Dissertation Data Analysis

Incredible Tips On Writing A Good Dissertation Data Analysis

Every dissertation/thesis writer has to submit the outcomes and results of their performed investigation. In this chapter of your dissertation, you are required to explain & analyze the data collected after extensive research. Data analysis is an ongoing activity that not only answers your question but also provide you the direction for future data collection. Dissertation data analysis is the process of systematically using statistical/ analytical techniques to evaluate the collected data.

Following are some tips to apply while writing a dissertation data analysis.

• Write an introductory paragraph in a succinct manner that explains the chapter excellently.
• Reference the analysis with the literature review.
• Cross referencing is surely an appropriate way to relate the common points that arise from the research and literature review.
• Using & Applying a structure akin to the structure followed in the literature review.
• Presenting your opinion & critical view for the results that the analysis has thrown.
• If any new theme sprang from the analysis the researcher to acknowledge it and link it the appropriate conclusion that is drawn from the analysis.
• Evade jargons and giving a definition of technical terms used in the analysis.
• All data presented should be relevant and appropriate to your objective. Irrelevant data dictates a lack of concentration and incoherence of thought.
• Explain & justify the methods used to collect the data. Discern significant patterns and trends in the data and present these findings meaningfully.
• Every collected data must show some evidence of scrutiny and measurements of credibility, validity & importance.
• Be presentable & use charts, graphs, diagrams to show the collected data.
• If some of the collected data are hard to organize in the text then, remove it from an appendix. Data sheets, questionnaires, interviews and transcripts must be included in the appendix.
• Write down the essential findings that emerge from the analysis of the collected data.
• Do not forget to compare the data with the already published work. Are your results meeting expectations? Do not forget to discuss reasons as well as provide suggestions.

Challenges/Problems In Dissertation Data Analysis

The researcher must be aware of the issues that arise in dissertation analysis. These are following:-

• Training of staff conducting analyzes
• Reliability and Validity
• Having the essential skills to analyze
• Concurrently selecting data collection methods and appropriate analysis
• Designing unbiased inference
• Environmental concerns
• Data recording method & procedure
• Partitioning ‘text’ when analyzing qualitative data
• Inappropriate & irrelevant subgroup analysis
• Following acceptable norms for disciplines
• Determining statistical importance
• Lack of clearly defined and objective outcome measurements
• Delivering original and accurate analysis
• Manner of submitting data
• Extent of analysis

Types Of Data Analysis Methods

ANOVA: - The one-way analysis of variance (ANOVA) is applied to determine whether there are any major differences between the means of three or more independent (unrelated) groups.

Cluster Analysis: - Cluster analysis is a data analysis tool that aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.

Discriminant Analysis: - Statistical analysis using a discriminant function to assign data to one of two or more groups.

Factor Analysis: - it is a process in which the values of observed data are expressed as functions of many possible causes to find which are the most significant.

Multidimensional Scaling Overview: -The purpose of multidimensional scaling (MDS) is to provide a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects.

Regression Analysis: - Regression analysis is a statistical process for evaluating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables.

Sampling: - A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population.

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