Running the test is ten minutes. Justifying it is the dissertation
Software will run almost any test on almost any data and return a p value that looks convincing. Whether that test was the right one, whether its assumptions held, and what the result actually licenses you to claim are separate questions, and they are the ones a committee asks.
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Design, then test, then software. Not the other way around
The most expensive mistake in quantitative work is collecting data first and choosing the analysis afterwards. By then the design has already decided what is possible, and some questions simply cannot be answered with what was collected. A conversation before data collection is worth more than any amount of help afterwards.
The second most expensive is choosing a test by looking at what other papers in the field used. Test selection follows from your research question, your variables and their measurement level, your design, and whether assumptions hold. Convention is a reasonable starting point and a poor justification.
The third is treating a p value as the finding. Effect size, confidence intervals and practical significance are what a reader actually needs, and reporting standards increasingly require them. A statistically significant result with a trivial effect size is a real thing, and saying so in your own discussion is a strength rather than an admission.
- Test selection justified from your question and your variables
- Assumptions checked and reported, not assumed
- Effect sizes and confidence intervals alongside significance
- Output turned into APA 7 tables that a committee will accept
What we work through
On your data, with you, so you can explain it afterwards
- Which test answers your question, and why the alternatives do not
- Assumption checks, and what to do when one fails
- Running the analysis in SPSS, R, Stata or Excel
- Reading the output properly, including what it does not tell you
- Writing results in APA 7, with the tables formatted correctly
Four things committees send back
The wrong test for the measurement level
Treating ordinal Likert responses as continuous without argument, or running parametric tests on clearly skewed data without checking. Both are defensible in some circumstances and neither is defensible silently.
Assumptions never mentioned
Normality, homogeneity of variance, independence, linearity, multicollinearity. A results chapter that does not mention them reads as one where they were not checked.
p values without effect sizes
Significance tells you the result is unlikely under the null hypothesis. It does not tell you the effect is large enough to matter. APA 7 expects both, and so will your committee.
Claims the design cannot support
Causal language on cross-sectional data is the single most common correction. The analysis may be perfect and the sentence describing it still wrong.
Specific help within quantitative work
Before you collect
- Sample size and statistical powerPower analysis, and what to do when recruitment falls short
Choosing and running
- Choosing a statistical testMatching test to question, variables and assumptions
- SPSS supportRunning it, reading the output, exporting usable tables
Writing it up
- Interpreting and reporting resultsTurning output into a results chapter that holds up
Your data, your analysis, your results
- We do not fabricate, alter or supply data.
- We do not run your analysis without you and hand back results.
- We do not write your results or discussion chapter.
- We do not help you search for a significant result by rerunning tests until one appears.
- We teach the method, work through your own data with you, and review your write-up.
On that fourth point: running tests repeatedly until something reaches significance inflates your error rate and is a research integrity problem, not a technique. If it comes up, we will say so and help you report what you actually found. Read the full policy.
Frequently Asked Questions
No. Beyond it being your work, an analysis you did not run is one you cannot defend, and a committee will ask you to explain your choices. We work through it with you on your own data so that you can.
It depends on your research question, how many groups or variables you have, whether they are independent or related, what measurement level they are at, and whether assumptions hold. That sounds like an evasion and it is genuinely the answer. Our statistical tests page walks through the decision, and one session usually settles it for a specific project.
No. There are non-parametric alternatives for most common tests, transformations that are sometimes appropriate, and robust methods. Some tests are also more tolerant of non-normality than their reputation suggests, particularly at larger sample sizes. What matters is that you check, choose deliberately, and report what you did.
You report it. A non-significant result is a result, and a discussion that engages honestly with it, including power and effect size, is stronger than one that quietly reframes the question. Committees generally penalize the reframing far more than the null finding.
Yes, and in Stata and Excel. Tell us what you are using and what your department expects, and we will work in that rather than pushing you toward a tool you then have to maintain alone.
Yes, and this is a common starting point. Send the output and the questions. We will tell you which are straightforward to answer, which need a rerun, and which are pointing at something more fundamental.
Data analysis support starts at $165 per session. Most projects need a small number of sessions at specific points rather than continuous support.
Send the dataset and the research question
We will tell you which analysis actually answers it, what has to be checked first, and where a committee is most likely to push back.
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