6 ECTS credits
180 h study time
Offer 1 with catalog number 1019624BNR for all students in the 1st semester at a (B) Bachelor - advanced level.
Statistics III: univariate data analysis deals with frequently used methods for statistical analysis of univariate data, both parametric and nonparametric. Methods are presented in an overview based on the measurement level (Nominal, Ordinal, Interval or higher) and the number of samples to be compared (1, 2, or more). In this course, the use of statistical software is explored and broadened: students work with larger and more complex data files, albeit “ready for use”. All topics dealt with in the lectures are put into practice in both the exercises and by means of software. The APA format is systematically used and required in the reporting of statistical results.
Exercises sessions comprise on the one hand "hands-on computations", in which the theory lessons are deepened with exercises that must be made by the student themselves, under the supervision of an assistant. Only a limited number of exercises are completed during exercises sessions, further practicing is part of the SELF required for this course.
In addition to the above exercises, we also practice with statistical software. This aspect of the exercises requires a significant amount of self-study in order to achieve that students can answer questions independently and quickly by analyzing provided data using statistical software.
Topics covered
Power analysis
Inference of population means: t-tests / ANOVA
t distributions
inference about the expected value for 1 population (confidence interval / hypothesis test)
Comparing 2 expected values
t-test for dependent measurements
t test for independent samples
Inference on non-normal distributed populations (data transformation)
Power of t-tests (with reference to G * power)
Inference about nominal variables: numbers and proportions
approximation on the basis of normal distributions for large samples
adjusted approaches for smaller samples (plus four)
determining a required sample size
inference about the difference between 2 proportions
inference for contingency tables
Non-parametric tests
Sign test for related data
Wilcoxon rank sign test
Wilcoxon rank sum test / Mann-Whitney U-test
Kolmogorov-Smirnov test / McNemar / Bartlett's test
Chi-square goodness-of-fit test
Kruskal-Wallis test
Linear models
Comparing Multiple Expected values: ANOVA (Oneway)
Univariate Regression Analysis
Slides shown in the lectures can be downloaded from Canvas.
Apart from a very thorough knowledge of the theory, it is mainly expected that the student can independently apply the learned techniques to realistic data and can use them to solve presented problems.
Explain Type I and Type II errors by means of a graph
Determine and explain power of a statistical test
Independently translate a research question into a statistical hypothesis.
Explain how the concept of "sampling distribution" plays an important role in inferential statistics.
Depending on the level of measurement and the number of samples to be compared, make an appropriate choice for a parametric or non-parametric test
Manually carry out methods that have been dealt with in lectures and WPOs (as described in content) independently.
Independently apply methods that have been discussed in lectures and WPOs (as described in content) with statistical software.
Interpret the results of self-performed significance tests and report on them in APA style.
Help fellow students with processing the contents of the course
Atively participate in the discussion form for this course
The final grade is composed based on the following categories:
Written Exam determines 80% of the final mark.
Practical Exam determines 20% of the final mark.
Within the Written Exam category, the following assignments need to be completed:
Within the Practical Exam category, the following assignments need to be completed:
Evaluation consists of:
Marks attaining at least 60% of the maximum score can be transferred from the first to the second exam session. Transfers of marks to a next academic year are not allowed. For each part of the evaluation only the last obtained score counts.
This offer is part of the following study plans:
Bachelor of Psychology: Profile Profile Work and Organisational Psychology (only offered in Dutch)
Bachelor of Psychology: Initial track (only offered in Dutch)
Bachelor of Psychology: Profile Profile Clinical psychology (only offered in Dutch)
Bachelor of Psychology: Profile Profile Work & Organisational Psychology (only offered in Dutch)
Bachelor of Psychology: Profile Profile Clinical Psychology (only offered in Dutch)
Bridging Programme Master of Science in Psychology: Traject van 90 studiepunten met Profiel Arbeids- en Organisatiepsychologie (only offered in Dutch)
Bridging Programme Master of Science in Psychology: Profile Profile Clinical Psychology (only offered in Dutch)
Bridging Programme Master of Science in Psychology: Profile Profile Clinical Psychology (only offered in Dutch)
Bridging Programme Master of Science in Psychology: Profile Profile Work and Organisational Psychology (only offered in Dutch)
Bridging Programme Master of Teaching in Behavioural Sciences: Psychology (only offered in Dutch)
Preparatory Programme Master of Science in Psychology: Profile Profile Work and Organisational Psychology (only offered in Dutch)
Preparatory Programme Master of Science in Psychology: Profile Profile Clinical Psychology (only offered in Dutch)