1. Political Science 104 Wednesday, November 12 Agenda Evaluation Report Lecture: Proportionate Reduction of Error Due Tomorrow: Assignment #5 SPSS: Recoding Variables Assignment #6/Final Paper Assigned Readings Tufte, Visual and Statistical Thinking Cartograms of 2004 presidential election (link)(link) Leonhardt, “A Diploma’s Worth?” Blau and Kahn, “The Gender Pay Gap” Shively, Craft, Chs. 8,9 Salkin, Statistics, Ch. 5,14

2. Evaluation Report From the evaluations, the things I need to work on are: I need to stimulate critical evaluation of the course material. I need to stimulate your interest in the subject matter. I need to spend more time clarifying the readings. I need to spend more time clarifying the lectures. I need to facilitate discussion by welcoming questions and comments. The most frequent suggestions/comments: “A better overview of lecture would be helpful”/”Not everything we read or discuss in lecture is elaborated”/”Section would help more for exams if we could do examples of using the theories”/”We should spend more time on the practical applications of lecture”/”We should spend more time on the lectures and the readings” “I’m not sure how well I understand SPSS”/”I am unsure about SPSS in terms of reading the output” “Section has limited time and we have to rush through everything”/”It feels like we are rushed to get through new information” What you guys liked: Clarity of material compared to lecture, website, my availability/accessibility, my organization, Jeopardy game, knowledge of material, attempt to get everyone involved

3. Evaluation Report Some Minor Adjustments: Now that we’ll be working and talking about the papers in section, it is my sincere hope that you will be much more interested in the subject matter. In addition, I think you’ll find the concepts in the course making much more sense and put together now that we’re beginning to wrap it up in the seventh/eighth weeks. Stats is exciting, but you really have to approach it as such. I will attempt to cover both the lectures and readings much more, especially when they seem to be relatively unclear. (We covered the reading in Assignment #5 last week and a short summary of the Tufte pamphlet is up online. We’ll spend time going through PRE today.) SPSS does seem relatively unclear because we haven’t spent much time on it. However, we should be looking at SPSS much more from now on now that the paper will soon be due. I will also attempt to facilitate a better discussion amongst the class. If you ever have a question about anything, feel free to ask. If you don’t feel comfortable asking, you’re also welcome to ask outside of class.

4. Proportionate Reduction of Error Yule’s Q: Income LowHigh Age YoungA = 10B = 20 OldC = 40D =50 Q = (B*C) – (A*D) (B*C) + (A*D) Q = (20*40) – (10*50) (20*40) + (10*50) Q = (800) – (500) (800) + (500) Q = 0.23 (AD) and (BC) are picked because they are the only pair in which we differ on BOTH variables. Hence, we’re measuring the correlation of two possibly dichotomous events (AD) and (BC). We’ll end up getting a Q value. If Q=1, then if AD happens, BC happens. If BC happens, AD happens. If AD never happens, BC never happens. If BC never happens, AD never happens. If Q=-1, if AD happens, BC never happens. If BC happens, AD never happens. If AD never happens, BC happens. If BC never happens, AD happens. If Q=0, the two events are statistically independent. Q = 300 1300

5. Proportionate Reduction of Error Yule’s Q: Income LowHigh Age YoungA = 10B = 20 OldC = 40D =50 So what is proportionate reduction of error? PRE means that the measure we’re calculating reflects the percentage reduction in errors predicting the dependent given knowledge of the independent. Yule’s Q and Gamma are PRE measures. It’s measuring the goodness of fit, meaning how well are the independent variables predicting our dependent variable. Q = (B*C) – (A*D) (B*C) + (A*D) Q = (20*40) – (10*50) (20*40) + (10*50) Q = (800) – (500) (800) + (500) Q = 0.23 Q = 300 1300

6. Proportionate Reduction of Error Yule’s Q: Income LowHigh Age YoungA = 10B = 20 OldC = 40D =50 We can already predict 50% of a relationship by chance. PRE proportionally reduces the remaining 50% of error (23%). Q = (B*C) – (A*D) (B*C) + (A*D) Q = (20*40) – (10*50) (20*40) + (10*50) Q = (800) – (500) (800) + (500) Q = 0.23 Q = 300 1300

7. Proportionate Reduction of Error Yule’s Q: Income LowHigh Age YoungA = 10B = 20 OldC = 40D =50 Yule’s Q simply measures the difference between non-tied pairs as a percentage of all non-tied pairs. The surplus of consistent data pairs over non-consistent data pairs is 23%. An association of 0.23 between the two variables means that this association is not very strong. Yule’s Q is the equivalent of gamma in a 2x2 cell for ordinal variables. Q = (B*C) – (A*D) (B*C) + (A*D) Q = (20*40) – (10*50) (20*40) + (10*50) Q = (800) – (500) (800) + (500) Q = 0.23 Q = 300 1300

8. Assignment #5 What is the question motivating this research? Can you formulate the (implicit) normative theory about the gender pay gap? What is the key (dependent variable) concept? How have the authors measured that concept? How and why does the measurement change as the analysis progresses? Enumerate the alternative explanations (“hypotheses”) they give for the gender pay gap, and explain the causal mechanism for each. What questions are left unanswered at the end of the article? Suggest some ideas for collecting the information needed for answering those questions.

9. SPSS: Recoding Variables Many times we are limited in the types of tests that we can run in SPSS. SPSS actually gives you the ability to go through and recode your variables so you can run tests on variables with different levels of measurement. When would you use this? You might use this when you have a variable like age, but you want to use that variable in a crosstab or t-test. Age is typically an interval variable, but you might want to see it in terms of two groups (young and old). SPSS can make these changes. You are now equipped to go through multiple tests through your paper now that you have the power to re-code variables. Theoretically, you can go through and start doing the analysis for your paper. Are there any tests that you would like for me to go over? Did we go over anything too fast last week that you want me to go over once again?

10. Assignment #6 With Assignment #4, this is where you should be: At this point in the course, you should have a couple of things ready in your paper: Some sort of testable hypothesis with both a cause and effect. (I hypothesize that this might affects something else.) (ie., I hypothesize that wealth affects voter participation.) Have extracted the concepts from your hypothesis. (Don’t overcomplicate.) I hypothesize that wealth affects voter participation. CONCEPTS: WEALTH VOTER PARTICIPATION Think of some way to operationalize (measure) these concepts: CONCEPTS: WEALTHVOTER PARTICIPATION income, net worth voter turnout, times worked on campaign Assign #2 Assign #4

11. Assignment #6 You have your Assignment #4. Get into small groups (2 or 3) and begin working on your own papers. Go through the variables and see if you can run a couple of tests.