SPSS Statistical Tests Explained — Which Test Should You Use?

How to Choose the Right SPSS Statistical Test

Before you open SPSS, three questions decide which test you need:

What type of data do you have? Nominal, ordinal, interval, or ratio. This alone rules out most of the wrong tests.
How many groups or variables are you comparing? Two groups, three or more groups, or the relationship between two continuous variables.
Are your samples independent or related? Independent means different people in each group. Related means the same people measured more than once, or matched pairs.

A fourth question only matters once you know the answer to the first three: does your data meet the assumptions of a parametric test — approximately normal distribution and, where relevant, homogeneity of variance? If yes, use the parametric test. If an assumption is meaningfully violated, use its non-parametric counterpart instead.

Parametric Tests {#parametric-tests}

Parametric tests assume your continuous data is approximately normally distributed. They are more statistically powerful than their non-parametric equivalents when that assumption holds.

Comparing one sample mean to a fixed value → One-Sample T-Test
Comparing two independent groups → Independent Samples T-Test
Comparing two related measurements → Paired Samples T-Test
Comparing three or more independent groups → One-Way ANOVA
Comparing groups across two independent variables → Two-Way (Factorial) ANOVA
Comparing three or more related measurements → Repeated Measures ANOVA
Comparing groups while controlling for a covariate → ANCOVA
Comparing groups across multiple dependent variables → MANOVA / MANCOVA

Correlation and Regression {#regression}

Use these when you’re testing relationships between variables rather than differences between groups.

Linear relationship between two continuous variables → Pearson Correlation
Predicting one continuous outcome from one predictor → Simple Linear Regression
Predicting one continuous outcome from several predictors → Multiple Linear Regression
Testing predictors in theory-driven blocks → Hierarchical Regression
Predicting a two-category outcome → Binary Logistic Regression
Predicting an outcome with three or more categories → Multinomial or Ordinal Logistic Regression
Testing whether one variable explains, or changes, the relationship between two others → Mediation or Moderation Analysis (PROCESS macro)

Non-Parametric Tests (When Assumptions Are Violated) {#non-parametric-tests}

Every parametric test above has a non-parametric counterpart for when normality or variance-homogeneity assumptions fail, or when your data is ordinal rather than interval/ratio.

Parametric test	Non-parametric alternative
Independent Samples T-Test	Mann-Whitney U Test
Paired Samples T-Test	Wilcoxon Signed-Rank Test
One-Way ANOVA	Kruskal-Wallis H Test
Repeated Measures ANOVA	Friedman Test
Pearson Correlation	Spearman’s Rank Correlation

Two more non-parametric tests don’t map onto a parametric equivalent:

Association between two categorical variables → Chi-Square Test
Change in a binary variable across two related measurements → McNemar’s Test

Multivariate and Advanced Analysis {#multivariate}

Once your design involves more than one dependent variable, latent constructs, or data collected over time, you move into multivariate territory:

Identifying underlying factors behind a set of items → Exploratory Factor Analysis (EFA)
Reducing variables into fewer uncorrelated components → Principal Component Analysis (PCA)
Testing internal consistency of a scale → Reliability Analysis (Cronbach’s Alpha)
Grouping cases by similarity → Cluster Analysis
Predicting group membership from continuous predictors → Discriminant Analysis
Testing a hypothesised model of latent variables → Structural Equation Modelling (SPSS Amos)
Time-to-event data with censoring → Survival Analysis (Kaplan-Meier, Cox Regression)
Data collected sequentially over time → Time Series Analysis
Synthesising effect sizes across studies → Meta-Analysis
Determining the sample size you need → Power Analysis
Nested data (e.g. students within classrooms) → Multilevel / Hierarchical Linear Modelling

Statistical Test Decision Guide by Research Question

If your research question sounds like…

“Is there a difference between groups?” → parametric or non-parametric group-comparison test, chosen by group count and independence.
“Is there a relationship between variables?” → correlation or regression, chosen by outcome type (continuous, binary, ordinal, multi-category).
“Does this scale measure one consistent construct?” → reliability analysis, often paired with factor analysis.
“What predicts who ends up in each category?” → logistic regression or discriminant analysis.

Still not sure? Tell us your research question and data type when you get SPSS assignment help, and we’ll confirm the right test before any analysis starts.