Course Syllabus

Communication Studies 783

Research Methods II – Quantitative Methods for Research on Mass Media 

Syllabus, Fall 2016

Instructor: Prof. Stuart Soroka, Office Rm. 5388 North Quad, ssoroka@umich.edu.

Objectives: COMM 781 and 783 are the first courses in research methodology and statistics in the Communication Studies PhD program. This class, 783, is intended as a primer in social science research methods and statistical analysis. For students whose work relies primarily on qualitative methods, this term provides the tools required to discuss and evaluate social scientific work in communication studies. For those working mainly with social scientific methods, this is just the beginning of your training, particularly in statistical methods.

The class is set up in a way that links (a) substantive communication studies topics, (b) issues in research methods and design, and (c) statistical analysis. The degree to which we focus on one or the other of these varies by week. Even so, the typical week involves at least some reading on either statistics or research design, a discussion of those readings, an in-class lab on statistical techniques, and a statistical assignment due the following week. The class meets for 2 hours twice a week. Typically, the first class on a topic is focused more on a discussion of methods and statistics, and the other(s) focus more on applying what we’ve learned.

Note that topics 2-6 (below) focus mainly on statistical topics. These weeks will be pretty intense. Topics 8-11 focus on research design, however, and we will use these weeks to both connect research design with statistics, and review and practice the statistical material we learn in the first half of the course.

Software: Students are strongly encouraged to bring laptops to class. This is not a requirement, but it is generally useful to not just to watch but also do what we talk about in class; and about half our class time will be dedicated to statistical analysis.

We will be exploring data, in ways that hopefully illustrate the material in the text, using R. You will need to have installed R on a computer before class begins. To do so, please visit this website: http://www.r-project.org. R itself is not especially user-friendly, so after installing R, I strongly recommend that you install RStudio, which you get here: http://www.rstudio.com. Both R and RStudio are free, and work on most operating systems.

There is a 6-hour statistics/R ‘prep’ class, taught by Dan Hiaeshutter-Rice, during the first week of the term. This is not required, but it is very strongly recommended. We will leap into work with R in the second week of class the assumption that students have installed and are loosely familiar with R.

Course Design & Readings: Readings will be drawn from a combination of textbooks and journal articles. We rely heavily on the following texts, enough that I strongly recommend that you add the first to your library.  Both are readily available from Amazon, and elsewhere, and the second is also available digitally through the library.

John Fox. 2016. Applied Regression Analysis and Generalized Linear Models, 3^rd ed. Sage.

Nathan Yau. 2011. Visualize This: The FlowingData Guide to Design, Visualization and Statistics. Wiley. Available electronically through the Wiley Online Library.

I also recommend the following as an R resource, which is nicely in line (sort of) with the Fox text listed above.  We'll be using our own scripts in class, but this book has a good number of useful examples:

John Fox and Sanford Weisberg. 2011. An R Companion to Applied Regression, 2^nd ed. Sage.

We also rely in part on the 'little green books' from Sage, which are available online through Sage ResearchMethods; and a combination of journal articles and book chapters, which will be made available through Canvas.

Throughout the course, I encourage you to take advantage of online resources on both statistics and R. On statistics, consider:

StatSoft Electronic Textbook, http://http//www.statsoft.com/textbook/

HyperStat Online, http://davidmlane.com/hyperstat/

And on R, you can find answers to almost all your questions (though you can ask me too) at:

Verzani’s, simpleR, https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf

Venables et al., An Introduction to R, https://cran.r-project.org/doc/manuals/R-intro.pdf

Comprehensive R Archive, http://cran.r-project.org/

R Tutor, http://www.r-tutor.com/

Quick R, http://www.statmethods.net/

There are also course notes, distributed through Canvas, and intended to offer brief summaries of some of the statistical issues each week.  These are not included in the readings.

All seminars will proceed on the assumption that students have read all required readings beforehand, and participation grades are determined based on students’ discussion, command and critique of both methods and issue-related readings. You also should arrive at class having downloaded the appropriate dataset. These datasets will be used to apply what we’ve learned, through replication of analyses in weekly readings, or the production of new analyses, both in class and in weekly assignments.

Note that the readings below are just a start - I will be adding to the readings, and adding datasets, as the term progresses.  This allows me to tailor the course (a little) to student interests.  Those interests will be the focus of our first meeting; and the course syllabus will fall into place relatively quickly after that.

Requirements: 1. Assignments will comprise 70% of your grade. All assignments must be submitted electronically, in pdf format, though Canvas. There will be one assignment for topics 2 through 11 – nearly weekly, though with some exceptions. Datasets for assignments, and in-class work, will be posted online as the class progresses.  The other 30% of your grade will be based on your participation in the seminar. Lateness in assignments, or in attendance, will not be tolerated; which is to say that participation and/or assignment grades will be penalized accordingly.

