Are 'Webographic' or Attitudinal Questions Useful for Adjusting Estimates From Web Surveys Using Propensity Scoring?
Inference from Web surveys may be affected by non-random selection of Web survey participants. One approach to reduce selection bias is to use propensity scores and a parallel phone survey. This approach uses demographic and additional so-called Webographic or lifestyle variables to balance observed differences between Web survey respondents and phone survey respondents. Here the authors investigate some of the Webographic questions used by Harris Interactive, a commercial company specializing in Web surveys. Their Webographic questions include choice of activities such as reading, sports and traveling and perceptions about what would constitute a violation of privacy. They use data from an existing probability sample of respondents over 40 who are interviewed over the phone, and a corresponding sample of respondents interviewed over the Web. They find that Webographic questions differentiate between on and offline populations differently than demographic questions. In general, propensity score adjustment of variables in the Web survey works quite well for a number of variables of interest (including home ownership and labor force participation). For two outcomes, (having emotional problems and often experiencing pain) the process of adjusting for demographic variables leads to the discovery of an instance of SimpsonÕs paradox, implying a differential mode effect or differential selection. They interpret this mainly as the result of a mode effect, where sensitive questions are more likely to receive a positive response over the Internet than over the phone.
|Date of creation:||Jun 2007|
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- Guido Imbens, 2000.
"Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,"
Econometric Society World Congress 2000 Contributed Papers
1166, Econometric Society.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
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