1. ‘How to evaluate your own work’ Dr. Catrin Eames Centre for Mindfulness Research and Practice c.eames@bangor.ac.uk Workshop for the ‘Mindfulness Now’ conference, CMRP, Bangor University 9 th -11 th April, 2011

2. Rationale for conducting your own evaluations How to manage the numbers Suggested evaluation material Scoring, inputting and analysing Presentation of results

3. To evaluate whether an intervention is worth doing (efficacy trial) To evaluate whether it works in real life settings (effectiveness trial) To establish for whom is might work and why (moderators and mediators of outcome) To establish what service users think about the intervention (qualitative methodologies)

4. To challenge beliefs What we feel might be true anecdotally is not always supported by large scale research studies To explore new applications Research plays a role in exploring whether existing interventions can be applied to new sub-groups

5. NHS Trusts or organisations may require you to conduct evaluation It is imperative to be able to demonstrate improvement associated with your groups Helps to maintain funding and/or win new funding 5

6. The most important decisions to make when considering evaluation are: 1) Design 2) Evaluation measures 6

7. It is very important that you have baseline (before) and outcome measures (after) It is also important that you have the same measures on everyone Use evidence based interventions Deliver intervention with fidelity 7

8. Be aware of ethical issues Consent, information sheet, free to withdraw Protection of participants, e.g. wellbeing Anonymity Data storage Disposal of data 8

9. One group post-test only design Audit of satisfaction with a service One group pre-test -post-test design Common design in clinical practice Problem of attributing change to treatment (i.e., causality)

10. Non-equivalent groups post-test only design No pre-test data available Cannot assume similarity before treatment Non-equivalent groups pre-test -post-test design Often one group is control Classic effectiveness study design

11. Comparison against norms Published data in other studies

12. For…. Many of our evaluations we have used: Demographic Questionnaire Beck Depression Inventory Hospital Anxiety and Depression Questionnaire Five Factor Mindfulness Questionnaire WHO Well-being Index 5 Warwick Edinburgh Mental Health Wellbeing Scale 12

13. What do you need to know? Do you want to compare outcomes of:- Older versus younger participants? Males versus females? Different areas? Any other ideas? 13

14. Working/ Unemployed? Prior mood disorder history? Progression/take up training/employment Been on another course/taster? Cultural background/family history Teacher effect on outcomes Gender Level of engagement prior to course 14

15. Family income Rurality - access issues First language in the home / how many languages? Any current medication? 15

16. Mean & SD Change scores Effect sizes Excel Inputting data Analysing data Graphs/chart production Writing up results 16

17. For evaluation purposes you are most interested in change from start to end. Easiest way is to look at MEAN difference Add up all baseline scores and divide by number of participants, do same for follow-up. Standard Deviation: the standard deviation is the most commonly used measure of statistical dispersion. Simply put, it measures how spread out the values in a data set are. 17

18. Mean  Plus 1, 2, 3… SDMinus 1, 2, 3… SD 

19. Even simple spreadsheet programmes like Excel will allow you to conduct simple statisticsExcel

20. 20 Intervention N= 19Control N = 11 Gender16 female, 3 male9 female, 2 male AgeM = 41.89 (SD = 13.05) Range 24-64 M = 44.54 (SD = 11.60) Range 24-58

21. FREE! 2-5 minutes to complete 14 positively phrased items Total score (min 14 max 70)

22. 22 Cohen’s 1988 guidelines: difference between means divided by pooled SD. 0.3 = clinically useful change, 0.5 medium effect, 0.8 = large effect Mean before SD before Mean after SD after Mean change Pooled SD Effect size WEM WBS- I 14.163.6617.843.303.683.481.06 WEM WBS-C 15.183.4914.453.50-0.723.50-.21

23. Change scores are useful Easy and simple way of evaluating change Change scores should demonstrate improvements in behaviour outcome 23

24. 24

25. Cohen’s D - difference between mean of two groups divided by pooled S.D. of both groups Glass Delta - difference between mean of two groups divided by mean SD of control group Note: both of these can be used to look at post treatment group differences or treatment group pre and post differences

26. Cohen’s D Mean of intervention - Mean of control/ (SD of intervention + SD of control)/2 Glass’s delta Mean of intervention - Mean of control/ (SD of control)

27. There were XX participants in total from two group conditions (Intervention N = XX, Control N = XX). The mean age was XX (range xx-xx, SD = XX ). At baseline the two groups DID/DID NOT differ significantly on XX/YY. The mean at baseline was ??(SD=XX) and at follow-up was ?? (SD = XX), respectively. The mean change score was therefore?? with an effect size of ?? This study suggests the intervention has impacted on participants’ self reported well-being. Furthermore this change is statistically significant as demonstrated by t-test analyses, t(20), =2.61, p<.05 27

28. 28

29. Title Abstract (summary) Introduction Method Participants Intervention Measures Design Results Discussion References 29

30. 21-item s elf-report inventory measuring the severity of characteristic attitudes & symptoms associated with depression Each item contains four possible responses which range in severity from 0 ( I do not feel sad) to 3 ( I am so sad or unhappy that I can’t stand it) Score of 10-18 = mild to moderate depression Score of 19-29 = moderate to severe depression 30-63 = severe depression Purchase from: http://www.pearson-uk.com 30

31. 39-item self-report questionnaire used to assess five different facets of mindful awareness. non-reactivity to inner experience, observing, acting-with-awareness, describing and non-judging of experience. 5-point Likert scale (1= never o very rarely true; 5 = very often or always true).

32. Rationale for conducting your own evaluations How to manage the numbers Evaluation measures Scoring, inputting and analysing Presentation of results 32

33. Mean score =AVERAGE(data) Standard deviation =STDEV(data) T-test Data Analysis -> t-test Independent = Different groups Paired = Matched groups