LibGuides: Scoping Reviews: Step 9: Synthesize the Results

Data Analysis

Data extraction is os often the first step in data analysis, as you will structure your extraction to suit the analysis you intend to do. You should limit the amount of interpretation in a scoping review. If your project requires more sophisticated analysis methods, that may be an indication that a systematic review is a better fit for your project.

What Types of Analysis Are Appropriate for Scoping Reviews?

Descriptive statistics
Basic qualitative content analysis

Remember: regardless of how you choose to analyze and present your data, you always need to do a narrative synthesis as part of a scoping review!

Reporting Standards

Scoping Reviews are CONDUCTED according to the JBI Guidelines.

They are REPORTED according to the PRISMA standards.

These two processes must be documented within your methodology section.

It is highly recommended that you view the PRISMA-ScR guidelines prior to conducting your review, so you have an understanding of what information you need to keep track of during your research. If you need help with the PRISMA guidelines, reach out to your liaison librarian.

PRISMA-ScR This checklist provides a birds-eye view of all the information you will need to report your scoping review.
PRISMA-ScR Statement and Explanation This article provides the rationale for each item in the PRISMA-ScR checklist as well as examples on how to meet the guidelines.
JBI Manual - Chapter 10 Chapter 10 of the JBI Manual details all the information needed to conduct and report a scoping review.
PRISMA Flow Diagram Tool This tool allows you to produce a flow diagram for your own review that conforms to the PRISMA2020 Statement.

What is Risk of Bias?

The Cochrane manual defines risk of bias as "a systematic error, or deviation from the truth, in results." It further states that "Biases can lead to underestimation or overestimation of the true intervention effect and can vary in magnitude: some are small (and trivial compared to the observed effect) and some are substantial, so that an apparent finding may be due entirely to bias." (Cochrane Manual: https://training.cochrane.org/handbook/current/chapter-07). The risk of bias is not typically assessed in a scoping review, but rather in a systematic review.

Risk of Bias Resources:

Risk of Bias Assessment Tools:

Modified Downs and Black Checklist It provides both an overall score for study quality and a numeric score out of a possible 30 points. It has five sections. The tool can be administered either within a systematic review process or as a quality assessment tool for individual articles.
Newcastle-Ottawa Quality Assessment Scale A tool used to assess the quality of non-randomized studies. Allocates a maximum of nine stars for quality of selection, comparability, exposure, and outcome of study participants.
Cochrane Risk of Bias Tool (RoB 2) Tool used to assess the risk of bias for randomized controlled trials. Required for all Cochrane group systematic reviews.
Cochrane Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I Tool) The ROBINS-I is a tool developed to assess the risk of bias in the results of non-randomized studies that compare the health effects of two or more interventions.
MMAT (Mixed Methods Appraisal Tool) The MMAT is intended to be used as a checklist for concomitantly appraising and/or describing studies included in systematic mixed studies reviews (reviews including original qualitative, quantitative, and mixed methods studies)
Critical Appraisal Skills Program (CASP) There are several checklists available for various study designs, including qualitative studies.
QUADAS-C tool For diagnostic test accuracy studies

What is Data Visualization?

Data visualization is the representation of data using standard graphics, such as charts, plots, infographics, and even animations. These visual displays of information effectively communicate complex data relationships and data-driven insights, making them easy to understand.

Best Practices for Data Visualization

Your choice of chart type, colors, or style will make a tremendous difference in how others will perceive your data. Fortunately, there are simple guidelines that, if you follow, can make your data visualization both visually appealing, compelling, and captivating.

Simple is always better
Choose the correct type of chart

Visualize one aspect per chart
Make your axis ranges and labels interesting
Be careful with your color scheme

Scoping Reviews