OBJECTIVES: Systematic literature reviews (SLR) often yield large amounts of data, which may prove challenging to analyse. This study aims to explore whether factor analysis can be used as a data reduction strategy in SLRs. METHODS: This study employs factor analysis to interrogate the data set to identify complex interrelationships among items and group items that are part of unified concepts. Using the data variables captured during an SLR of 168 full-text articles, a categorical coding structure was constructed. The dataset was further evaluated using Kaiser- Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. KMO measure of sampling adequacy is a test to assess the appropriateness of using factor analysis on the data set. Bartlett’ test of sphericity is used to test the null hypothesis that the variables in the population correlation matrix are uncorrelated. To confirm the component structure of the data set, a non-linear rotation (direct Oblimin with Kaizer normalisation) was performed with delta set at zero. The Kaiser's normalisation tends to decrease the standard errors of the loadings for the variables with small commonalities and to increase those of the correlations among oblique factors. RESULTS: The KMO value was 0.538 and Bartlett’s test of sphericity significant with a p-value of < 0.0001. The results indicate that the data set is adequately sampled and that factor analysis of the data can is appropriate. The non-linear rotation analysis converged after 20 iterations, and the pattern matrix overlaps the findings seen in the initial structure matrix, thus confirming the factor analysis. CONCLUSIONS: Factor analysis may complement and strengthens the summary of findings by the use of regression modelling techniques to identify groups of inter-related variables in SLRs.