SCOTT I. DONALDSON is a Senior Research Scientist in the Department of Preventive Medicine at the Keck School of Medicine of USC. Scott completed his Postdoctoral Scholarship in Evaluation, Statistics, and Measurement at the University of California, San Diego School of Medicine. Scott received his PhD in Psychology with a concentration in Evaluation and Applied Research Methods and a co-concentration in Positive Organizational Psychology from Claremont Graduate University. He received an MS in Organizational Psychology from the University of Southern California, and a BA in Psychology from the University of California, Los Angeles. His research uses cutting-edge methodologies to design, measure, and evaluate health and well-being programs. He currently works in the USC Social Media Analytics Lab on a multi-year monitoring and evaluation grant funded by the California Department of Public Health.
Postdoctoral Scholar in Evaluation, Statistics, and Measurement, 2021
University of California, San Diego
PhD in Evaluation and Applied Research Methods, 2019
Claremont Graduate University
MS in Organizational Psychology, 2015
University of Southern California
BA in Psychology, 2014
University of California, Los Angeles
Evaluating Health Promotion Programs
How can we best design individual, workplace, and community-based health promotion programs to improve well-being and prevent lifestyle diseases?
This research question spurred me to systematically review and meta-analyze over 20 years' worth of intervention research that examined positive functioning and performance outcomes (Donaldson et al., 2019). Across 6,027 employees representing 10 nations, I showed that five workplace interventions had a small positive effect on improving desirable outcomes, and a small to moderate effect on reducing undesirable outcomes. I also developed and validated my own scale of Positive Functioning at Work to contribute to the design and measurement of such interventions (Donaldson, 2019; Donaldson & Donaldson, 2021). The Positive Functioning at Work Scale provides a comprehensive measurement tool that can inform future workplace health promotion programs and interventions. It also predicts important performance outcomes, such as turnover intentions, job related affective well-being, plus individual, team, and organizational adaptivity, proactivity, and organizational proficiency.
Statistical Computing and Advanced Analytics in R
My research utilizes R to conduct cutting-edge methodologies, such as item response theory, factor analytic methods, multitrait-multimethod analyses, and interactive dashboards to improve health outcomes.
My doctoral dissertation evaluated the psychometric properties of Positive Functioning at Work using exploratory factor analysis, confirmatory factor analysis, and multiple regression to establish incremental and criterion validity (Donaldson, 2019; Donaldson & Donaldson, 2021). Some of my other scale work includes validating a measure of imposter phenomenon in organizations (Lavelle, Donaldson, & Jones, in press). I am also interested in exploring the role of employee self-report and monomethod bias using multitrait-multimethod analyses. In sample of 220 coworker pairs, I found that positive functioning significantly predicted life satisfaction above and beyond self-report and mono-method bias (Donaldson et al., 2019). This was one of the first empirical studies to test Positive Functioning at Work beyond the sole use of self-reports, and illustrates that Positive Functioning can be a strong predictor of well-being. In my organizational consulting practice, I attempt to produce data-driven insights for my clients, some of which include companies such as Accenture. For example, I build data visualizations and analytic dashboards based on implementation and outcome intervention data so that human resource managers can easily use the information to make evidence-based decisions. My philosophy of practice is spring boarded on a storytelling narrative of data that makes findings digestible to clients that may not have a background in quantitative methodologies.
Research Design and Methodology - Teaching Evaluations
Statistics - Teaching Evaluations