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.

English

**Learning outcomes**- After completing this assignment, the student will be able to:
- Construct frequency distributions tables and graphs.
- Calculate summary statistics and describe the dataset.
- Assess outliers and missing values.

- SPSS functionalities covered:
- Constructing frequency distribution tables (absolute, cumulative, relative).
- Constructing statistical plots (histogram, boxplot, scatterplot).
- Calculating measures of central tendency (mean, median, mode).
- Calculating measures of dispersion (variance, standard deviation, range).
- Calculating quantiles (percentiles, quartiles, IQR).
- Calculating confidence intervals.
- Calculating correlation coefficients (Pearson, Spearman).
- Analyse missing values.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Perform basic operations on datasets.
- Create and change variables.
- Calculate confidence intervals for proportions.

- SPSS functionalities covered:
- Splitting datasets.
- Drawing random samples.
- Compute new variables.
- Recode variables.
- Create dummy variables

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct t-tests.
- Interpret the results of t-tests.
- Check the validity of the underlying assumptions of t-tests.

- SPSS functionalities covered:
- Conducting a one-sample t-test.
- Conducting an independent-sample t-test.
- Conducting a paired-samples t-test.
- Conducting a test for normality.
- Constructing a QQ-plot.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct chi-square tests.
- Interpret the results of chi-square tests.
- Check the validity of the underlying assumptions of chi-square tests.

- SPSS functionalities covered:
- Create crosstabs.
- Conducting a chi-square Goodness of fit test.
- Conducting a chi-square test for independence.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct analyses of variance.
- Interpret the results of an analysis of variance.
- Check the validity of the underlying assumptions of analyses of variance.
- Conduct and interpret post hoc analyses.

- SPSS functionalities covered:
- Conducting a one-way ANOVA.
- Conducting a factorial ANOVA.
- Conducting a repeated-measures ANOVA.
- Conducting a Levene’s test for homogeneity of variances.
- Conduct Tukey’s HSD post hoc test.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct a simple linear regression analysis.
- Interpret the results of simple linear regression.
- Check the validity of the underlying assumptions of simple linear regression.
- Evaluate the explanatory power of a linear regression model.

- SPSS functionalities covered:
- Conducting a simple linear regression.
- Calculate prediction intervals.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct a multiple linear regression analysis.
- Interpret the results of multiple linear regression.
- Check the validity of the underlying assumptions of multiple linear regression.
- Compare the explanatory power of multiple linear regression models.
- Add categorical variables as dummy variables to a regression model.

- SPSS functionalities covered:
- Conducting a multiple linear regression.
- Use of dummy variables in regression analysis.
- Adding additional variables to an existing regression model.

**Learning outcomes**- After completing this assignment, the student will be able to:
- Conduct a logistic regression analysis.
- Interpret the results of logistic regression.
- Check the validity of the underlying assumptions of logistic regression.
- Evaluate the explanatory power of a logistic regression model.

- SPSS functionalities covered:
- Conducting a logistic regression.