A Fitting Approach to Construct and Measurement Alignment

Abstract

Many phenomena of interest to management and psychology scholars are dynamic and change over time. One of the primary impediments to the examination of dynamic phenomena has been challenges associated with collecting data at a sufficient frequency and duration to accurately model such changes. Emerging technologies that produce nearly continuous streams of big data offer great promise to address those challenges; however, they introduce new methodological challenges and construct validity concerns. We seek to integrate the emerging big data technologies into the existing repertoire of measurement techniques and advance an iterative process to enhance their measurement fit. First, we provide an overview of dynamic constructs and temporal frameworks, highlighting their measurement implications. Second, we discuss different data streams and feature emerging technologies that leverage big data as a means to index dynamic constructs. Third, we integrate the previous sections and advance an iterative approach to achieving measurement fit, highlighting factors that make some measurement choices more suitable and viable than others. In so doing, we hope to accelerate the advancement of dynamic theories and methods.

The Evolution and Maturation of Teams in Organizations: Convergent Trends in the New Dynamic Science of Teams

“…As this work matures, measurement and theory development will have to address linkages between these lower level biological states and processes, and higher level, abstract constructs such as mutual trust and support or other team processes and emergent states. Innovative approaches to handling these issues have been introduced (Luciano et al, 2018), but much more remains to be done.…”

The Role of Psychological Testing As an Effort to Improve Employee Competency

“…From an HRM perspective, competence is seen as the ability of people. It is necessary to examine the construct that must be measured by the test and then examine how the test approaches this construct (Luciano et. al., 2018).…”

Time to go wild: How to conceptualize and measure process dynamics in real teams with high-resolution

“…Further resources for handling data from different high resolution approaches: Key considerations for collecting video-recordings of teams, see Waller & Kaplan (2018) For examples of collecting data using live observation, see Farh & Chen (2018), Liu et al 2019For collecting team lexical/text data, see Driskell et al (2017), Riedl & Wooley (2017) For tutorials on accessing and processing big data on team dynamics (e.g., sports archives, email corpuses, computer games) see Braun et al (2018). Online archives: www.basketballreference.com; www.nhl.com; https://developer.riotgames.com ; https://x-culture.org/for-researchers/data/ For demonstrative case studies and guidelines using sociometric sensors/wearables, see Chaffin et al (2017), Kim et al (2012), Santaro et al 2015For helpful suggestions on aligning dynamic team constructs with non-traditional measurements, see Luciano et al (2018) How will "raw data" be transformed into a dynamic measure of the phenomenon?…”

“…Crucial to understanding effective team processes is recognizing that they are dynamic phenomena that change over time (e.g., Kozlowski, 1999;Kozlowski, 2015;Leenders, Contractor, & DeChurch, 2016;Luciano, Mathieu, Park, & Tannenbaum, 2018;Rousseau, Aubé, & Savoie, 2006;Schecter, Pilny, Leun, Poole, & Conractor, 2017). First, the nature and extent of team processes can change in response to internal contingencies (e.g., voicing frustrations might stimulate team conflict management) and external contingencies (e.g., during surgery, rapid changes in a patient's body temperature will trigger backup behaviors).…”