Type of Credit: Elective
Credit(s)
Number of Students
This advanced course in communication research methods aims to introduce students to multivariate analysis, focusing on the practical application of various statistical tests. The course emphasizes understanding the concepts and principles that underpin these analytical techniques.
Through years of teaching research methods and statistics, we have found that the most effective way to teach multivariate data analysis is by providing students with real-world data and guiding them to manipulate variables using various techniques. While this is not a pure statistics course, students are expected to have a basic understanding of elementary statistics and SPSS operations before enrolling.
The course is divided into three parts. The first part introduces the theory and underlying logic of multivariate methods. The second part focuses on specific techniques, including multiple regression, logistic regression, factor analysis, and discriminant analysis. The third part covers advanced topics such as path analysis and structural equation modeling.
能力項目說明
At the end of this semester students will learn to do the following:
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Weekly Schedule
Week 1 (2-18), Introduction to Multivariate Analysis and Summated Rating Scale Construction
Readings:
Food for thought
Readings:
Food for thought
Week 3 (3-4), Factor Analysis II
Readings:
Food for thought
Presentation #1
Week 4 (3-11), Final Project Preparation
Week 5 (3-18), Partial Correlation and Multiple Regression I
Readings:
Food for thought
Week 6 (3-25), Partial Correlation and Multiple Regression II
Readings:
Food for thought
Presentation #2
Week 7 (4-1), Logistic Regression I
Readings:
Food for thought
Week 8 (4-8), Logistic Regression II
Readings:
Food for thought
Presentation #3
Week 9 (4-15), Mid-term Examination
Week 10 (4-22), Path Analysis and the Logic of Causal Order
Readings:
Food for thought
Week 11 (4-29), Mediation and Moderation Analyses
Reading:
Food for thought
Presentation #4
Week 12 (5-6), Structural Equation Modeling I
Readings
Food for thought
Week 13 (5-13), Structural Equation Modeling II (SEM workshop)
Readings:
Food for thought
Week 14 (5-20), University Anniversary Celebration: No Classes Scheduled.
Week 15 (5-27), Structural Equation Modeling III (SEM Guest lecture)
To be arranged
Week 16 (6-3), Discriminant Analysis I
Readings:
Food for thought
Presentation #5
Week 17 (6-10), Final Examination
Week 18 (6-17), Final Project Preparation
Mid-term exam 30%
Final exam 30%
Assignments 15%
Final project 20%
Presentation 5%
Books
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (second edition). New York: Routledge.
Collier, E. (2020). Applied structural equation modeling using Amos: Basic to advanced techniques. New York, Routledge.
Hair, J, F. Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate data analysis: A global perspective (seventh edition). New Jersey: Pearson Education Inc.
Hayes, A. F. (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis. New York: Guilford Press.
Ho, R. (2013). Handbook of univariate and multivariate data analysis and interpretation with SPSS. New York: Chapman & Hall/CRC.
Jose, P. E. (2013). Doing statistical mediation & moderation. New York: Guilford Press.
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th edition). New York: Guilford Press.
Leech, N. L. (2014). IBM SPSS for intermediate statistics: Use and interpretation. New York: Psychology Press.
Spector, P. E. (1992). Summated rating scale construction: An introduction. Newbury Park, CA: Sage.
Journal Articles
Cohen, J. & Tsfati, Y. (2009). The influence of presumed media influence on strategic voting. Communication Research, 36(3), 339-378. (Logistic regression)
Drew, D., & Weaver, D. (2006). Voter learning in the 2004 presidential election: Did the media matter? Journalism Quarterly, 83 (1): 25-42. (Multiple regression)
Eveland, W. (2002). News information processing as mediator of the relationship between motivations and political knowledge. Journalism & Mass Communication Quarterly, 79, 26-40. (Path analysis)
Gunther, A., & Storey, D. (2003). The influence of presumed influence. Journal of Communication, 53 (2), 199-215. (SEM)
Lin, C. (2006). Predicting satellite radio adoption via listening motives, activity, and format preference. Journal of Broadcasting and Electronic Media, 50(1): 140-159. (Discriminant analysis)