Building Good Data Collection & Study Management Tools
Raymond R. Balise, PhD‐ Statistical Programmer and Biostatistician, HRP, SPCTRM
Target Audience:
Research coordinators, fellows, post‐doctoral scholars, students. For anyone who
creates
or helps design data collection/management tools, case report forms, or
research timelines.
Series Description:
Getting good data is the whole key to successful research. A poorly designed
data
capture
system or case report form, or a badly organized and validated spreadsheet, can
render data
un‐analyzable
and useless. Horrible things can go wrong with study data, at every stage of
the collection
and
management process, even here at Stanford. This series of three lectures and a
hands‐on workshop
describes
typical data collection problems and presents ways to avoid common mistakes. The first
course provides an overview of common data collection errors,
describing subject selection, data
collection,
data storage, and data summary break down. The
second course teaches some simple
techniques
(whi ch can be implemented
in a program like Microsoft Excel) that will alleviate many data
collection
and management problems. The final course explains how to visualize data and locate
problems throughout the data collection process.
What Can Possibly Go
Wrong? Do’s and Don’ts of Data Collection
Setting
Up a Tool (Like Microsoft Excel) to Prevent Data Problems and Enable Validation