Schedule: The schedule on the following pages is preliminary, and subject to change over the term based on student abilities and interests.  Consider these readings a starting point.  Once I’ve a good sense for the students in the class, I will be adding and adjusting the readings below.  In the meantime, I’ve listing preliminary readings below in the order in which I think they should read.

1. Philosophies of Research in the Social Sciences (Sept 6, 8)

• There are no readings for the class on the 6th, but read these for September 8th:
• Brian Fay and J. Donald Moon. “What Would an Adequate Philosophy of Social Science Look Like?” Ch 2 in Michael Martin and Lee C. McIntyre, eds., Reading in the Philosophy of Social Science (MIT Press, 1994).
• Simon, H. A. “On judging the plausibility of theories” (pp. 25-48). In H. A. Simon, Models of Discovery (Dordrecht, Holland: D. Reidel Publishing, 1977).

2. Descriptive Statistics (Sept 13, 15)

• Sean Gailmard. 2014. Statistical Modeling and Inference for Social Science (New York: Cambridge University Press). Chapter 2, pages 21-43.
• Also see Variance, Covariance and Correlation in class notes.

3. Measurement, Reliability, Validity, and Indices (Sept 20, 22)

• Gailmard, Statistical Modeling and Inference for Social Science. Chapter 2, pages 12-21, and Chapter 3.
• Edward Carmines and Richard Zeller. 1979. Reliability and Validity Assessment, Sage, Chapters 1, 2 and 4. Available through Sage
• Kristen Harrison and Veronica Hefner. 2014. “Virtually Perfect: Image Retouching and Adolescent Body Image,” Media Psychology 17: 1-20.
• Sara Lindberg et al. 2006. “A Measure of Objectified Body Consciousness for Preadolescent and Adolescent Youth.” Psychology of Women Quarterly 30: 65-76.
• Also see Factor Analysis in class notes.

4. Ordinary Linear Least-Squares Regression (Sept 27, 29)

• John Fox, Applied Regression Analysis, Chapters 2 & 5.
• supplementary (not required, but helpful): Gailmard, Statistical Modeling and Inference for Social Science. Chapter 2, pages 43-61.
• Jan Van den Bulck. 2004. "Television Viewing, Computer Game Playing, and Internet Use and Self-Reported Time to Bed and Time out of Bed in Secondary-School Children." SLEEP 27(1): 101-104.

5. Analysis of Variance (Oct 4, 6)

• Fox, Applied Regression Analysis, Chapter 8 (esp 8.1 and 8.2).
• supplementary (not required, but helpful): Fox, Applied Regression Analysis, Chapter 7.
• James D. Ivory and Sriram Kalyanaraman. 2007. "The Effects of Technological Advancement and Violent Content in Video Games on Players' Feelings of Presence, Involvement, Physiological Arousal, and Aggression." Journal of Communication 57: 532-555.

6. Complexities in OLS Regression (Oct 11, 13,  20)

• Fox, Applied Regression Analysis, Chapters 7.3, 11-13.
• Scott W. Campbell and Nojin Kwak. 2011. “Political Involvement in ‘‘Mobilized’’ Society: The Interactive Relationships Among Mobile Communication, Network Characteristics, and Political Participation.” Journal of Communication 61: 1005-1024. (Individual-level survey data)

7. Data Visualization (Oct 25, 27)

• Fox, Applied Regression Analysis, Chapter 3.
• Tufte, The Visual Display of Quantitative Information.
• Yau, Visualize This, Chapters 1, 3-8.
• TBA

8. Content Analysis (Nov 1, 8, 15)

• Klaus Krippendorff and Mary Angela Bock, eds., The Content Analysis Reader Sage, 2009, Chapters 1.2, 1.7, 2.3, 2.4, 2.7, 3.4, 3.9, 5.8, 7.7.
• Sonya Dal Cin et al. 2008. “Youth exposure to alcohol use and brand appearances in popular contemporary movies,” Addiction 103: 1925-1932.
• TBA

9. Sampling & Survey Research (Nov 17, 22)

• Suman Mishra. 2014.”Doing Survey Research in Media Studies.” The International Encyclopedia of Media Studies, First Edition.
• TBA

10. Experimental Design (Nov 29, Dec 1)

• Leshner, Glenn. 2014. “The Basics of Experimental Research in Media Studies.” The International Encyclopedia of Media Studies, First Edition.
• Druckman, J. N., D. P. Green, J. H. Kuklinski, and A. Lupia, eds. 2011. Cambridge handbook of experimental political science. New York: Cambridge Univ. Press. Introduction.
• TBA

11. Review Dec 6,8