Midterm Review

1. Midterm Review Renan Levine POL 242 June 14, 2006

2. Theories and Data I • An empirical theory of politics is an attempt to explain why people behave the way they do politically.  • We observe characteristics of people, governments, organizations, states, etc. • Characteristics that differ from one person, (etc) to another are called variables. 

3. Recall: Types of Variables • Nominal variables have categories that have no numerical properties and cannot be “ordered” or “ranked”. Example: Provinces • Ordinal variables are ordered or ranked, but there is no equal unit size between categories • Interval variables are measured using a scale in which the units of measurement are all equal in size. • A36 ‘Arik’ Sharon Feeling Thermometer • Number of police officers per province per 100,000 people

4. Theories and Data II • We scrutinize variables that are related to each other. • We strive to identify causes for effects we observe. • Why do people vote for the Green Party? • Why do some countries enjoy a high degree of sustained growth but not others? • Why has there been a big increase in gun-related homicides in the GTA? • We try to explain the dependent variable. 

5. Hypotheses and Variables • A statement positing a relationship between two variables is called a hypothesis. • A hypothesis posits a relationship between independent variable(s) and dependent variables. Independent variables influence the values of the dependent variable.  • Hence, the dependent variable depends on independent variable(s).

6. Descriptive Statistics • Mean, median, mode. • T-tests, ANOVA to compare differences. • Variance / standard deviation, • Skewness and kurtosis • You should know which measures are best for which types of variables.

7. Crosstabs • Relationship between two variables can be compared in a tabular form. • Remember to recode data in order to make the crosstab easier to interpret. • Collapse categories with few observations. • Measures of association (Cramer’s V, Kendall’s Tau) can be used to observe strength of comparison

8. Are you sure the relationship is not due to chance? • Chi-Square tells you whether the relationship is statistically significant. In other words, is the relationship unlikely due to chance. • Chi-Square tests whether the distribution of observations in the contingency table is different than the null hypothesis. • A significant relationship (measured by Chi-Square) is not the same as a strong relationship (measured by Kendall’s Tau, Cramer’s V, etc.) • Recall: difference between one-tailed and two-tailed hypothesis tests.

9. Tests • Are two variables different? • Are two variables measuring the same thing? • Does it “make sense” to suggest that there is a relationship between these two variables?

10. Differences • T-test looks at difference in mean level of dependent variable between two groups. • Whether or not the difference between the means depends primarily on two factors: • How big the difference is between the means. • How big the sample is – can we be confident that if we drew the same number – or more – from the population again, that we will get similar results. • ANOVA will allow you to use ordinal or nominal independent variables with more than two categories.

11. Similarities: Indexing • Are two variables measuring the same thing? Or, how well do two or more variables “go together”? • Cronbach’s alpha used to determine if more than one item is measuring the same thing. • If the alpha is high, then the variables are presumably measuring the same thing, and they can be combined into an index. • Remember that the computer doesn’t know what each variable really is – it just looks at the data.

12. Plausibility? • Over break, many of you commenced doing qualitative research. • Ways of: • Seeing whether your hypotheses “make sense.” • Generating new hypotheses. • Identifying important variables. • Getting a picture of what is almost always a complicated world.

13. Focus Groups • Loosely structured, open-ended discussion • Best when exploring areas of personal experience and when comments of one participant may provoke ideas and responses from other participants that they might not have thought of in a one-on-one setting. • Designed to explore ideas/generate hypotheses • Not good at testing hypotheses (small N! non-random sample!) • Except to see whether hypothesis is reasonable – good to observe whether there is evidence of your hypothesized relationship or your subjects think there is a relationship. • Sometimes better when there is no evidence or the focus group suggests a different relationship!

14. In-Depth Interviews • More flexibility than surveys to dig deep. • How do people really think? Why? • Dig deep! • Give and take with interviewer – much fewer constraints than mass surveys where questions must be same for many people. • See Babbie and Benaquisto or Bryman and Teevan for more guidance.

15. Expert Interviews • What have other scholars found? • What is still unexplained by scholars? • What variables are important? • Is your theory plausible? • What are some possible alternative explanations?

16. Eyes on the prize • Ideally, your research either enlightens work you have already done or will inform research you will do this term… • Because this term we will be learning multivariate analysis. • Culminating in multivariate regression.