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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

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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 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 LicenseCreative 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.

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Session pm Reading and interpreting a meta- analysis Overview of challenges to effective meta-analysis 3.00 pmBreak 3.15 pmSummary, conclusions and evaluation 4.00 pmFinish

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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

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Stages of synthesis What is the question? Theories and assumptions in the review question What is the result? What new research questions emerge? What data are available? By addressing review question according to conceptual framework How does integrating the data answer the question? To address the question (including theory testing or development). What does the result mean? (conclusions) How robust is the synthesis? For quality, sensitivity, coherence & relevance. 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. What are the patterns in the data? Including study, intervention, outcomes and participant characteristics Can the conceptual framework be developed?

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

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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

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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

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

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Exercise 1.What is the effect size in the Bletchman et al. study 2.Is the effect size in the Kelley et al. study bigger or smaller than in the Patrick & Marsh study? 3.How many subjects were in the Patrick and Marsh Study? 4.What is the 95% confidence interval of the pooled effect size? 5.What is the weighting given to the Bletchman study in the meta-analysis? 6.How does the confidence interval differ between the Kelley et al. study and the other two?

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A Systematic Review of the Research Literature on the Use of Phonics in the Teaching 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).

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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?

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Forest plot

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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

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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

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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

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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

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

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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

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

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

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Statistical Due to chance Unexplainable

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Statistical methods to identify heterogeneity Presence –Q statistic (Cooper & Hedges, 1994) Significance level (p-value) – 2 – 2 Extent –I 2 (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)

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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

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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

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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.

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

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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

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Random effects model Assumes there are two component of variation 1.Due to differences within the studies (e.g. different design, different populations, variations in the intervention, different implementation, etc.) 2.Due to sampling error

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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

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

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“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 Which model?

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

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Other forms of data presentation Box plotsMean and range

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Presenting Results Stem and Leaf Plot

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

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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?

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

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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).

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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?

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

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

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

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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

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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)

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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?

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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

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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

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Evaluation Please complete the evaluation sheet Feedback will be used: –to inform other sessions/ revise materials for independent use –For ESRC evaluation

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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 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 LicenseCreative 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.

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