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Title: Indiana - Analysis of Categorical Data

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Title Indiana - Analysis of Categorical Data
Contributor/Contact Siu L. Hui, PhD
Institution Indiana
Acknowledgment Please cite the appropriate contributors/authors/contacts when using or adapting these materials.
Format PDF slides
Attachment Analysis of Categorical Data
URL_Web_Link

Type of Course Single Presentation
Level of Course Beginning
Audience Graduate Student
Topics Description Biostatistics Course for Health Care Providers: A Short Course

Objectives of this course:
* Know the basic principles, assumptions, and a few basic methods in analyzing categorical data
* Understand and interpret the results of categorical data analyses in the literature.
* Know the assumption needed for sample size estimation
Software Program

Datasets

Data

Keywords Inference
Estimation
Hypothesis testing
Categorical variables
Binary variables
Examples of one-sample problems
Point estimate
Confidence interval
Example: Phase 2 clinical trial
Example: Aminocentesis and pregnancy loss
Prospective randomized controlled trial
Retrospective case control study
Cross-sectional study
Example: Randomized study to reduce antibiotic use
Chi-Square Tests: Large Sample (RxC)
Chi-Square Tests: Fisher's exact test
Chi-Square Tests: McNemar's test
See Also

Type of Activity Course Slides
Disclaimer The views expressed within CTSpedia are those of the author and must not be taken to represent policy or guidance on the behalf of any organization or institution with which the author is affiliated.
Topic revision: r3 - 08 Oct 2012 - 14:40:23 - MaryBanach
 

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