By Emma 
Data analysis refers to the process of organization and classification of data that you collect for your research. The process involves tabulation, summarization and making a comparison of the results. For the synthesis of data analysis, you will have to present your findings in a clear and understandable manner. The process entails systematically applying statistical tools and logical procedures to describe and evaluate the data. The primary aim of data analysis through evaluation and comparison is to extract useful information from the data relevant to your research problem. Data analysis is different for qualitative and quantitative research. Although sometimes researchers apply quantitative data analysis tools to analyze qualitative data. Following are the examples of data analysis for both qualitative and quantitative data.
Table of Contents
Qualitative Data Analysis
- Content Analysis
- Discourse Analysis
Quantitative Data Analysis
- Statistical Analysis
- Multivariate Analysis
- Frequential Analysis
For both qualitative and quantitative data analysis, the researcher looks for emerging patterns in the data. The researchers try to determine similarities and differences in the gathered data and look for discrepancies. If there are any differences, researchers try to determine whether they are significant to alter the findings or negate the hypotheses.
On the other hand, the synthesis of data refers to amalgamating the findings and looking for patterns among them. Synthesis of data requires seeking consistent patterns among the gathered data. It also requires looking for disparities and contradictions in the gathered data. Synthesis of data involves efficiently organizing the gathered data. Researchers must find the best possible way to sort and compare the gathered data. Synthesis of data requires triangulation of data and comparison of findings by corroborating them with various approaches and viewpoints. You can get PhD dissertation help if you are facing any issues.
How do you synthesize data analysis?
To synthesize the gathered data, a researcher must consider the following things:
Relate the data with your Research Problem
Synthesis of data requires a clear and focused approach to analyzing the gathered data by keeping the research goals in mind. A clear, focused, and detailed analysis is more likely to elicit engagement and action than a rambling, disconnected, and unclear one.
- Identify your expectations from the research
- Once you have a clear view of your research problem and research questions, it will be easier to wade through the gathered data
- Synthesis of data requires matching your research objectives with the gathered data
- Data synthesis does not necessarily entail that you should review the entire data but keep your focus by keeping in mind your research problem
Examine your Data
Data synthesis requires organization and examination of gathered data by making notes. You can also listen to the recordings (interviews, audio recordings) if you have missed any vital information. Examine alternative methods for recording and organizing your research notes. VLOOKUP Google Sheets can help you to appropriately collect and summarize data, using functions to assess it more easily. You need to consider the following things:
- Reread your notes to enhance your familiarity with the data
- Try to identify emerging patterns in the data
- Data synthesis requires flexibility and keeping an open mind towards the data
- Pay close attention to discrepancies and divergences even if they are contrary to your hypotheses
- Do not succumb to confirmation bias which is a tendency among researchers to look out for such information that validates their pre-conceived notions
- Overcome your prejudices and consciously work on them to avoid biasness in your analysis
Find Commonalities and Themes in the Data
While reviewing the data, you will come across affinities and similar trends in the data. Your goal should be the identification of recurring themes in the data. You should consider the following things:
- Map the identical ideas and patterns in the data
- Identify the ideas and themes that are recurrent
- If you are unable to identify any recurrent themes, it is more likely that you will have to do additional research
Sum up your results
After identifying and organizing the data, the next step is to focus on the key findings of your research. You should consider the following things while summarizing your findings when working on synthesis of data:
- Make a list of the essential points that you think are directly relevant to your research problem
- Look for consistencies and contradictions in your data
- Evaluate and compare your key findings with the theoretical frameworks you established for your research
- Do your findings answer the research questions?
- Limitations of your data and identification of potential biases
- Possible implications of your results
Presentation
The final step in data synthesis is to decide the medium through which you will share your findings. You need to consider the best possible medium to display your data, such as tables, pie charts, flow charts and forest plots.
Is data synthesis the same as data analysis?
Data analysis and synthesis are two separate concepts. Data analysis is a process of dissecting the data into smaller fragments and analyzing them separately. It requires breaking down the rich data into smaller pieces to relate them to the research goals. On the other hand, data synthesis is the process of assembling the minor details of data and merging them to have a comprehensive understanding of the data.
What is data synthesis in research?
Data synthesis in research refers to a macro approach towards the gathered data that gives a bird’s eye view of the data. Data synthesis in research seeks to thoroughly understand the implications of collected data and comprehension of a relevant research problem in its entirety. Data synthesis in research is an abstraction of the entire findings of the research study. It offers a broad overview of the research study.
Data synthesis methods
The following methods are common in synthesis of data analysis in qualitative studies:
- Meta-Ethnography
- Meta-studies
- Narrative Synthesis
- Realist Synthesis
- Meta-Narrative Reviews
- Critical Interpretative Synthesis
- Qualitative Interpretative Meta-Synthesis
- Thematic Analysis
For quantitative data synthesis, researchers utilize the following methods:
- Statistical Summary of the Data
- Pooling the results for statistical analysis
- Heterogonous Approach for Data Analysis
- Cumulative Meta-Analysis
- Meta-Regression and Multivariate analysis
Conclusion
Researchers attempt to make sense of the gathered data for the relevant research problem in data analysis and synthesis. Synthesis of data leads to emerging patterns and clusters in the data. Those clusters and emerging patterns can provide deep insights into a research problem.
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