Pub Date : 1900-01-01DOI: 10.31523/glmj.046001.003
Brenna Curley, Debra Wetcher-Hendricks
{"title":"Correction for Attenuation of the Multiple Correlation Coefficient Given Non-Independent Error Scores","authors":"Brenna Curley, Debra Wetcher-Hendricks","doi":"10.31523/glmj.046001.003","DOIUrl":"https://doi.org/10.31523/glmj.046001.003","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"400 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114226800","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}
Pub Date : 1900-01-01DOI: 10.31523/glmj.046001.004
R. Schumacker, Lauren F. Holmes
A true experimental design requires random selection and random assignment of subjects to control and experimental groups. A hypothesized statistically significant mean difference in the dependent variable between these two groups is typically specified. This methodology is also referred to as a randomized clinical trial when testing for group differences. Individual differences are generally not considered, rather the focus is on the average control group and experimental group difference. This article offers another approach that illustrates testing for individual differences over time. h e true experimental design conducts a test of control group versus experimental group average dependent variable difference using analysis of variance statistical tests (Maxwell & Delaney, 2004). This methodology is also referred to as a randomized clinical trial when testing for group mean differences (Machin & Fayers, 2010). Oftentimes a true experimental design is not possible, so the researcher uses a quasi-experimental design. A quasi-experimental design uses a comparison group rather than a control group. The typical quasi-experimental design considers a pre-test measure, followed by treatment, and then a similar post-test measure for the subjects in the comparison group and the experimental group. In the statistical analysis, individual post-test measure differences are adjusted for individual pre-test measure differences to control for bias. This adjustment is referred to as analysis of covariance and expressed in the general linear model as: are
{"title":"Testing Individual vs Group Mean Differences in Social Science Research","authors":"R. Schumacker, Lauren F. Holmes","doi":"10.31523/glmj.046001.004","DOIUrl":"https://doi.org/10.31523/glmj.046001.004","url":null,"abstract":"A true experimental design requires random selection and random assignment of subjects to control and experimental groups. A hypothesized statistically significant mean difference in the dependent variable between these two groups is typically specified. This methodology is also referred to as a randomized clinical trial when testing for group differences. Individual differences are generally not considered, rather the focus is on the average control group and experimental group difference. This article offers another approach that illustrates testing for individual differences over time. h e true experimental design conducts a test of control group versus experimental group average dependent variable difference using analysis of variance statistical tests (Maxwell & Delaney, 2004). This methodology is also referred to as a randomized clinical trial when testing for group mean differences (Machin & Fayers, 2010). Oftentimes a true experimental design is not possible, so the researcher uses a quasi-experimental design. A quasi-experimental design uses a comparison group rather than a control group. The typical quasi-experimental design considers a pre-test measure, followed by treatment, and then a similar post-test measure for the subjects in the comparison group and the experimental group. In the statistical analysis, individual post-test measure differences are adjusted for individual pre-test measure differences to control for bias. This adjustment is referred to as analysis of covariance and expressed in the general linear model as: are","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116236046","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}
Pub Date : 1900-01-01DOI: 10.31523/glmj.045001.003
W. H. Finch
{"title":"A Comparison of Clustering Methods when Group Sizes are Unequal, Outliers are Present, and in the Presence of Noise Variables","authors":"W. H. Finch","doi":"10.31523/glmj.045001.003","DOIUrl":"https://doi.org/10.31523/glmj.045001.003","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115172482","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}
Pub Date : 1900-01-01DOI: 10.31523/glmj.046001.001
W. H. Finch
{"title":"SEM Estimation in the Context of Small Samples: Comparison of Latent Variable Models, Single Indicator, Regularized 2-Stage Least Squares and Observed Variable Models","authors":"W. H. Finch","doi":"10.31523/glmj.046001.001","DOIUrl":"https://doi.org/10.31523/glmj.046001.001","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123569417","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}
Pub Date : 1900-01-01DOI: 10.31523/glmj.046001.002
Pornchanok Ruengvirayudh, Gordon Brooks
{"title":"Sample Size for Parallel Analysis and Not-So-Common Criteria for Dimensions in Factor Analysis: Modifying the Eigenvalue > 1 Kaiser Rule","authors":"Pornchanok Ruengvirayudh, Gordon Brooks","doi":"10.31523/glmj.046001.002","DOIUrl":"https://doi.org/10.31523/glmj.046001.002","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131695201","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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