Cjt 765: Special Topics in Quantitative Communication

CJT 765: SPECIAL TOPICS IN QUANTITATIVE COMMUNICATION RESEARCH METHODS: STRUCTURAL EQUATION MODELING Spring, 2007

Professor: Rick Zimmerman Office: 245 Grehan Phone: 257-4099 Office hours: Mon. 11:45-12:45 and by appointment Classroom: B35 W.T. Young Library Class meeting hours: Mon. 1-3:30; labs 3:45 – 4:45 p.m. Monday, and TBA B35 W.T. Young Library Teaching Assistant: Olga Dekhtyar TA’s office: 308 Breckinridge Hall TA’s phone number: 257-8133 TA’s office hours: TBA

Course Philosophy

While for most students, some of the material will be review, the course will be taught at the graduate student level. That is, students are expected to have a level of commitment to the course well beyond that expected for undergraduates in their courses, as the material covered in this course may be of use throughout the students' careers. It is expected that multiple readings of material will have been undertaken before class, and that an all-out effort will be made to understand the material and to work on assignments for the class. The instructor will do his best to make sure that the information presented is understandable, but expects that students will have first spent some time trying to assimilate the material on their own. Quizzes in this class will be completed in-class, will be designed to be difficult, and will be graded on a curve, so that students who have superior ability and/or have expended much effort will be able to demonstrate these on the exam. Late assignments will not be accepted and make-up exams will not be given, except for extenuating circumstances. Each student must keep up with the material as we go along; statistical methods is typically not a subject for which several weeks of material can be crammed into one's brain in several hours. Laboratories to become familiar with computer programming, model testing, and completing exercises will be required. Students are strongly urged to stop in during office hours with Dr. Zimmerman or Ms. Dekhtyar if they have any questions.

Primary Course Objectives . to familiarize graduate students in the social and behavioral sciences with the language, logic, and implementation of structural equation modeling; . to compare and contrast structural equation modeling with more commonly used statistical strategies in the social and behavioral sciences such as multiple regression analysis and factor analysis; . to teach the criteria associated with the decisions that must be made at each phase of a structural equation modeling analysis; . to consider the philosophical and statistical criticisms of structural equation modeling as an approach to research design and data analysis; . to provide firsthand experience reviewing research reports that feature structural equation modeling and writing up and presenting orally the results of structural equation modeling analyses. Elements of the Course Readings There are three required books: 1. Kline, R.B. (2005). Principles of Structural Equation Modeling (2nd edition). New York: Guilford. 2. Hoyle, R.H. (ed.) (1995). Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, CA: Sage. 3. Byrne, B. M. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Mahwah, NJ: Erlbaum.

Most weeks, additional readings are also required. More advanced topics are occasionally covered in suggested (but not required) readings, marked by an asterisk (*). All additional readings (generally as pdf files) will be posted on the course website by the first day of class.

Quizzes Two quizzes will comprise the testing in the course. While we hope you will read and learn the material just for learning sake, sometimes in the mix of other activities and coursework, it is easy to let readings and mastery of the material go by the wayside. So, I think some grades related to mastery of the material may help students keep on the top of the material, and have decided to include 2 quizzes as part of your grade. Each will occupy about 30 minutes of class time on Feb. 26 and Apr. 2. Most will be short answer or short essay but some writing of computer programming, simple calculations, path diagrams, and /or interpreting output may also be included.

Homework Assignments Three homework assignments will also be required. All will use the dataset we are using for the course, a 3-wave, longitudinal sample of about 5000 rural high school students from the beginning of 9th to the end of 10th grade. We will discuss the dataset and the codebook for the dataset in the first laboratory section of the course, to be held the week of Jan. 29. Laboratory sessions will focus on preparing students for these homework assignments, including practice questions.

Research Project The major product of the course will be a written report of a structural equation modeling analysis you conduct on data of your choosing. On Feb. 19 I will ask you to specify a dataset that you will analyze and write up for the course. On Mar. 26 I will ask you to prepare a document in which you specify the names and characteristics of the variables your analysis will include and the nature of the model you plan to fit. All models should include both measurement and structural components for this assignment. About two-thirds of the way through the course I will ask you to meet outside of class with another member of the class to discuss your data and plan of analysis and to exchange feedback on your projects. The final draft of the research project is due by May 3th at 10 a.m. An 8-10-minute oral presentation will also be given on either April 23 or April 30.

