A Longitudinal Study of the Well-being of Students Using the Student Wellbeing Process Questionnaire (Student WPQ)

Background: Previous research shows that the Student Well-being Process Questionnaire (Student WPQ) has good content validity, construct validity

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INTRODUCTION
The Well-Being Process Questionnaire [ The well-being of university students has also been studied for a long time [14] and high levels of mental health problems have been reported in undergraduate students [15,16].Many of the same concepts in the WPQ have also been applied in this research.A student version has been developed [17] and this has also been shown to have good construct validity.
Most of the studies that have used the WPQ have had cross-sectional designs which makes interpretation of causality difficult.Galvin [18] carried out a longitudinal study and showed the predictive value of the WPQ scales in a sample of clinical students.The aim of the present study was to examine the test-re-test reliability of the Student WPQ scales in a short longitudinal study (10 weeks).It also examined whether measures taken at time one could predict outcomes at time 2 (a cross-lag analysis) and whether changes in predictors were associated with changes in wellbeing.

METHODS
This study was carried out with the approval of the ethics committee, School of Psychology, Cardiff University, and the informed consent of the volunteers.

Participants
Eighty-seven undergraduate and postgraduate students aged 18 to 47 years from across the university took part in the study.Mean age was 21 years, 75% were female.The majority (71%) identified as white, with the remaining identifying as Indian (2%) or Chinese (17%).Nine percent did not indicate ethnicity.

Design
Short-term longitudinal design over 10 weeks.

Materials
Single-item measures of positive personality (self-efficacy; self-esteem; and optimism) social support (esteem support; tangible support; belonging support; and emotional support), student-focused demands (challenges to your development; time pressure; academic dissatisfaction; romantic problems; societal annoyance; social mistreatment; and friendship problems) and negative coping (wishful thinking; blame self; and avoidance) were the well-being predictors.The wellbeing outcomes covered positive items (happiness, positive affect, and life satisfaction) and negative ones (life stress, anxiety and depression).An overall wellbeing score was calculated by subtracting the sum of the negative items from the sum of the positive ones.All items were responded to using a 10 point rating scale.The actual questions are shown in [8].

Procedure
The study was advertised on the university notice board and those interested were provided with further information and a unique identifier in order to link responses at each time point.Scores at time 1 were taken 4 weeks into the academic year, those at time 2 were taken 10 weeks later.Each stage of the study included a consent and instructions sheet at the beginning of the questionnaire and respondents could not continue beyond the consent form on the first page before consenting to participate.Debrief was presented at the end of the final stage of the research.Respondents were paid £5 at the completion of each time period.Ethical approval was provided by Cardiff University Psychology Department Ethics Committee.

Analysis Procedure
Test-retest correlations were used to assess the stability of scores on the scales at 10 weeks (time 1 -time 2).Cross-sectional analyses of the time 1 and time 2 data were used to assess the associations between the predictor variables and the outcomes.A cross-lag analysis was carried out to examine associations between the time 1 predictors and time 2 outcomes.Changes between time one and time two were calculated and associations between changes in the predictors and outcomes examined.

RESULTS
Table 1 shows the test-re-test correlations for the predictor variables and the well-being outcome score.All of these correlations were significant showing some stability over time.This was especially true for the positive personality score and the social support score, although even variables that one might expect to reflect current state (e.g.demands and well-being) showed quite high test-re-test correlations.
Cross-sectional analyses were carried out on the time 1 and time 2 data.These results are shown in Tables 2 and 3.At time 1, well-being was predicted by positive personality and demands, whereas at time 2 well-being was predicted by these variables and social support.
The next analysis (a cross-lag analysis) examined whether scores at time 1 predicted well-being at time 2 (see Table 4).These results showed that positive personality was the strongest predictor of positive well-being.
A final analysis examined change scores (time 2time 1).These results are shown in Table 5.Once again, positive personality (changes in positive personality) predicted positive well-being (changes in positive well-being).

DISCUSSION
The present study aimed to continue to extend the use of an adapted DRIVE model and the use of the WPQ to a student sample.A measure of student-specific demands was used and a measure of social support created [17].Other measures (coping and personality) were from the original WPQ.The results from a previous crosssectional study [17] showed that these measures could predict both positive and negative outcomes.In the cross-sectional study, there was no evidence of interactions between the variables and a combined effects score was calculated by summing the predictors.All three outcomes showed the predicted changes across the quartiles of the combined effects score.Overall, these cross-sectional results confirm that a short measuring instrument can demonstrate predicted variation in well-being measures.A limitation of the original student WPQ was that it was cross-sectional and it was suggested that a longitudinal study was highly desirable.
The study described in this article is the first to use the Student WPQ in a longitudinal study.The test-re-test correlations for the predictor variables and the well-being outcome score were all significant.Cross-sectional, cross-lag and change score analyses all showed significant effects of the predictor variables, with positive personality being the strongest predictor of positive well-being.Future research can extend the present approach by examining individual components of well-being in more detail.Similarly, other dimensions can be incorporated into the research to investigate the influence of lifestyle (e.g.diet, exercise, sleep, alcohol, and drugs) and mental fatigue [19], while controlling for the established predictors studied here.
Another extension will be to use the WPQ in intervention studies which aim to promote positive well-being or reduce negative outcomes such as stress, anxiety, and depression.

CONCLUSIONS
In summary, the present study has shown that the Student WPQ has good test-re-test reliability.Similar results were found in the cross-sectional analyses of the two-time points, cross-lag analyses, and change score analyses.Positive personality (high levels of optimism, self-efficacy, and self-esteem) was found to be the strongest predictor of positive well-being in all of the analyses.
Pl e a s e n o t e: C h a n g e s m a d e a s a r e s ul t of p u blis hi n g p r o c e s s e s s u c h a s c o py-e di ti n g, fo r m a t ti n g a n d p a g e n u m b e r s m a y n o t b e r efl e c t e d in t his ve r sio n.Fo r t h e d efi nitiv e ve r sio n of t hi s p u blic a tio n, pl e a s e r ef e r t o t h e p u blis h e d s o u r c e.You a r e a d vis e d t o c o n s ul t t h e p u blis h e r's v e r sio n if yo u wi s h t o cit e t hi s p a p er. Thi s v e r sio n is b ei n g m a d e a v ail a bl e in a c c o r d a n c e wit h p u blis h e r p olici e s.