Hi there,
I got data of multiple observers that measured the weight of an animal (the same). It's literally just one column with categorial value for the observer and another for the measured weight. Each observer measured the animal 3 times.
One particular observer is new in the field (so has no or little experience) and I would like to test if that observer might deviate from the others in his measurements -- I would go for a one-way ANOVA here, is that correct?
And another hypothesis is that whoever measured the animal has a significant effect on the estimated weight -- how do I test for that? Isn't it both the same? It's two different questions for this research, so I am a bit confused if it's actually different tests?
Thanks a lot!
Saskia
Dear Saskia Martin,
You can use different statistical tests such as the variance test, chi-squared test, students test, GLR…
For more details and information about this subject, I suggest you to see links and attached files on topic.
https://www.scribbr.com/methodology/types-of-reliability/
http://web1.sph.emory.edu/observeragreement/CIA_repeated_manuscript.pdf
https://www.researchgate.net/publication/330607765_The_diaphragm_thickness_measurements_on_computed_tomography_Intra-_and_inter-observer_reliability/figures?lo=1
https://catalogofbias.org/biases/observer-bias/
Best regards
We first consider the problem of estimating the variation between repeated measurements for the same subject. Essentially, we want to know how far from the true value a single measurement is likely to be. This estimation will be simplest if we assume that the error is the same for everybody, irrespective of the value of the quantity being measured. This will not always be the case, and the error may depend on the magnitude of the quantity, for example being proportional to it. Measurement error is assumed to be the same for everyone. This is a simple model, and it may be that some subjects will show more individual variation than others. If the measurement error varies from subject to subject, independently of magnitude so that it cannot be predicted, then we have to estimate its average value. We estimate the within-subject variability as if it were the same for all subjects.
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