Download presentation

Presentation is loading. Please wait.

Published byColin Ivory Modified over 2 years ago

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

Similar presentations

OK

Quantile Regression Prize Winnings – LPGA 2009/2010 Seasons www.lpga.com Kahane, L.H. (2010). “Returns to Skill in Professional Golf: A Quantile Regression.

Quantile Regression Prize Winnings – LPGA 2009/2010 Seasons www.lpga.com Kahane, L.H. (2010). “Returns to Skill in Professional Golf: A Quantile Regression.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Best ppt on body language Ppt on online library management Human rights for kids ppt on batteries Ppt on peak load pricing example Ppt on action research in education Ppt on you can win book Ppt on rainwater harvesting structures Ppt on image classification using fuzzy logic Ppt on business plan of any product Ppt on drainage system for class 9