Have you been searching for a method where you can collate all your research findings and analyze them statistically? If yes, have you considered meta-analysis? If not, grab a seat as we go through the concept of meta-analysis, what it can be used for, and how you can use it to improve how you collect data as a researcher/investigator. 

Alright, to begin with, what is a meta-analysis?

What is a meta-analysis?

Meta-analysis is a method primarily used to determine the prevalence of truth or the common differences in research with similar research questions. It puts together the results from different scientific researches and analyzes them using statistical methods.

The term meta-analysis was first mentioned and clearly used in the 1970s. This happened when a statistician known as Gene Glass used the word as he made a call for statisticians to find more improved ways to conclude their field research findings.

For over a century or more now, researchers have been collecting data from numerous studies regardless of the call for improved data collection techniques. One of these cases is that of immunity and mortality in soldiers of 1904. While searching for averages in the results that may provide insight on the effects of the typhoid vaccine being administered to the soldiers, all the results from the findings were mixed up.

This incident and many others are why Gene Glass demanded better data recording techniques from all statisticians.

Purpose of meta-analysis in research 

It is unarguable the volume of new studies that are being published by researchers. One thing meta-analysis does is review all these studies and narrow them down.

The purpose of meta-analysis is that it seeks to determine whether an effect is present in a study and also determine whether the present effect is a positive one or a negative one. Meta-analysis examines the strengths of the results of a study. It checks whether there is substantial evidence to back up the findings of a study.

Another aim of meta-analysis is that it analyzes all the results of previously published theses on a subject. This is to detect if a common trend in all the studies. When a researcher establishes a trend among many studies it has higher statistical significance than when the study is conducted alone. This is because the validity of research is increased significantly when there are visible differences.

Meta-analysis can be used in fields such as medical research, psychology, and other studies.

Read: Systematic Errors in Research: Definition, Examples

Why Meta-Analysis?

Meta-analysis is designed to review the information and put it into simpler terms. Meta-analysis however follows some principles which are:

  • Meta-analysis must be done systematically
  • The basis of meta-analysis is quantitative analysis.
  • There must be a number of results for meta-analysis to occur.

The reassessment carried out by meta-analysis provides trends that improve decisions and subsequent research and this is why these reviews 

Hence, the reason why meta-analysis is helpful in research are as follows:

1. In research, meta-analysis evaluates effects in diverse participants that are in subsets of a study subgroup.

2. Meta-analysis establishes another hypothesis that can set precedence for future studies.

3. Applying meta-analysis in your study can prove statistical significance.

4. A meta-analysis helps to overcome the issue of a small sample size in research since it tests the outcome of various studies across similar subjects or topics.

Read: Margin of error – Definition, Formula + Application

How to conduct a meta-analysis

1. Select your topic: The first thing to do before conducting a meta-analysis review is to choose the topic you want the meta-analysis to be focused on. You can develop your research questions using the PICO model which stands for Population Intervention Comparison and Outcome.

For example, are women at a higher risk of cervical cancer if they used oral contraceptives in 10 years or more than women who never used oral contraceptives? This research hypothesis would help the researcher to identify the population for the study and also cervical cancer can be identified as the result of long-term use of contraceptives.

To avoid duplication of research, you should confirm that there has never been published research on the same topic as yours.

2. Review the guidelines for conducting a meta-analysis: there are many guidelines available to help a researcher when conducting a meta-analysis review. Have them by your side and follow the instructions step by step.

3. Establish your standards: Before commencing your meta-analysis review, determine your research criteria such as your sample size, the type of study you want it to be, and even the language you want to publicize the journal in. Define the variables that will be obtained from your research clearly.

4. Apply for systematic review: make use of systematic review to detect your primary data in your database. This will help to reduce the probability of not finding out about all the published articles that are the same as your topic. 

You can also request unavailable data or missing data from the authors of the published articles.

5. Plan your questions: Ensure you use the most efficient statistical model to plan your research questions. Once you are done planning your question, run your meta-analysis test.

6. Record and Report: Once your meta-analysis test results are available, record your research findings carefully and report your findings. Make sure your reports are transparent and replicable. Also, provide sufficient data or information about your just-concluded study. Let your report also include the software, the standard, and the method used to carry out the research.

7. Draw your conclusion: the last part is drawing a conclusion as the researcher that is bias-free and represents the accurate outcome of the study. Enumerate your research findings and explain how they can be generalized to the population.

Meta-analysis methodologies 

Step One: The first step is to develop the objective of the research in the form of a hypothesis or questions. This should be done before conducting day research to reduce the risk of insignificant variables appearing in the study.

Step Two: The second step is to take precautions concerning the objective developed for the test. This means that secondary or supporting objectives should be formulated to back up the primary objectives. This is in case the primary objectives do not cover the complete study.

The purpose of the meta-analysis methodology is to set primary and secondary objectives in advance for the research to be carried out.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Systematic review vs Meta-Analysis

The systematic review is a rigorous method of research but not as rigorous as compared to meta-analysis. This is because a systematic review focuses on analyzing only one research question.

For example, a researcher conducts a study about contraceptives and cervical cancer. The systematic review will only focus on the association between using oral contraceptives for the long term and having cervical cancer.

