Understanding and Teaching Unequal Probability of Selection
AbstractThis paper focuses on econometrics pedagogy. It demonstrates the importance of including probability weights in regression analysis using data from surveys that do not use simple random samples (SRS). We use concrete, numerical examples and simulation to show how to effectively teach this difficult material to a student audience. We relax the assumption of simple random sampling and show how unequal probability of selection can lead to biased, inconsistent OLS slope estimates. We then explain and apply probability weighted least squares, showing how weighting the observations by the reciprocal of the probability of inclusion in the sample improves performance. The exposition is non-mathematical and relies heavily on intuitive, visual displays to make the content accessible to students. This paper will enable professors to incorporate unequal probability of selection into their courses and allow students to use best practice techniques in analyzing data from complex surveys. The primary delivery vehicle is Microsoft Excel®. Two user-defined array functions, SAMPLE and LINESTW, are included in a prepared Excel workbook. We replicate all results in Stata® and offer a file for easy analysis. Documented code in Excel and Stata allows users to see each step in the sampling and probability weighted least squares algorithms.
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Bibliographic InfoPaper provided by DePauw University, Department of Economics and Management in its series Working Papers with number 2011-01.
Date of creation: Oct 2011
Date of revision:
Publication status: Published in Journal of Econometric Methods, Volume 2, Issue 1, March 2013, pages 101-112
Contact details of provider:
Web page: http://www.depauw.edu/academics/departments-programs/economics--management/
More information through EDIRC
complex survey; simulation; unequal probability; weighted regression;
Other versions of this item:
- Raghav, Manu & Barreto, Humberto, 2011. "Understanding and teaching unequal probability of selection," MPRA Paper 32334, University Library of Munich, Germany.
- A2 - General Economics and Teaching - - Economic Education and Teaching of Economics
- A22 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Undergraduate
- A23 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Graduate
- C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
- C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- William J Carrington & John L Eltinge & Kristin McCue, 2000. "An Economist's Primer on Survey Samples," Working Papers 00-15, Center for Economic Studies, U.S. Census Bureau.
- Barreto,Humberto & Howland,Frank, 2006. "Introductory Econometrics," Cambridge Books, Cambridge University Press, number 9780521843195, December.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Manu Raghav).
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