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How to Start an Analysis Paragraph in PhD Dissertation?

Analysis Paragraph

You can begin by creating an analysis paragraph that summarizes your key idea and establishes the paragraph’s topic. Commonly referred to as a topic sentence. It should be concise enough to be covered in a single paragraph yet broad enough to be expanded upon in several additional sentences.

The Analysis Paragraph Should Be:

  • Unified: Every sentence is related to the same main topic or theme.
  • Coherent: The sentences make sense and are connected logically.
  • Relevant: The sentence contributes to the paper’s overarching theme and goal.

We have developed some guidelines to follow to help you to start an analysis paragraph:

Contextual Relevance

Make sure your initial study objectives determine which data does and does not make it into your analysis before blindly following the data you have obtained. The information you give should all be pertinent to your objectives. Relevant data will show a lack of concentration and coherence in your thinking. In other words, you must treat the data you use with the same level of rigour that you did the literature research. By explaining to the reader the academic justification for your data collection and analysis, you demonstrate your capacity for critical thought and ability to get to the heart of a problem. This is the fundamental idea of higher education.


You must use techniques that are appropriate for the goals of your research as well as the type of data you are collecting. You should rigorously explain and defend these procedures with the same rigour that your collecting methods were justified. Remember that you must constantly demonstrate to the reader that your strategy was not chosen randomly. But, after extensive investigation and careful consideration. The main objective is to locate significant data patterns and trends and properly present these discoveries. Get dissertation help online if you can’t do this at your own.

Quantitative Work:

Rigid statistical analysis is needed for quantitative data to start an analysis, typical of scientific and technological study and, to some extent, social and other disciplines. By gathering and analyzing quantitative data, you can make inferences that can be applied to a larger population (provided that the sample is representative, which is one of the fundamental checks to make in your research). This methodology, which has its roots in the natural sciences, is sometimes referred to as the “scientific method” in the social sciences.

Qualitative Work

The majority of the time, but not always, qualitative data is not numerical and is referred to as “soft” data. But it doesn’t imply it calls for less analytical acumen; you still need to thoroughly examine the data gathered (e.g. through thematic coding or discourse analysis). This can take a lot of time because qualitative data analysis is an iterative process that occasionally calls for the use of hermeneutics. It is important to remember that research using a qualitative technique aims to unearth deeper, transferable information, not to produce statistically representative or accurate conclusions.

Thoroughness in Analysis Paragraph:

The data never just “speaks for itself.” In qualitative studies, where students frequently provide a selection of quotes and expect this to be sufficient, believing it does is a particularly prevalent error. Instead, you should carefully analyse all the information you want to use to support or disprove academic claims, exhibiting comprehensive participation and a critical viewpoint in all contexts, especially about any potential biases and sources of mistake. When you start an analysis section, you must identify the weaknesses and positives of your data since doing so demonstrates your academic credentials.

Presentational Devices

It might be challenging to logically express vast amounts of data. Consider every presentational option to solve this issue with the information you have gathered. Charts, graphs, diagrams, quotes, and formulas are beneficial in some circumstances. Another great method for concisely presenting data, both qualitative and quantitative, is to use tables. The most important thing to remember is to always put your reader, not yourself, first when presenting your data. While a certain layout might be obvious to you, consider whether it would be just as obvious to someone less familiar with your study. Frequently, at least for your first draught, the response will be “no,” and you may need to rethink your presentation.

Appendix for Analysis Paragraph

You might see that your data analysis chapter is filling, but you don’t want to drastically reduce the data you have worked so hard to obtain. You might want to relocate information to an appendix if it is pertinent but difficult to organize within the text. The appendix should contain data sheets, sample questionnaires, transcripts of interviews, and focus group materials. In the PhD dissertation, just the most pertinent bits of data, such as statistical analysis or quotes from interviewees, should be utilized.


When you describe your findings, you must show that you can recognize trends, patterns, and themes in the data. Consider possible theoretical interpretations and weigh the advantages and disadvantages of these distinct viewpoints. Assess the relevance and impact of both anomalies and consistencies. Include representative quotes from the interviews you used in your discussion.

Findings in Analysis Paragraph

What key ideas come to light from the analysis of your data? You should present these conclusions succinctly, and any claims about them should be backed up by evidence.

The Connection To Literature

It is advisable to start an analysis by comparing your data with that released by other academics at the end of your data analysis, taking into account areas of agreement and disagreement. Are your results in line with your expectations, or do they support a debatable or fringe opinion? Discuss the causes and consequences in your analysis paragraph. At this point, it’s critical to recall the precise words you used in your literature review. What were the main themes that you found? What were the gaps? And what relevance does this have to your research? Something is amiss if you can’t connect your research findings to your evaluation of the literature; your data should always make sense in light of your research question(s), which should be based on the literature. You must explain and demonstrate this point.


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