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982: USP/SOC Methods of Research and Analysis for Urban Social Institutions II Fall, 2008 Monday, 4:30 p.m. - 7:10 p.m. Room 293 Bolton Hall Professor Nancy Mathiowetz e-mail: nancym2@uwm.edu Phone: 414-229-2216 Office hours: Mondays, 1:00 - 3:00 p.m. and by appointment Course Description and Goals: The focus of this course is on the application of statistical techniques to analyze social science data. Students will chose a data set and prepare a research paper based upon the data; the final product for the course will be an original research paper suitable for presentation at a profession conference or for publication in a scholarly journal. This is not a statistics course, per se, but rather a course in which statistical theories and techniques will be reviewed (and some new techniques introduced). The emphasis in this course will be on the appropriate use and interpretation of statistical techniques in the analysis of social science data. The material covered in this course will enable students to intelligently and critically read professional publications as well as prepare students to successfully complete the steps in the empirical research process, moving from a research idea through data analysis and presentation of findings. Prerequisites: At least one graduate statistics course (that included multivariate methods) and a passing grade of 85% or higher on the diagnostic exam for this course. The course work assumes that students understand principles of statistical inference, including confidence intervals and hypothesis testing, measures of central tendency and variation, multiple linear regression, analysis of variance, logistic regression and bivariate and multivariate contingency tables (and associated statistical tests of association and relationship strength). Required Texts: There are three required texts for this class and a coursepack (available at Clark Graphics, 2915 N. Oakland Ave. Telephone number: 414-962-4633). The required texts are: Carol Aneshensel (2002) Theory-Based Data Analysis for the Social Sciences. Thousand Oaks: Pine Forge Press. J. Scott Long and Jeremy Freese (2006) Regression Models for Categorical Dependent Variables Using Stata (2nd Edition) College Station, TX: Stata Corporation Barbara G. Tabachnick and Linda S. Fidell (2007) Using Multivariate Statistics (5th Edition) Boston: Pearon/Allyn and Bacon 1 In addition, I have found the following this book helpful for conducting analysis with Stata: Lawrence C. Hamilton (2006) Statistics with Stata. Belmont, CA: Duxbury (an imprint of Thompson). Computing This course will make extensive use of statistical software for the analysis of complex survey data. The course will be taught using Stata. Many of the statistical tests and models we will be examining are not available in SPSS nor can one use the standard SPSS package for the analysis of complex survey data. Stata is available in the computer labs throughout campus. In addition, I have set up a Grad Plan with the Stata corporation. I will forward an email from Stata to class members with the direct link. You will be able to order either of the following: Intercooled Stata 10 One-year license, Getting Started manual $95 Intercooled Stata 10 Perpetual license, Getting Started manual $155 Grading. Grading for the course will be based on the following: 1. Homework assignments (25% of your grade). There will 5 homework assignments. The homework is intended to reinforce the lecture materials and readings and allow you the opportunity to apply your skills to real datasets. I encourage collaboration among students with respect to mastering the material and debugging statistical software runs. However, you are required to write up your own answers to the homework, independent of other students. You are also required to produce and attach your own computer printout, whenever printout is needed. No photocopies or second printings of other students printouts are allowed. Violations of this policy will result in serious penalties. Late assignments will be downgraded by one letter grade per day that they are late. 2. In class Exam (15% of your grade). An in-class cumulative exam will be given on Monday, November 24th. This will be an open-book, open note exam, focusing on application and interpretation. 2 2. Empirical research paper (45% of your grade). The final project will require you to select a dataset of interest to you, draft a hypothesis or hypotheses you wish to examine using these data (theory or empirically based hypothesis), motivate the research with a literature review, complete the statistical analysis, and write up the results. The final paper is expected to be of the quality one would find in a leading social science peerreviewed journal. This is a great opportunity to find the data for your MA or Ph.D. thesis and begin to conduct preliminary analysis! Students will peer-review and critique papers by fellow students. Work on the final paper should begin the first week of class! Empirical research paper: presentation (15% of your grade). In addition to writing a final you paper, will present a profession-level presentation to the class, 20-30 minutes of your findings. This presentation will be similar to a presentation you would present at a professional meeting. All students will grade and critique all of the presentations. 