admin

This exercise is designed to consolidate the basic operations of importing data, recoding items, and calculating scale means.

📘 Scenario You conducted a short survey with 6 statements. Three items measure everyday stress, and 3 items measure recovery behavior. Some items are negatively worded and need to be reverse-scored.The scale ranges from 1 (strongly disagree) to 8 (strongly…

Imagine that you have collected a large amount of survey data with 30 questions on various psychological attitudes. You suspect that only a few underlying constructs, such as "achievement motivation" or "social desirability," lie behind them. This is where exploratory factor analysis (EFA) comes in: It helps you identify a smaller number of latent dimensions from a large set of observed variables. Here, we will look at how to implement it in SPSS.

You conducted a survey with 10 items on job satisfaction. An exploratory factor analysis (EFA) was then conducted in R. First (just to be on the safe side), a few definitions: The following R output shows the most important results:…

Inhalt Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 Task 7 Task 1 3 + 5 * 2 What is the result? Task 2 x <- c(4, 7, 2, 9)x What is the result? Task 3…

Inhalt Aim of the exercise The data: ALLBUS 2021 The variables Your task Step by step through the task Solution video for R Step 1: Calculate measures of central tendency Step 2: Calculate measures of dispersion Step 3: Grouped analysis…

Inhalt Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 Task 7 Task 1 x <- c(10, 20, 30, 40)x[x > 25] What is the result? Task 2 df <- data.frame(a = 1:3, b = c("a", "b",…

Inhalt Task 1 Task 2 Task 3 Task 4 Task 5 Task 1 # ────────────────────────────────────────────────────────────────# Task 1: Arithmetic Mean of a Small Group of Scores# We have 5 scores, calculate the mean, and interpret it.# ────────────────────────────────────────────────────────────────# 1. Create the datasetscores <-…

In this article, I’ll show you step by step how to perform a t-test with just one sample in R – a so-called one-sample t-test. It is useful whenever you want to compare the mean of a single group with a specific reference value.

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.