We anticipate this primer will be broadly applicable across many disciplines, especially for health, social, and behavioral sciences. To do this, we have created a primer for best practices for scale development. Therefore, our goal is to describe the process for 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. Despite the availability of a large amount of technical literature on scale theory and development ( 1– 7), there are a number of incomplete scales used to measure mental, physical, and behavioral attributes that are fundamental to our scientific inquiry ( 8, 9). Further, many health and behavioral science degrees do not include training on scale development. There are many steps to scale development, there is significant jargon within these techniques, the work can be costly and time consuming, and complex statistical analysis is often required. Scale development is not, however, an obvious or a straightforward endeavor.
Thousands of scales have been developed that can measure a range of social, psychological, and health behaviors and experiences.Īs science advances and novel research questions are put forth, new scales become necessary. Leads to more accurate research findings. The use of multiple items to measure an underlying latent construct can additionally account for, and isolate, item-specific measurement error, which Scales are typically used to capture a behavior, a feeling, or an action that cannot be captured in a single variable or item. Scales are a manifestation of latent constructs they measure behaviors, attitudes, and hypothetical scenarios we expect to exist as a result of our theoretical understanding of the world, but cannot assess directly ( 1). 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. We have also added examples of best practices to each step. In the third phase, scale evaluation, the number of dimensions is tested, reliability is tested, and validity is assessed. 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 second phase, the scale is constructed. In the first phase, items are generated and the validity of their content is assessed. We identified three phases that span nine steps. 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. To do this, we have created a primer for best practices for scale development in measuring complex phenomena. 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. Further, it is often not a part of graduate training. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and resource-intensive. Scale development and validation are critical to much of the work in the health, social, and behavioral sciences.