ACCP Spring Forum
ACCP Academy Programming
April 25-26, 2020

Statistical Issues

Saturday, April 25, 2020 from 8:00 AM to 12:00 PM CDT

Available for 4.00 hours of CPE credit
Activity Number: 0217-0000-18-140-L04-P

This module, part of the Research and Scholarship Academy core programming, will guide participants through data analysis considerations based on outcome variables for their research projects. Faculty will review the appropriate statistical tests applied to specific types of data. Data associations (correlation and regression) will be discussed. Analysis considerations when projects use qualitative data sets will also be discussed.

To receive credit toward a certificate of completion, participants must be enrolled in the ACCP Academy and sign the sign-in sheet provided during the session. For those enrolled in the ACCP Academy, this module must be completed before subsequent programming.

Faculty: Gary L. Cochran, Pharm.D., S.M.
Associate Professor of Pharmacy Practice, University of Nebraska Medical Center, College of Pharmacy, Omaha, Nebraska
View Biography
Gary L. Cochran, Pharm.D., S.M.
Faculty: Jacqueline McLaughlin, Ph.D., M.S.
Assistant Professor, Educational Innovation and Research; Director, Office of Strategic Planning and Assessment, University of North Carolina Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
View Biography
Jacqueline McLaughlin, Ph.D., M.S.
Learning Objectives
1. Describe basic statistical concepts.
2. Describe commonly used descriptive statistics for continuous variables.
3. Explain the use of common statistical test for continuous variables.
4. Explain correlation and linear regression.
5. Define qualitative research.
6. Describe basic approaches to analyzing qualitative data.
7. Describe analysis of a dataset with a dichotomous outcome variable.
8. Describe what variables should be included in a demographic table.
9. Identify the appropriate statistical test for demographic variables and dichotomous outcome variable.
10. Determine whether a regression model is needed.
11. Determine what variables should be included in a logistic regression model.