Pub Date : 1900-01-01DOI: 10.31523/glmj.044002.002
Kim F. Nimon
{"title":"appsl2lme: A Model-Selection Diagnostic Tool for Hierarchical Linear Models","authors":"Kim F. Nimon","doi":"10.31523/glmj.044002.002","DOIUrl":"https://doi.org/10.31523/glmj.044002.002","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"57 8 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":"133648184","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.045001.002
Fatimah Al-Abdullatif, Mohammed Al-Abdullatif
{"title":"MANOVA Post Hoc Techniques Used in Published Research Articles: A Systematic Review","authors":"Fatimah Al-Abdullatif, Mohammed Al-Abdullatif","doi":"10.31523/glmj.045001.002","DOIUrl":"https://doi.org/10.31523/glmj.045001.002","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"249 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":"132448223","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.045001.001
J. Williams
{"title":"Memories of Isadore Newman","authors":"J. Williams","doi":"10.31523/glmj.045001.001","DOIUrl":"https://doi.org/10.31523/glmj.045001.001","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"4 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":"116975352","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.047001.003
John Morris, Mary G. Lieberman
Amelioration of the perpetual difficulty students have upon encountering typical statistical methods introduced in a first statistics course, or combined into a research methodology course, is the objective of this effort. In addition, an evaluation of the proposed method is included.
{"title":"Integration of ANOVA and Multiple Regression for Beginning Statistics Students","authors":"John Morris, Mary G. Lieberman","doi":"10.31523/glmj.047001.003","DOIUrl":"https://doi.org/10.31523/glmj.047001.003","url":null,"abstract":"Amelioration of the perpetual difficulty students have upon encountering typical statistical methods introduced in a first statistics course, or combined into a research methodology course, is the objective of this effort. In addition, an evaluation of the proposed method is included.","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"8 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":"127933589","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.044002.004
Sebastian Moncaleano, L. Ludlow
{"title":"Career-Oriented Historic Events and Their Impact on Student Ratings: A Longitudinal Study","authors":"Sebastian Moncaleano, L. Ludlow","doi":"10.31523/glmj.044002.004","DOIUrl":"https://doi.org/10.31523/glmj.044002.004","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"6 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":"124462131","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.044002.001
Janet Holt
{"title":"In Memoriam: Isadore Newman","authors":"Janet Holt","doi":"10.31523/glmj.044002.001","DOIUrl":"https://doi.org/10.31523/glmj.044002.001","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"39 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":"133448159","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.047001.002
Mokshad Gaonkar, T. Beasley
Several tests for heteroscedasticity in a two-group between-subject variances were compared with a simulation study. Two common rank-based procedures inflated test size with skewed error distributions. Nonparametric Levene test performed well but has notable limitations. Tests based on the absolute value of OLS residuals also inflated test size with skewed error distributions. Procedures based on squared OLS residuals performed better; however, the original Breusch-Pagan and Variance Function Regression are sensitive to even slight departures from the normality assumption. The Brown-Forsythe test based on taking the absolute value of median centered data performed the best; however, generalization to more complex analyses would not be straightforward.
{"title":"Comparison of Tests for Heteroscedasticity in Between-Subject ANOVA Models","authors":"Mokshad Gaonkar, T. Beasley","doi":"10.31523/glmj.047001.002","DOIUrl":"https://doi.org/10.31523/glmj.047001.002","url":null,"abstract":"Several tests for heteroscedasticity in a two-group between-subject variances were compared with a simulation study. Two common rank-based procedures inflated test size with skewed error distributions. Nonparametric Levene test performed well but has notable limitations. Tests based on the absolute value of OLS residuals also inflated test size with skewed error distributions. Procedures based on squared OLS residuals performed better; however, the original Breusch-Pagan and Variance Function Regression are sensitive to even slight departures from the normality assumption. The Brown-Forsythe test based on taking the absolute value of median centered data performed the best; however, generalization to more complex analyses would not be straightforward.","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"8 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":"121193152","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.045001.004
T. Beasley
{"title":"Multiple R IS the Square Root of R2: Multiple Correlation Coefficient Using Matrix Formulation","authors":"T. Beasley","doi":"10.31523/glmj.045001.004","DOIUrl":"https://doi.org/10.31523/glmj.045001.004","url":null,"abstract":"","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"5 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":"124845265","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 determination of an appropriate sample size is a difficult, yet critically important, element in the research design process. Sample sizes in ANOVA are most often based on an overall standardized difference in the means
{"title":"For Post Hoc's Sake: Determining Sample Size for Tukey Multiple Comparisons in 4-Group ANOVA","authors":"G. Brooks, Q. An, Yanju Li, G. Johanson","doi":"10.31523/glmj.047001.00","DOIUrl":"https://doi.org/10.31523/glmj.047001.00","url":null,"abstract":"The determination of an appropriate sample size is a difficult, yet critically important, element in the research design process. Sample sizes in ANOVA are most often based on an overall standardized difference in the means","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"28 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":"133734570","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.044002.003
W. H. Finch
