This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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425
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #308541 |
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Title:
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Bias and Efficiency Effects from Unaccounted Correlation
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Author(s):
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Adam Sima*+ and Roy Sabo
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Companies:
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Virginia Commonwealth University and Virginia Commonwealth University
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Address:
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Theater Row, 730 E. Broad St., Richmond, VA, 23298,
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Keywords:
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repeated measures ;
bias ;
correlation ;
longitudinal
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Abstract:
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Cannon et al. provide an example of the detrimental consequences that may occur when a researcher ignores correlation in longitudinal settings. This work has been extended to show exactly how bias is introduced in the regression parameter estimates in models with either a common or separate covariates among the repeated measures. A simulation study shows how these biases are affected for different parameter values and also how the results from hypotheses are affected. Data examples are also provided to show how these traits appear in practice. The results show that major bias is introduced in both testing and estimation stages if the correlation is not taken into account.
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