# Problem of Missing Data

(Difference between revisions)
 Revision as of 08:21, 3 February 2008 (view source)Stenstro (Talk | contribs)← Older edit Latest revision as of 20:53, 7 September 2009 (view source)Doug (Talk | contribs) (One intermediate revision not shown) Line 1: Line 1: - '''Why is missing data a problem?''' Missing values means reduced sample size and loss of data. [[Image:Fe40.png]] - You conduct research to measure empirical reality so missing values thwart the purpose of research. The less data collected, the less data that can be analyzed. + '''Why is missing data a problem?''' Missing values means reduced sample size and loss of data. [[Image:Fe40.png]] - You conduct research to measure empirical reality so missing values thwart the very purpose of research. The less data collected, the less data that can be analyzed, and reducing the data that can be analyzed reduces statistical power, which is the ability to detect real relationships in the data. Missing values may also indicate bias in the data. [[Image:Fe40.png]] - If the missing values are non-random, then the study is not accurately measuring the intended constructs. The results of your study may have been different if the missing data was not missing. See [[Missing Values | Why do missing values occur?]] for the difference between random and non-random missing data. Missing values may also indicate bias in the data. [[Image:Fe40.png]] - If the missing values are non-random, then the study is not accurately measuring the intended constructs. The results of your study may have been different if the missing data was not missing. See [[Missing Values | Why do missing values occur?]] for the difference between random and non-random missing data. + + + ---- ---- - ◄ Back to [[Research_Tools |Research Tools mainpage]] + ◄ Back to [[Analyzing Data]] page

## Latest revision as of 20:53, 7 September 2009

Why is missing data a problem? Missing values means reduced sample size and loss of data. - You conduct research to measure empirical reality so missing values thwart the very purpose of research. The less data collected, the less data that can be analyzed, and reducing the data that can be analyzed reduces statistical power, which is the ability to detect real relationships in the data.

Missing values may also indicate bias in the data. - If the missing values are non-random, then the study is not accurately measuring the intended constructs. The results of your study may have been different if the missing data was not missing. See Why do missing values occur? for the difference between random and non-random missing data.

◄ Back to Analyzing Data page