SPSS help
From a messy dataset to output you can defend. We run the right procedure in SPSS and write up what it means.
SPSS help takes your dataset and research question and returns a correct, reproducible analysis: setting up the variables and measurement levels, running the right procedure, checking its assumptions, and writing the output up so the result answers your question and survives examination. It covers the whole path from raw data to a defensible finding, not a single number dropped in an email.
Where SPSS work actually goes wrong
Most SPSS problems are not about clicking the wrong menu. They start earlier, in how the data was set up, and they surface later, in how the output was read. A continuous variable mislabelled nominal vanishes from the dialog that needs it; a blank cell left without a declared missing value code is silently dropped; a test is run before its assumptions are checked, so the p-value means nothing. We fix the setup first, because a clean procedure on a broken dataset is still a broken result. If you are finding your feet in the software, our guide to how to use SPSS explains the Data and Variable Views and why we work in saved syntax.
This service is execution-focused. It is narrower than full dissertation statistics help, which also plans the design, and it pairs naturally with dissertation data analysis help when you have a complete dataset that needs a results chapter.
The procedures we run most
- 1
Group comparisons
t-tests and ANOVA with the right post hoc and the Levene or Welch correction, as in our t-test in SPSS and one-way ANOVA guides.
- 2
Relationships and prediction
Correlation and regression, including the multicollinearity and influence diagnostics covered in multiple regression in SPSS.
- 3
Categorical and binary outcomes
Chi-square tests of independence and logistic regression with the odds ratios and fit checks from logistic regression in SPSS.
- 4
Scales and latent structure
Factor analysis and reliability for survey scales, following factor analysis in SPSS and Cronbach's alpha.
For doctoral datasets that need modelling beyond standard tests, see doctoral dissertation statistics.
How we work an SPSS analysis
A defensible analysis follows a fixed order, and we paste syntax at every step so the whole thing is reproducible in one click rather than fifty. Working out of sequence, for example running a regression before screening for outliers, is how a result ends up resting on an entry that should never have been there.
- Define and label every variable, with value labels, missing codes, and the correct measurement level.
- Import and screen the data for impossible values and outliers.
- Handle missing data with a documented rule, not a silent deletion.
- Run descriptive statistics and visual checks to understand the distributions.
- Test the assumptions the planned procedure depends on.
- Run the inferential procedure, pasting the syntax as we go.
- Report the result with its effect size and confidence interval.
The assumption stage is where the analysis is won or lost; our guide to checking normality and other assumptions shows what we test and why, and our walkthrough of SPSS output tables shows how we read the tables that result.
What you receive
The deliverables are built for transparency, because a result nobody can reproduce is a result a viva can unpick. Everything is handed over in a form your committee can re-run.
- The cleaned .sav dataset, ready to re-analyse.
- The full syntax file so every step is reproducible.
- The complete output from every procedure that was run.
- Correctly formatted results tables in your required style.
- A written interpretation that ties each number to a question.
- A short plain-language summary you can speak to under questioning.
If you are still deciding which procedure your data calls for, our guide to picking the right test for your design walks through how the question and variable types point to one method, and our free statistics calculators let you plan and check before you hand the data over.
Send your dataset and research questions, and we will confirm the SPSS procedure and turnaround before any work starts.
Frequently asked questions
- Can you help me run my analysis in SPSS?
Yes. Send your dataset and research questions and we set up the variables correctly, run the procedure your design calls for, check its assumptions, and return the output with a written interpretation and the syntax so the whole analysis is reproducible. You receive a result you can defend, not just a screenshot of a table.
- How do I know which SPSS test to use?
The test follows from your research question and your variable types: how many groups you compare, whether the outcome is continuous or categorical, and whether the data meet parametric assumptions. We match the test to the design rather than the other way around, and our guide on choosing a statistical test walks through the same logic.
- Do you fix the data or just run the test?
Both, and the data setup usually matters more. We define and label variables, set measurement levels, declare missing-value codes, and screen for impossible values and outliers before any test is run, because a clean procedure on a badly set-up dataset still produces a wrong answer.
- Will the SPSS results be reproducible for my viva?
Yes. We work in pasted syntax rather than menu clicks, so you receive a saved syntax file alongside the cleaned dataset and the output. That lets you, or an examiner, rerun the entire analysis in one step and see exactly how each result was produced.