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Hierarchical Linear Modeling


3.0 Credit, 3 Lecture, 0 Lab
Conceptual and applied processes in hierarchical linear modeling with cross sectional nested data and longitudinal repeated measures data.

Understand basic concepts.

Understand the basic concepts and notational conventions used in multilevel modeling (e.g., nested levels of analysis and within-level dependencies; intercepts, slopes, and residuals; within-level versus between-level variance; intraclass correlation coefficients; conditional versus unconditional models; fixed versus random model components; within-level versus cross-level interactions; cross-sectional versus longitudinal designs; time-varying versus time invariant predictors; growth trajectories; etc).

Demonstrate proficiency.

Demonstrate proficiency in using multilevel software (e.g., SPSS, HLM, Mplus, or SAS) to analyze hierarchically structured data including (a) preparing the data files, (b) generating the input commands, (c) executing an analysis, and (d) interpreting and evaluating the output.

Apply appropriate strategies.

Apply appropriate strategies to analyze hierarchically-structured data sets by building and testing alternative models (representing both cross-sectional and longitudinal designs) and prepare written reports of the results.


Design an original, multilevel study including a description of (a) the purpose of the research, (b) the primary questions of hypotheses to be addressed, (c) procedures for collecting relevant data, and (d) appropriate strategies for analyzing the data.

Summarize, interpret, and critique

Summarize, interpret, and critique written reports of completed research studies that used multilevel modeling.