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Finite Population Corrections For Two-Level Hierarchical Linear Models With Binary Predictors, Steven Svoboda

College of Education and Human Sciences: Dissertations, Theses, and Student Research

Answering social science research questions about clustered data necessitates collecting data using sampling schemes, which may result in hierarchical data structures. Hierarchical liner modeling (HLM) techniques are required to account for the interdependency of observations due to clustering. However, traditional HLM assumes the target population is infinitely large or near enough to infinitely large for practical purposes (i.e., the sample consists of less than 5% of the target population). Often times, the assumption of an infinitely large target population may not hold.

The current study was conducted in two separate phases using Monte Carlo simulation methods. First, the continuous predictors …