Data Analysis and Findings
Share this on:
Data analysis and findings chapter is the core of your research paper and ineffective results can considerably affect your research quality. Data analysis is termed as a process of cleaning, converting and modelling of data to find out useful information for decision-making. Researchers use this process to reduce data into a story and interpret it to gain insights.
Process of data analysis helps in minimising an enormous amount of data into smaller bits that give some meaning. Three key things occur during this process. First is, data organization followed by summarisation and categorisation to reduce data and find patterns for simpler identification and relation. Last is data analysis that can take place through a bottom-up or top-down approach. In short, data analysis and findings is a process to represent the application of inductive and deductive logic to the dissertation.
7 day delivery
Why Data Analysis?
When doing research work, data collection, organization, analysis and its interpretation is very crucial. This chapter helps in testing the hypothesis and interpret the results. It defines the statistical processed used to test the hypothesis along with qualitative analysis techniques used to analyse the responses collected. As Gay stated, analysis of data is critical as any other element of the research process. Irrespective of how effectively the study is carried out, unsuitable analysis can result in ambiguous or biased results and conclusion, which can affect the quality of the entire research.
Data Considered in Analysis and Findings Chapter
Every type of data has the quality to describe things after giving it a specific value. Data can be in various forms and the most common data used are qualitative and quantitative data.
Qualitative data are presented using descriptions and words. Even though one can observe this data, it is subjective in nature and difficult to analyse in research, particularly when used for comparison. This data is generally collected through personal interviews, focus groups or open-ended survey questionnaires.
Quantitative data on the other hand is expressed in numerical figures and can be categorised into grouped, categories, calculated, measured or ranked. This data can be presented in form of charts or graphs or statistical analysis techniques can be applied to it. Outcomes Measurement Systems questionnaires in the survey approach is an effective way to collect numerical data.
Effective Tools For Data Analysis
Different data sets require different tools for analysis. Common methods used to analyse qualitative data include content analysis or discourse analysis. Most frequently used qualitative analysis tool is content analysis. This tool analyses the documented information from images, text and physical items. On the other hand, discourse analysis is used for analysing the interactions with individuals. This approach considers the social context in which interaction between the research and participant takes place and focuses on the environment when coming to any conclusions.
In case of quantitative data, researchers can use different methods to derive meaningful insights. The most favoured approach to analyse quantitative data is statistical analysis. Two kinds of statistical analysis can be conducted – descriptive analysis to describe the data using tools like SPSS and inferential statistical analysis to compare the data.
Tips to Write an Effective Data Analysis and Findings Chapter
Writing a data analysis and findings chapter is not an easy task. Following tips should be considered when writing your data analysis chapter:
Difficulty in Writing Analysis and Discussion Chapter
When attempting to write your own analysis and discussion chapter, students tend to face a lot of difficulties.
- Lack of aptitude or skill to conduct analysis or use different analysis tools
- Issue of choosing right type of data for the research study.
- Unable to extract the true meaning and findings from the analysis conducted, thus giving unreliable results.
• Inexperienced in relating the analysis and findings chapter to the entire research, thus not being able to generate precise results and conclusions.