1. Exam 1 Review GOVT 120

2. Review: Levels of Analysis Theory: Concept 1 is related to Concept 2 Hypothesis: Variable 1 (IV) is related to Variable 2 (DV) Operational Definition: IV: Definition of Cause DV: Definition of Effect

3. Types of Hypotheses (19) Types of Hypotheses: Univariate: making a statement about only one property or variable. (19) Multivariate: a statement about how two or more variables are related. Most hypotheses are multivariate and Directional: that is, they suggest not only how the variables are related but what the direction of the relationship is. (19) Null Hypothesis: There is in fact no relationship between the stated independent and dependent variables.

4. Hypothesis Hypothesis: Variables (IV) Independent Variable: the cause of something (DV) Dependent Variable: the effect It is not always easy to determine the IV and DV. Control Variables: when they are used the intent is to ensure their effects are excluded. …

5. Types of Hypotheses (19) Types of Directional Relationships: Positive/Negative Positive: variables move in the same direction: Example: 1. As income rises, so does voting, 2. As income drops, so does voting. Negative (or Inverse): Variables move in opposite directions: Example: 1. As income rises, homelessness drops.

6. EXAMPLES: Levels of Research: (18) Hypothesis: IV: Cause DV: Effect Positive: IV: Cause DV: Effect They go up together. They go down together.

7. EXAMPLES: Levels of Research: (18) Hypothesis: IV: Cause DV: Effect Negative: IV: Cause DV: Effect The variables move in opposite directions. They have an inverse relationship to each other.

8. Examples of IV and DV Hypothesis: The better the state of the economy, the greater the proportion of votes received by the party of the president. Independent Variable: State of the Economy Dependent Variable: votes Direction: positive Hypothesis: The more negative the advertising in a Senatorial campaign, the lower the turnout rate. Independent Variable: negativity of ads Dependent Variable: turnout Direction: negative

9. Examples of IV and DV: Hypothesis: Media attention is necessary for a candidate to succeed in a primary election. Independent Variable: media attention Dependent Variable: electoral success Direction: positive Hypothesis: Southern states have less party competition than Northern states. Independent Variable: region Dependent Variable: party competition Direction: negative

10. Three (3) Requirements of Causality 1) Correlation: two things tend to occur at the same time (not sufficient to establish causation) Examples: Whenever there is a foreign policy crisis, presidential popularity increases If Catholic, then more likely to oppose abortion. 2) Time Order: cause has to happen before the effect. 3) Non-Spuriousness: to make sure any correlation we observe between the independent and dependent variables is not caused by other factors. …

11. The Quasi Experimental (Natural Experiment) 2) Quasi Experimental It is also called the before and after test: you compare the DV (a Pretest and Posttest) before and after the IV has been applied. Differs from Experimental Design in several ways: Groups are not assigned (we observe some happen, and then go back and sort into experimental and control groups.) Requires a Pretest of DV so amount of change can be measured. …

12. Quasi Experimental Design

13. Meeting Conditions of Causality: Quasi Experimental Correlation: change between pretest and post-test has to be significant (indicating IV had an effect) Time Order: includes measure of DV before and after IV. Non-Spurious: effect of all outside forces is theoretically equal on all subjects. (they are all exposed to same amount of TV ads, thus any changes comes from the IV) …

14. The Nature of Social Science Research Using Numbers in Political Research: Levels of Measurement Numbers serve important functions for researchers, depending on the level of measurement employed. Nominal: Refers to discrete or mutually exclusive categories. Individual cases can only fit into one category at a time. Used to classify, categorize or label. Example: party affiliation, voter, non-voter. Ordinal: Involves the ranking or ordering of cases in terms of the degree to which they possess a certain characteristic. Example: Social class, Measurements of attitudes. Interval-Ratio: Measurements for all cases are expressed in the same units. There are equal intervals between points on a scale and either a real or theoretical zero point. Example: Income, temperature, SAT scores, weight

15. Measures of Central Tendency Measures of Central Tendency: Averages Mean: (Applies to Interval) The mean is average: is calculated by adding up all of the individual values and dividing by the number of cases. Can only be computed for Interval Data. Median: (Applies to Ordinal and Interval) Median is the middle: “half cases have higher values and half have lower values.” Often used to calculate income. Mode: (Applies to Nominal) It refers to the “most frequently occurring value or category.”

16. Skewness 16 Source: http://www.websters-online-dictionary.org/definitions/skewness?cx=partner-pub-0939450753529744%3Av0qd01-tdlq&cof=FORID%3A9&ie=UTF-8&q=skewness&sa=Search#922 __ + LEFT RIGHT LEFT

17. Types of Sampling Probabilistic Sampling (Random): Types: Random, systematic, stratified, cluster Random (most common): everyone has a equal and independent chance of being selected. Challenges: Telephone sampling Not everyone has a phone Not everyone is listed Busy street Not random: Not typical of the population.

18. Disadvantages of Surveys (44) Here are some the disadvantages of polls: 1) Poor response rates. 2) Reactivity: people react to the fact that they are being studied. 3) Resources needed to conduct poll. 4) Meaning of terms change over time (think of polls conducted since 1952) 5) Response Set: disinterested respondents complete poll without much concern. 6) Challenges of being random.