The SPSS Survival Guide – 7 Mistakes That Could Cost You Your Degree

 

Introduction

SPSS is powerful — but dangerous in the wrong hands.  We have seen a gazillion of them but here are the Top 5. This guide exposes 5 deadly mistakes students make during their data analysis… and how to avoid them like an expert


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1. Running Tests Without Checking Assumptions

You can’t run t-tests, ANOVA, or regression without checking normality, homogeneity, or linearity. If the assumptions fail, your results are invalid.

🧠 Here is an Analogy: It’s like trying to fry eggs on a broken stove — no matter how hot the pan looks, the eggs won’t cook.

2. Using the Wrong Test for the Data Type

Many students run t-tests on ordinal data or use Chi-square for mean differences. You must know your data scale (nominal, ordinal, interval, ratio) to choose the right test.

✅ Fix: Learn what each test was designed for.

3. Blindly Interpreting p-values Without Context

A p-value of 0.04 isn’t automatically “good.” What does it mean in your study? What variable is significant? And what’s the direction?

🧠 Pro Tip: Always pair your p-value with effect size and interpretation.

4. Submitting Raw SPSS Output

Supervisors don’t want to see 18 pages of unedited output. Format your tables, label your variables, and present clean charts.

🧠 Here is an Analogy: Would you serve food without removing the pot cover?

5. Ignoring Data Cleaning

If you didn’t check for duplicates, outliers, or inconsistent entries — your entire analysis may be trash.

✅ Always clean, code, and define variables before running tests.

📩 Want to avoid these mistakes in your own thesis or dissertation?
🎁 Then Download our Free SPSS Mistakes Checklist Here: 
Or DM “SPSS FIX” and we’ll review your output.

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