Remote Workers Well-being and Productivity during the COVID-19 Pandemic
Creators: name: Hanel, Paul
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Physical Sciences and Mathematics
subject: Computer Sciences
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Pre-registration Abstract: The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the spreading rate of the COVID-19 virus, these restrictions have far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software professionals currently working from home. Although software professionals are already highly accustomed to working with digital tools in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In particular, we propose to investigate predictors of their well-being and productivity through a three-wave longitudinal study covering psychological, social, situational, and physiological factors, with a focus on the causal relationship of these variables. The results of our study can be used to assess the effectiveness of current work-from-home and general well-being and productivity support guidelines, and to provide tailored insights for software professionals. Design and general approach: We will use a three-wave longitudinal design, with the three waves set one-week apart from each other. A three-wave design allows to test for internal replication: Are the associations of the variables between wave 1 and wave 2 the same as those between waves 2 and 3? The data analysis will consist of two parts. First, we will use the data from time 1 to identify the variables that explain variance in well-being and productivity beyond the other variables. Second, we will use the variables that explained unique variance in well-being and productivity, to test whether they causally predict our two outcomes. Power analysis: The dataset we propose to collect will be composed of a 3-wave study design, covering an extensive set of 62 predictors as identiﬁed in the prior section. Given that we will ﬁrst determine with the data of time 1 which of the in total 62 predictors uniquely explain variance in well-being and productivity, we base our power analysis on it. We expect the strength of the association between the predictors and the outcome to vary between small to large. We assume for our power analysis a medium-to-large effect size of f.square = .25 and a power of .80. A power analysis with G*Power 3.1.9.4 revealed that we would need a sample size of 178 participants. Through pre-screening we identified 220 software engineers who are currently working at least 20h/week and declared to be willing to participate in our three-wave longitudinal study. We invited all of them. Data analysis: In a first step, we will perform an attrition analysis to test whether drop-out between the time points was systematic. For this, we will perform three MANOVAs (time 1 vs time 2, time 1 vs time 3, and time 2 vs time 3). If one of them is significant, we will perform a series of follow-up univariate ANOVAs. In a second step, we will test whether the responses are invariant across time, separately for each variable that is measured by more than one item (e.g., well-being). Establishing measurement invariance allows us to be confident that participants understood the items in the same way across time waves (Davidov et al., 2014). If we fail to reach measurement invariance, this would indicate that how participants understand one or more item has changed over time. Using the R-package lavaan (Rossel, 2012), we will test for configural, metric, and scalar measurement invariance, using the recommended threshold of Delta.CFI &lt; .01 (Cheung &amp; Rensvold, 2002). That is, if the difference in model fit between two models (e.g., metric and scalar) is larger than .01, a scale will not be invariant. For example, if we are unable to establish scalar invariance, this would suggest that the factor score is associated with different item scores across time. If measurement invariance is not established, we will free (unconstrain) one or more items based on their modification indices until measurement invariance is established (Byrne, 1989). Scales that are not invariant after 50\% of the items were unconstrained will be excluded. However, previous research found full time invariance even if the time-points were a year apart (Daniel &amp; Benish-Weisman, 2019), so we are confident that measurement invariance will not be an issue. To analyze the data from all three time-points, we will use a random intercept cross-lagged panel model (Hamaker et al., 2015). We will use the R-package lavaan (Rosseel, 2012) to perform the analysis (Mund &amp; Nestler, 2019). We will include all variables together in the model. Importantly, our statistical approach only allows us to infer Granger causality (Granger, 1969), because we do not (and cannot) address the omitted variable problem. This problem states that we do not know whether a variable we have not measured is driving the effect between, for example, well-being and productivity. Nevertheless, in line with the literature (Hamaker et al., 2015), we use the terms causal and causality when referring to longitudinal data.