F: Data Extraction (Data Abstraction) Step

What is Data Extraction?

Data extraction or data abstraction (in North America) or data charting (in social sciences) is the process of data collection from the existing relevant literature. To start the process, you need to design a Data Extraction Form.

How to design a Data Extraction Form for Systematic Reviews?

Data Extraction Form can be designed in online systems, in Word or Excel, or Google Docs, or Google Forms, or Survey Monkey, or Qualtrics, or simply on A4 paper.

What is important about data extraction forms is that they should collect all the required data points. You need to know what data are 1. needed 2. enough to answer your question. Since it could be tricky for non-standard systematic reviews, it is usually expected that you should pilot your data extraction form for a percentage of your studies before using it for all studies. The main reason is that if you miss one data point, you will have to return to every study again and that could be time consuming. The best data extraction forms are those minimising your need to refer back to the original studies again.

Depending on the protocol/proposal of your research, you might need to extract the certain types of data from the Included Studies:

1: What is Meta-Analysis?

Meta-analysis is statistical combination of numerical results of studies. If there are more than one study that are trying to answer the same research question it is very likely that they report the same or similar type of data. Such data from similar studies can be statistically combined. If you are planning a meta-analysis you will need the following data and meta-data extracted from each and every study:

1.1: Characteristics of study

1.2: Qualitative data (Quality of Study) or risk of bias data

Having the same or similar types of data does not mean that we should combine them. Combining very low quality studies (apples) with high quality studies (oranges) cannot give us interpretable results. It is the same if we only find and combine low quality studies hoping to see a miracle: garbage in, garbage out – the quality of the meta-analysis depends on the quality of the studies included in meta-analysis.

1.2.1: Sensitivity Analysis

1.2.2: Cross-studies bias or publication bias

1.2.2: Quality of evidence per outcome

1.3: Quantitative data

2: What is Meta-Synthesis?

Sometimes, the included studies in systematic review are from qualitative nature not numerical and as a result, we combine such qualitative results during meta-synthesis.

2.1: Characteristics of study

2.2: Qualitative data (Quality of Study) or risk of bias data

2.3: Qualitative data

3: Summarizing

If you are doing a literature review just to support your research not to perform and analysis/synthesis based on their data, then you just need to summarize the main information from the Included Studies and present them as Tables, Figures or text.

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