Coping with COVID: Use,factor structure,and health behavior implications of specific emotion regulation strategies during the coronavirus pandemic

This series of studies examined U.S. individuals' use of specific emotion regulation/coping strategies during the early months of the COVID-19 pandemic, investigated the factor structure among strategies during this universally experienced stressor, and the extent to which these factors predicted engagement in COVID-related health-promoting behaviors. In Study 1, participants (N = 520) rated their use of 17 strategies for coping with pandemic-related stress during the past 24 h. Differences emerged in strategy use across demographic groups (age, race, income). Results of exploratory factor analysis suggest a factor structure grouping strategies in terms of goals beyond emotion regulation per se, rather than phases of the emotion process or a binary adaptive versus maladaptive distinction. In Study 2 (N = 264), participants reported daily on their coping strategy use and weekly on their engagement in COVID-specific health behaviors for 22 days. Results of confirmatory factor analysis replicate the factor structure found in Study 1. Some significant associations of coping strategy use with health-promoting behaviors were observed, but these were sporadic and largely involved baseline measures rather than predicting change over time. Theoretical and practical implications are discussed.     

The relationship between numerical mapping abilities,maths achievement and socioeconomic status in 4- and 5-year-old children

Background

Early numeracy skills are associated with academic and life-long outcomes. Children from low-income backgrounds typically have poorer maths outcomes, and their learning can already be disadvantaged before they begin formal schooling. Understanding the relationship between the skills that support the acquisition of early maths skills could scaffold maths learning and improve life chances.

Aims

The present study aimed to examine how the ability of children from different SES backgrounds to map between symbolic (Arabic numerals) and non-symbolic (dot arrays) at two difficulty ratios related to their math performance.

Sample

Participants were 398 children in their first year of formal schooling (Mean age = 60 months), and 75% were from low SES backgrounds.

Method

The children completed symbolic to non-symbolic and non-symbolic to symbolic mapping tasks at two difficulty ratios (1:2; 2:3) plus standardized maths tasks.

Results

The results showed that all the children performed better for symbolic to non-symbolic mapping and when the ratio was 1:2. Mapping task performance was significantly related to maths task achievement, but low-SES children showed significantly lower performance on all tasks.

Conclusion

The results suggest that mapping tasks could be a useful way to identify children at risk of low maths attainment.     

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.