Felix Christian Grün

Felix Christian Grün
Technische Universität Berlin | TUB · Department of Psychology and Ergonomics

Master of Science


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Publications (3)
Full-text available
Even the most effective drug product may be used improperly and thus ultimately prove ineffective if it does not meet the perceptual, motor, and cognitive capacities of its target users. Currently, no comprehensive guideline for systematically designing user‐centric drug products that would help prevent such limitations exists. We have compiled a l...
Full-text available
Wie ändert sich unser Arbeitsverhalten durch digitale Arbeitsformen? Welche positiven oder auch negativen Auswirkungen haben moderne, digitale Arbeitsformen auf das Arbeitsverhalten? Und welche Empfehlungen kann die Wissenschaft geben, damit digitale Neuerungen produktive als auch humane Arbeit unterstützen? Um Fragen wie diese zu beantworten, verw...
Cockpit design is a core area of human factors and ergonomics (HF/E). Ideally, good design compensates for human capacity limitations by distributing task requirements over human and interface to improve safety and performance. Recently, empirical findings suggest that the mere spatial layout of car cockpits may influence driver behaviour, expandin...


Questions (3)
I have a base model: leader ~ gender + posture leader is the dependent variable. gender and posture independent variables.
I want to control for control variable, like probands' gender, age etc. To solve this, I use an Anova to compare the base model with complex model ( leader ~ gender + posture + probandGender ). If it's significant, the control variable probandGender will have a meaningful influence on the model, and thus it should be kept for further analysis. Is that correct so far?
If yes, it leads me to my actual question:
Variable gender has two categories (female and male), but it consists the pictures of 2 males (male1, male2) and 2 females (female1, female2). Hence, there is the control variable single_person. I want to control, if the gender effects is reliable, or a single person stands out and make it significant. Is it allowed put single_person into a term like leader ~ gender + posture + single_person and to compare it to the basic model like before? it feels wrong, because it's like single_person is in gender. Are they nested?
Thank you for any idea.
For a project, I want to show pictures of different faces to participants. I want to control for the individual impact of the face (for example, one of the faces is outstanding attractive and it may biase my results),
Is there a good practice or an etablished pre-survey to check for attractiveness and other sources of biases?
I intend to run a replication. In this replication participants rank 4 persons in 4 different body postures. They rank them on intelligence, confidence etc.
For this study we have 4 picture sets with 4 pictures each. Each set has each posture and each person one time, but in different Combinations.
I want to know if being female or male on the picture or having a closed or open posture on the picture, influence the rankings.
I want to use a (binary) logistic regerssion
Now my question:
Can I use Logistic regression for this data?
To me it looks like, I do not have 200 participants, but 800 because each participant ranks 4 picture and each picture has a ranking position.
Would I hurt assumptions if I feeded spss like this,? The 800 'data points' are not independent, because every participant created 4.
Thanks for any help and advice!


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