Quantitative Data Analysis
Descriptives, difference tests, correlation and regression
Descriptive statistics, difference tests, correlation and regression analyses — with the assumption checks done and the right test actually chosen.
Most of the statistical problems that reach peer review are not exotic. They are an independent-samples t-test run on dependent data, an ANOVA reported without a check on homogeneity of variance, or a significant result presented without an effect size.
We check the assumptions before choosing the test, report effect sizes with confidence intervals alongside significance, and use the non-parametric or robust alternative where the assumptions do not hold rather than proceeding regardless.
Software: SPSS, R or JASP. Tell us if your journal or institution prefers a particular package.
What Is Included
Assumption checks
Normality, homogeneity of variance, independence and multicollinearity checked and reported.
The right test
Parametric, non-parametric or robust, chosen on the basis of the data rather than convention.
Effect sizes
Cohen’s d, eta squared, R² and confidence intervals reported alongside p-values.
How the Process Works
- Initial assessmentYou share your research question, your dataset and your target journal. Together we decide which analysis is appropriate.
- Proposal and planScope, timeline and fee are agreed, and what you will receive is set out in writing.
- AnalysisAssumption checks, model specification and estimation, with interim findings shared with you.
- Delivery and handoverAPA-format tables, a written interpretation and a handover conversation. We do not request authorship for standard analysis work.
Request This Analysis
Send us your research question and the structure of your dataset and we will tell you which tests apply.
Or write to us directly: info@badeinstitute.com