3. Class website: Lecture notes (to the extent there are any), exercises, and any supplemental readings not included in the texts or coursepack can be found on the course website, using D2L. I encourage you to check the D2L website prior to class so that you can have copies of the material during the lecture. Lecture and Lab format: The course runs from 4:30 to 7:10 p.m. on Monday; the course will be divided between lecture and lab, depending upon the nature of the material to be covered. For example, during the weeks that we are focusing on statistical techniques, lab time will be spent applying (that is trying out) the various techniques. I encourage you to have either a floppy disk/CD/flash drive with you for all classes, so that you can save your work. You are expected to have read the material prior to the lecture. The lectures only make sense when you have prepared adequately for the class. Since this is a small, Ph.D.-level course, all students will be expected to contribute to the discussion and explication of material during the class. Attendance at both the lecture and lab are required. Office hours are intended to be used for clarification of material and assistance with assignments, readings, and the final project and not to reiterate material that was covered in lecture and/or lab. 3 Schedule of Topics Date Topic Readings Section I: Analytic Thinking and Data Preparation 9.8 Introduction; Theory Based Data Analysis Theory-Based Data Analysis pp. 1-245 Assignment 1: Draft proposal for research question. Due 9.22.08 9.15 Review of basic concepts, hypothesis testing, bivariate analysis; presenting empirical data Introduction to Stata Issues in Data Screening, Cleaning, and Dealing with Missing Data Tabachnick and Fidell, Chapters 1-3 Review your favorite stat text book univariate through bivariate analyses Long and Freese, pp. 3-72 9.22 Tabachnick and Fidell Chapter 4 Innis Imputation in Surveys: Coping with Reality Kalton and Kasprzyk The Treatment of Missing Survey Data Schafer and Graham Missing Data: Our View of the State of the Art 9.29 Analyzing Complex Survey Data: Weights and Variance Estimates Biemer and Christ Weighting Survey Data Stapelton Analysis of Data from Complex Surveys Johnson and Elliot Sampling Design Effects: Do They Affect the Analyses of Data from the National Survey of Families and Households? Assignment 2: Data Cleaning, Imputation and Weighting Exercise: Due 10.6.08 4 II. Advanced Multivariate Statistical Techniques for Data Analysis 10.6 Review of Linear Regression; Estimation in Stata Tabachnick and Fidell: Chapter 5 Application: Klebanov, Brooks-Gunn, and Duncan Does Neighborhood and Family Poverty Affect Mothers Parenting, Mental Health and Social Support Tabachnick and Fidell: Chapter 10 DeMaris A Tutorial in Logistic Regession Long and Freese, pp. 75-181 pp. 415-427 Application: Sigelman, Tuch, and Martin What s In a Name? Assignment 3: Preliminary review of data and outline of data issues Due 10.20 10.20 Limited Dependent Variables: Log linear models; Ordinal logit models; Tabachnick and Fidell: Chapter 16 Sloan and Morgan An Introduction to Categorical Data Analysis Long and Freese, Chapter 5 Application: Sammer, Lykens, and Singh Physician Characteristics and the Reported Effect of EvidenceBased Practice Guidelines 10.27 Limited Dependent Variables: Multinomial Logit Models; Count Data Long and Freese, Chapter 6 & Chapter 8 Applications: Sakinsian and Gerstel, Kin Support among Blacks and Whites: Race and Family Organization Sakinsian and Gerstel Explaining the Gender Gap in Help to Parents: The Importance of Employment Assignment 4: Analysis with Limited Dependent Variables Due: 11.3.08 10.13 Review of Logistic Regression; Estimation in Stata 5 III. Topics in Data Analysis 11.3 Reliability, validity and multi-item scales; Principal Components and Factor Analysis Warner Reliability, validity and multi-item scales Tabachnick and Fidell: Chapter 13 Application: Ross and Hill The Family Unpredictability Scale: Reliability and Validity Assignment 5: Factor Analysis and Scale Creation Due 11.10.08 11.10 Causal Pathways and Structural Equation Models Klem Path Analysis Tabachnick and Fidell: Chapter 14 Application: Chesley Blurring Boundaries? Linking Technology Use, Spillover, Individual Distress, and Family Satisfaction 11.17 Multi-level (hierarchical) models Tabachnick and Fidell: Chapter 15 Teachman and Crowder Multilevel Models in Family Research: Some Conceptual and Methodological Issues Application: Wharton, Rotolo, and Bird Social Context at Work: A Multilevel Analysis of Job Satisfaction 11.24 12.1 12.8 12.15 In-class Exam Final Presentations Final Presentations Final Papers Due 6

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