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Full-Text Articles in Social and Behavioral Sciences
Factors That Influence Cross-Validation Of Hierarchical Linear Models, Tracy Widman
Factors That Influence Cross-Validation Of Hierarchical Linear Models, Tracy Widman
Educational Policy Studies Dissertations
While use of hierarchical linear modeling (HLM) to predict an outcome is reasonable and desirable, employing the model for prediction without first establishing the model’s predictive validity is ill-advised. Estimating the predictive validity of a regression model by cross-validation has been thoroughly researched, but there is a dearth of research investigating the cross-validation of hierarchical linear models. One of the major obstacles in cross-validating HLM is the lack of a measure of explained variance similar to the squared multiple correlation coefficient in regression analysis.
The purpose of this Monte Carlo simulation study is to explore the impact of sample size, …
Sample Size In Ordinal Logistic Hierarchical Linear Modeling, Allison M. Timberlake
Sample Size In Ordinal Logistic Hierarchical Linear Modeling, Allison M. Timberlake
Educational Policy Studies Dissertations
Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study.
Much research has been conducted on the impact of …