Data Acquisition and Preprocessing in Studies on Humans: What is Not Taught in Statistics Classes?

Yeyi Zhu, Ladia M. Hernandez, Peter Mueller, Yongquan Dong and Michele R. Forman

The American Statistician, 2013, vol. 67, issue 4, 235-241

Abstract: The aim of this article is to address issues in research that may be missing from statistics classes and important for (bio-) statistics students. In the context of a case study, we discuss data acquisition and preprocessing steps that fill the gap between research questions posed by subject matter scientists and statistical methodology for formal inference. Issues include participant recruitment, data collection training and standardization, variable coding, data review and verification, data cleaning and editing, and documentation. Despite the critical importance of these details in research, most of these issues are rarely discussed in an applied statistics program. One reason for the lack of more formal training is the difficulty in addressing the many challenges that can possibly arise in the course of a study in a systematic way. This article can help to bridge the gap between research questions and formal statistical inference by using an illustrative case study for a discussion. We hope that reading and discussing this article and practicing data preprocessing exercises will sensitize statistics students to these important issues and achieve optimal conduct, quality control, analysis, and interpretation of a study.

Date: 2013
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DOI: 10.1080/00031305.2013.842498

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