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Syllabus, HS510a Applied Design and Analysis Spring 2017 Time: Tu&Th 5:30-6:50pm, Location: Schneider Building, Room G-1 Instructor: Grant A. Ritter Office: Heller Rm 268 Phone: 781-736-3872 Office Hours: 4:00pm-5:30pm Tu&Th Email: [email protected] Text: Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach 5th ed., Cengage Learning, ISBN 978-81-315-24-65-7 Prerequisite: Knowledge of basic statistics and use of statistical software (such as HS404 or its equivalent) Course Objectives: Course continues a presentation of quantitative methods covering experimental design issues, statistical analyses, and other topics relevant to researchers in the social sciences. Course Requirements: The course will include four problem sets to be solved using a statistical software package, plus a set of five writing assignments which together will form the framework for a proposed research project. As the Final, the student must combine the five writing assignments together and edit to produce a potential research proposal. The course is graded pass/fail. To pass the student must regularly attend class and turn in both problem sets and written assignments. Outline of Topics (26 classes of one hour twenty minutes each): Linear Regression Topics Review of Probability; mean, variance, standard deviation; random variable, independence, correlation Review of Statistics; population vs sample, sample mean, sample variance, The Central Limit Theorem Causality Designs for Social Science: experimental, quasi-experimental, or observational Data Preparation and Preliminary Analyses Bivariate Analyses Linear regression models; OLS; reading Stata output Interpretation of linear regression output Inclusion of Interaction terms in linear regressions; interpretation of their estimates Additional Diagnostics for Linear Regression Models: Goodness of fit, VIFs, tests on the residuals The F-test for comparing nested linear regression models Multiple Comparison Tests: Bonferroni, Dunnett, Tukey The Chow Tests The Linear Probability Model Logistic Regression Topics Introduction and Background for dichotomous dependent variable Graphic representation of relevant empirical data Modeling the ‘log odds’ – justification for using logit transformation Fitting the model, Intro to maximum likelihood method of solution Interpretation of the model estimates – the odds ratios, constructing confidence intervals Calculating marginal effects in logistic regression Further topics in model building – interaction terms, adding blocks of variables, comparing results Interpreting the interaction term in logistic regression models Assessing model fit and comparing among models - 2LLN versus AIC versus BIC, pseudo R-square Pros and cons of logistic modeling versus linear probability modeling Application to observational, cohort, and case-control study designs Diagnostics: the ROC curve, concordance and discordance, Somer’s D statistic Additional Social Science Topics Poisson, Negative Binomial, ZIP, and ZINB models for counting measures Mediators and Moderators Difference in Difference Models Matching and Propensity Score Matching