multivariate regression 11/19/2013. readings chapter 8 (pp 187-206) chapter 9 dummy variables and...
TRANSCRIPT
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Multivariate Regression
11/19/2013
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Readings
• Chapter 8 (pp 187-206)
• Chapter 9 Dummy Variables and Interaction Effects (Pollock Workbook)
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OPPORTUNITIES TO DISCUSS COURSE CONTENT
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Office Hours For the Week
• When– Tuesday 10-12– Thursday 8-12– And by appointment
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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.
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BIVARIATE REGRESSION ANALYSISRatio and Intervals!
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Bivariate Linear Regression
• Bivariate linear regression is an important statistical technique in the Social Sciences. It allows us to measure the effects of an independent variable on a dependent variable.
• It regresses all the values on to a line that best describes the relationship.
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Rules for Regression
• If you have a Ratio/Interval Dependent variable that takes on at least 11 values
• You need data that are not nominal• If you have 30 or more cases (N>30) • If you have a linear relationship. It will not
work with curvilinear or exponential relationships.
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The Dependent Variable
The Constant, where the line crosses the y-axis
The Regression Equation!
The Slope and direction of the line
The independent variable
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Weight and MPG
• What is the Constant?• A positive or Negative relationship?• Is it significant relationship and and Why• What is the predicted MPG of a car that
weighs 2000 lbs?
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MULTIPLE REGRESSION
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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!
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The Model
• Y is the dependent variable• a is the constant• b1x1- first beta coefficient and first
independent variable• b2x2- first beta coefficient and first
independent variable
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This allows us to model additive relationships
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Computing a Multiple regression
• You put more than one independent variable where you say "independents“
• D.V. Women09 (% of women in parliament) – IV1- womenyear2 (date
of enfranchisement) – IV2- pr-sys (pr system) – IV3- pmat12_3
(postmaterialism)
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Regression Outputs
• These have 3 parts1. The Model Summary 2. ANOVA3. The Variables/Model
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THINGS THAT BEGIN WITH “R”Part I
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With So Many, How do we know?
• There are many R's out there: – lower case "r" for correlation – upper case "R" for regression
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Correlation (small r)
• r- the pearson’s product movement
• r2- The squared pearson correlation coefficient.
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The R-Square (large R)• this is a measure of association
for the entire model • This is a PRE measure that tells
us what percent of the total variation in the dependent variable is explained by our model.
• The higher the number, the better our model predicts.
• We can increase the R value of our model, by increasing the number of variables, even insignificant ones!
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Adjusted R-Square
• this "adjusts" for the addition of independent variables. In equations with more than 1 independent variable, it will always be smaller than the R Square.
• This is the preferred measure and a PRE model
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What the R’s look like
The R Square
Adj R-Square, the preferred measure
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THE ANALYSIS OF VARIANCE (ANOVA)
Part II
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ANOVA
• A Way of testing the null hypothesis for the entire model- We Look at the F-Score
• H0 = that there is not relationship between our variables and the dependent variable
• HA = There is at least 1 significant variable in the model
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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.
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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
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THE MODELPart III
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The Model
• What it tells us– Variable relationships and direction– Variable significance– Variable Strength
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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
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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.
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Looking at our Model
Beta Values
T-Score- Significance
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TRYING IT OUT
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Turning Texas Blue (through Turnout)
• Data• Dependent Variable – Turnout 2012
• Independent Variables- – COLGRADCollege %– HISPPER Hispanic percent – BLACKPER African-
American percent
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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
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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
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State Example
• Dependent Variable- gay_support (higher is more supportive on Lax-Phillips)
• 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)
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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)
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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.
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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
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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.
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Data collection
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Collecting Primary Data
• Direct Observation
• Document Analysis
• Interview Data
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DOCUMENT ANALYSIS
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Document Analysis (The Written Record)
• What is it
• When to use it
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Types of Document Analysis
• The Episodic Record
• The Running Record
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Limitations and Advantages