‘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 9th-11th April, 2011 • Rationale for conducting your own evaluations • How to manage the numbers • Suggested evaluation material • Scoring, inputting and analysing • Presentation of results • 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) • 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 • 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 The most important decisions to make when considering evaluation are: 1) Design 2) Evaluation measures 6 • 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 Be aware of ethical issues • • • • • Consent, information sheet, free to withdraw Protection of participants, e.g. wellbeing Anonymity Data storage Disposal of data 8 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) 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 Comparison against norms Published data in other studies 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 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 • 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 • Family income • Rurality - access issues • First language in the home / how many languages? • Any current medication? 15 • Mean & SD • Change scores • Effect sizes • Excel • Inputting data • Analysing data • Graphs/chart production • Writing up results 16 • 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 Minus 1, 2, 3… SD Mean Plus 1, 2, 3… SD Even simple spreadsheet programmes like Excel will allow you to conduct simple statistics Intervention N= 19 Control N = 11 Gender 16 female, 3 male 9 female, 2 male Age M = 41.89 (SD = 13.05) Range 24-64 M = 44.54 (SD = 11.60) Range 24-58 20 • FREE! • 2-5 minutes to complete • 14 positively phrased items • Total score (min 14 max 70) Mean before SD before Mean after SD after Mean Pooled Effect change SD size WEM WBS- I 14.16 3.66 17.84 3.30 3.68 3.48 1.06 WEM WBS-C 15.18 3.49 14.45 3.50 -0.72 3.50 -.21 Cohen’s 1988 guidelines: difference between means divided by pooled SD. 0.3 = clinically useful change, 0.5 medium effect, 0.8 = large effect 22 • Change scores are useful • Easy and simple way of evaluating change • Change scores should demonstrate improvements in behaviour outcome 23 24 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 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) 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 • Title • Abstract (summary) • Introduction • Method Participants Intervention Measures Design • Results • Discussion • References 29 • 21-item self-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 • 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). • Rationale for conducting your own evaluations • How to manage the numbers • Evaluation measures • Scoring, inputting and analysing • Presentation of results 32 • Mean score • =AVERAGE(data) • Standard deviation • =STDEV(data) • T-test • Data Analysis -> t-test • • Independent = Different groups Paired = Matched groups

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# Evaluation workshop