A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data

We extend propensity score methodology to incorporate survey weights from complex survey data and compare the use of multiple linear regression and propensity score analysis to estimate treatment effects in observational data from a complex survey. For illustration, we use these two methods to estimate the effect of gender on information technology (IT) salaries. In our analysis, both methods agree on the size and statistical significance of the overall gender salary gaps in the United States in four different IT occupations after controlling for educational and job-related covariates. Each method, however, has its own advantages which are discussed. We also show that it is important to incorporate the survey design in both linear regression and propensity score analysis. Ignoring the survey weights affects the estimates of population-level effects substantially in our analysis.

並列關鍵字

Complex survey data ； information technology careers ； multiple linear regression ； propensity scores ； salary ； gender gap ； SESTAT

被引用紀錄

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國際替代計量

A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data

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