SPSS will happily run a test that makes no sense for your data
It does not check whether the procedure suits your design, and it does not warn you when a variable is measured at the wrong level. That judgment is yours, and it is what the output is worth nothing without. We work through your own dataset with you.
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Most SPSS problems are data problems wearing a software costume
The error message is rarely the issue. Variables typed as string when they should be numeric, missing values coded as 99 and then treated as a real score, reverse-scored items never reversed, measurement level set to scale on a nominal variable: these produce output that runs perfectly and means nothing.
Setting up the variable view properly takes half an hour at the start and saves the entire analysis. Names, labels, values, missing value codes and measurement level, done once, deliberately. It is also what makes your output readable six months later when you are writing up.
The other recurring problem is that SPSS output is verbose and most of it is not what you need. Knowing which three numbers in a five-table block go into your results chapter is a skill, and it is quicker to be shown than to work out.
- Variable view set up so the output is readable and correct
- Missing data handled deliberately rather than by default
- The right procedure for your design, with assumptions checked
- Output read properly, and turned into APA 7 tables
What sessions cover
Driven by your dataset and your deadline
- Getting data in from Excel, Qualtrics or Google Forms without corruption
- Recoding, computing scale scores and reverse-scoring items
- Descriptives, reliability, correlation, t-tests, ANOVA, regression, chi-square
- Reading output: which numbers matter and which are noise
- Exporting tables that go into your thesis without reformatting
Four SPSS problems we see constantly
Missing data treated as a score
Coding missing as 99 and not declaring it in the missing values column. Your means are then wrong and nothing flags it. This alone invalidates more student analyses than any other single error.
Scale scores computed wrongly
Summing items without reverse-scoring the negatively worded ones, or using Mean rather than the scale's own scoring rule. Cronbach's alpha usually reveals it, if you run it.
Reading the wrong table
Reporting the Sig. value from Levene's test as the result of the t-test is a genuinely common slip, and it changes the conclusion completely.
Output pasted raw into the thesis
SPSS tables are not APA 7 tables and pasting them in is an instant correction. They need rebuilding, and there is a fast way to do it.
We teach the software on your data
- We do not run your analysis for you and return results.
- We do not fabricate, alter or supply data.
- We do not write your results or discussion chapter.
- We do not rerun tests until something reaches significance.
- We work through your dataset with you and review what you produce.
SPSS is licensed software from IBM. Most universities provide access, and it is worth checking before paying for a personal license. Read the full policy.
Frequently Asked Questions
No. Your committee will ask you to explain your procedure choices and your output, and an analysis you did not run is one you cannot explain. We work through it with you on your own data, which takes slightly longer and leaves you able to defend it.
Usually by exporting to Excel or CSV first, then importing. The step people skip is checking what happened to variable names, value labels and missing data during the export, which is where most silent corruption occurs. It is worth doing carefully once.
It means the assumption of equal variances is not supported, so you read the row of the t-test output that does not assume equal variances. SPSS gives you both rows for exactly this reason, and choosing between them is one of the most commonly missed steps.
If you are using a multi-item scale, almost certainly yes, and it should be computed on your own sample rather than quoted from the original validation study. If alpha is low, report it and discuss it rather than omitting it.
Neither is better. SPSS is faster to learn and the menus make the procedure explicit, which suits a one-off dissertation analysis. R is free, reproducible and far better if you will keep doing this. Use whichever your department supports, because being able to ask someone nearby matters more than the feature list.
Usually a version or a settings difference rather than an error. Send a screenshot of the output and what you expected, and it is normally resolved in a few minutes.
Send the dataset and tell us what you are trying to find out
We will tell you what needs cleaning first, which procedure answers your question, and what the output will actually let you claim.
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