Data extraction is 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 or systematic 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.
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!
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.
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.
Bias can occur in the design and methodology of a study, potentially distorting its findings. The presence of bias may prevent the study from accurately reflecting the actual results. No study is entirely free from bias. Through critical appraisal, the reviewer should systematically check that the researchers have minimized and acknowledged all forms of bias.
Selection bias: Differences in the characteristics of the intervention and the comparator groups. Randomly assigning subjects to the intervention and control groups will reduce the risk of selection bias.
Performance bias: Differences between groups in the care that is provided, or in the exposure to factors other than the intervention of interest. Blinding of participants, researchers, and outcome assessors will reduce the risk of performance bias.
Attrition bias: Loss of participants in either the control or intervention group through withdrawal or dropout. This impacts the comparison between the intervention and control groups. Enrolling a greater number of study participants than deemed necessary can help prevent the loss of needed data points and compensate for expected withdrawals.
Detection bias: Differences between groups in how outcomes are determined. To minimize detection bias, the methodology applied to the intervention and control groups must be equivalent, except for the measured intervention. Blinding of participants, researchers, and assessors also minimizes detection bias.
Reporting bias: Difference between reported and unreported findings. All data collected during a study must be presented objectively, regardless of the results, to minimize reporting bias.
For more information: Cochrane Training Handbook for Assessing Risk of Bias
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.
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