Quantitative methods

Language of instruction

English

Course content

Type of course: Methods

The course introduces the most common statistical methods to analyse quantitative data.

The course aims to give students an overview of methods available for analyzing quantitative data.

The course will cover main methods for analyzing relationships between

(a) continuous predictor and continuous outcome variables

(b) continuous predictor and categorical outcome variables

(c) categorical predictor and continuous outcome variables

(d) categorical predictor and categorical outcome variables.

In addition, it will be introduced in which conditions must be met to use parametric methods and now

one has to use non-parametric methods.

Learning outcomes

Knowledge

After completion of the course, the candidate has knowledge of the most common statistical methods

to analyze quantitative data. An overview will be given about what are categorical and continuous data

as well as what are predictor and outcome variables. Afterwards this terminology will be used to

introduce methods for analyzing relationships within the four possible combinations of categorical vs.

continuous and predictor vs. outcome variables:

[a] continuous predictor and continuous outcome variables (correlation and linear regression)

[b] continuous predictor and categorical outcome variables (logistic regression)

[c] categorical predictor and continuous outcome variables (analysis of variance)

[d] categorical predictor and categorical outcome variables (chi-squared)

In addition, it will be introduced which conditions have to be met to use parametric methods and when one has to use non-parametric methods.

Skills

After completion of the course, the candidate will be able:

[a] to prepare data collection in a way that makes evaluation of these data as easy as possible

[b] to select an appropriate evaluation method

[c] to carry these out in a software package for statistical analyses.

General competence

After completion of the course, the candidate will

[a] be able to organize own data

[b] have basic knowledge about how to handle missing data

[c] be able to choose an appropriate method to evaluate these data statistically

[d] have knowledge about how to carry out common statistical procedures. This knowledge will also

enable the candidate to critically reflect upon methods used by other researcher in publications.

Study period

Spring 2022

Credits (ECTS)

2 ECTs

Pre-requirements

Master Degree in disciplines relevant to educational sciences, psychology and public health.

Recommended previous knowledge

Primary target group are Ph.D. candidates. Other can ask for permission to attend

Compulsory Requirements

Compulsory assignment is an essay preferably in the form of a study preregistration. In addition,

attendance is compulsory (for at least 80% of the teaching).

Supplementary course information

Compulsory assignment is an essay with a description of a study design (preferably related to the own

dissertation) incl. a plan for suitable qualitative methods to analyze the data collected in that study

(can be in the form of a study preregistration). The other option is the conduct a data analysis (with

own data) and write it up (i.e., the statistical analyses part of the methods section [and other parts of

the methods that might be necessary to understand the analyses] and the results section).

Programme

The course will contain lectures (introducing several methods for analyzing quantitative data) and

exercises (where the methods are applied to evaluate own data statistically). Altogether 4 days with 4

+ 3 hours each will be used for these lectures and exercises

Academic responsibility

Sebastian Jentschke, Faculty of Psychology