Systematic reviews are therefore used to reduce research bias.

Because meta-analysis is quantitative and more rigorous than a systematic review, it will not only provide the researcher with an overview of the subject, it also provides a quantitative analysis of whether a treatment performs better.

The meta-analysis also takes it a step further to provide a prediction or probability of the likelihood of a person developing an illness or disease if the person shows some characteristics.

It is noteworthy that meta-analysis is a subset of systematic review; however, meta-analysis is not always present in a systematic review.

Because systematic review focuses on one specific relationship, a researcher is able to draw conclusions and make reliable decisions from the findings of the study.

A meta-analysis, on the other hand, analyses multiple outcomes from different studies which, if a researcher is not careful, may draw a biased conclusion. 

Advantages of meta-analysis

Here are the advantages of using meta-analysis in a study.

  1. Meta-analysis gives a study better statistical power and this, in turn, allows generalization of the result to the entire population.
  2. Meta-analysis provides accurate evidence as a result of analyzing different studies trends.
  3. Meta-analyses are used by researchers to review large and sometimes complex research.
  4. In a meta-analysis, the tested hypothesis and the results are essential because meta-analysis is a delicate method.
  5. Meta-Analysis has the ability to be totally objective in analyzing and evaluating research outcomes.  

From the points listed above, you can see that the advantages of the meta-analysis are the qualitative reviews obtained from a large sample which in most cases is also complex. Also because meta-analysis is sensitive in research the results and the hypothesis that is studied are very important.

Read: Type I vs Type II Errors: Definition, Examples & Prevention

Disadvantages and limitations of meta-analysis

A meta-analysis is indeed a powerful tool in research, however, it has some disadvantages. Some of its disadvantages are:

  1. Meta-analysis is time-consuming. This is because it reviews outcomes from diverse studies.
  2. To perform meta-analysis in a research complex statistical techniques and relevant skills and needed. 
  3. Because of the complex techniques needed to perform a meta-analysis, it is difficult to conduct.
  4. Meta-analysis is not useful in all research. This means that a researcher cannot perform meta-analysis in all research.
  5. If meta-analysis cannot detect most existing research on a subject, it can lead the researcher to draw wrong conclusions.

FAQs about meta-analysis

  • How do I know if I can do a meta-analysis?

If your studies seek to address the same results, they are measured or reviewed using the same method and the same analysis approach but still produce different results, then you should consider introducing a meta-analysis review.

If these studies provide you with sufficient information to estimate and understand the effects on the size of interest, then it may be possible to use meta-analysis.

So as a researcher or investigator, you can be sure to use a meta-analysis review if your study has similar topics or subjects, similar treatments, similar interventions, and similar results.

You should also be careful to take a wider perspective into consideration while applying meta-analysis as it is often appropriate than applying meta-analysis to just one clinical research or study.

  • When should a meta-analysis not be used?

In some cases, meta-analysis can be a hindrance rather than being helpful.

1. Meta-analysis should not suffice if the research is a combination of diverse studies. For example combining pineapples with grapes. 

If there are no similarities in the subjects of study meta-analysis is best avoided because the study may lose its meaning.

Combining topics that should be conducted separately or extremely different results of studies in a single meta-analysis review, should not be encouraged.

2. Conducting a meta-analysis study on already published results that has high biases may lead the researcher to an erroneous conclusion. This is because if there are biases in some of the individual research, meta-analysis will combine the errors and give incorrect results which the researcher may take credible.

3. When confronted with serious reporting and publication biases meta-analysis we most likely produce an inaccurate conclusion.

Read: Research Bias: Definition, Types + Examples

In these three instances, it is best to not use meta-analysis to review your studies. Be quick to shield your research from the high risk of biases and time and resources wastage that can be frustrating. 

Analyze your data and your research objectives, understand what the most appropriate review method for your data is before conducting any study.

  • What is a good sample size for a meta-analysis?

Meta-analysis is best conducted when the sample size is small. To understand what a good sample size is for meta-analysis, a researcher must know that it is best to keep the sample size small if it meets the researcher’s outlined requirements.

This is to say that the criteria developed by the researcher for the study determine the sample size be adopted.

Conducting meta-analysis with sample size is not only easy but it produces rather interesting results than a large sample size. In reality, studies with significant and interesting results have a higher chance of getting published. 


Meta-analysis is helpful if your studies are based on finding the similarity in the Trent between existing research and the new one. However, be mindful of the studies you combine so that your research will not be at risk of biases which can lead to erroneous conclusions.

  • busayo.longe
  • on 10 min read


You may also like:

Research Questions: Definitions, Types + [Examples]

A comprehensive guide on the definition of research questions, types, importance, good and bad research question examples

11 min read
Selection Bias in Research: Types, Examples & Impact

In this article, we’ll discuss the effects of selection bias, how it works, its common effects and the best ways to minimize it.

8 min read
Research Bias: Definition, Types + Examples

Simple guide to understanding research bias, types, causes, examples and how to avoid it in surveys

12 min read
Reliability vs Validity in Research: Types & Examples

In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it.

11 min read

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. Try Formplus and transform your work productivity today.
Try Formplus For Free