STA 5126 - Statistical Methods for the Social SciencesDr. Mohr, Fall 2011Instructor: Dr. Donna Mohr, Building 14/2702, Phone: 620-2884 (do NOT use voice mail)FAX: 620-2818 (put my name on cover sheet)email: [email protected] materials: once you are registered, go to http://blackboard.unf.edu,prior to the course beginning, some materials at http://www.unf.edu/~dmohr/sta5126Office Hours: Mondays and Wednesdays 10:30-11:30 am, 4:30 – 5:30 pmTuesdays and Thursdays 1:30 – 3:30 pmOther times, just call for an appointment Course Goals, Content and Requirements:This course is intended for graduate students in psychology, sociology, education or the other social sciences. It is assumed that you have already had a course in elementary statistics, and now need to apply more advanced statistical methods. (See ‘A Note on Prerequisite Material’, below.) No mathematical theory will be developed, but intensive computer use is required. By the end of this course, you should be able to choose appropriate statistical techniques for the most common experimental situations, use SPSS to perform the appropriate analyses, and interpret the results to a reader. We will briefly review prerequisite material on one-sample, paired and two-sample t-tests. Among the statistical techniques we will consider in more detail are analysis of contingency tables, the one-way analysis of variance including multiple comparisons, the two-way and higher order analysis of variance, simple and multiple regression. If time allows, we will add a brief introduction to repeated measures experiments. In addition to homework problems and exams, you will be required to undertake several statistical analyses of real data sets on the computer, describing the methods and results in a formal report. Text: I will have copies of my course notes posted to the course's Web page. These notes contain extensive explanations of the heuristics behind the formulas, homework problems, and numerous computer examples. In addition, I will provide handouts on the use of SPSS. No other text will be required. However, if at all possible, you should use the money you save by not purchasing a text to buy a copy of SPSS for your home computer. See the document SPSSinfo.pdf on the Blackboard site.While the course notes on the Web page are available for your use, they are my intellectual property and should not be cited without appropriate reference.Calculators: You must have a decent calculator and you must bring it to class. Your cell phone will not be enough. It must be a real scientific calculator, able to do sample means and standard deviations, but it does not have to be a graphing calculator. The TI-83/84 series are excellent but pricey (~$100). The TI-30 series are quite cheap and will do a lot of good stuff (under $25). Look for symbols like STAT,xor Son the keyboard. Those are good signs that the calculator will do simple statistics.Optional Reference Texts. You should certainly have a copy of an elementary statistics text for basic materials. The one you used in your undergraduate elementary course will certainly suffice. If you got rid of that book, I may have some old ones in my office. First come, first serve. Some people say that they would like to have a real reference text to read or use later.In that case, I suggest you go on line and find a recent version (4th edition or later) of David Howell’s Statistical Methods for Psychology, which is quite complete.Course Format: The course will meet twice a week for lecture and discussion. An important part of every class will be the presentation of examples illustrating statistical techniques, and comparison of those techniques to others so far discussed. Attendance is important. Grading: Grade will be based on - 1) Two mid-term exams, each worth 20% of the semester grade2) Final exam, worth 20% of the semester grade3) Reports - 25% of the semester grade. There will be 4 written reports in which you analyzedata sets using techniques studied and give the results in a formal report.4) Homework problems - 15% of grade. Problems will be assigned from each chapter of the notes, along with several review assignments. Problems more than a few hours late will normally not be accepted.Exam DatesExam 1 Thursday September 29Exam 2 Thursday November 3Final exam Thursday December 9 from 9am – 10:50 amCONTENTS:Chapter 1: PREREQUISITE - ONLY BRIEF REVIEW IN CLASS. Review of elementary terminology: populations and samples, parameters and statistics, quantitative and qualitative variables, sampling variability, ‘provability’ of an effect, simple t-test and p-values.Chapter 2: PREREQUISTE – ONLY BRIEF REVIEW IN CLASS. Some truly useful basic tests for quantitative variables: random samples, independent and dependent variables, paired t-tests, independent sample t-tests, F-test and Levene test to compare variances, normality assumption.Chapter 3: Some truly useful basic tests for qualitative variables: Z-test for proportion in one group, z-test comparing proportions in two groups, Fisher’s exact test, Chi-squared test comparing multiple groups, independence.Chapter 4: Comparing means in several groups, the One-Way Analysis of Variance: hypotheses in one-way ANOVA, graphical displays, notation and data layout, sums of squares, mean squared error, the F-test statistic, normal probability plots.Chapter 5: Advanced topics in the One-Way ANOVA: Multiple comparisons, family/experimentwise significance, Bonferroni Inequalities, Tukey’s Honestly Significant Differences, Scheffe’s contrasts, posthoc versus apriori comparisons.Chapter 6: (brief coverage) When the assumptions for the one-way ANOVA look shaky: Levene’s test for homogeneity of variances, Kolmogorov-Smirnov statistic, transformations of variables, nonparametric alternatives to the one-way ANOVA.Chapter 7: The Two-Way Analysis of variance: graphical summaries, data layout and notation, mathematical model, main effects and interactions, development of the F-test.Chapter 8: Advanced Topics in the two-way ANOVA: Types of sums of squares in computer packages, multiple comparisons with and without interactions, R-square.Chapter 9: (brief coverage) Examples of higher-order ANOVA: Interpreting interactions, fitting less than the largest possible model.Chapter 10: Simple linear regression: algebraic fundamentals, scatterplots, fitting the best straight line, testing hypotheses about the