GRA 5917: Input Politics and Public Opinion Basic regression (including interaction effects) in political economy GRA 5917 Public Opinion and Input Politics.

Slides:



Advertisements
Similar presentations
GRA 5917 Public Opinion and Input Politics. Lecture September 16h 2010 Lars C. Monkerud, Department of Public Governance, BI Norwegian School of Management.
Advertisements

11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Multiple Regression W&W, Chapter 13, 15(3-4). Introduction Multiple regression is an extension of bivariate regression to take into account more than.
There are at least three generally recognized sources of endogeneity. (1) Model misspecification or Omitted Variables. (2) Measurement Error.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
Chapter 11 Contingency Table Analysis. Nonparametric Systems Another method of examining the relationship between independent (X) and dependant (Y) variables.
The Simple Linear Regression Model: Specification and Estimation
Common Factor Analysis “World View” of PC vs. CF Choosing between PC and CF PAF -- most common kind of CF Communality & Communality Estimation Common Factor.
Statistics 350 Lecture 21. Today Last Day: Tests and partial R 2 Today: Multicollinearity.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Multivariate Data Analysis Chapter 4 – Multiple Regression.
GRA 5917: Input Politics and Public Opinion Logistic regression in political economy GRA 5917 Public Opinion and Input Politics. Lecture, September 9th.
GRA 5917: Input Politics and Public Opinion Introduction GRA 5917 Public Opinion and Input Politics. Introductory Lecture, August 19th 2010 Lars C. Monkerud,
Week Lecture 3Slide #1 Minimizing e 2 : Deriving OLS Estimators The problem Deriving b 0 Deriving b 1 Interpreting b 0 and b 1.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
GRA 5917: Input Politics and Public Opinion Data manipulation and descriptive statistics GRA 5917 Public Opinion and Input Politics. Lecture, August 26th.
Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Lecture II-2: Probability Review
Simple Linear Regression Analysis
Multivariate Probability Distributions. Multivariate Random Variables In many settings, we are interested in 2 or more characteristics observed in experiments.
SPSS Statistical Package for Social Sciences Multiple Regression Department of Psychology California State University Northridge
Lecture 5 Correlation and Regression
Week 9: QUANTITATIVE RESEARCH (3)
Objectives of Multiple Regression
Lecture 8 Analysis of Variance and Covariance Effect of Coupons, In-Store Promotion and Affluence of the Clientele on Sales.
Research Tools and Techniques The Research Process: Step 7 (Data Analysis Part C) Lecture 30.
1 MF-852 Financial Econometrics Lecture 10 Serial Correlation and Heteroscedasticity Roy J. Epstein Fall 2003.
Correlation and Regression Used when we are interested in the relationship between two variables. NOT the differences between means or medians of different.
Research Project Statistical Analysis. What type of statistical analysis will I use to analyze my data? SEM (does not tell you level of significance)
Correlation Patterns.
Constructing/transforming Variables Still preliminary to data analysis (statistics) Would fit comfortably under Measurement A bit more advanced is all.
1st meeting: Multilevel modeling: introduction Subjects for today:  Basic statistics (testing)  The difference between regression analysis and multilevel.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Examining Relationships in Quantitative Research
6-1 Introduction To Empirical Models Based on the scatter diagram, it is probably reasonable to assume that the mean of the random variable Y is.
Introduction to SPSS. Object of the class About the windows in SPSS The basics of managing data files The basic analysis in SPSS.
Multiple Regression Petter Mostad Review: Simple linear regression We define a model where are independent (normally distributed) with equal.
CADA Final Review Assessment –Continuous assessment (10%) –Mini-project (20%) –Mid-test (20%) –Final Examination (50%) 40% from Part 1 & 2 60% from Part.
Measurement Models: Exploratory and Confirmatory Factor Analysis James G. Anderson, Ph.D. Purdue University.
1 11 Simple Linear Regression and Correlation 11-1 Empirical Models 11-2 Simple Linear Regression 11-3 Properties of the Least Squares Estimators 11-4.
Correlation & Regression Chapter 15. Correlation It is a statistical technique that is used to measure and describe a relationship between two variables.
Scatterplots & Regression Week 3 Lecture MG461 Dr. Meredith Rolfe.
Analysis of Variance and Covariance Effect of Coupons, In-Store Promotion and Affluence of the Clientele on Sales.
G Lecture 81 Comparing Measurement Models across Groups Reducing Bias with Hybrid Models Setting the Scale of Latent Variables Thinking about Hybrid.
Examining Relationships in Quantitative Research
The Simple Linear Regression Model: Specification and Estimation ECON 4550 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s.
Mixed ANOVA Models combining between and within. Mixed ANOVA models We have examined One-way and Factorial designs that use: We have examined One-way.
Chapter 6: Analyzing and Interpreting Quantitative Data
Tutorial I: Missing Value Analysis
Using SPSS Note: The use of another statistical package such as Minitab is similar to using SPSS.
THE SCIENTIFIC METHOD: It’s the method you use to study a question scientifically.
Nonparametric Statistics
Getting the most out of interactive and developmental data Daniel Messinger
Copyright © 2008 by Nelson, a division of Thomson Canada Limited Chapter 18 Part 5 Analysis and Interpretation of Data DIFFERENCES BETWEEN GROUPS AND RELATIONSHIPS.
Statistics and probability Dr. Khaled Ismael Almghari Phone No:
BINARY LOGISTIC REGRESSION
Dr. Siti Nor Binti Yaacob
Research Methodology Lecture No :25 (Hypothesis Testing – Difference in Groups)
Multiple Regression.
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Dr. Siti Nor Binti Yaacob
12 Inferential Analysis.
6-1 Introduction To Empirical Models
Multiple Regression Chapter 14.
12 Inferential Analysis.
Simple Linear Regression
Getting the most out of interactive and developmental data
Presentation transcript:

