Quantitative dissertations rise or fall on one thing: whether the statistical test actually answers the research question as worded. Our specialists handle correlational, experimental, quasi-experimental, and survey designs — from power analysis through to results reporting in SPSS, R, or Stata.
| Research Question Type | Typical Test |
|---|---|
| Relationship between two continuous variables | Pearson or Spearman correlation |
| Predicting an outcome from one or more variables | Linear or multiple regression |
| Difference between two group means | Independent or paired-samples t-test |
| Difference across three or more groups | ANOVA / ANCOVA |
| Relationships among several latent constructs | Structural equation modeling (SEM) |
| Categorical outcome prediction | Logistic regression |
A sample size chosen without a power analysis is a common reason quantitative proposals stall. Power analysis (typically via G*Power or equivalent) calculates the minimum sample needed to detect a meaningful effect given your expected effect size, significance level, and desired power — usually 80%. Running this before data collection, not after, also strengthens your proposal by showing the study is adequately resourced to actually answer the question.
Check your assumptions before you run the test, not after. Normality, homogeneity of variance, linearity, and independence assumptions vary by test. Violated assumptions don't necessarily disqualify your analysis — but they need to be checked, reported, and addressed (e.g. with a non-parametric alternative) rather than ignored.
Power-justified sample size, the correct statistical test, and results reported the way your field expects.
Yes. If your data collection is complete, we can run the appropriate analysis in SPSS, R, or Stata and write up the results chapter with output tables explained clearly enough for you to defend in your viva.
We check this before running anything and recommend the appropriate alternative — a non-parametric test, a transformation, or a robust regression approach — and document why the change was necessary.
Yes, including selecting or adapting validated scales where they exist for your constructs, rather than writing untested items that would weaken your instrument's reliability.