1. Research Design 10/16/2012

2. Readings Chapter 3 Proposing Explanations, Framing Hypotheses, and Making Comparisons (pp. 58-76) Chapter 5 Making Controlled Comparisons Chapter 4 Making Comparisons (Pollock Workbook)

3. Homework Short homework assignment on the paper due todaypaper Time Series Data

4. OPPORTUNITIES TO DISCUSS COURSE CONTENT

5. Office Hours For the Week When – Wednesday 11-1 – Thursday 11-12 – Friday 10-11 – And appointment

6. Course Learning Objectives Students will learn the research methods commonly used in behavioral sciences and will be able to interpret and explain empirical data. Students will learn the basics of research design and be able to critically analyze the advantages and disadvantages of different types of design.

7. Research Design

8. Types of Observational Studies Cross-Sectional Time-Series Panel Studies

9. Case Study Design What is it? N=1 (one unit in your study) Problems

10. Bivariate Data Analysis CROSS-TABULATIONS

11. Variables Dependent Variable- the variable/result that you want to explain. Independent Variable(s)- the variables that you believe will cause/explain/change your dependent variable

12. Univariate Statistics answer discrete questions

13. What are Cross Tabs? a simple and effective way to measure relationships between two variables. also called contingency tables- because it helps us look at whether the value of one variable is "contingent" upon that of another

14. When to use them When you have 2 variables They can only be used for categorical variables – ordinal (variables are ranked, but the differences between them are not certain (Less than HS, Hs, College, Grad School), – nominal variables (the variables are simply given names Gingrich, Perry, Romney, Santorum)

15. You can use them if you have two ordinal variables one nominal and one ordinal variable two nominal variables. Your variable can take less than 10 categories

16. When it is a bad method If you have ratio or interval variables You have a variable with more than 10 values You want to test multiple independent variables against a single d.v.

17. Useless

18. DOING CROSS-TABS IN SPSS

19. Open Up the GSS Open GSS2008

20. A Simple Hypothesis

21. What is it Dependent variable- Happiness Independent variable- Intelligence What would be the hypothesis

22. Running Cross Tabs Select, Analyze – Descriptive Statistics – Cross Tabulations

23. Running Cross-Tabs Dependent variable is usually the row Independent variable is usually the column. We have to use the measures available

24. Case Processing Summary Ignore the case Processing Summary Delete it from your outputs

25. Cross-Tab Terminology Rows (appear along the side of the table) and Columns (appear at the top) the categories formed by the intersection of a column is called a cell

26. The Outputs As “Education increases, unhappiness increases” Raw Counts are not very helpful Most are “pretty happy”

27. Lets Add Some Percent's Click on CellsCell Display

28. Row %'s- This measures data across the row 15% of people who are very happy have >11 years of education Overall 27.7% have 16+ and 17.5% are less than 12. This allows us to measure change across one category and compare to the total

29. Column %'s This measures data down each column Compare across each column – 37.0% of 16+ are very happy vs 27.0% of >11’s – 6.5 of 16+ are not too happy, vs 23.3% of people with low education – Overall, 31.6% of people are very happy

30. How we interpret Using Face Validity to interpret x-tabs – is there a pattern? – does one column stand out? When we have two ordinal variables we can state directional relationships!

31. THE COMPARE MEANS TEST

32. When do we use this? A way to compare ratio variables by controlling for an ordinal or nominal variable – One ordinal vs. a ratio or interval – One nominal vs. a ratio or interval This shows the average of each category

33. In SPSS Open the States.SAV Analyze – Compare Means – Means

34. Where the Stuff Goes Your categorical variable goes in the independent List Your continuous variable goes in the Dependent List

35. Reading the Output We can compare each region against each other and the total For ordinal variables, we can state relationships This is all face validity!

36. The Practical Significance Why do some regions smoke more? (possible i.v.’s) What are the policy effects? Is smoking harmless?harmless?