The analysis is an integral part of the research, no matter what type of study design you opt for your study. Whether you opted for qualitative or quantitative methods of investigation, without data analysis, you cannot interpret data to give valuable insights. Thus, this article will describe an important method of analysis named ‘Meta-Analysis’ that seems difficult to conduct for the majority of students. If you also think that Meta-analysis is difficult, the following simple five-step guide is for you.
What is Meta-Analysis?
As a matter of course, Meta-Analysis is the examination of data from various independent studies stating the same subject to determine the overarching trends. It is the central method for the cumulation of knowledge in different scientific fields. Like a literature review, it serves as a synopsis of a field and research questions. Interestingly, in addition, giving a narrative summary of the main findings also helps quantitative researchers in assessing the relationship between different variables. All in all, Meta-Analysis is a systematic process of cumulation of knowledge based on the result reported in the previous studies.
Steps to conducting a Meta-analysis
Meta-Analysis is the process of systematically merging or compiling the findings of independent studies using a statistical method to calculate the absolute effect. It not only stuffs all relevant information in a place; rather, it also uses some quantitative analysis steps that make it different from the simple or systematic literature review. The following are five simple steps that can help you in conducting a Meta-Analysis effectively by solving all possible problems:
Select Research Questions:
Like all other secondary research designs, Meta-Analysis also starts by formulating some research questions. The research questions will help you in making search strategies, keyword selection, identification of variable and independent variables, and making inclusion and exclusion criteria. Additionally, the research questions must be clear, focused, and straightforward. If you are facing any issues in making a clear research question, get dissertation help from experts.
Conduct Systematic Review:
Good research questions help you search all relevant studies that can warrant your inclusion criteria. At this stage, you must use different databases to find all previously reported studies on similar issues. For the systematic literature review, some researchers use randomised control traits for finding high-quality evidence, while others use experimental or quasi-experimental studies if they fit in the frame of the inclusion requirements.
Extract And Synthesis Data:
Once all studies containing high-quality information about a subject under study are collected, the next step must be to extract information and synthesise data. In other words, the summary and results outcomes of all selected studies are reviewed to calculate any chance of bias or data fabrication. Furthermore, depending on the research questions and objectives of the research, results in the systematic review can be reported in categorical measures or numerical values.
Standardisation And Weighting Studies:
After assembling all data in one place (in the form of a literature review), the fourth step must be to calculate summary measures of all studies independently to conduct further analysis. This step can also be named ‘the measure of effect size’, which aims to identify the difference in average scores between the control group and the intervention. Additionally, in different studies, the parameters to measure an outcome may differ in different studies; thus, to interpret the results, it is usually necessary to standardise outcomes. This standardisation is essential to produce comparable estimates for reaching a final outcome.
The Meta-Analysis also needs to estimate the weightage of each individual study. For example, large-scale studies must account for more weightage than the small scales studies. Likewise, studies with less data variability are considered ‘better quality’ than ones with more data variability. Moreover, the weightage, including both of these factors, is known as an inverse variance.
Final Estimates Of Effect:
Last but not least is the selection or application of an appropriate model to relate the effect size of different studies involved in a study. In this regard, Fixed Effect as well Random Effect both models can help you equally. The fixed Effect model gives inference based on the assumption that every study included in a review gives a common effect size. In contrast, Random Effect emphasises that the common effect treatment is different among different studies. Based on the interpretation of both of these models, you can quantitatively reflect on a given matter under study by conducting Meta-Analysis.
Meta-Analysis resembles a typical literature review. However, its ability to give quantitative interpretations makes it different from the literature review. Achieving some extra milestones in analysis makes the former a little bit difficult to conduct, especially for a fresher. Thus, by following simple steps, whether you are a fresher or an expert, Meta-Analysis can be made easier.