Attendance and Participation Students are expected to attend all class sessions, as both hearing about statistics material and reading it as important elements to learning it. Attendance is also required at laboratory sessions (1 per week), as doing statistics is probably the most important learning component of all. I also expect students’ participation in class; both the quality and quantity of student’s participation will be considered in their evaluation.

Published Article Presentation Each student will give a 2-minute presentation in which he/she describes and evaluates a published study in which the data are analyzed using structural equation modeling. Students can choose from a list of recently published articles in top-tier journals in their field of study; references and abstracts for psychology, communication, and business/economics/marketing will be available on the course website by January 31, over two months before the presentations begin. Presentations will take place on April 9th. Details about the selection of an article and the contents of the presentation will be provided around January 31st as well.

Grading Three computer assignments, an oral presentation about a published article using structural equation modeling, a research project (both an oral presentation and a written paper), and a midterm exam will comprise the grading in this course. The total grade will be distributed as follows: Homework Assignments 24% (3 @8% each) Published Article Presentation 10% Research Project—written component 26% Research Project—oral presentation 10% Quizzes (2 @ 10%) 20% Attendance/participation 10%

Everyone should receive an “A” or a “B” barring poor attendance or not doing the work, so that students can spend more time and energy on learning the material rather than on their grade.

Course Website

The website for the course is at www.uky.edu/centers/hiv/cjt765/cjt765.html. The course syllabus, assignments, dataset to be used throughout the course (in SPSS format), additional readings (in PDF files), articles for the published article presentation, datasets, and a variety of other materials will be available on the course website.

Acknowledgments

I would like to acknowledge the following faculty members, whose syllabi helped provide some suggestions for assignments, readings, or course organization. I either spoke to these faculty members and/or their syllabi were available through publicly accessible websites. Copies of my syllabus have been shared with them.

Rick Hoyle, Duke University, Psychology/Sociology 779, Structural Equation Modeling, taught Fall, 2000 at UK. (I have especially drawn on this syllabus for readings and assignments.) Robert Hauser, University of Wisconsin, Sociology 952, Mathematical and Statistical Applications in Sociology, Topic: Path Analysis and Structural Equation Models, taught Spring, 2004. Stephen West, Cathy Cottrell, & Oi-Man Kwok, Arizona State University, Psychology 533, Structural Equation Modeling, taught Spring, 2004. Gregory Elliott, Brown University, Sociology 226, Structural Equation Models in the Social Sciences, taught Fall, 2004. Course Outline

Jan. 22 Introduction to Structural Equation Modeling: Kline: 1; Hoyle: 7 Ancestry, History, and Philosophy of Science Blalock (1991) Berk (1988) Hershberger (2003) Freedman (1991)

Jan. 29 Review of correlation and regression Kline: 2; Byrne: 1 Cohen et al. (2003): Ch. 2-3

Feb. 5 Review of data preparation, screening, Kline: 3; Byrne: 2 measurement issues Allison (2003) Cohen et al. (2003) pp.225-251 DeVellis (1991), pp. 1-41 Schaefer & Graham (2002)

Feb . 12 Overview of SEM notation, path diagrams, Kline: 4; Hoyle: 1, 2, 8 programs; Homework 1 due Byrne: 3 *Byrne (1994), Chapters 1 & 2 *Kelloway (1998),Ch. 4-7

Feb. 19 Path Analysis 1: Basic theorems, mediation, coefficients, Kline: 5 pp. 93-105; Hoyle: 3 Choose dataset Kenny (1979), Chapters 3-4 Baron & Kenny (1986) MacKinnon et al. (2002) Cole & Maxwell (2003) Gionta et al. (2005) *Shrout & Bolger (2002)

Feb. 26 Path Analysis 2: decomposing a correlation, direct and Kline: 5 pp. 105-122 indirect effects, identification; Quiz 1 Alwin & Hauser (1975) Holbert & Stephenson (2001) Pedhazur (1982), pp. 614-628 Fox (1980)

Mar. 5 Path Analysis 3: fitting a model, fit indices, comparing Kline: 6; Hoyle 3, 5 models, statistical power; Homework 2 due Hayduk et al., (2003) Bollen & Long (1993) Tanaka (1993) Marsh et al. (2004) Fan & Sivo (2005) *Reichardt (2002) *Dormann, 2001 *Muthén & Muthén (2002) Course Outline (cont.)

