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Direct linkEddy, Colleen L.; Huang, Francis L.; Cohen, Daniel R.; Baker, Kirsten M.; Edwards, Krista D.; Herman, Keith C.; Reinke, Wendy M. – School Psychology Review, 2020
Teacher emotional factors influence the classroom environment. The purpose of the study was to examine the association of teacher emotional exhaustion and teacher efficacy with student office discipline referrals (ODRs), in-school suspensions (ISSs), and out-of-school suspensions (OSSs) using multilevel logistic regression models. The sample…
Descriptors: Psychological Patterns, Fatigue (Biology), Self Efficacy, Predictor Variables
Download full textEddy, Colleen L.; Huang, Francis L.; Cohen, Daniel R.; Baker, Kirsten M.; Edwards, Krista D.; Herman, Keith C.; Reinke, Wendy M. – Grantee Submission, 2020
Teacher emotional factors influence the classroom environment. The purpose of the study was to examine the association of teacher emotional exhaustion and teacher efficacy with student office discipline referrals (ODRs), in-school suspensions (ISSs), and out-of-school suspensions (OSSs) using multilevel logistic regression models. The sample…
Descriptors: Psychological Patterns, Fatigue (Biology), Self Efficacy, Predictor Variables
Huang, Francis L. – School Psychology Quarterly, 2018
The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their…
Descriptors: Hierarchical Linear Modeling, School Psychology, Misconceptions, Correlation
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
Huang, Francis L.; Cornell, Dewey G. – Journal of Psychoeducational Assessment, 2016
Advances in multilevel modeling techniques now make it possible to investigate the psychometric properties of instruments using clustered data. Factor models that overlook the clustering effect can lead to underestimated standard errors, incorrect parameter estimates, and model fit indices. In addition, factor structures may differ depending on…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Factor Structure, Measures (Individuals)
Huang, Francis L. – Journal of Experimental Education, 2016
Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Sample Size, Error of Measurement
