General Linear Model Journal最新文献_第2页

Testing Individual vs Group Mean Differences in Social Science Research 检验社会科学研究中的个体与群体平均差异

General Linear Model Journal Pub Date : 1900-01-01 DOI: 10.31523/glmj.046001.004

R. Schumacker, Lauren F. Holmes

{"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}

引用次数: 0

Comparison of Measurement Invariance Testing using Penalized Likelihood and Maximum Likelihood Estimators: A Monte Carlo Simulation Study 惩罚似然估计与极大似然估计测量不变性检验的比较:蒙特卡罗模拟研究

General Linear Model Journal Pub Date : 1900-01-01 DOI: 10.31523/glmj.044002.003

W. H. Finch

{"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}

引用次数: 0

SEM Estimation in the Context of Small Samples: Comparison of Latent Variable Models, Single Indicator, Regularized 2-Stage Least Squares and Observed Variable Models 小样本背景下的SEM估计:潜在变量模型、单指标、正则化2阶段最小二乘和观测变量模型的比较

General Linear Model Journal Pub Date : 1900-01-01 DOI: 10.31523/glmj.046001.001

W. H. Finch

引用次数: 0

A Comparison of Clustering Methods when Group Sizes are Unequal, Outliers are Present, and in the Presence of Noise Variables 分组大小不等、存在异常值和存在噪声变量时聚类方法的比较

General Linear Model Journal Pub Date : 1900-01-01 DOI: 10.31523/glmj.045001.003

W. H. Finch

引用次数: 1

Sample Size for Parallel Analysis and Not-So-Common Criteria for Dimensions in Factor Analysis: Modifying the Eigenvalue > 1 Kaiser Rule 平行分析的样本量和因子分析中不常见的维度标准:修改特征值> 1 Kaiser规则

General Linear Model Journal Pub Date : 1900-01-01 DOI: 10.31523/glmj.046001.002

Pornchanok Ruengvirayudh, Gordon Brooks

引用次数: 0