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Reading and interpreting quantitative intervention research syntheses: an introduction

Reading and interpreting quantitative intervention research syntheses: an introduction

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Reading and interpreting quantitative intervention research syntheses: an introduction

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  1. Reading and interpreting quantitative intervention research syntheses: an introduction Steve Higgins, Durham University Robert Coe, Durham University Mark Newman, EPPI Centre, IoE, London University James Thomas, EPPI Centre, IoE, London University Carole Torgerson, IEE, York University Part 2

  2. Acknowledgements • This presentation is an outcome of the work of the ESRC-funded Researcher Development Initiative: “Training in the Quantitative synthesis of Intervention Research Findings in Education and Social Sciences” which ran from 2008-2011. • The training was designed by Steve Higgins and Rob Coe (Durham University), Carole Torgerson (Birmingham University) and Mark Newman and James Thomas, Institute of Education, London University. • The team acknowledges the support of Mark Lipsey, David Wilson and Herb Marsh in preparation of some of the materials, particularly Lipsey and Wilson’s (2001) “Practical Meta-analysis” and David Wilson’s slides at: (accessed 9/3/11). • The materials are offered to the wider academic and educational community community under a Creative Commons licence: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License • You should only use the materials for educational, not-for-profit use and you should acknowledge the source in any use.

  3. Session 2 1.30 pm Reading and interpreting a meta- analysis Overview of challenges to effective meta-analysis 3.00 pm Break 3.15 pm Summary, conclusions and evaluation 4.00 pm Finish

  4. Recap • Should be conducted as part of a systematic (or at least transparent) review • Meta-analysis is the statistical combination of research study findings to answer a specific question • Uses a common metric - effect size - to aggregate and explore the findings across studies

  5. Stages of synthesis What data are available? By addressing review question according to conceptual framework What are the patterns in the data? Including study, intervention, outcomes and participant characteristics What is the question? Theories and assumptions in the review question How does integrating the data answer the question? To address the question (including theory testing or development). Can the conceptual framework be developed? What new research questions emerge? How robust is the synthesis? For quality, sensitivity, coherence & relevance. What does the result mean? (conclusions) What is the result? Cooper, H.M. (1982) Scientific Guidelines for Conducting Integrative Research Reviews Review Of Educational Research 52; 291 See also: Popay et al. (2006) Guidance on the Conduct of Narrative Synthesis in Systematic Reviews. Lancaster: Institute for Health Research, Lancaster University.

  6. Procedures for a meta analysis What is the question? • Key question • Search/ retrieval strategy • Inclusion/ exclusion criteria • Coding • Analysis • Synthesis What data are available? What patterns are in the data? How does integrating the data answer the question? How robust is the synthesis? What us the result? What are the conclusions?

  7. Overview • Interpreting a meta-analysis • Forest plots and other forms of data presentation • Issues in meta-analysis • Research designs and quality • Heterogeneity • Models for pooling the results

  8. Forest plots • Effective way of presenting results • Studies, effect sizes, confidence intervals • Provides an overview of consistency of effects • Summarises an overall effect (with confidence interval) • Useful visual model of a meta-analysis

  9. Anatomy of a forest plot N of study Line of no effect Study effect size (with C.I.) C.I Studies Study effect size Weighting of study in meta-analysis Pooled effect size Pooled effect size

  10. Exercise • What is the effect size in the Bletchman et al. study • Is the effect size in the Kelley et al. study bigger or smaller than in the Patrick & Marsh study? • How many subjects were in the Patrick and Marsh Study? • What is the 95% confidence interval of the pooled effect size? • What is the weighting given to the Bletchman study in the meta-analysis? • How does the confidence interval differ between the Kelley et al. study and the other two?

  11. A Systematic Review of the ResearchLiterature on the Use of Phonics in theTeaching of Reading and Spelling Torgerson, Brooks and Hall, 2006 Department for Education and Skills (DfES) commissioned the Universities of York and Sheffield to conduct a systematic review of experimental research on the use of phonics instruction in the teaching of reading and spelling. This review is based on evidence from randomised controlled trials (RCTs).