Comparison of Measurement Invariance Testing using Penalized Likelihood and Maximum Likelihood Estimators: A Monte Carlo Simulation Study W. Holmes Finch Ball State University Invariance testing remains a widely used and important issue for social scientists. At its heart, assessment of factor invariance involves an examination of the suitability of a scale’s use across an entire population. Traditionally, invariance testing has been carried out using a Chi-square difference test in conjunction with multiple group confirmatory factor analysis. However, research has demonstrated that this approach can result in inflated Type I error rates, or findings of a lack of invariance when in fact invariance is present. As a result, statisticians and methodologists have been investigating alternative approaches to testing invariance, which control the Type I error rate without sacrificing much in terms of power. The current study investigated one such alternative, based on a penalized likelihood estimator. This estimator has been previously investigated in the context of fitting structural equation models, and found to perform well in terms of parameter estimation accuracy. Results of the current Monte Carlo simulation study found that the PLE approach is in fact promising in the context of invariance assessment. It was able to control the Type I error rate better than did the Chi-square test, and it exhibited power rates that were as good as or better than those of the Chi-square. Implications of these findings are discussed. he invariance of latent variable models is an important issue in a wide variety of fields within the social sciences. Invariance refers to the case where latent variable model parameters, such as factor loadings, factor intercepts, or error variances, are equivalent across subgroups within the population. It is key for users of educational and psychological scales, as its presence allows for the use of such instruments with the entire population of interest. On the other hand, when invariance cannot be demonstrated, users of the scale cannot be certain that scores produced by it have the same meaning across subgroups, such as different ethnic groups, genders, or individuals with different socioeconomic status (Dorans, & Cook, 2016; Millsap, 2011; Wu, Li, & Zumbo, 2007). Thus, researchers who do plan to use scales with broad populations of individuals need to demonstrate scale invariance. The investigation of latent trait model parameter invariance typically involves the use of multiple groups confirmatory factor analysis (MGCFA). In this paradigm, the fit of models with, and without group equality constraints on the model parameters are compared, and if the fit of the models differs, we conclude that invariance does not hold (Millsap, 2011). Perhaps the most common statistical approach used in such invariance assessment involves the calculation of the Chi-square difference statistic, which is discussed in more detail below. However, r
{"title":"Comparison of Measurement Invariance Testing using Penalized Likelihood and Maximum Likelihood Estimators: A Monte Carlo Simulation Study","authors":"W. H. Finch","doi":"10.31523/glmj.044002.003","DOIUrl":"https://doi.org/10.31523/glmj.044002.003","url":null,"abstract":"Comparison of Measurement Invariance Testing using Penalized Likelihood and Maximum Likelihood Estimators: A Monte Carlo Simulation Study W. Holmes Finch Ball State University Invariance testing remains a widely used and important issue for social scientists. At its heart, assessment of factor invariance involves an examination of the suitability of a scale’s use across an entire population. Traditionally, invariance testing has been carried out using a Chi-square difference test in conjunction with multiple group confirmatory factor analysis. However, research has demonstrated that this approach can result in inflated Type I error rates, or findings of a lack of invariance when in fact invariance is present. As a result, statisticians and methodologists have been investigating alternative approaches to testing invariance, which control the Type I error rate without sacrificing much in terms of power. The current study investigated one such alternative, based on a penalized likelihood estimator. This estimator has been previously investigated in the context of fitting structural equation models, and found to perform well in terms of parameter estimation accuracy. Results of the current Monte Carlo simulation study found that the PLE approach is in fact promising in the context of invariance assessment. It was able to control the Type I error rate better than did the Chi-square test, and it exhibited power rates that were as good as or better than those of the Chi-square. Implications of these findings are discussed. he invariance of latent variable models is an important issue in a wide variety of fields within the social sciences. Invariance refers to the case where latent variable model parameters, such as factor loadings, factor intercepts, or error variances, are equivalent across subgroups within the population. It is key for users of educational and psychological scales, as its presence allows for the use of such instruments with the entire population of interest. On the other hand, when invariance cannot be demonstrated, users of the scale cannot be certain that scores produced by it have the same meaning across subgroups, such as different ethnic groups, genders, or individuals with different socioeconomic status (Dorans, & Cook, 2016; Millsap, 2011; Wu, Li, & Zumbo, 2007). Thus, researchers who do plan to use scales with broad populations of individuals need to demonstrate scale invariance. The investigation of latent trait model parameter invariance typically involves the use of multiple groups confirmatory factor analysis (MGCFA). In this paradigm, the fit of models with, and without group equality constraints on the model parameters are compared, and if the fit of the models differs, we conclude that invariance does not hold (Millsap, 2011). Perhaps the most common statistical approach used in such invariance assessment involves the calculation of the Chi-square difference statistic, which is discussed in more detail below. However, r","PeriodicalId":259786,"journal":{"name":"General Linear Model Journal","volume":"44 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":"128677668","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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