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Data Analysis Skills and Practices Necessary for Dissertation

by ChrisGreenwalty
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Data Analysis

Data analysis is a primary component of a dissertation or a research paper. It is essential to use appropriate methods for data analysis. A haphazard analysis to complete the dissertation leads to illogical conclusions and meaningless findings. In your dissertation, you have to demonstrate to the reader that you employed the best analytical tools by doing extensive research. The main aim of the analysis is to indicate trends and patterns in the data to reach meaningful conclusions. The data analysis methods should be relevant to the collected data and research objectives. A researcher needs to have suitable analysis skills for the aforementioned purposes.

Data analysis pertains to two principal research methodologies, qualitative and quantitative. Quantitative data is a standard research method for scientific, technical, and social sciences subjects. On the other hand, qualitative analysis is non-numerical. It also requires critical analytical skills. This article aims to inform its readers about different data analysis skills crucial for dissertations and research papers.

Skills Required for Quantitative Data Analysis

Quantitative analysis requires meticulous statistical analysis. Having a working knowledge of statistical tools is good, but it’s not the whole data analysis story. Statistical analysis requires precision and accuracy. You need to have a proper methodology to find meaningful results for that purpose. So, what are those essential techniques?

Make a Plan

Quantitative data analysis can bear fruitful results if you work with a plan. A plan requires you to contemplate critical decisions and saves you from future revisions. The efficiency of statistical analysis depends on the research problem, research questions, methodology, variables and gaps in data. Synthesizing all these factors together before delving into analysis helps to save a lot of time. However, you still have an option to buy dissertation online if you don’t know how to proceed with planning.

Collect Data Relevant to your Research

Your plan should include looking for data relevant to your research problem. It requires collecting that information that can tackle your research questions and hypotheses.

Collect large samples of data

Keep in mind the constraints of time and money and collect a large sample of data to reach meaningful conclusions. A sufficiently large sample of data improves the precision of your analysis.

Learn to Deal with Missing Data

There are instances when there are significant gaps in data sets due to small sample sizes and external factors. Recognizing the missing data and dealing with it if it will tamper with your results is a crucial data analysis skill.

Deal With Each Variable Separately

The central component of data analysis is to deal with each variable separately. Use graphical displays to carry out an exploratory analysis. Do not analyze all variables together since it will lead to confusion. This practice ensures to locate the errors in data and enables you to comprehend the patterns.

GIGO (Garbage In, Garbage Out)

The concept most commonly used in mathematics and computer sciences is also relevant to data analysis. Beware of your data and filter out unnecessary and irrelevant information in analysis. After cleaning the data, code and format the variables and structure them for analysis.

Keep it Simple

It would help to be mindful that you are doing data analysis to simplify things for your reader. For that purpose, choose sufficient statistical procedures and provide reasons for using them.

Check Your Assumptions And Deal With Violations

Every statistical model and test have assumptions, so it is essential to check for premises. It is vital to ensure that there is no violation of study assumptions.

Present Your Data In A Straightforward Manner

It is essential to present a large amount of data intelligibly. You can do the following:

  1. Use charts, graphs, diagrams and formulae
  2. Use tables to show your data
  • If there is too much data, place it in the appendix section for your readers


Discussing the findings from your data demonstrates trends and patterns in the collected data. Employ various theoretical interpretations and provide a balanced perspective. It is also essential to indicate the significant impact of your findings. Discuss the limitations and inconsistencies in your results.

Skills Required for Qualitative Data Analysis

Qualitative analysis requires a different skill set. There are various approaches to analyzing qualitative data. Some of those approaches are the following:

Content Analysis

The content analysis looks for themes and hypotheses that might emerge from the collected data. Identify and label themes of any kind within the text. Use coding and colouring methods to group the ideas and themes.

Contextual Analysis

Reformulate the data collected through interviews or survey questionnaires. Contextualize the data within the different experiences of respondents. Narrative analysis documents the data into small narratives to spot story-like patterns. In contextual or narrative analysis, you let the data speak for itself. It can emerge themes from the evidence gathered relevant to your research problem.

Sort out the Data

Review the data to identify patterns and to determine relationships between different variables. It is a valuable analytical technique to highlight similar patterns and themes. It also helps to spot qualitative analysis inconsistencies.

Discourse Analysis

Discourse analysis is a helpful method for textual and oral analysis. It focuses on comprehending ideas and themes transmitted through verbal speech. It enables the researcher to understand the epistemological implications behind the communicated ideas. Discourse analysis leads to the formulation of meta-themes and patterns.

Framework Analysis

Framework analysis consists of coding, indexing, charting, mapping and interpreting. It analyses the text from a specific framework or concept. Framework analysis enables the researcher to define the specific parameters of the study. It is helpful for tunnel vision studies.

Grounded theory

Grounded theory analysis encompasses the synthesis of content, narrative and discourse analysis to study a specific case. It enables the researcher to take advantage of insights gained from examining a single topic. The gathered insights are helpful to evaluate other similar issues. The grounded theory approach leads to the discovery of the foundational theory


It is crucial to compare data with the relevant literature for both quantitative and qualitative data analysis. The potency of your data depends upon its power to agree or disagree with the existing body of knowledge. In the discussion section of your study, justify whether you successfully achieved the research objectives. Your data should be in tandem with research questions and formulated hypotheses in the analysis section. Read and implement the strategies mentioned above to improve your analytical skills.

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