You've collected your data. Now you're staring at thousands of rows in SPSS or hundreds of transcripts in NVivo, and you have no idea what to do next. Data analysis is where many dissertations stall: students don't know which statistical test to run, whether their data meet the assumptions of their chosen test, how to interpret the output, or how to write the results in academic language. This guide covers what data analysis help looks like, when it's appropriate to hire support, and what you can realistically expect.
Types of data analysis help
Data analysis support comes in three forms:
| Type | What it includes | When to use it | Cost |
|---|---|---|---|
| Consulting | A statistician or qualitative analyst reviews your data and recommends which analysis to use. You run it yourself; they interpret the output with you. | You collected data but don't know the next step. You want guidance but want to do the analysis yourself. | $100–200/hr, typically 2–4 hours |
| Analysis service | You provide raw data; the analyst runs the analysis, produces output, and delivers tables and charts for the dissertation. | You don't have software access or statistical expertise. You want the analysis done by an expert. | $1,500–3,500 for a quantitative study; $1,000–2,500 for qualitative depending on data volume |
| Chapter writing | Data analysis + results chapter writing. The analyst analyzes the data and writes Chapter 4 (or equivalent) explaining findings in academic prose. | You want analysis done and written up simultaneously, ready for committee review. | $2,500–4,000 depending on data complexity |
Quantitative data analysis
For quantitative dissertations (surveys, quasi-experiments, RCTs), data analysis typically involves:
- Data cleaning: Checking for missing values, outliers, data entry errors; deciding how to handle them.
- Descriptive statistics: Means, standard deviations, frequencies for all variables.
- Assumption checks: Normality, homogeneity of variance, linearity (depending on your chosen test).
- Primary analysis: Your main statistical test (t-test, ANOVA, regression, etc.) and results.
- Follow-up tests: Effect sizes, confidence intervals, post-hoc tests if needed.
- Output tables: Formatted in APA 7th, ready for your dissertation.
What you need to provide: your raw data (Excel, SPSS, or CSV file), your research questions or hypotheses, and your data collection procedures (so the analyst understands variable coding).
Qualitative data analysis
For qualitative dissertations (interviews, focus groups, document analysis), analysis typically involves:
- Transcription: If you recorded but haven't transcribed yet, transcription services are available (or use NVivo's AI transcription).
- Coding: Reading transcripts systematically and assigning codes to chunks of text. Codes may be applied by hand (NVivo) or using an AI-assisted coding tool.
- Theme development: Organizing codes into broader themes, comparing themes across participants, identifying patterns.
- Validity checking: Member checking (returning themes to participants for feedback), inter-rater reliability (second coder reviews a subset of transcripts), triangulation across data sources.
- Output: Themed code list, quote tables, visual thematic map, summaries ready for the results chapter.
What you need to provide: interview transcripts (verbatim text), your research questions, and your population details (so themes are interpreted in context).
Results chapter writing
After analysis, many students struggle with writing Chapter 4 (results or findings). The chapter should:
- Report descriptive statistics first (sample characteristics)
- Report primary findings addressing each research question
- Present results in tables/figures, not just prose
- Write in past tense, neutral language ("the results indicated" not "this proves")
- Save interpretation for the discussion chapter
If you've had analysis done but are stuck writing the results, hiring a writer to draft the chapter based on your findings and analyst's output can accelerate completion.
Important ethical note
You must understand your data analysis. It's okay to hire a statistician to run the tests and interpret output — that's routine. But you need to be able to explain your analysis to your committee and understand what the results mean. Read the analyst's output carefully, ask questions about what it means, and don't submit results you can't defend. Your committee may ask you in the defense: "Why did you use a t-test instead of Mann-Whitney U?" You need to know the answer.
Cost and timeline
- Consulting (hourly): $100–200/hr for a statistician, $75–150/hr for a qualitative analyst. Most projects take 2–5 hours, so $200–1,000 total.
- Full analysis service: $1,500–3,500 for quantitative (SPSS, basic stats). $1,000–2,500 for qualitative (coding, theming) depending on transcript volume.
- Analysis + Chapter 4 writing: $2,500–4,000.
- Timeline: Consulting turnaround is 1 week. Full analysis is 2–3 weeks depending on data volume. Chapter writing adds 3–5 days.
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Frequently asked questions
No. Data analysis support is routine and common. Statisticians work with researchers all the time. The key is that you understand the analysis and can explain it. If your committee asks "Why did you use ANOVA?" you should be able to answer. If you can't, that's a problem — but not because you used help, but because you didn't engage with it carefully.
Yes. Some analysts will run tests and deliver tables; you handle interpretation. This is cheaper (usually $1,000–1,500 for basic analysis) than full consulting. Just make sure you understand the output before writing your results chapter.
This is a question for a statistician. They'll advise on whether to transform data, use a non-parametric alternative, or take a different approach. Assumption violations are common and usually fixable, but they need to be addressed intentionally, not ignored.