Presentation is loading. Please wait.

Presentation is loading. Please wait.

Sociology 601 Class 24: November 19, 2009 (partial) Review –regression results for spurious & intervening effects –care with sample sizes for comparing.

Published byColin Ivory Modified over 9 years ago

Similar presentations

Presentation on theme: "Sociology 601 Class 24: November 19, 2009 (partial) Review –regression results for spurious & intervening effects –care with sample sizes for comparing."— Presentation transcript:

1 Sociology 601 Class 24: November 19, 2009 (partial) Review –regression results for spurious & intervening effects –care with sample sizes for comparing models Dummy variables F-tests comparing models Example from ASR 1

2 Review: Types of 3-variable Causal Models Spurious x 2 causes both x 1 and y e.g., age causes both marital status and earnings Intervening x 1 causes x 2 which causes y e.g., marital status causes more hours worked which raises annual earnings No statistical difference between these models. Statistical interaction effects: The relationship between x 1 and y depends on the value of another variable, x 2 e.g., the relationship between marital status and earnings is different for men and women. 2

3 Review: Regression models using Stata see: http://www.bsos.umd.edu/socy/vanneman/socy601/conrinc.do 3

4 Review: Regression models with Earnings Marital status, Age, and Hours worked. 4 Model 0Model 1Model 2 Married10,383.4***8,243.1***7,328.5***7,465.1*** Age702.1***631.6***640.2*** Hours worked281.3***278.3*** Constant35,065.3***8,836.3*-232.1n.s.-493.8n.s. N725 664725 R-square0.0420.0910.1020.133

5 Regression with Dummy Variables 5 Agresti and Finlay 12.3 (skim 12.1-12.2 on analysis of variance) Example: marital status, 5 categories married widowed divorced separated never married

6 Regression with Dummy Variables: example 6 Example: marital status, 5 categories married widowed divorced separated never married. tab marital marital | status | Freq. Percent Cum. --------------+----------------------------------- married | 969 52.12 52.12 widowed | 48 2.58 54.71 divorced | 337 18.13 72.83 separated | 98 5.27 78.11 never married | 407 21.89 100.00 --------------+----------------------------------- Total | 1,859 100.00

7 Dummy Variables: stata programming 7 * create 5 dummy variables from marital status: gen byte married=0 if marital<. replace married=1 if marital==1 gen byte widow=0 if marital<. replace widow=1 if marital==2 gen byte divorced=0 if marital<. replace divorced=1 if marital==3 gen byte separated=0 if marital<. replace separated=1 if marital==4 gen byte nevermar=0 if marital<. replace nevermar=1 if marital==5 * check marital dummies (maritalcheck should =1 for all nonmissing cases) egen byte maritalcheck=rowtotal(married widow divorced separated nevermar) tab marital maritalcheck, missing * shortcut method: tab marital, gen(mar) describe mar* * check new mar dummies (marcheck should =1 for all nonmissing cases) egen byte marcheck=rowtotal(mar1-mar5) tab marital marcheck, missin

8 Regression with Dummy Variables: example 8. regress conrinc mar1-mar4 if sex==1 Source | SS df MS Number of obs = 725 -------------+------------------------------ F( 4, 720) = 9.78 Model | 2.4002e+10 4 6.0006e+09 Prob > F = 0.0000 Residual | 4.4177e+11 720 613572279 R-squared = 0.0515 -------------+------------------------------ Adj R-squared = 0.0463 Total | 4.6577e+11 724 643334846 Root MSE = 24770 ------------------------------------------------------------------------------ conrinc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mar1 | 14111.68 2316.232 6.09 0.000 9564.302 18659.05 mar2 | 11331.78 7143.717 1.59 0.113 -2693.223 25356.79 mar3 | 6709.996 2970.39 2.26 0.024 878.3349 12541.66 mar4 | 8404.298 5074.261 1.66 0.098 -1557.817 18366.41 _cons | 31336.99 1958.271 16.00 0.000 27492.38 35181.59 ------------------------------------------------------------------------------ Omitted category = never married (mar5) b 1 = 14111; Currently married men earn on average $14,111 more than never married men. t= 6.09; p<001; so, statistically significant (more than single men).

