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Two reviewers independently performed data extraction and method quality assessment.
We have endeavoured to reduce bias by using a priori inclusion/exclusion criteria, data extraction and method quality assessments ensuring a range of supporting quotes from across the included studies.
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Study inclusion criteria, data extraction, and methods of the analysis were specified in advance and documented in a protocol.
Detailed data extraction and quality assessment methods have been described in a previous publication.
In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation.
A study protocol outlining the search strategy, outcomes, and methods of data extraction and statistical analysis was designed and prospectively registered with the Prospero database (CRD42013005698).
Search methods, data extraction and risk of bias assessment were thorough.
The review authors (PS and RJH) conducted the data extraction and assessed the quality of the method used in each included trial.
Using the primary datasets reduces potential sources of errors introduced when compiling data from published studies that may have used different methods of data extraction and aggregation.
Attached at the end of the manuscript are the PRISMA statement and flowchart detailing the methods of data extraction and abstraction.
We did not determine inter-rater reliability of data extraction, and it is possible that our methods of ensuring consistent agreement were insufficient.
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