Mar. 19 Measurement Models and Confirmatory Kline: 7; Hoyle: 10, 12 Factory Analysis; item parcels Byrne: 4 Lance et al., 2002 Noar, 2003 Quilty et al. (2006) Hagtvet & Nasser (2004)

Mar. 26 Putting it all together: Structural and measurement Kline: 8; Byrne: 6 components in SEM; Turn in dataset description; Anderson & Gerbing (1988) Homework 3 due Holbert & Stephenson (2002) McDonald & Ho (2002) Stephenson & Holbert (2003) MacCallum & Austin (2000) *Hayduk & Glaser (2000)

Apr. 2 Nonrecursive structural models; Quiz 2 Kline: 9 James & Singh, 1978 *Berry, 1984

Apr. 9 Advanced topics: Multi-group SEM, Kline: 10, 11; Hoyle: 11, 13 Latent Growth Models Bentler & Dudgeon (1996) Published article presentations McCallum et al. (1993) Byrne (2004) Kim (2005) *Gonzalez & Griffin (2001) *Raykov (2005)

Apr. 16 Pitfalls in using SEM; Critique of SEM Kline: 12, 13 and future directions Tomarken & Waller (2005) Raykov & Marcoulides (2001) Schumacker (2002) deJong (1999)

Apr. 23 Research report presentations 1

Apr. 30 Research report presentations 2

May 3 Research reports due, 10 a.m. Additional Readings

January 22 Berk, R.A. (1988). Causal inference for sociological data. In N.J. Smelser (Ed.), Handbook of Sociology (pp. 155-172). Newbury Park: Sage. Hershberger, S. L. (2003). The growth of structural equation modeling: 1994-2001. Structural Equation Modeling, 10, 35-46. Blalock, H. M., Jr. (1991). Are there really any constructive alternatives to casual modeling? P. V. Marsden (Editor), Sociological Methodology, 21, 325-335. Cambridge, MA: Basil Blackwell. Freedman, D. A. (1991). Statistical models and shoe leather. P. Marsden (Editor), Sociological Methodology, 21, 291-313. Cambridge, MA: Basil Blackwell.

January 29 Cohen, J., Cohen, P., West, S. G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd Ed.). Hillsdale, NJ: Erlbaum.

February 5 Cohen, J., Cohen, P., West, S. G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd Ed.). Hillsdale, NJ: Erlbaum. DeVellis, R. F. (1991). Scale Development: Theory and Applications. Newbury Park, CA: Sage. Schafer, J.L., & Graham, J.W. (2002). Missing data: our view of the state of the art. Psychological Methods, 7, 147-177. Allison, P. D. (2003). Missing data techniques for Structural Equation Modeling. Journal of Abnormal Psychology, 112(4), 545-557.

February 12 *Byrne, B. M. (1994). Structural Equation Modeling with EQS and EQS/Windows: Basic Concepts, Applications, and Programming. Thousand Oaks, CA: Sage. *Kelloway, E. K. (1998). Using LISREL for Structural Equation Modeling: A Researcher’s Guide. Thousand Oaks, CA: Sage.

February 19 Kenny, D. A. (1979). Correlation and Causality. New York, NY: John Wiley. Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 173-182. MacKinnon, D. P., Lockwook, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of the mediation and intervening variable effects. Psychological Methods, 7, 83-104. Cole, D. A., & Maxwell, S. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of Structural Equation Modeling. Journal of Abnormal Psychology, 112(4), 558-577. Gionta, D. A., Harlow, L. L., Loitman, J. E., & Leeman, J. M. (2005). Testing a mediational model of communication among medical staff and families of cancer patients. Structural Equation Modeling, 12(3), 454–470. *Shrout, P.E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445. Additional Readings (cont.)

February 26 Alwin, D.F., and Hauser, R.M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40, 37-47. Pedhazur, E.J. (1982). Multiple Regression in Behavioral Research, Chapter 15, pp. 577-588. New York, NY: Holt. Fox, J. (1980). Effect analysis in structural equation models. Sociological Methods and Research, 9, 3-26. Holbert, R. L., & Stephenson, M. T. (2001). The importance of indirect effects in media effects research: Testing for mediation in structural equation modeling. Journal of Broadcasting and Electronic Media, 47, 556-572.

