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8 Key Points to Remember When Interpreting Data in a PhD Thesis

Quality data analysis forms the basis of any PhD thesis and determines the capability of the researcher to address the posed research objectives and questions. Unfortunately, many students fail to succeed in becoming a PhD due to some common mistakes in this sphere. Below, you will find 8 key points to keep in mind when interpreting data in a PhD thesis.

Interpreting Data in a PhD Thesis

1. Data Collection Design Is Crucial

Even the best data interpretation method cannot address the problems of missing, inaccurate or biased data in PhD thesis writing. If you are planning to work with statistical tools, make sure that you have a sufficient sample size, validate your data sets, and run some basic significance tests such as Cronbach’s alpha.

data analysis

2. Select the Right Instruments

Even experienced students are prone to methodological mistakes such as the use of the Mann-Whitney test for comparing dependent data or building linear regressions of mean values. If possible, consult with your tutor or a reliable PhD services agency to ensure that you are using the right tools for the job.

3. Prepare Your Data in Advance

Many researchers make the mistake of not revising their numeric datasets or database records prior to conducting statistical analyses. Try to convert all your information into a common format in advance to recognise any existing inconsistencies, missing respondent statements or unusual trends before it is too late to address these issues.

4. Analyse the Work of Your Colleagues

If you are using a popular PhD research design or a widely applied statistical instrument, chances are you can easily find 5-10 published projects with similar methodological choices. Study these examples to gain some interesting ideas or learn about better data interpretation instruments and techniques you may have missed.

5. Question Your Assumptions

If you are working with qualitative data, your interpretations may be prone to various forms of researcher and respondent bias. Approach all your assumptions with caution and never exaggerate your findings or overstate their significance. The same is true for the statements made by your respondents. If you are working with thematic analysis, make sure that you only analyse the ‘quantitative’ factors such as code frequency. Your respondents may be biased or uninformed in some spheres, which is why basing your assumptions on a single interviewee statement is never a good idea.

6. Avoid Unwarranted Claims

Some research projects do not produce ground-breaking results and there is nothing you can do to influence their outcomes. If your study confirms the assumptions based on the previous projects in this field, this still does not detract from its significance or novelty. Remember that your responsibility as a researcher is to enlist the facts of your hard work and not make these facts sensational by distorting their objectivity or accuracy.

7. Use Graphical Analysis

Besides the improved readability of your findings, data visualisation allows you to recognise some major problems or unusual trends within your findings. An instantly visible deviation on one of your graphs is much easier to note than several problematic lines within a large table. Most software packages including SPSS and NVivo allow you to build graphs with several clicks of your mouse. Try to produce these visuals after each analysis you perform to promptly identify any possible issues.

8. Have a Backup Plan

Things do not always go as planned. If you encounter data quality problems, sample size issues or other unexpected factors reducing the statistical significance and statistical power of your findings, you may need to return to the data collection stage. Make sure that you always have a contingency plan for any emerging difficulties including personal ones. In some scenarios, it may be better to return to your analysis phase several times to improve its quality than to work with sub-optimal data and suffer the consequences.