Open AccessScale Development and Validation: Methodology and Recommendations
Author(s) -
Kevan W. Lamm,
Alexa J. Lamm,
Don W. Edgar
Publication year - 2020
Publication title -
journal of international agricultural and extension education
Language(s) - English
Resource type - Journals
eISSN - 2831-5960
pISSN - 1077-0755
DOI - 10.5191/jiaee.2020.27224
Subject(s) - scale (ratio) , computer science , context (archaeology) , data science , process (computing) , data collection , set (abstract data type) , rigour , external validity , management science , psychology , mathematics , engineering , paleontology , social psychology , statistics , physics , geometry , quantum mechanics , biology , programming language , operating system
The importance of valid and reliable data and its collection is fundamental to empirical research; however, there remain inconsistent approaches to creating robust scales capable of capturing both valid and reliable data, particularly within international agricultural and extension education contexts. Robust scale development consists of five areas for validation: content, response process, internal structure, external structure, and consequential. The purpose of this guide was to provide methodological recommendations to improve scale development rigor and adoption and to provide a set of functional principles to aid researchers and practitioners interested in capturing data through developed, or adapted, scales. Additionally, the information summarized provide a benchmark upon which to evaluate the rigor and validity of reported scale results. A consistent framework should provide a common lexicon upon which to examine scales and associated results. Proper scale development and validation will help ensure research findings accurately describe intended underlying concepts, particularly within an international agricultural and extension education context. Keywords: scale development, validity, quantitative analysis