  12. Interpreting a forest plot Have a look at the forest plot from the meta-analysis of phonics interventions on the handout. These are RCTs with a separate analysis for lower attaining (Cluster 0) and normally attaining pupils (Cluster 1) • What do you notice? • Work in a pair or small group to ‘read’ it to each other • What questions can you raise about the meta-analysis?

  13. Forest plot

  14. Issues to consider Is it reasonable to combine the results of the individual studies ? i) Study design/ quality ii) Are the studies too different (heterogeneity) • Methodological heterogeneity • Educational heterogeneity • Statistical heterogeneity

  15. Assessing between study heterogeneity • When effect sizes differ consistent with chance error, the effect size estimate is considered to be homogeneous (unique true effect). • When the variability in effect sizes is greater than expected by chance, the effects are considered to be heterogeneous • Presence of heterogeneity affects the process of the meta-analysis

  16. Methodological quality • Traditional reviews privilege methodological rigour • Low quality studies have higher effect sizes (Hattie Biggs & Purdie, 1996) • No difference (Marzano, 1998) • High quality studies, higher effect sizes (Lipsey & Wilson, 1993) • Depends on your definition of quality • Assessing quality • Dimensions of quality • Exploring its impact

  17. Methodological quality • What about ‘low quality’ studies? • All studies are likely to have weaknesses (methodological quality is on a range or continuum) • Exclusivity restricts the scope and scale of the analysis and generalizability • Inclusivity may weaken confidence in the findings • Some methodological quality is in the “eye-of-the-beholder” • Needs a balance appropriate to the key research question

  18. Which designs? • RCTs only? • RCTs plus rigorously controlled experimental and quasi-experimental designs? • All RCTs, and experimental designs? • All pre-post comparisons?

  19. Task: Diamond Ranking • Have a look at the different descriptions of research • Which do you think it would be most appropriate for a meta-analysis?Which would be the least appropriate? • Can you place or rank the others? Most important Least important

  20. Methodological heterogeneity • Study design • Sample characteristics • Assessment (measures, timing)

  21. Educational heterogeneity • ‘Clinical’or ‘pedagogical’ heterogeneity • Systematic variation in response to the intervention • Teacher level effects • Pupil level effects

  22. Statistical • Due to chance • Unexplainable

  23. Statistical methods to identify heterogeneity • Presence • Q statistic (Cooper & Hedges, 1994) • Significance level (p-value) • 2 • 2 • Extent • I2 (Higgins & Thompson, 2002) • If it exceeds 50%, it may be advisable not to combine the studies All have low power with a small number of studies (Huedo-Medina et al. 2006)

  24. Exploring heterogeneity • In a meta-analysis, exploring heterogeneity of effect can be as important as reporting averages • Exploring to what extent the variation can be explained by factors in the coding of studies (age, gender, duration of intervention etc) • Forming sub-groups with greater homogeneity • Identifying the extent of the variation through further analysis

  25. Coding for exploration • Factors which may relate to variation • The intervention • E.g. duration, intensity, design, implementation • The sample • E.g. age, gender, ethnicity, particular needs • The research • E.g. design (RCT, quasi-experimental), quality, tests/outcomes, comparison group

  26. Pooling the results • In a meta-analysis, the effects found across studies are combined or ‘pooled’ to produce a weighted average effect of all the studies-the summary effect. • Each study is weighted according to some measure of its importance. • In most meta-analyses, this is achieved by giving a weight to each study in inverse proportion to the variance of its effect.

