From Raw Data to a Defensible Analysis Chapter
Data analysis is where many strong theses stall. You have spent months collecting questionnaires or compiling secondary data — but choosing the right statistical tests, running them correctly, and writing an interpretation that satisfies both your supervisor and the external examiner is a specialist's job. Using the wrong test, ignoring assumption checks, or misreading a p-value can undermine an otherwise excellent study.
Our statisticians work with research scholars every day. We start from your objectives and hypotheses — not from a menu of fancy techniques — and select methods that genuinely answer your research questions. Every output is converted into properly formatted, APA-style tables and figures ready to drop into your analysis chapter, and every result is interpreted in plain academic English that connects back to your hypotheses.
Techniques We Cover
- Descriptive statistics: frequencies, cross-tabulations, means, reliability (Cronbach's alpha)
- Hypothesis testing: t-tests, ANOVA/ANCOVA/MANOVA, chi-square, non-parametric equivalents
- Relationships and prediction: correlation, simple/multiple/logistic regression, mediation and moderation analysis
- Multivariate methods: exploratory and confirmatory factor analysis (EFA/CFA), cluster and discriminant analysis
- Structural Equation Modelling (SEM) in AMOS and R (lavaan), including model-fit reporting
- Econometrics and panel data in STATA and R for commerce and economics scholars
- Questionnaire support: scale construction, pilot testing, validity and reliability analysis
What's Included
- Data cleaning, coding and missing-value treatment of your dataset
- Assumption checks (normality, homogeneity, multicollinearity) documented properly
- All outputs as APA-formatted tables and publication-quality figures
- Written interpretation linked explicitly to each objective and hypothesis
- Software files (SPSS .sav/.spv, AMOS models, R/STATA scripts) so your work is reproducible
- A one-on-one session where the analyst walks you through every output before your presentation
Our Process
- Share your instrument and objectives: questionnaire, hypotheses and university expectations.
- Analysis plan approval: we propose the exact tests, and why, before running anything.
- Analysis and reporting: cleaned data, outputs, tables and interpretation delivered together.
- Walkthrough session: your analyst explains every table so you can defend it independently.
- Revisions: supervisor-requested changes and additional tests within scope are free.
How Long Will It Take?
Every project is different, so we don't quote one-size-fits-all deadlines. In your free consultation we understand your scope, your university's requirements and your target dates, agree a realistic schedule together, and put it in writing before work begins — then we stick to it.
Frequently Asked Questions
My data is already collected but messy. Can you still work with it?
Yes. Cleaning real-world data — inconsistent coding, missing responses, reverse-scored items — is a standard first step in every project. If the data has a fatal problem (for example, a sample too small for the planned technique), we tell you before charging anything and suggest workable alternatives.
Will I be able to explain the analysis in my viva?
That is exactly what the walkthrough session is for. Your analyst explains each test's purpose, its assumptions, and how to read every table — in plain language. Scholars regularly tell us this session was the difference between fearing and enjoying their statistics questions.
Which software should I choose — SPSS, AMOS, R or STATA?
It depends on your discipline and design: SPSS suits most survey-based social science research; AMOS is the standard for SEM in management and psychology; R and STATA shine for econometrics and advanced modelling. We recommend the right tool in your free consultation — and can match whatever your department prefers.