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:
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. Units of Analysis (22) Two common Units of Analysis: (26)
Individuals: indicates either people in general, or a specific type of
person (elected official, union member, etc). It can also refer to
institutions, such as interest groups, corporations, political parties. What
you are doing is looking at how an “individual” unit, a person, a party is
behaving. Polls are the best source of data on people in general, whereas
their can be other sources of data on specific classes of individuals. (26)
Groups: analyze group behavior, such as performance on some test. You don’t go down to the individual. How did Democratic state legislators vote on a particular issue, as a group? You use aggregates, as opposed to individual data points.
It is not always easy to determine the unit of analysis. Yet the choice of which unit to use is extremely important. (22)

9. Units of Analysis (22)
Units of Analysis: Exam Scores Individuals: Student Score Compare to: Other Students
Groups: Average Class Score
Compare to: Other Classes
Student: 85
Class: 90

10. Units of Analysis (22)
Units of Analysis: Political Parties Individuals: Dem. Or Rep. Party Compare to: Other Parties
Groups: Party System
Compare to: Other Party Systems
Democrats
Amer. Party System
Republicans

11. Ecological Fallacy: (22-23)
Ecological Fallacy erroneously drawing conclusions about individuals from groups. Solution: only draw conclusion about the units of analysis from which the data is actually drawn.
Example of Ecological Fallacy: Afro-Americans and Wallace
Student found a strong positive (directional) relationship between proportion of a county that was Afro-American and those that voted for George Wallace and assumed Afro-Americans voted for Wallace. (22-23)
In fact, virtually no minorities supported Wallace. All the student really could say is that counties with a high number of Afro-Americans voted for Wallace. The county, not Afro-Americans was the unit of analysis.

12. Units of Analysis (22) Individuals: Voters Compare to: Other Voters
Units of Analysis: Votes for Wallace
Counties, not necessarily Black voters supported Wallace.
Individuals: Voters Compare to: Other Voters
Groups: County
Compare to: Other Counties
Black
Supported Wallace
Black
White

13. 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

14. 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

15. 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. …

16. 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. …

17. Quasi Experimental Design

18. 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) …

19. Three levels of statistical analysis (17)
Nominal: Mutually Exclusive Data
Most basic level: Refers to discrete or mutually exclusive categories: age, party affiliation, voter, non-voter. Individuals can only fit into one category at a time.
Ordinal: Ranked Data
Next level of analysis: ranked data. It enables us to able to “rank” cases in relation to each other. It is data that can be placed in Comparative order: which candidate received the most votes?
Interval/Ratio
Allows use to measure how far apart measurements are. It has a zero point so it is effective at measuring change. …

20. 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.”

21. 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.