  27. Fixed effect model • The difference between the studies is due to chance • Observed study effect = Fixed effect + error

  28. Fixed effect model Each study is seen as being a sample from a distribution of studies, all estimating the same overall effect, but differing due to random error

  29. Random effects model Assumes there are two component of variation • Due to differences within the studies (e.g. different design, different populations, variations in the intervention, different implementation, etc.) • Due to sampling error

  30. Random effects model There are two separable effects that can be measured 1. The effect that each study is estimating 2. The common effect that all studies are estimating Observed study effect = study specific (random) effect + error

  31. Random effects model Each study is seen as representing the mean of a distribution of studies There is still a resultant overall effect size

  32. Which model? • “Random effects” model assumes a different underlying effect for each study. • This model gives relatively more weight to smaller studies and wider confidence intervals than fixed effect models. • The use of this model is recommended if there is heterogeneity between study results. • Usually recommended in education

  33. Extent (on % scale) Study effect size (with C.I.) Degrees of freedom Significance level

  34. Box plots Mean and range Other forms of data presentation

  35. Presenting Results • Stem and Leaf Plot

  36. Interpreting meta-analysis results What is the question? • Conceptual • Scope and scale - searches • Robustness of evidence • Wider applicability How does integrating the data answer the question? How robust is the synthesis? What does the result mean?

  37. How many studies? • How many studies and of what quality would be needed to make a ‘strong recommendation’ or for ‘strong evidence of effect’? • On what scale? • How many participants/ sites, 350, 500? • Is there an empirical answer?

  38. Issues and challenges in meta-analysis • Conceptual • Comparability • Reductionist • Atheoretical • Technical • Heterogeneity • Methodological quality • Publication bias

  39. Comparability • Apples and oranges • Same test • Different measures of the same construct • Different measures of different constructs • What question are you trying to answer? • How strong is the evidence for this? “Of course it mixes apples and oranges; in the study of fruit, nothing else is sensible; comparing apples and oranges is the only endeavor worthy of true scientists; comparing apples to apples is trivial” (Glass, 2000).

  40. Reductionist or ‘flat earth’ critique The “flat earth” criticism is based on Lee Cronbach’s assertion that a meta-analysis looks at the “big picture” and provides only a crude average. According to Cronbach: “… some of our colleagues are beginning to sound like a Flat Earth Society. They tell us that the world is essentially simple: most social phenomena are adequately described by linear relations; one-parameter scaling can discover coherent variables independent of culture and population; and inconsistencies among studies of the same kind will vanish if we but amalgamate a sufficient number of studies…The Flat Earth folk seek to bury any complex hypothesis with an empirical bulldozer…” (Cronbach, 1982, in Glass, 2000). Over simplification - the answer is .42?

  41. Empirical … so not theoretical? • What is your starting point? • Conceptual/ theoretical critique • Marzano, 1998 • Hattie, 2008 • Sipe and Curlette, 1997 • Theory testing • Theory generating

  42. Remaining technical issues • Interventions • Publication bias • (Methodological quality) • (Homogeneity/ heterogeneity)

  43. Interventions • “Super-realisation bias” (Cronbach & al. 1980) • Small-scale interventions tend to get larger effects • Enthusiasm, attention to detail, quality of personal relationships

  44. Publication bias • The ‘file drawer problem’ • Statistically significant (positive) findings • Smaller studies need larger effect size to reach significance • Large studies tend to get smaller effect sizes • Replications difficult to get published • Sources of funding

  45. Dealing with publication bias • Trim and fill techniques • ‘Funnel plot’ sometimes used to explore this Scatterplot of the effects from individual studies (horizontal axis) against a study size (vertical axis)

  46. Dealing with heterogeneity • Tackle variation in effect sizes • Investigate to find clusters (moderator variables) • Explore against coded variables • Evaluate whether a pooled result is an appropriate answer to the question .42?

  47. Summary • “Replicable and defensible” method for synthesizing findings across studies (Lipsey & Wilson, 2001) • Identifies gaps in the literature, providing a sound basis for further research • Indicates the need for replication in education • Facilitates identification of patterns in the accumulating results of individual evaluations • Provides a frame for theoretical critique

  48. Some useful websites EPPI, Institute of Education, London The Campbell Collaboration Best Evidence Encyclopedia, Johns Hopkins Best Evidence Synthesis (BES), NZ Institute for Effective Education (York) Google Scholar Keyword(s) + meta-analysis