Winson Peng, Ph.D.

Winson Peng, Ph.D.

Professor of Communication

Communication Research Design II

Graduate course, Michigan State University, Department of Communication, 2023

This course is the second in the Ph.D. statistics sequence at the Department of Communication, MSU. It has a prerequisite of COM 901 or its equivalent. It is a four-credit class. The course aims to provide PhD students with a working knowledge of the assumptions, concepts, and theories underlying the most frequently used multivariate analysis techniques in quantitative social and behavioral sciences. These techniques include, but are not limited to, multiple regression, exploratory and confirmatory factor analysis, path analysis, structural equation modelling (SEM), multilevel analysis, and time series analysis. The selection of specific topics may be tailored to students’ research needs. The focus will be on practical issues such as selecting the appropriate analysis, preparing data for analysis in the popular statistical tools (e.g., SPSS, AMOS, or R), interpreting output, and presenting results of a complex nature. This course is a mixture of lectures, discussions, and hands-on practices.

Textbook

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates.
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.
  • Babbie, E. R. (2016). The practice of social research (14th ed.). Nelson Education.

Weekly Topics

  • Basics in Quantitative Research: A Review
  • From Correlation to Regression
  • Mediation and Moderation Effects
  • Regression Analysis with Categorical Variables
  • Structural Equation Modeling (SEM): Theory and Development
  • From Exploratory to Confirmatory Factor Analysis
  • From Path Analysis to Structural Models: Specification, Identification, Estimation and Assessment in SEM
  • Advanced Measurement Models: Second-order Factor Analysis and MTMM
  • Measurement Equivalence in SEM and Multi-Sample Analysis
  • Mean Structure and Latent Growth Models in SEM
  • Multi-level Research and Hierarchical Linear Models
  • Panel Data Analysis