85
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Scale development and validation are critical to much of the work in the health, social, and behavioral sciences. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and resource-intensive. Further, it is often not a part of graduate training. Therefore, our goal was to concisely review the process of scale development in as straightforward a manner as possible, both to facilitate the development of new, valid, and reliable scales, and to help improve existing ones. To do this, we have created a primer for best practices for scale development in measuring complex phenomena. This is not a systematic review, but rather the amalgamation of technical literature and lessons learned from our experiences spent creating or adapting a number of scales over the past several decades. We identified three phases that span nine steps. In the first phase, items are generated and the validity of their content is assessed. In the second phase, the scale is constructed. Steps in scale construction include pre-testing the questions, administering the survey, reducing the number of items, and understanding how many factors the scale captures. In the third phase, scale evaluation, the number of dimensions is tested, reliability is tested, and validity is assessed. We have also added examples of best practices to each step. In sum, this primer will equip both scientists and practitioners to understand the ontology and methodology of scale development and validation, thereby facilitating the advancement of our understanding of a range of health, social, and behavioral outcomes.

          Related collections

          Most cited references143

          • Record: found
          • Abstract: not found
          • Article: not found

          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

            Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Coefficient alpha and the internal structure of tests

              Psychometrika, 16(3), 297-334
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                11 June 2018
                2018
                : 6
                : 149
                Affiliations
                [1] 1Department of Anthropology and Global Health, Northwestern University , Evanston, IL, United States
                [2] 2Division of Prevention Science, Department of Medicine, University of California, San Francisco , San Francisco, CA, United States
                [3] 3Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina , Columbia, SC, United States
                [4] 4Institute for Global Food Security, School of Human Nutrition, McGill University , Montreal, QC, Canada
                [5] 5Institute for Policy Research, Northwestern University , Evanston, IL, United States
                Author notes

                Edited by: Jimmy Thomas Efird, University of Newcastle, Australia

                Reviewed by: Aida Turrini, Consiglio per la Ricerca in Agricoltura e L'analisi Dell'Economia Agraria (CREA), Italy; Mary Evelyn Northridge, New York University, United States

                *Correspondence: Godfred O. Boateng godfred.boateng@ 123456northwestern.edu

                This article was submitted to Epidemiology, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2018.00149
                6004510
                29942800
                bf531702-9478-4ef3-ac7c-1e067ea11f78
                Copyright © 2018 Boateng, Neilands, Frongillo, Melgar-Quiñonez and Young.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 February 2018
                : 02 May 2018
                Page count
                Figures: 1, Tables: 2, Equations: 0, References: 141, Pages: 18, Words: 15827
                Funding
                Funded by: National Institute of Mental Health 10.13039/100000025
                Award ID: R21 MH108444
                Categories
                Public Health
                Review

                scale development,psychometric evaluation,content validity,item reduction,factor analysis,tests of dimensionality,tests of reliability,tests of validity

                Comments

                Comment on this article