Data is the key element in conducting research studies because it helps to make or break the arguments in a systematic manner. Shortlisting the relevant data from the shortlisted journal articles, books, clinical study reports, interviews, and so on, is the main function of doing data extraction in systematic review.
Scientific or non-scientific researchers often have to deal with complex data sets to pick the most relevant pieces for their dissertations, assignments, etc. It has been observed that while shortlisting data, most of the researchers fall prey to biases associated with it.
That’s why getting help from fellow researchers or experts is recommended to keep the studies free from errors or biases. Data extraction is no less than an art – it’s an artistic phenomenon that needs to be learned by the research professionals.
What is Data Extraction in Systematic Review?
Data extraction in systematic review is the process of collecting, analysing, and organising data to form evidence and summary tables upon the characteristics of a study, its results, or both. These data collection tables help researchers to determine which studies need to be considered for synthesis, containing an overview of research findings.
It has been observed that data used in systematic reviews are usually collected from unstructured reviews or observation groups. Professionals recommend that a research team must have two reviewers to eliminate biases and errors from being at work.
Make sure to perform data extraction on the studies you include in your dissertation literature review and mark the extracted points to use while conducting data analysis.
Importance of Data Extraction in Systematic Review
Data extraction helps the professionals in compiling the data that matches the intervention question. These data tables allow researchers to assess the applicability of the findings in their area of interest.
When you collect the data relevant to your questions in the data graphs or tables, you get the touch points in one place for managing the relevant data easily. Data extraction allows you to accumulate the data relevant to systematic review questions with ease.
What Type of Data to Extract for a Systematic Review?
There are several sources of data collection, including primary and secondary resources, that are being used by professional researchers across the scientific or non-scientific world of research writings.
Here is a list of data collection sources that you can use for accumulating data for a systematic review.
- Research Articles from Journals
- Clinical Study Reports (CSRs)
- Regulatory Reviews
- Participant or Interview Data
- Errata and Letters
- Trial and Experimentation Registers
- Conference Papers Abstracts
To extract data from the above-mentioned sources, you need to follow the pre-established guidelines to specify the types of data and sources that need to be analysed based on the purpose of conducting the review.
Make sure to consider the following points while conducting the research study.
- Title of the Publication
- Name of the Author or Authors
- Date of Publication
- Journal Name
- Research Questions, Aims, or Abstract
- Research Methodology or Framework
- Interventions
- Outcomes
- Conclusion
Data Extraction Methods in Systematic Review
We have outlined a few steps for you to follow for extracting data in a systematic review based on the three phases named database planning, building, and data manipulation.
1. Specify the Collected Data Items
Position the data collected in the tables based on the different features to pick the relevant items easily when needed.
2. Divide Specified Items into Distinct Entities
Organise the filtered data items into distinct entities using the terminologies that represent the specific set of items.
3. Figure Out the Relationships Among Entities
Connect the different data sets through one-to-one or one-to-many relationships depending on how each instance relates to the other available in different data entities.
4. Build a Dictionary
After building the relationships among different entities, it is time to build a data dictionary based on entity items and the database structure.
5. Layout Data Entry Forms and Set up the Database
Create data forms that directly communicate with the reviewer and the author to set up the database. The database contains structured tables showing the data in an organised form.
6. Data Export and Compilation
Export the data set individually from each database set and then combine the exported files into a single data file.
7. Compare Exported Data
Compare your data file with other reviewers to identify any discrepancies to avoid any error in matching the same results for perfect data extraction in the systematic review.
How to Write the Data Extracted in a Systematic Review?
Make sure to skim the text of collected data articles and then extract the information in a table format to summarise the studies and make it easier to compare reviewers. Also, You can use the following tips for data extraction in a systematic review to conduct error-free research studies.
- Ensure the complete text of collected data is available
- Pinpoint the pieces of information you want to use in your study
- Select the data collection methodology
- Layout the data extraction table to organise the collected data
- Conduct A/B testing
- Highlight the Required Datasets
- Review the data collection table to eliminate errors
Data Extraction Tools
Data extraction can be performed using a conventional mode of writing and modern tools available online as well. Let’s explore different tools to use for conducting the perfect data extraction phenomenon.
- A Pen and Paper
- Excel Spreadsheet (Google, Microsoft)
- E-Form for Data Extraction
- Covidence Software
- RevMan
- DistillrSR
Conclusion
Conducting research studies can be a daunting task for the researchers dealing with large data sets, but familiarity with data extraction in systematic review methods can ease your research writing process.
No doubt, organising data in a logical order makes it easy for the reviewer to compare with other studies and deduce the best results possible to rely on your study. Data available in clear tables increases the chances of being more credible in the world of research studies.
Frequently Asked Questions
The best outcome of data extraction in systematic review is the elimination of biases in individual studies and reports from reviewers.
Yes, you can consult BuyAssignmentOnline to get help in conducting the data extraction process in systematic review online at nominal prices.
If there are errors in your tables containing data extracted from other journal articles and sources then there will be issues in doing data analysis.
