Information
It is challenging to state the number of studies required for a meta-analysis. Factors affecting the decision may involve discipline-specific context, fixed or random-effects models used in the analysis, population values of effect sizes, and other considerations (Cheung & Vijayakumar, 2016). Designing a database is an art; a well-designed one can instil a sense of preparedness and confidence in you.
The basic rules are the same as those for an empirical study; you should ensure the datasheet contains all the information you need and is set out logically so that it is easy to complete and difficult to enter data in the wrong place. Use the most extended, most complicated-looking papers you have to trial test your datasheet. If it can handle them, you are off to a good start.
The main difference between an empirical study and a meta-analysis is that you must record which subjects you did and did not collect data from. In short, keep a bibliographic library of studies and explain why some were excluded (e.g., irrelevant, missing critical information needed to satisfy inclusion criteria, not possible to extract an effect size and variance estimate).
A suitable protocol makes it relatively easy to encode information for study moderators. It provides a structured approach that simplifies the process, making it more manageable. In contrast, extracting effect sizes is among the most challenging parts of meta-analysis. It can lead to self-doubt, especially during your first meta-analysis. To extract effect sizes, you often must make subjective decisions. This process requires careful consideration and thoroughness to ensure the accuracy of your analysis.
Finally, a protocol should be established for studies reporting multiple effect sizes. Specifically, if treatment effects, which are the changes in the outcome of interest due to the treatment, are measured repeatedly over time, a structured approach will help you determine which comparisons to use, providing a sense of guidance and control.
