Activity Number:
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419
- Contributed Poster Presentations: Mental Health Statistics Section
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #323449
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Title:
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Evaluating Paired Categorical Data When the Pairing Is Lost
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Author(s):
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Robert Neal Montgomery* and Amber Watts and Nicole Burns and Eric Vidoni and Jonathan Mahnken
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Companies:
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The University of Kansas Medical Center and The University of Kansas and The University of Kansas and The University of Kansas Medical Center and The University of Kansas Medical Center
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Keywords:
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Bootstrap ;
Alzheimer's Disease ;
Affect Grid ;
Center of Mass
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Abstract:
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We encountered a problem in which the experimental design for a trial called for the use of paired data, but the pairing between subjects was lost during the data collection procedure. Thus we were presented with a data set consisting of pre and post responses but with no way of determining the dependencies between our observed pre and post values. We utilized a bootstrap approach to create a null hypothesized distribution for out test statistic, which was the Euclidean distance between the Center of Mass from two Affect grids. Using this test statistics we addressed whether Self Revelatory performance impacted perceptions of Alzheimer's disease. We rejected the null hypothesis and concluded that the intervention did influence perceptions about the disease, specifically the intervention resulted in more positive perceptions of the disease.
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Authors who are presenting talks have a * after their name.