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You have two groups — for example, men and women — and want to know whether they differ in a particular characteristic such as perceived stress, learning outcomes, or reaction time. For this, you need the t-test for independent samples. In this article, I will show you how to conduct and interpret it in R.

Imagine you want to know whether students from two fields of study experience different levels of exam stress. Or whether men and women differ in their average self-esteem. If you want to compare two independent groups, you need the independent-samples t-test in SPSS.

Sometimes you collect data before and after an intervention – for example, stress levels before and after mindfulness training. Or you work with pairs, twins, or repeated measurements. In such cases, you use the paired-samples t-test, also called the paired t-test.

Scenario:You are investigating whether a motivation workshop significantly increases employees’ motivation. To do this, motivation is measured before and after the workshop. Dataset: Mittel_Motivationsvergleich.csv Download data Variables: Solution in R 1. Import the data daten <- read.csv("Mittel_Motivationsvergleich.csv") 2. Check the…

If you have data from the same people at two points in time – e.g., before and after training – you need the paired-samples t-test in SPSS. This test examines whether the two measurements differ significantly. It is typically used for pre-post measurements, but also for measurements involving pairs or twin studies.

Scenario:You collect data from people in three groups (e.g., professional fields) on their interest in technology using three items. Examine whether Group 1 and Group 2 differ significantly in their scale mean. Group 3 should not be taken into account.…

You already know what an analysis of variance (ANOVA) is and when to use it. Now it’s time to put it into practice: You’ll get a complete workflow for conducting a one-way ANOVA in R—including assumption tests, model specification, post-hoc tests, and visualizations.

Inhalt Example: Study Methods and Exam Performance 1. Entering Data in SPSS 2. Checking the Assumptions a) Normality (Shapiro–Wilk test) b) Homogeneity of variances (Levene’s test) c) Independence 3. Conducting a One-Way ANOVA Example Output: Descriptive Statistics Test of Homogeneity…

Inhalt Example: Mindfulness Training and Stress 1. Entering data in SPSS 2. Conducting a Repeated Measures ANOVA a) Defining the factor b) Assigning the measurement time points c) Setting the options 3. Interpreting the results a) Mauchly’s test of sphericity…

One-Way ANOVA is great when you want to examine the influence of one factor on a numerical outcome variable. But what if you have two—and want to know whether they jointly or even interactively influence the outcome?

Then you need a Two-Way ANOVA, also called factorial ANOVA.