Category Thesis

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.

In this exercise, you’ll learn how to use a one-way analysis of variance (ANOVA) in R to test whether average job satisfaction differs significantly between three departments. You’ll prepare the data manually, check the assumptions of ANOVA, and conduct the analysis, including a Tukey post hoc test. This practical exercise will help you apply statistical methods to real-world questions in a targeted way.

You examine whether the average stress level differs significantly between male and female participants. Using a t-test (and, if appropriate, a Mann–Whitney U test), you learn how to turn a research question into a testable hypothesis—and how to test it in R or SPSS.