Exam 1 Review

GOVT 120

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

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.

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.

…

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.

EXAMPLES: Levels of Research: (18)

Hypothesis:

IV: Cause DV: Effect

Positive:

IV: Cause DV: Effect

They go up together.

They go down together.

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 .

Units of Analysis (22)

Two common Units of Analysis: (26)Individuals: indicates either people in general, or a specific type ofperson (elected official, union member, etc). It can also refer toinstitutions, such as interest groups, corporations, political parties. Whatyou are doing is looking at how an “individual” unit, a person, a party isbehaving. Polls are the best source of data on people in general, whereastheir 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)

Units of Analysis (22)

Units of Analysis: Exam Scores

Individuals: Student Score

Compare to: Other Students

9

Student: 85Student: 85

Groups: Average Class Score

Compare to: Other Classes

Class: 90Class: 90

Units of Analysis (22)

Units of Analysis: Political Parties

Individuals: Dem. Or Rep. Party

Compare to: Other Parties

10

DemocratsDemocrats

Groups: Party System

Compare to: Other Party Systems

Amer. Party SystemAmer. Party System

RepublicansRepublicans

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 WallaceStudent 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.

Units of Analysis (22)

Individuals: Voters

Compare to: Other Voters

12

BlackBlack

Groups: County

Compare to: Other Counties

Supported WallaceSupported Wallace

WhiteWhite

Units of Analysis: Votes for WallaceCounties, not necessarily Black voters supported Wallace.

BlackBlack

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 EconomyDependent 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: turnoutDirection: negative

Examples of IV and DV:

Hypothesis: Media attention is necessary for a candidate to succeed in a primary election.

Independent Variable: media attentionDependent Variable: electoral successDirection: positive

Hypothesis: Southern states have less party competition than Northern states.

Independent Variable: regionDependent Variable: party competitionDirection: negative

Three (3) Requirements of Causality

1) Correlation: two things tend to occur at the same time (not sufficientto establish causation)

Examples:Whenever there is a foreign policy crisis, presidential popularity increasesIf 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 observebetween the independent and dependent variables is not caused byother factors.…

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.

…

Quasi Experimental Design

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)

• …

Three levels of statistical analysis (17)Nominal: Mutually Exclusive DataMost 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 DataNext 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/RatioAllows use to measure how far apart measurements are. It has a zero point

so it is effective at measuring change. …

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.”

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 phoneNot everyone is listed

Busy streetNot random: Not typical of the population.