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 |
This table is a starting point, not a lookup table to apply blindly. The right-hand column only holds if the underlying assumptions of that test are actually met by your data and design. A correlation between two continuous variables assumes a roughly linear relationship; a t-test assumes reasonably normal distributions and, for the independent-samples version, similar variances between groups; ANOVA assumes independence of observations and homogeneity of variance across groups; SEM requires a sample large enough to estimate what are often dozens of parameters simultaneously, typically far larger than a simple regression would need. Picking the test that matches your question type is step one; verifying your actual data can support that test is step two, and skipping it is one of the more common reasons a seemingly correct analysis still draws committee objections.
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.
Three inputs drive every power calculation, and each one is a judgment call worth making deliberately rather than defaulting blindly. Effect size is your best estimate of how large the relationship or difference you're studying actually is. Ideally drawn from a meta-analysis or closely related prior study in your field, and when no such estimate exists, a conservative standard like Cohen's conventions (small, medium, large) is used instead, with the conservative choice justified explicitly. Significance level (alpha) is the false-positive rate you're willing to accept, conventionally α = .05 in most social science fields, though some disciplines and some multiple-comparison situations call for a stricter threshold. Power is the probability of correctly detecting a real effect if one exists, conventionally set at .80. Meaning you accept a 20% chance of missing a true effect (a Type II error) in exchange for a more feasible sample size; some fields and funders now expect .90 given the cost of underpowered research.
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.
Every quantitative research question needs to be translated into a null hypothesis (H0, no relationship or no difference exists) and an alternative hypothesis (H1, a relationship or difference exists) before any test is run. This translation step is where a surprising number of quantitative chapters go wrong, because the hypotheses need to match the statistical test precisely. A hypothesis about "differences between groups" cannot be tested with a correlation, and a hypothesis about "prediction" cannot be tested with a t-test. The p-value itself is routinely misinterpreted even by experienced researchers: it is the probability of observing data this extreme (or more extreme) if the null hypothesis were true, not the probability that the null hypothesis is true, and not the probability that your results are due to chance. A p-value below your chosen alpha (typically .05) lets you reject the null hypothesis; it does not "prove" the alternative, and it says nothing about the size or practical importance of the effect, which is why effect sizes are reported alongside it, never in place of it.
Quantitative sampling decisions come down to two linked questions: how will participants be selected, and how many are needed? Probability sampling (simple random, stratified, cluster, systematic) supports the strongest generalizability claims because every member of the population has a known chance of selection; non-probability sampling (convenience, purposive, snowball) is common in applied doctoral research where a full sampling frame isn't available, but it requires an honest limitation statement about restricted generalizability. Sample size should never be chosen by convention or by matching what a similar study used. It should come from an a priori power analysis specific to your design, expected effect size, alpha, and desired power (see the tip above and our dedicated statistics help guide for how this is calculated in practice). A sample that is too small risks a Type II error, failing to detect a real effect, which can quietly invalidate months of data collection after the fact.
Suppose a doctoral candidate is testing whether remote-work flexibility predicts employee job satisfaction, controlling for tenure and department. H0: remote-work flexibility does not significantly predict job satisfaction after controlling for tenure and department. H1: remote-work flexibility significantly predicts job satisfaction after controlling for tenure and department. The test selected is hierarchical multiple regression, entering tenure and department in the first block and remote-work flexibility in the second block, to isolate its unique contribution to variance explained. A power analysis targeting a small-to-medium effect size (f² = .15), alpha = .05, and power = .80, with three predictors, indicates a minimum sample of roughly 77 respondents. The candidate recruits 120 to allow for incomplete surveys. Before running the regression, the data are checked for linearity, multicollinearity (via VIF), homoscedasticity, and normally distributed residuals. The results report the R² change from adding remote-work flexibility, its standardized beta, its p-value, and a 95% confidence interval. Giving the committee both statistical significance and a sense of practical magnitude.
Master's-level quantitative projects are often evaluated mainly on whether the correct test was run and interpreted correctly. Doctoral-level work is expected to go further: a defensible a priori power analysis, explicit hypotheses tied precisely to the test, assumption checks reported transparently even when they required a change in analytic strategy, and language that never overstates what the design and statistics can support. Doctoral committees, especially any member with a methodological or statistical background, are also more likely to probe alternative explanations for a significant finding (confounds, common-method variance, multicollinearity) rather than accepting a significant p-value at face value.
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.
We write the null and alternative hypothesis for each research question in the exact language the chosen test can actually evaluate, comparative wording for a t-test or ANOVA, predictive wording for regression, so there's no gap between what you claim to test and what the analysis actually shows.
Yes. A non-significant finding is still a legitimate, reportable result, not a failed study. We help you discuss it honestly against your power analysis and existing literature rather than searching for ways to make it look significant after the fact.