Practitioner's Guide to Using Research for Evidence-Informed Practice. Allen Rubin

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not well designed, then a particular study would merit a lower level on the hierarchy. For example, a randomized experiment with egregiously biased measurement would not deserve to be at Level 3 and perhaps would be so fatally flawed as to merit dropping to the lowest level. The same applies to a quasi-experiment with a severe vulnerability to a selectivity bias.

      Typically, however, replications of experiments produce inconsistent results (as do replications of studies using other designs). Moreover, replications of studies that evaluate different interventions relevant to the same EIP question can accumulate and produce a bewildering array of disparate findings as to which intervention approach is the most effective. The studies at the top level of the hierarchy – systematic reviews (SR) and meta-analyses – attempt to synthesize and develop conclusions from the diverse studies and their disparate findings. Thyer (2004) described systematic reviews (SR) as follows:

       In an SR, independent and unbiased researchers carefully search for every published and unpublished report available that deals with a particular answerable question. These reports are then critically analyzed, and – whether positive or negative, whether consistent or inconsistent – all results are assessed, as are factors such as sample size and representativeness, whether the outcome measures were valid, whether the interventions were based on replicable protocols or treatment manuals, what the magnitude of observed effects were, and so forth. (p. 173)

      Although systematic reviews often will include and critically analyze every study they find, not just randomized experiments, they should give more weight to randomized experiments than to less controlled studies in developing their conclusions. Some systematic reviews, such as those registered with the Campbell or Cochrane collaborations, require researchers to meet strict standards related to methods used to find studies and quality standards for the studies that will or will not be included in the review itself.

      Some meta-analyses will compare different interventions that address the same problem. For example, a meta-analysis might calculate the average strength of treatment effect across experiments that evaluate the effectiveness of exposure therapy in treating PTSD, then do the same for the effectiveness of eye movement desensitization and reprocessing (EMDR) in treating PTSD, and then compare the two results as a basis for considering which treatment has a stronger impact on PTSD.

      You can find some excellent sources for unbiased systematic reviews and meta-analyses in Table 2.2 in Chapter 2. Later in this book, Chapter 8 examines how to critically appraise systematic reviews and meta-analyses. Critically appraising them is important because not all of them are unbiased or of equal quality. It is important to remember that to merit a high level on the evidentiary hierarchy, an experiment, systematic review, or meta-analysis needs to be conducted in an unbiased manner. In that connection, what we said earlier about Table 3.1 is very important, and thus merits repeating here:

      This hierarchy assumes that each type of study is well designed. If not well designed, then a particular study would merit a lower level on the hierarchy.

      For example, a randomized experiment with egregiously biased measurement would not deserve to be at Level 3 and perhaps would be so fatally flawed as to merit dropping to the lowest level. The same applies to a quasi-experiment with a severe vulnerability to a selectivity bias.

Qualitative Experimental Quasi-Experimental Single Case Correlational Systematic Reviews or Meta-analyses
What factors predict desirable and undesirable outcomes?
What can I learn about clients, service delivery, and targets of intervention from the experiences of others?
What assessment tools should be used?

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