Transcript Political Research and Statistics

Multivariate Regression and Data
Collection
11/21/2013
Readings
• Chapter 8 (pp 187-206)
• Chapter 9 Dummy Variables and Interaction
Effects (Pollock Workbook)
OPPORTUNITIES TO DISCUSS
COURSE CONTENT
Office Hours For the Week
• When
– Friday 7:00AM-3:00PM
– Monday 7:00AM-1:00 PM
– Tuesday 8-12
– And by appointment
Course Learning Objectives
1. Students will be able to interpret and explain
empirical data.
2. Students will achieve competency in
conducting statistical data analysis using the
SPSS software program.
3. Third, students will learn the basics of
polling and be able to analyze and explain
polling and survey data.
MULTIPLE REGRESSION
What we can do with it
• Test the significance, strength and direction of
more than one independent variable on the
dependent variable, while controlling for the
other independent variables.
• We can compare the strength of each
independent variable against each other
• We can examine an entire model at one time!
This allows us to model additive
relationships
Regression Outputs
• These have 3 parts
1. The Model Summary
2. ANOVA
3. The Variables/Model
Part I
THINGS THAT BEGIN WITH “R”
With So Many, How do we know?
• There are many R's out there:
– lower case "r" for correlation
– upper case "R" for regression
What the R’s look like
The R Square
Adj R-Square,
the preferred
measure
Part II
THE ANALYSIS OF VARIANCE
(ANOVA)
What The F-Score tells us
• It is like a chi-square for
Regression. The F-score tells
us if we have a significant
regression model
• If the F-Score is not significant,
we accept the null hypothesis
(no relationship).
• It usually tells us at least one
of our variables is significant.
• It is a way of examining the
entire regression.
The F-Score
• We look at the Sig value and use the p<.05
measurement
• In the model above, our p value is .001
– We Reject the null hypothesis
– At least one variable is significant
Part III
THE MODEL
The Model
• What it tells us
– Variable relationships and direction
– Variable significance
– Variable Strength
Old Friends
Beta Values
• Measure the change in the
dependent variable
• Show the direction of the
relationship
T-Tests
• Test the significance of each
independent variable on the
dependent variable
• Accept or reject the null for
that variable
Standardized Beta Coefficients
• They show us the
variables which have
the greatest influence.
• These are measured in
absolute value
• The larger the
standardized beta, the
more influence it has on
the dependent variable.
Looking at our Model
Beta Values
T-ScoreSignificance
TRYING IT OUT
Another One
• D.V. Palin_therm-post
(Feeling thermometer for
Palin 0-100)
• IV's
– enviro_jobs (Environment
vs. jobs tradeoff) 0=envir,
1=middle, 2=jobs
– educ_r- education in years
– Gunown- do you own a
gun (1=yes, 5=no)
– relig_bible_word (Is Bible
actual word of God?)
1=yes, 0=No
Another one from the states
• Gay Rights involves many concepts. The Lax-Phillips index
uses content validity to address this issue at the state level.
It examines the support for the following issues
–
–
–
–
–
–
–
–
–
Adoption
Hate Crimes legislation
Health Benefits
Housing Discrimination
Job Discrimination
Marriage Laws
Sodomy Laws
Civil Unions
It then averages these to get a statewide level
State Example
• Dependent Variablegay_support (higher is
more supportive on LaxPhillips)
• Independent Variables
– relig_import (% of people
in state that say religion
provides a great deal of
guidance)
– south (1=south, 0=
NonSouth
– abortlaw (restrictions on
abortion)
Tautology
• it is tempting to use independent variables
that are actually components of the
dependent variable.
• How you will notice this:
– if the dependent variables seem to be measures
of each other (human development vs. education)
they probably are, (female literacy and literacy
rate)
– High Adj. R-Squares (above .900)
Multicollinearity
• Your independent variables should not only be
independent of the d.v. (non tautological) but
they should be independent of each other!
• Picking independent variables that are very
closely related, or are actually part of the same
measure What can happen here is these variables
will negate the influence of each other on the
dependent variable.
Symptoms of Multicollinearity
• the multiple regression equation is statistically
significant (big R values, even a significant
ANOVA), but none of the t-ratios are statistically
significant
• the addition of the collinear independent variable
radically changes the values of the standardized
beta coefficients (they go from positive to
negative, or weak to strong), without a
corresponding change in the ADJ R-square.
• Variables, that you would swear on a stack of
bibles should be related, are not
Solving Tautology and Multicolinearity
• Solving tautology- Drop the independent
variable
• What to do About Multicollinearity
– run bivariate correlations on each of your
variables. If the r-square value is >.60.
– You will want to drop one of the variables, or
combine them into a single measure.
Data collection
Collecting Primary Data
• Document Analysis
• Direct Observation
• Interview Data
DOCUMENT ANALYSIS
Document Analysis (The Written
Record)
• What is it
• When to use it
Types of Document Analysis
• The Episodic Record
• The Running Record
Limitations and Advantages
OBSERVATION
Observation
• What is it
• Types of Observation
Problems of Observation
• Reactivity
• Ethics
Which Method to use?
THE LITERATURE REVIEW
What it Should Contain
• Bring the reader up to
speed on the status of the
research (what has been
done)
• Establish face validity
(why I am using these
variables)
• Point out potential
problems with previous
research
What it should Contain
• what are the main texts
in this area
• what are the general
theories in this area
• how has the question
been measured in the
past
QUESTIONNAIRE CONSTRUCTION
Question Style
• Open Ended (advantages & disadvantages)
• Closed Ended (advantages & disadvantages)
Demographic Questions
• Who are you?
• These tend to be
overrated
• Don’t get too personal!
Behavior Questions
• What do you do, and
how often?
• Knowing behavior is a
good dependent and
independent variable
Opinion and Attitude Questions
• What do you think?
• Easy to Answer
Knowledge/Factual Questions
• Use sparingly
Question Order is Key
• Intro and Filter
• First Questions
• Major Questions
• Final Questions- demographics
How you should Phrase Questions
• Language (be clear)
• One question 1 concept
Information Level