TheRightTest.Explained,NotJustRun.
One-to-one SPSS consulting for dissertations, theses, and coursework — correct test selection, assumption checks, and output interpretation. You run the analysis in your own SPSS; we make sure every step is statistically sound.
A misapplied statistical test undermines everything built on top of it.
SPSS makes it easy to run a test in three clicks — it doesn't tell you whether that test was the right one for your data and question. Getting this step wrong is one of the most common reasons quantitative chapters get sent back for revision.
You Run The Analysis
We sit with your dataset and your questions — you drive SPSS yourself while we guide test selection and check every output.
Assumption-Checked
Every test we recommend comes with the assumption checks (normality, homogeneity, multicollinearity) an examiner will look for.
Results-Chapter Ready
Output translated into properly formatted tables and reporting language matching APA, Harvard, or your institution's style guide.
Which test answers which kind of question.
| Test | Used To | Example |
|---|---|---|
| Independent Samples t-Test | Compare means between two independent groups | Test scores: male vs. female students |
| Paired Samples t-Test | Compare means from the same group at two time points | Pre-test vs. post-test scores |
| One-Way ANOVA | Compare means across three or more independent groups | Satisfaction across three age brackets |
| Pearson / Spearman Correlation | Measure the strength of association between two variables | Study hours and exam performance |
| Linear / Multiple Regression | Predict a continuous outcome from one or more predictors | Predicting sales from marketing spend and season |
| Chi-Square Test | Test association between two categorical variables | Gender and product preference |
| Factor Analysis | Identify underlying constructs behind a set of survey items | Reducing 20 survey items into 4 latent factors |
| Cronbach's Alpha | Measure internal consistency reliability of a scale | Checking a 10-item job satisfaction scale |
Where most students need a second pair of eyes.
Test Selection Guidance
Matching your research questions and data type to the correct statistical test — before you run anything, so you don't discover a mismatch after the fact.
Learn more about Test Selection GuidanceData Cleaning & Coding Review
Checking variable coding, missing data handling, and outlier treatment against accepted practice before analysis begins.
Learn more about Data Cleaning & Coding ReviewOutput Interpretation
Walking through SPSS output tables line by line — what's significant, what it means substantively, and how to report it.
Learn more about Output InterpretationResults Chapter Guidance
Structuring your findings chapter around your output: tables, figures, and narrative that connect back to your research questions.
Learn more about Results Chapter GuidanceReliability & Validity Checks
Cronbach's alpha, factor analysis, and construct validity checks for survey-based and psychometric studies.
Learn more about Reliability & Validity ChecksViva & Defense Prep
Mock questioning on your statistical choices so you can explain and defend every test and result under scrutiny.
Learn more about Viva & Defense PrepWhat gets quantitative chapters flagged.
Skipping Assumption Checks
Running a t-test or ANOVA without checking normality and homogeneity of variance first — the most commonly flagged statistical error in student work.
Confusing Correlation With Causation
Reporting a significant correlation as if it proves one variable causes the other, without acknowledging the limits of the design.
P-Value Without Effect Size
Reporting statistical significance (p < .05) without an effect size (Cohen's d, r², eta squared) to show how meaningful the result actually is.
Wrong Test For Data Type
Using a parametric test on ordinal or non-normally distributed data instead of the appropriate non-parametric alternative.
Mismanaged Missing Data
Deleting cases with missing values without justifying the approach, skewing sample size and representativeness.
Over-Interpreting a Marginal Result
Treating a p-value of .049 as strong evidence when the practical effect size is negligible — examiners look for calibrated language here.
From raw data to a results chapter you can defend.
Share Your Dataset & Questions
Send your research questions, hypotheses, and (anonymized) dataset or variable list.
Test Selection
We map your questions and data types to the correct statistical tests and flag any assumption issues up front.
Guided Analysis Session
A working session where you run the tests in SPSS with us guiding syntax, options, and output checks.
Interpretation & Reporting
We help you translate raw output into APA-formatted tables and results-chapter narrative.
Common questions.
No — this is a consulting service. You run the analysis in SPSS yourself, on your own machine and your own login, with us guiding test selection and checking your output. You stay the person who actually performed and can explain the analysis, which matters both academically and when you're questioned on it in a viva.
Yes. Sessions are calibrated to your starting point. For first-time users we start with data entry, variable coding (nominal/ordinal/scale), and navigating the Data View and Variable View before moving into any actual tests.
It depends on three things: your research question (comparing groups, finding a relationship, or predicting an outcome), the type of data you have (categorical, ordinal, or continuous), and whether your data meets the assumptions of parametric tests. We walk through this decision with you rather than just naming a test — so you understand why it's the right choice, not just that it is.
A p-value tells you whether a result is statistically significant — unlikely to have occurred by chance. An effect size tells you how large or meaningful that result actually is. A large sample can produce a 'significant' result that's practically trivial, which is why most reviewers now expect both reported together.
Yes — many students come to us with SPSS output tables already generated and need help understanding what they mean and how to write them up. Send the output and your research questions and we'll work through it with you.
SPSS consulting here focuses on quantitative analysis. If your study is qualitative or mixed-methods and involves NVivo coding, get in touch and we'll scope that separately — the consulting approach (you drive the software, we guide the methodology) is the same.
Guidance applies across current SPSS Statistics versions (26 through the latest release) — the core menus, dialog boxes, and output format have stayed largely consistent, so version differences rarely matter for the concepts we're covering.
Send your questions and data.
Share your research questions, variables, and any deadline — we'll respond within a few hours.