TY - JOUR
T1 - A Fitting Approach to Construct and Measurement Alignment
T2 - The Role of Big Data in Advancing Dynamic Theories
AU - Luciano, Margaret M.
AU - Mathieu, John E.
AU - Park, Semin
AU - Tannenbaum, Scott I.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was conducted with support from the United States Army Research Institute (Contract: W911NF-15-1-0014; The Development of Construct Validation of Unobtrusive Dynamic Measures of Team Process and Emergent States).
Publisher Copyright:
© 2017, © The Author(s) 2017.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
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U2 - 10.1177/1094428117728372
DO - 10.1177/1094428117728372
M3 - Article
AN - SCOPUS:85047466168
SN - 1094-4281
VL - 21
SP - 592
EP - 632
JO - Organizational Research Methods
JF - Organizational Research Methods
IS - 3
ER -