The correct method depends on your measurement level and your research questions. The table below shows the most common pairings.
| Research goal | Method | Notes |
|---|
| Summarize responses | Descriptive statistics | Means, medians, frequencies, percentages |
| Check scale consistency | Reliability analysis | Cronbach's alpha, omega |
| Validate an instrument | Factor analysis | Exploratory or confirmatory |
| Compare two groups | Independent or paired t-test | Mann-Whitney if non-normal |
| Compare three or more groups | ANOVA | Kruskal-Wallis if assumptions fail |
| Association between categories | Chi-square test | With effect size (Cramer's V) |
| Strength of a relationship | Correlation | Pearson or Spearman |
| Predict an outcome | Regression | Linear, logistic, or ordinal |
Before you collect data, our free sample size calculator helps you recruit enough respondents, and our linear regression calculator and Pearson correlation calculator let you explore relationships in your data. For analysis beyond surveys, our research data analysis service and biostatistics services cover experimental and clinical designs.
How We Handle Likert Scales
Likert scale analysis is where most survey projects stumble. A single Likert item (for example, strongly disagree to strongly agree) is ordinal, and treating it as continuous is hard to defend. A summated scale built from several Likert items, however, can often be analyzed with parametric methods once its reliability is established. We make that decision explicitly, depending on your scale structure: we test the internal consistency of each scale, justify whether items are summed or analyzed individually, and choose parametric or non-parametric tests accordingly. That justification is written into your methods so reviewers see the reasoning, not just the result.
- Scope and design. You share your questionnaire and dataset; we confirm your hypotheses and the analysis your design supports.
- Data cleaning. We recode variables, reverse-score where needed, document missing-data handling, and check assumptions.
- Scale validation. We run reliability and, where relevant, factor analysis on your validated instruments.
- Analysis. We run the descriptive and inferential tests, spanning Likert scale handling, reliability (Cronbach's alpha), exploratory and confirmatory factor analysis, structural equation modeling, and regression, in SPSS, R, or Stata with annotated, reproducible syntax, where the sample supports it.
- Reporting. You receive tables, figures, and a results-section draft written in the conventions of your field.
Our survey statistics clients span health sciences, nursing, psychology, education, social sciences, and beyond: PhD and master's students analyzing dissertation surveys, academics running questionnaire studies under publication deadlines, and applied teams who need defensible numbers from staff or patient surveys. Every project is matched with a PhD methodologist published in your field, so the reporting conventions fit your discipline. In each case the deliverable is the same: clean output, honest interpretation, and a write-up you can defend in a viva or in peer review. You can review published survey and measurement work in our samples library to confirm the standard of the deliverables.
For clinical studies and registries that need the database built and validated first, our study data management team handles collection, cleaning, and lock before analysis.
Ready to move? Get a free quote with a short note about your survey and sample size, or explore the full list of research services to combine analysis with writing, editing, or visualization.
