Importance of research
Responding adaptively to the vicissitudes of life is key to wellbeing (Sapolsky, 2007). Vital to adaptively responding to adversity is the ability to regulate emotions in helpful ways (Gross, 2015). Also important is the capacity to grow from stressful occurrences via posttraumatic growth (PTG; Brooks et al., 2021). In the current climate of a global pandemic, our ability to adapt to adversity is especially salient. In this proposed programme of research, we pose two related questions: (1) What role does emotion regulation play in PTG? (2) To what extent do PTG and emotion regulation influence mental and physical health?
Whilst research indicates that emotion regulation and greater PTG are separately associated with better health (e.g., Schenider et al., 2019), little research has investigated how PTG and emotion regulation interact to predict health. Some research has found that, following an adverse event, more adaptive emotion regulation is related to greater PTG in high intensity emotional situations (Orejuela-Dávila et al., 2019), and that different emotion regulation strategies relate differentially to PTG (Larsen & Berenbaum, 2015), but research is sparse.
The results of the proposed research will help us develop resources to enable greater PTG in the general population. As we move into the future, imbuing people with growth strategies may protect against mental and physical illnesses.
We will complete two studies with a representative sample. In study 1 we examine the extent to which emotion beliefs and emotion regulation influence PTG and symptoms of psychological and physical pathology. In study 2 we longitudinally examine the cause-effect relationships between the variables examined in study 1.
We will employ a cross-sectional design to examine the latent profiles that characterise adaptive and maladaptive responses. Study 1 reflects an exploratory investigation that, for the first time, examines links between important cognitive and emotional variables for PTG. These variables are:
• Major stressful life events. The Social Readjustment Rating Scale (SRRS; Holmes & Rahe, 1967) is a 43-item measure of life events occurring in the last six months.
• Emotion beliefs. The Cognitive-Mediation Beliefs Questionnaire (CMBQ; Turner et al., 2021) is a 15-item measure of two specific emotion beliefs: cognitive-mediation beliefs, and stimulus-response beliefs. In addition, the Emotion Beliefs Questionnaire (EBQ; Becerra et al., 2020) is a 16-item measure of two other specific emotion beliefs: controllability, and usefulness.
• Emotion regulation. The Regulation of Emotion Systems Survey (RESS; De France & Hollenstein, 2017), the Cognitive Emotion Regulation Questionnaires (CERQ; Garnefski & Kraaji, 2007), and the Dysfunctional Attitude Scale (form A; DAS-SF1; Beevers et al., 2007) are 38-item, 36-item, and 18-item (respectively) indicators of emotion regulation.
• Posttraumatic growth. The Posttraumatic Growth Inventory-X (PTGI-X; Tedeschi et al., 2017) is a 25-item measure of positive changes in the aftermath of highly stressful events.
• Mental health. The Depression Anxiety and Stress Scale (DASS-21; Lovibond & Lovi, 1995) is a 21-item questionnaire that measures depression, anxiety, and stress.
• Physical health. The physical health questionnaire (PHQ; Schat et al., 2005) is a 14-item measure assessing quality of sleep, digestion problems, headaches, and respiratory problems.
We will stratify participants in relation to the major stress they reported on the SRRS. Those scoring <300 (low stress) will form one group (group 1), and those scoring >300 (moderate-high stress) will form another group (group 2). We will conduct LPA for the two groups separately to account for the effects of major stressful life events on results.
Using LPA in the R package tidyLPA (Rosenberg et al., 2019), we hypothesise that two classes will emerge from the data. Class 1 will be characterised by more adaptive emotion beliefs and emotion regulation tendencies, greater PTG, and greater mental and physical health. Class 2 will be characterised by less adaptive emotion beliefs and emotion regulation tendencies, less PTG, and lower mental and physical health.
In study 2 we use the same measures as in study 1, but collect data across two waves, six months apart. We can longitudinally assess the cause-effect relationships between emotion beliefs and emotion regulation, and PTG and symptoms of psychopathology and physical pathology. In study 2, we will apply cross-lagged panel (path) analysis in SPSS AMOS (Arbuckle, 2009) to test the hypothesised longitudinal effects (e.g., Maxwell & Cole, 2007). It is hypothesised that more adaptive emotion beliefs and emotion regulation tendencies at wave 1, will predict greater PTG, and greater mental and physical health at wave 2 (when controlling for wave 1 PTG, and mental and physical health). It is also hypothesised that greater PTG at wave 1 will predict greater mental and physical health at wave 2 (when controlling for wave 1 mental and physical health).
Sample sizes for LPA can be influenced by number of variables (Tein et al., 2013), but N = 500 is deemed to be sufficient (Spurk et al., 2020). We also conducted a power analysis in G*Power for multiple linear regression calculations (15 predictors, α error probability = 0.05, 1 – β error probability = 0.80). To detect a small effect size (chosen because this study is exploratory), we require 954 participants.
Sample size guidelines for cross-lagged panel analysis (i.e., structural equation modelling) indicate N = 10 participants per variable is suitable (Boateng et al., 2018). To account for attrition at wave 2, and to ensure generalisability of findings, we will use a 20:1 participant:variable criteria. Thus, with the proposed 15 predictors at two waves (totalling 30) we will recruit 600 (30x20) participants for study 2.
1000 participants at £7.20 per hour for 25 minutes = £4,080.
600 participants at £7.20 per hour for 25 minutes on two separate occasions (wave 1 and wave 2, six months apart) = £4,896.
£8,976. We are requesting 10% extra to account for bonus payments. We do not require custom screening.
Total requested = £9,874.
Manchester Metropolitan University has a dedicated space for data storage: (https://researchdata.mmu.ac.uk/). Data will be anonymised and deposited here to showcase and disseminate the data. Following University policy, we are committed to making this data freely available.
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