1. Research methods in clinical psychology:An introduction for students and practitionersChris Barker, Nancy Pistrang, and Robert Elliott CHAPTER 12 Analysis, interpretation and dissemination

2. Overview • Interpretation • What is the strength and significance of the findings? • What are their scientific and professional implications? • Dissemination • Making the findings known

3. Qualitative analysis: overview • Analysis is an inductive process • Many different approaches • vary in depth of interpretation / inference • method should fit research questions and the data • Generic processes / general principles • Within-case and cross-case analysis

4. Frequently used approaches • Grounded theory • Interpretative phenomenological analysis (IPA) • Discourse analysis • Content analysis

5. Preliminaries to data analysis • Transcriptions • different conventions • anonymity • “Immersion” in the data

6. Generic processes in analysis • identifying meaning • categorising • integrating Note: cyclical, not linear

7. Identifying meaning • identifying and labelling ideas • line-by-line (microanalysis) • meaning units • codes (labels): ‘in vivo’ v. abstract • implicit v. explicit meaning

8. Categorising • themes or categories • method of ‘constant comparison’ • “saturation”

9. Integrating • linking themes / categories • conceptual framework or hierarchical structure

10. Computer packages for qualitative analysis • Good for sorting and searching, linking categories • e.g., ATLAS-ti, NUD*IST

11. Writing up the results • Different models • conventions for different genres of qualitative research • what best captures the essence of the data? • be guided by the research questions • Narrative account • tell a story • describe the phenomenon • illustrate with examples • Table of themes/ tree diagrams

12. Good practice in qualitative analysis • guidelines for evaluating qualitative research, e.g.: • credibility checks • have the research questions been answered? • is the analysis coherent and integrated? Elliott et al. (1999); Willig (2001); Yardley (2000)

13. Quantitative approaches • Measures of strength and significance of the findings

14. Statistical conclusion validity • Was the study sensitive enough? • Large enough sample? • Error minimised in measurement and design? • Do the variables covary? • Were the statistical methods appropriate? • If so, how strongly? • Significance (Shadish, Cook & Campbell, 2002)

15. Significance of the findings • Statistical significance • Effect sizes • Clinical significance

16. Statistical significance • p-value (alpha level) of statistic • e.g., 2 (1) = 4.7, p = 0.03 • null hypothesis testing framework • currently controversial • replace with confidence intervals? • value dependent on sample size

17. Effect size • measure of magnitude • independent of sample size • depends on statistical test • often classified into small, medium and large (see Cohen)

18. Effect sizes: Meta-analysis • Pioneered by Smith & Glass (1977) • Aggregates several studies, using effect sizes • Advantages: • Quantitative effect size index • Can also examine study variables (e.g., investigator allegiance) • However: GIGO (garbage in, garbage out)!

19. Clinical significance • Measure of meaningfulness • do patients actually improve? • “endstate functioning” • Jacobson and Truax (1991) • reliable change • clinical significance cut-offs • “Number needed to treat” • used in evidence-based medicine

20. External validity • Can the findings be generalised across: • persons • settings • times? • Replication • Literal • Operational • Constructive

21. How research is used and interpreted • Dissemination • research as a public activity • feedback to staff and managers • feedback to participants • Publication • Research utilisation • does research affect policy? • models of research utilisation (Weiss, 1986) • Political issues