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Baek, Eunkyeng; Luo, Wen; Henri, Maria – Journal of Experimental Education, 2022
It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Comparative Analysis, Statistical Inference
Huang, Francis L. – Journal of Experimental Education, 2018
Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…
Descriptors: Hierarchical Linear Modeling, Least Squares Statistics, Regression (Statistics), Comparative Analysis
King, Ronnel B. – Journal of Experimental Education, 2017
Students have various social reasons for doing well in school (social-academic goals). However, most studies have focused on competence-oriented achievement goals with little attention paid to social-academic goals. This study aims to examine the role of social-academic goals in students' general well-being (Study 1) and socioemotional functioning…
Descriptors: Well Being, Goal Orientation, Academic Achievement, Social Responsibility
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data