GRA 5917: Input Politics and Public Opinion Basic regression (including interaction effects) in political economy GRA 5917 Public Opinion and Input Politics. Lecture, September 2nd 2010 Lars C. Monkerud, Department of Public Governance, BI Norwegian School of Management

Excercise from last week… 3)The median measures in the WVS *AGGR.sav file are simply the response category code medians. For some variables (e.g. x011 - ”number of children”) this is an appropriate estimate of the substantive median. For other (continuous scale) phenomena a more reasonable median measure can be constructed. For instance, this is done in Gable and Hix (2005; see note 6) for the country-year median of the WVS e033 – ”left-right self positioning” variable.Gable and Hix (2005; see note 6) a)Using the methodology of Gable and Hix (2005), calculate the median for e033 for all combinations of countries and years in the WVS surveys. Save the estimates md_est in a file called lr_md.sav containing country-year observations for the median estimate and the identifiers (cname and year). (Tip: Work with a trivariate individual level file, count individuals in and out of the median category, aggregate and keep aggergates in the file until the final stage…)

Regression analysis… Given the correct model… … and X A and X B are correlated… and e (as usual) a random individual error unrelated to any X… excluding X B from etsimation will give biased estimate of X A … (unmeasured X B will be included in the error term) but, if X A and X B are uncorrelated, omitting X A or X B will give correct effect estimates (betas)…

Regression analysis in SPSS OLS regression with Analyze > Regression > Linear…

Regression analysis in SPSS Move the dependent and independent variables to Dependent and Independent(s) frames respectively + a host of options (for selecting different models, assessing improved model fit, requesting covariances etc.)

Excercises (I) 1)Like Gabel and Hix (2005), you would like to look into the relationship between a country’s electoral system and form of governement on the one hand and governement spending on the other, and how this might be viewed after one takes into account popular spending preferences. Download and save P&T’s 85cross…sav set from It’s Learning (the folder containing today’s lecture material) and… a)manipulate the lr_md.sav data that you have just assembled, keeping the earliest record from the 1990s with a valid md_est value. Are there any differences between records in this data and the data put to use by Gable and Hix (2005; Appendix)? b)Combine the manipulated lr_md.sav data with the data in the 85cross…sav and peform a regreession analyses where the spending varaiable cgexp is regressed on the variables in Gabel and Hix’s analyses named Original and model 1 (Gabel and Hix 2005; table 1). Next, do a regression where you also include md_est as a regressor. Compare the results to G&H’s result and P&T’s original result?

Estimated marginal effect (  ): A large effect in substantive terms? Standard error: Measure of uncertainty of  Corresponding t-test (t=  /std.err.) for rejecting H 0 :  =0

Interaction effects in basic regression analysis Given the model… …simple rearrangment yields that is…

Interaction effects in basic regression analysis Model with interaction terms… …entails symmetry: Effect of one variable contingent on the other and vice versa …terms are mostly not to be interpreted in isolation:  A effect of X A when X B =0 (but, consider centering of variables to rescale an interesting value of X B to 0) …additive terms are not to be seen as unconditional effects

Interaction effects in basic regression analysis In model with interaction terms… …significance of effect of one varaible varies with value of other variable: that is…

Interaction effects in basic regression analysis Need estimated variances and covariances. In SPSS: Click statistics Request variance- covariance matrix

Interaction effects in basic regression analysis Variance-covariance matrix: