Dissertation Data Analysis Chapter:
The methodology chapter of the dissertation must tell the readers about the methods you have used for data analysis. Inform readers about how the primary data will be looking through and analyzed through various experiments and research discussed in this chapter. Data analysis is the process of transforming and re-modeling data with the vision of extracting something useful out of that specific data material. Data analysis is no doubt, the most crucial as well as an important part of any research. The data analysis the main purpose is to summarize collected data. Students are always advised not to delegate their data analysis part or consider very much who will be reading your data while writing this part. While working on the data analysis part of your dissertation check the research models that will guide your research and recognize the main points of research models. Dissertation data analysis always:
- Provides the outline of the study purpose, research methodologies, data description, instruments used and assumptions made.
- Provides the readers with the research questions and major predictions/hypothesis.
- Has data collection through statistical, mathematical and qualitative data analysis.
- Mentions the concluding statement of each question.
- Provide the summary of chapter review.
- Always have an introduction to introduce this chapter of the dissertation.
- Always have links and references to literature reviews.
- Always provide the reader's definitions of complex terminologies used in the analysis.
- The writer includes his own judgment and point of views about results.
- Follows a definite structure.
- Always linked with the good conclusion.
Importance of Data Presentation:
Data can be presented in various forms depending on what type of data is collected. Data can be presented in various forms including:
- Graphical Displays:
Graphical displays are very interesting and affecting ways presenting any data. It makes the reader understand the data more.
Line chart – used while analyzing the trend of data.
Bar graphs – used while comparing the figures of two or more data present.
Pie chart – used for presenting the qualities of the entire data.
- Textual and Images:
This type of data presentation is in the form of texts and paragraphs. It is helpful while presenting key findings in research. Images are also used as charts or others to present data.
Showing the relations between data using tables. This is the most reliable form of data presentation. Than scorecards, scatter charts, area charts, bullet charts are, combo charts, bar charts are also other ways of representing data.
Importance of Presenting Data through Various Means:
- Makes the reader understand data more easily.
- Helps the writer explain data more carefully.
- There are fewer chances of errors while data is presented through charts and bars instead of writings.
- Finding the results are much easier when every data is written individually.
Data Analysis Methods:
There are two types of data analysis; qualitative data analysis and quantitative data analysis. Both of these types are different. The qualitative data analysis used research, experiments and others etc in its methods. In qualitative, critical analyses of data takes place to achieve research goals. The quantitative data analyses along with critical analyses involve figure and numbers for the reasons behind the main findings. For both of the types, a literature review is important.
The data analyses which has non-numeric information for example notes, videos and audios, images and written documents are called qualitative data analyses. Qualitative data analyses have following categories to it including:
- Narrative Analysis
- Content Analyses
- Discourse Analyses
- Framework Analyses
- Narrative Analysis:
Analyses that depends on the visual, spoken and written information of individuals.
- Content Analysis:
Methods of research for studying different documents that can be audio, video, pictures etc.
- Discourse Analysis:
Methods to approach a number of ways to analyze any written, spoke or sign language.
- Framework Analysis:
Analysis methods for the research that can have questions, a time frame or it can have a designed sample.
- A Grounded Theory of Analysis:
Method to generate systematic theory from systematic research.
Features of Qualitative Data Analysis:
- The focus on text is the most important feature of the qualitative data analysis. That text can be written or present in audios, videos, pictures etc.
- Qualitative data analysis has the hermeneutic text perspective which can never judge a text whether true or false.
- It is always inductive as only it contains the most important data, patterns through the discovery.
Qualitative Data Analysis Techniques:
- All data must be connected to represent this fact how one part can affect the other.
- Data collection methods and data present proper documentation.
- Categorizing the data by concepts.
- Proper reporting of the findings.
The ultimate technique of converting the data into a numeric form, which involves many techniques and major challenging of converting the whole data into knowledge, is called quantitative data analysis. There are stages of quantitative data analysis and that are
- Nominal – data has no logic and it is in basic form.
- Ordinal – values differences are not constant but the present data has some logical order.
- Interval – continuous type of data with major differences between values and the entire data is in logical form.
- Ratio – data has a logical order with values differences and a zero, occurred naturally.
Categories of Quantitative Data Analysis:
- This type of data analysis has two main flavors.
- Continuous data analysis.
- Discrete data analysis.
- Continuous data analysis – this type of data collected can be reduced to its finer levels.
- Discrete data analysis – these types involves integers and cannot be further specified.
Features of Quantitative Data Analysis:
- Results are usually based on large samples.
- The researcher must have defined the questions.
- Data must be well arranged in tables, charts, or other forms but non-textual.
- Various tools are used for collecting data that can be computer software.
- It predicts future results.
- Replication and repetition of data to give reliabilities to data.
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