstract":"Recommended Citation Beavers, Amy S.; Lounsbury, John W.; Richards, Jennifer K.; Huck, Schuyler W.; Skolits, Gary J.; and Esquivel, Shelley L. (2013) \"Practical Considerations for Using Exploratory Factor Analysis in Educational Research,\" Practical Assessment, Research, and Evaluation: Vol. 18 , Article 6. DOI: https://doi.org/10.7275/qv2q-rk76 Available at: https://scholarworks.umass.edu/pare/vol18/iss1/6","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79258545","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}
Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.
{"title":"Classroom Test Construction: The Power of a Table of Specifications.","authors":"Helenrose Fives, Nicole DiDonato-Barnes","doi":"10.7275/CZTT-7109","DOIUrl":"https://doi.org/10.7275/CZTT-7109","url":null,"abstract":"Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74766019","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}
{"title":"Factor Analysis using R","authors":"A Alexander Beaujean","doi":"10.7275/Z8WR-4J42","DOIUrl":"https://doi.org/10.7275/Z8WR-4J42","url":null,"abstract":"","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74775438","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}
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