Distress,wellbeing, and growth amidst COVID-19: Considering the dynamic interplay between positive and negative anticipatory emotions

Although negative anticipatory emotions are typically seen as risk factors for poorer psychological outcomes over COVID-19, emotion theorists suggest that this risk may be attenuated if balanced by the experience of positive emotion. Thus, the current study examined whether interactions between positive and negative anticipatory emotions were concurrently associated with psychological distress and greater personal wellbeing/posttraumatic growth (PTG) at three distinct periods (i.e., pre-lockdown, during lockdown, post-lockdown), and whether associations varied by these three COVID-19 time periods. The study utilizes two large longitudinal Australian samples, surveyed in 2020 prior to, during, and after a strict 4-month lockdown that occurred in Australia. Overall, positive emotions attenuated the adverse psychological outcomes arising from higher levels of negative emotion (i.e., higher psychological distress and lower personal wellbeing). Observed effects varied according to COVID-19 threat exposure. Specifically, the interaction was significantly associated with psychological distress prior to the lockdown for Sample 2, and during the lockdown for both samples. The interaction was significantly associated with wellbeing (Sample 2) prior to, and during, the lockdown but only marginally associated post-lockdown. The interaction, however, was not significantly associated with PTG (Sample 1). The results suggest that it is valuable for future research to consider greater emotional complexity (i.e., mixed emotions) over COVID-19, and other stressors more generally, to encompass a more nuanced understanding of resilience.     

Status faking on the SVIB-M

A study and replication of students faking interest in high status occupations on the Strong Vocational Interest Blank for Men. In general, the faking was successful in shifting the specific target occupational scales and especially the occupational level scale. Other scales are affected, many with decisive upward or downward shifts. Demonstration is made of the extent to which the faking condition led to differences in the overall test results. Various aspects of the general problem of test faking are reviewed.     

Factor analysis by generalized least squares

Aitken's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are seale free and asymptotically efficient. The estimates are computed by a rapidly converging Newton-Raphson procedure. A new technique is used to deal with Heywood cases effectively.The work on this project was done when the first author was Research Statistician at Educational Testing Service, Princeton, N. J. The second author was in part supported by a grant from the Research Committee of the University of Wisconsin Graduate School. The authors wish to thank Michael Browne for many helpful comments and Marielle van Thillo for valuable assistance in the numerical computations.     

An individual difference model for the multidimensional analysis of preference data

A model for the analysis of paired comparison data is presented which combines features of the BTL-model with features of the Unfolding model. The model is metric, mathematically tractable, and has an exact algebraic solution. Since it is multidimensional and allows for individual differences, it is thought to be more realistic for some choice situations than either the Thurstone model or the BTL-model. No claim is made that the present model will be appropriate for all conceivable choice situations. Rather, it is argued that the fact that it is explicitly falsifiable is a point in its favor.This work was supported by a grant of the United States Public Health Service (1 R03 MH19139 01 MSM) and a David Ross (XR) grant of the Purdue Research Foundation (PRF 2132), both of which are gratefully acknowledged. We also owe thanks to Dr. Terry Cooper, whose careful scrutiny of an earlier draft of this paper allowed us to reduce the number of errors from n to n-1.     

Some new results on factor indeterminacy

Some relations between maximum likelihood factor analysis and factor indeterminacy are discussed. Bounds are derived for the minimum average correlation between equivalent sets of correlated factors which depend on the latent roots of the factor intercorrelation matrix . Empirical examples are presented to illustrate some of the theory and indicate the extent to which it can be expected to be relevant in practice.