9 Regression with Dummy Variables: example 9. regress conrinc mar1-mar4 if sex==1 Source | SS df MS Number of obs = 725 -------------+------------------------------ F( 4, 720) = 9.78 Model | 2.4002e+10 4 6.0006e+09 Prob > F = 0.0000 Residual | 4.4177e+11 720 613572279 R-squared = 0.0515 -------------+------------------------------ Adj R-squared = 0.0463 Total | 4.6577e+11 724 643334846 Root MSE = 24770 ------------------------------------------------------------------------------ conrinc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mar1 | 14111.68 2316.232 6.09 0.000 9564.302 18659.05 mar2 | 11331.78 7143.717 1.59 0.113 -2693.223 25356.79 mar3 | 6709.996 2970.39 2.26 0.024 878.3349 12541.66 mar4 | 8404.298 5074.261 1.66 0.098 -1557.817 18366.41 _cons | 31336.99 1958.271 16.00 0.000 27492.38 35181.59 ------------------------------------------------------------------------------ Omitted category = never married (mar5) b 2 = 11331; Currently widowed men earn on average $11,331 more than never married men. t= 1.59; p=.11; so, not statistically significant. So, no earnings difference between widowed men and never married men.

10 Regression with Dummy Variables: example 10. regress conrinc mar1-mar4 if sex==1 Source | SS df MS Number of obs = 725 -------------+------------------------------ F( 4, 720) = 9.78 Model | 2.4002e+10 4 6.0006e+09 Prob > F = 0.0000 Residual | 4.4177e+11 720 613572279 R-squared = 0.0515 -------------+------------------------------ Adj R-squared = 0.0463 Total | 4.6577e+11 724 643334846 Root MSE = 24770 ------------------------------------------------------------------------------ conrinc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mar1 | 14111.68 2316.232 6.09 0.000 9564.302 18659.05 mar2 | 11331.78 7143.717 1.59 0.113 -2693.223 25356.79 mar3 | 6709.996 2970.39 2.26 0.024 878.3349 12541.66 mar4 | 8404.298 5074.261 1.66 0.098 -1557.817 18366.41 _cons | 31336.99 1958.271 16.00 0.000 27492.38 35181.59 ------------------------------------------------------------------------------ Omitted category = never married (mar5) b 3 = 6709.996; Currently divorced men earn on average $6,710 more than never married men. t= 2.26; p<.05; so, statistically significant (more than single men). Note that b 3 < b 2, but b 3 is statistically significant even though b 2 is not. High standard error of b 2 (because few widowed men 25-54).

11 Inferences: F-tests Comparing models 11 Comparing Regression Models, Agresti & Finlay, p 409: Where: R c 2 = R-square for complete model, R r 2 = R-square for reduced model, k = number of explanatory variables in complete model, g = number of explanatory variables in reduced model, and N = number of cases.

12 Next: Regression with Interaction Effects 12 Examples with earnings: age x gender marital status x gender

Download ppt "Sociology 601 Class 24: November 19, 2009 (partial) Review –regression results for spurious & intervening effects –care with sample sizes for comparing."

Similar presentations

Dummy Variables and Interactions. Dummy Variables What is the the relationship between the % of non-Swiss residents (IV) and discretionary social spending.

Dummy Variables and Interactions. Dummy Variables What is the the relationship between the % of non-Swiss residents (IV) and discretionary social spending.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.16 Original citation: Dougherty, C. (2012) EC220 - Introduction.

Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.16 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 4) Slideshow: interactive explanatory variables Original citation: Dougherty, C. (2012)

Christopher Dougherty EC220 - Introduction to econometrics (chapter 4) Slideshow: interactive explanatory variables Original citation: Dougherty, C. (2012)
Heteroskedasticity The Problem:

Heteroskedasticity The Problem:
1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…

1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…
Sociology 601, Class17: October 27, 2009 Linear relationships. A & F, chapter 9.1 Least squares estimation. A & F 9.2 The linear regression model (9.3)

Sociology 601, Class17: October 27, 2009 Linear relationships. A & F, chapter 9.1 Least squares estimation. A & F 9.2 The linear regression model (9.3)
Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: exercise 3.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.

Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: exercise 3.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Sociology 601 Class 19: November 3, 2008 Review of correlation and standardized coefficients Statistical inference for the slope (9.5) Violations of Model.

Sociology 601 Class 19: November 3, 2008 Review of correlation and standardized coefficients Statistical inference for the slope (9.5) Violations of Model.
Valuation 4: Econometrics Why econometrics? What are the tasks? Specification and estimation Hypotheses testing Example study.

Valuation 4: Econometrics Why econometrics? What are the tasks? Specification and estimation Hypotheses testing Example study.
Sociology 601 Class 21: November 10, 2009 Review –formulas for b and se(b) –stata regression commands & output Violations of Model Assumptions, and their.

Sociology 601 Class 21: November 10, 2009 Review –formulas for b and se(b) –stata regression commands & output Violations of Model Assumptions, and their.
Sociology 601 Class 25: November 24, 2009 Homework 9 Review –dummy variable example from ASR (finish) –regression results for dummy variables Quadratic.

Sociology 601 Class 25: November 24, 2009 Homework 9 Review –dummy variable example from ASR (finish) –regression results for dummy variables Quadratic.
Sociology 601 Class 28: December 8, 2009 Homework 10 Review –polynomials –interaction effects Logistic regressions –log odds as outcome –compared to linear.

Sociology 601 Class 28: December 8, 2009 Homework 10 Review –polynomials –interaction effects Logistic regressions –log odds as outcome –compared to linear.
1 Multiple Regression EPP 245/298 Statistical Analysis of Laboratory Data.

1 Multiple Regression EPP 245/298 Statistical Analysis of Laboratory Data.
Regression Example Using Pop Quiz Data. Second Pop Quiz At my former school (Irvine), I gave a “pop quiz” to my econometrics students. The quiz consisted.

Regression Example Using Pop Quiz Data. Second Pop Quiz At my former school (Irvine), I gave a “pop quiz” to my econometrics students. The quiz consisted.
Introduction to Regression Analysis Straight lines, fitted values, residual values, sums of squares, relation to the analysis of variance.

Introduction to Regression Analysis Straight lines, fitted values, residual values, sums of squares, relation to the analysis of variance.
Addressing Alternative Explanations: Multiple Regression 17.871 Spring 2007.

Addressing Alternative Explanations: Multiple Regression Spring 2007.
1 Review of Correlation A correlation coefficient measures the strength of a linear relation between two measurement variables. The measure is based on.

1 Review of Correlation A correlation coefficient measures the strength of a linear relation between two measurement variables. The measure is based on.
1 Michigan.do. 2. * construct new variables;. gen mi=state==26;. * michigan dummy;. gen hike=month>=33;. * treatment period dummy;. gen treatment=hike*mi;

1 Michigan.do. 2. * construct new variables;. gen mi=state==26;. * michigan dummy;. gen hike=month>=33;. * treatment period dummy;. gen treatment=hike*mi;
Sociology 601 Class 23: November 17, 2009 Homework #8 Review –spurious, intervening, & interactions effects –stata regression commands & output F-tests.

Sociology 601 Class 23: November 17, 2009 Homework #8 Review –spurious, intervening, & interactions effects –stata regression commands & output F-tests.
A trial of incentives to attend adult literacy classes Carole Torgerson, Greg Brooks, Jeremy Miles, David Torgerson Classes randomised to incentive or.

A trial of incentives to attend adult literacy classes Carole Torgerson, Greg Brooks, Jeremy Miles, David Torgerson Classes randomised to incentive or.

Similar presentations

Ads by Google