March 5 Hayduk, L., Cummings, G., Stratkotter, R., Nimmo, M., Grygoryev, K., Dosman, D., Gillespie, M., Pazderka-Robinson, H., & Boadu, K. (2003). Pearl’s D-separation: One more step into causal thinking. Structural Equation Modeling, 10, 289-311. Bollen, K.A., & Long, J.S. (1993). Introduction. In K.A. Bollen & J.S. Long (Eds.), Testing Structural Equation Models (pp.1-9). Newbury Park, CA: Sage. Tanaka, J.S. (1993). Multifaceted conceptions of fit in structural equation models. In K.A. Bollen & J.S. Long (Eds.), Testing Structural Equation Models (pp. 10-39). Newbury Park, CA: Sage. Marsh, H. W., Hau, K., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320-341. Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12(3), 343-367. *Reichardt, C. S. (2002). The priority of just-identified, recursive models. Psychological Methods, 7, 307-315. *Dormann, C. (2001). Modeling unmeasured third variables in longitudinal studies. Structural Equation Modeling, 8, 575-598. *Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620.

March 19 Lance, C. E., & Noble, C. L. (2002). A critique of the correlated trait-correlated method and correlated Uniqueness models for multitrait-multimethod data. Psychological Methods, 7, 228-244. Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling, 10, 622-647. Quilty, L. C., Oakman, J. M., & Risko, E. (2006). Correlates of the Rosenberg Self-Esteem Scale method effects. Structural Equation Modeling, 13(1), 99-117. Hagtvet, K. A., & Nasser, F. M. (2004). How well do item parcels represent conceptually defined latent constructs? A two-facet approach. Structural Equation Modeling, 11(2), 168-193. Additional Readings (cont.)

March 26 Holbert, R. L., & Stephenson, M. T. (2002). Structural equation modeling in the communication sciences, 1995-2000. Human Communication Research, 28, 351-551. McDonald, R.P., & Ho, M-H. R. (2002). Principles and practice in reporting structural equation analysis. Psychological Methods, 7, 64-82. Stephenson, M. T., & Holbert, R. L. (2003). A Monte Carlo simulation of observable latent variable structural equation modeling techniques. Communication Research, 30, 332-354. MacCallum, R.C., & Austin, J.T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-22. Anderson, J. & Gerbing, D. (1988). Structural equation modeling in practice: A review and recommended two-step procedure. Psychological Bulletin, 103, 411-423. *Hayduk, L. A., & Glaser, D. N. (2000). Jiving the four-step, waltzing around factor analysis, and other serious fun. Structural Equation Modeling, 7, 1-35.

April 2 James, L.R., and B.K. Singh. (1978). An introduction to the logic, assumption, and basic analytical procedures of two-stage least squares. Psychological Bulletin, 85, 1104-1122. *Berry, W. D. (1984). Nonrecursive Causal Models. Newbury Park, CA: Sage.

April 9 Bentler, P.M., & Dudgeon, P. (1996). Covariance structure analysis: Statistical practice, theory, directions. Annual Review of Psychology, 47, 563-592. MacCallum, R.C., Wegener, D.T., Uchino, B.N., & Fabrigar, L.R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185-191. Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling, 11(2), 272-300. Kim, K. H. (2005). The relation among fit indexes, power, and sample size in structural equation modeling. Structural Equation Modeling, 12(3), 368-390. *Gonzalez, R., & Griffin, D. (2001). Testing parameters in structural equation modeling: Every “one” matters. Psychological Methods, 6, 258-269. *Raykov, T. (2005). Analysis of longitudinal studies with missing data using covariance structure: Modeling with full-information maximum likelihood. Structural Equation Modeling, 12(3), 493–505.

April 16 Raykov, T., & Marcoulides, G. A. (2001) Can there be infinitely many models equivalent to a given covariance structure model? Structural Equation Modeling, 8, 142-149. Schumacker, R. E. (2002). Latent variable interaction modeling. Structural Equation Modeling, 9, 40- 54. deJong, P. F. (1999). Hierarchical regression analysis in structural equation modeling. Structural Equation Modeling, 6, 187-211. Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: Strengths, limitations, and misconceptions. Annual Review of Clinical Psychology, 1, 31-65.