Concept of Workshop

In addition to a reasonable research question, every good and successful study needs above all a good experimental design. In order to create such a study, it is necessary to have a basic understanding of the interdependent interrelationships between the question, empirical hypotheses, study designs, error probabilities, sample size, measuring instruments (including test theoretical quality criteria), analysis methods (including their application prerequisites and their testing) and power considerations. Important issues here are, on the one hand, the operationalisation and specification of research questions, especially in the case of random sample numbers in clinical studies that cannot be freely selected, and the determination of the probable study design including the treatment of disturbing and moderating factors, and, on the other hand, the understanding of scale levels and the associated choice of analysis procedure, error probabilities and how to deal with them as well as effect strengths.

The workshop will therefore provide an introduction to the basics of experimental design as well as descriptive and inferential statistics. These will be explained using clinical examples and short exercises, with participants invited to contribute and discuss their own (questions on) study plans.

Learning goals

• Understanding the fundamentals of experimental design
• Understanding the basics of descriptive and inferential statistics
• Understanding the basic principles of experimental design
• Understanding the selection criteria for measuring instruments
• Understanding of the relationships between questions, empirical hypotheses, study designs, error probabilities, sample size, measurement equipment, analysis methods and power basic rules for reporting experimental design and statistics in scientific publications

Workshop content

• Basic knowledge (scale levels, causality principles, test plan designs, control of confounding variables)
• Design of experiments (questions, dependent and independent variables, choice of measuring equipment, internal or ceteris paribus validity, study designs)
• Descriptive statistics (principle of large numbers, distribution assumptions, measures of central tendency and confidence intervals)
• Inference statistics (hypothesis derivation, error types, adjustment for multiple testing, effect sizes)
• Parametric procedures (assumptions, prerequisites, effect sizes and basic test procedures for interval data)
• Nonparametric methods (assumptions, prerequisites, effect sizes and basic test methods for ordinal/nominal data)
• Correlations (species and interpretation)

Concept of Workshop

In addition to a reasonable research question, every good and successful study needs above all a good experimental design. In order to create such a study, it is necessary to have a basic understanding of the interdependent interrelationships between the question, empirical hypotheses, study designs, error probabilities, sample size, measuring instruments (including test theoretical quality criteria), analysis methods (including their application prerequisites and their testing) and power considerations. Important issues here are, on the one hand, the operationalisation and specification of research questions, especially in the case of random sample numbers in clinical studies that cannot be freely selected, and the determination of the probable study design including the treatment of disturbing and moderating factors, and, on the other hand, the understanding of scale levels and the associated choice of analysis procedure, error probabilities and how to deal with them as well as effect strengths.

In the second part, an introduction to the basics of inference statistics will be given. (Clinical) Examples will be calculated using SPSS. Participants can bring and discuss their own (questions about) study designs.

Learning goals

• Understanding the basics of inferential statistics
• Understanding of the relationships between questions, empirical hypotheses, study designs, error probabilities, sample size, measurement equipment, analysis methods and power basic rules for reporting experimental design and statistics in scientific publications

Workshop content

• Parametric procedures (assumptions, prerequisites, effect sizes and basic test procedures for interval data)
• Nonparametric methods (assumptions, prerequisites, effect sizes and basic test methods for ordinal/nominal data)
• Correlations (species and interpretation)