Principle Assumptions of Regression Analysis: Testing, Techniques, and Statistical Reporting of Imperfect Data Sets

Candace Flatt, Ronald Lynn Jacobs

Research output: Contribution to journalArticlepeer-review

Abstract

The Problem: Journal pages are filled with articles that scarcely mention the assumptions behind the chosen statistical techniques and models. Based on questionable foundations, the ultimate conclusions are intended to shape academia and guide practitioners. Violations of the underlying assumptions can result in biased and misleading forecasts, confidence intervals, and scientific insights. The Solution: The field of human resource development (HRD) is equipped to present these assumptions clearly and concisely to ensure the integrity of statistical analysis and subsequent conclusions. Testing the principle assumptions of regression analysis is a process. As such, the presentation of this process in a systems framework provides a comprehensive plan with step-by-step guidelines to help determine the optimal statistical model for a particular data set. The goal of this article is to provide practitioners a Regression Development System that can be adapted to organizational performance as well as information that can be used to evaluate the strength of journal articles. The Stakeholders: Quantitative researchers, practitioners, instructors, and students.

Original languageEnglish (US)
Pages (from-to)484-502
Number of pages19
JournalAdvances in Developing Human Resources
Volume21
Issue number4
DOIs
StatePublished - Nov 1 2019

Keywords

  • assumptions
  • quantitative
  • regression

ASJC Scopus subject areas

  • Organizational Behavior and Human Resource Management

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