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Multivariate Regression and Data Collection

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

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Multivariate Regression and Data Collection

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  1. Multivariate Regression and Data Collection 11/21/2013

  2. Readings • Chapter 8 (pp 187-206) • Chapter 9 Dummy Variables and Interaction Effects (Pollock Workbook)

  3. Opportunities to discuss course content

  4. Office Hours For the Week • When • Friday 7:00AM-3:00PM • Monday 7:00AM-1:00 PM • Tuesday 8-12 • And by appointment

  5. Course Learning Objectives • Students will be able to interpret and explain empirical data. • Students will achieve competency in conducting statistical data analysis using the SPSS software program. • Third, students will learn the basics of polling and be able to analyze and explain polling and survey data.

  6. Multiple regression

  7. 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!

  8. This allows us to model additive relationships

  9. Regression Outputs • These have 3 parts • The Model Summary • ANOVA • The Variables/Model

  10. Part I Things that Begin with “r”

  11. With So Many, How do we know? • There are many R's out there: • lower case "r" for correlation • upper case "R" for regression

  12. What the R’s look like The R Square Adj R-Square, the preferred measure

  13. Part II The Analysis of variance (ANOVA)

  14. 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.

  15. 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

  16. Part III The Model

  17. The Model • What it tells us • Variable relationships and direction • Variable significance • Variable Strength

  18. Old Friends Beta Values T-Tests Test the significance of each independent variable on the dependent variable Accept or reject the null for that variable • Measure the change in the dependent variable • Show the direction of the relationship

  19. 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.

  20. Looking at our Model Beta Values T-Score- Significance

  21. Trying it out

  22. 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

  23. 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

  24. 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)

  25. 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)

  26. 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.

  27. 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

  28. 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.

  29. Data collection

  30. Collecting Primary Data • Document Analysis • Direct Observation • Interview Data

  31. Document Analysis

  32. Document Analysis (The Written Record) • What is it • When to use it

  33. Types of Document Analysis • The Episodic Record • The Running Record

  34. Limitations and Advantages

  35. Observation

  36. Observation • What is it • Types of Observation

  37. Problems of Observation • Reactivity • Ethics

  38. Which Method to use?

  39. The Literature Review

  40. 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

  41. 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

  42. Questionnaire Construction

  43. Question Style • Open Ended (advantages & disadvantages) • Closed Ended (advantages & disadvantages)

  44. Demographic Questions • Who are you? • These tend to be overrated • Don’t get too personal!

  45. Behavior Questions • What do you do, and how often? • Knowing behavior is a good dependent and independent variable

  46. Opinion and Attitude Questions • What do you think? • Easy to Answer

  47. Knowledge/Factual Questions • Use sparingly

  48. Question Order is Key • Intro and Filter • First Questions • Major Questions • Final Questions- demographics

  49. How you should Phrase Questions • Language (be clear) • One question 1 concept

  50. Information Level

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