Synthesising evidence


Systematic review

Literature review that selectively discusses evidence to build an argument

Can be found in essays or introductions of a research article

Systematically and methodically identifies, appraises and synthesises all high-quality available on a given research topic

Seek to reduce bias at all stages of the review process: detailed, systematic and transparent approach

1 of 9


Statistical technique for combining the results of multiple studies into a single pooled estimate

Should only be done in the context of a systematic review, not just selecting certain studies and creating a pooled estimate, which causes bias

An optional final step of a systematic review, where appropriate (but it’s not always appropriate, for example if studies are too different to combine meaningfully)

Happens at the synthesising evidence step of a systematic review

It’s not just a narrative synthesis, it’s also statistical analysis that calculates an overall estimate of effect

2 of 9

Review protocol

-          A complete recipe for how the review will be conducted

-          Should contain enough detail for someone else to follow exactly

-          Define the question

-          Outline all steps: how you will search, screen and appraise articles and synthesises and report your findings

-          Could pre-register this by making it available on OSF in advance, adds another layer of transparency

3 of 9

Search strategy

-          Relevant databases

-          Relevant search terms

-          Define dates

This all needs to be specified beforehand in the review protocol to be systematic

4 of 9

Inclusion criteria

Studies which meet the inclusion criteria have relevant info about the topic the review covers

5 of 9

Quality/bias assessment

-          The evidence needs to be high quality

-          Use a bias/quality assessment tool that scores the methodological quality of each study on aspects that are appropriate for the research question

6 of 9


a challenge with systematic reviews and especially meta-analyses, are we trying to compare things that can’t be compared because they’re too different?

has to do with variation in participants, methods and statistical analysis

Studies need to be sufficiently similar in order to meaningfully combine

7 of 9

Funnel plots

aim to detect publication bias, using number in study sample vs size of effect found

8 of 9

Grey literature

Unpublished literature

9 of 9


No comments have yet been made

Similar Psychology resources:

See all Psychology resources »See all research methods resources »