Difference between revisions of "Selection Bias"

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<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, nstrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>
 
  
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= Overview =
  
''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">Source:</font></span>''<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial"> ''Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.'' </font></span>
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A '''selection bias''' is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified.<ref name="Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.html">Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.htmlfckLR</ref>
  
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= Solutions =
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Randomized experiments solve the problem of selection bias by generating an experimental [[Control Groups|control group]] of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In [[Quasi-Experimental or Non-Experimental Designs|quasi-experimental designs]], statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.<ref name="Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)">Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)fckLR</ref>
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= Further Information<br/> =
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*[http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.]
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*[[Portal:Impacts|Impact Portal on energypedia]]
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*[[Impact_Evaluation_-_Mixed_Methods|Impact Evaluation - Mixed Methods]]
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= References =
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[[Category:Impacts]]
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[[Category:Questionnaires_/_Interviews]]

Latest revision as of 15:54, 6 October 2014

Overview

A selection bias is a statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified.[1]


Solutions

Randomized experiments solve the problem of selection bias by generating an experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples. In quasi-experimental designs, statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.[2]


Further Information


References

  1. Businessn Dictionary: http://www.businessdictionary.com/definition/selection-bias.htmlfckLR
  2. Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C.: http://siteresources.worldbank.org/INTISPMA/Resources/handbook.pdf (p.5)fckLR