# Data Analysis

Introductory course data analysis with the statistical software program SPSS.

Good fit for first-year (applied) university students. Also potentially interesting as a refresher course for students writing their (bachelor) thesis.

Available languages: English

## Course content

### 1. Descriptive statistics

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Construct frequency distributions tables and graphs.
2. Calculate summary statistics and describe the dataset.
3. Assess outliers and missing values.
• SPSS functionalities covered:
1. Constructing frequency distribution tables (absolute, cumulative, relative).
2. Constructing statistical plots (histogram, boxplot, scatterplot).
3. Calculating measures of central tendency (mean, median, mode).
4. Calculating measures of dispersion (variance, standard deviation, range).
5. Calculating quantiles (percentiles, quartiles, IQR).
6. Calculating confidence intervals.
7. Calculating correlation coefficients (Pearson, Spearman).
8. Analyse missing values.

### 2. Variables and datasets

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Perform basic operations on datasets.
2. Create and change variables.
3. Calculate confidence intervals for proportions.
• SPSS functionalities covered:
1. Splitting datasets.
2. Drawing random samples.
3. Compute new variables.
4. Recode variables.
5. Create dummy variables

### 3. Mean comparison

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Conduct t-tests.
2. Interpret the results of t-tests.
3. Check the validity of the underlying assumptions of t-tests.
• SPSS functionalities covered:
1. Conducting a one-sample t-test.
2. Conducting an independent-sample t-test.
3. Conducting a paired-samples t-test.
4. Conducting a test for normality.
5. Constructing a QQ-plot.

### 4. Chi-square tests

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Conduct chi-square tests.
2. Interpret the results of chi-square tests.
3. Check the validity of the underlying assumptions of chi-square tests.
• SPSS functionalities covered:
1. Create crosstabs.
2. Conducting a chi-square Goodness of fit test.
3. Conducting a chi-square test for independence.

### 5. Analysis of variance

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Conduct analyses of variance.
2. Interpret the results of an analysis of variance.
3. Check the validity of the underlying assumptions of analyses of variance.
4. Conduct and interpret post hoc analyses.
• SPSS functionalities covered:
1. Conducting a one-way ANOVA.
2. Conducting a factorial ANOVA.
3. Conducting a repeated-measures ANOVA.
4. Conducting a Levene’s test for homogeneity of variances.
5. Conduct Tukey’s HSD post hoc test.

### 6. Simple linear regression

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Conduct a simple linear regression analysis.
2. Interpret the results of simple linear regression.
3. Check the validity of the underlying assumptions of simple linear regression.
4. Evaluate the explanatory power of a linear regression model.
• SPSS functionalities covered:
1. Conducting a simple linear regression.
2. Calculate prediction intervals.

### 7. Multiple linear regression

• Learning outcomes
• After completing this assignment, the student will be able to:
1. Conduct a multiple linear regression analysis.
2. Interpret the results of multiple linear regression.
3. Check the validity of the underlying assumptions of multiple linear regression.
4. Compare the explanatory power of multiple linear regression models.
5. Add categorical variables as dummy variables to a regression model.
• SPSS functionalities covered:
1. Conducting a multiple linear regression.
2. Use of dummy variables in regression analysis.