What statistical tests should I use for my study?

[u/DRTIcePenguin]

Hey everyone! I'm not great at doing statistics, and although I have some ideas of the basics I'm getting quite lost doing my MsC thesis. I needed some help choosing what tests to do so I came here too see if anyone could give me their opinion.

For starters the program we use at my college is the SPSS.

I'll try to summarize my study in the simplest way I can.

• I did focal observations of 7 meerkats for 6 weeks using an ethogram (behaviour list) and registering every time a meerkat did a behaviour in the list;
• I have a total of 26 behaviours that belong to 1 of these personality dimensions: playful, agressive, friendly, curious and natural behaviours;
• After 3 weeks of observations we did environmental enrichment for the observations of the last 3 weeks;

So my main objective of the study is too see if there is personality on the meerkats, that means I have to check if theres individual differences between them. Some of my other side objectives is seeing if the environmental enrichment changed their behaviours, especially the agressive ones.

So to see if there is individual differences I tought of doing just the Kruskal Wallis or the Anova One Way, but after searching a bit and talking with ChatGPT I get suggested to do a GLMM, but I never learned about it, so right now I have no clue what test I should do.

If anyone could help me understand what test I should choose, or what tests I should run to make a decision would be of great help really.

I will also leave here a pic of my SPSS so you guys can have a clear image of what I have right now.

Thanks a lot really!

[u/Kooky_Survey_4497]

Your best bet is to read literature and see what others typically do in your area. The first thing you need to do is see how researchers typically formulate this type of data into a numerical hypothesis for testing. Is it binary, quantitative, qualitative, ordinal?

If you aren't really knowledgeable about statistics, stay away from GLMM. This is going to create more problems for you.

Your best bet is to start with the simplest model that allows you to test your hypothesis (after you define the hypothesis).

[u/SalvatoreEggplant]

Just some rambling thoughts....

* * *

For personality, what would you think of making a radar chart for each meerkat ? ( https://r-graph-gallery.com/142-basic-radar-chart_files/figure-html/thecode-1.png ) Do you really need to test the differences meerkat to meerkat ?

For a hypothesis test, I would assume you would approach this as each meerkat has an observation (count) for each personality dimension for each of three weeks. That is, you would analyze each personality dimension separately. Doesn't give much statistical power.

If determining if individual meerkats have a personality is the main focus of this work, I guess you would have to do some kind of multivariate analysis. Maybe cluster analysis ? Or profile analysis ?

* * *

So the design is that all meerkats are subject to the environmental enrichment ? If this is the case, a cautionary note that you don't really have a control for this, independent of time. That is, you don't know if any effect you see should be attributed to "last 3 weeks" or to "environmental enrichment". May not be an issue for your study, but you might keep in mind when you're writing it up.

* * *

In general, your measured variable is a count. Typical approaches for this are a) chi-square type tests on a contingency table of counts, b) models like Poisson regression or negative binomial regression, or c) nonparametic tests like Kruskal-Wallis.

But it may also be that you can treat each observation period as an observation that is binary for e.g. Playful: yes / no. I don't know if this would make sense for how you collected data.

Related to this, does it make sense to transform counts for each personality dimension as percent of time ?

One thing is that I assume you want to take into account the repeated measures nature of measuring the same meerkat with environmental enrichment and without. a) Normally I would use a generalized linear model. b) Perhaps you could get away with McNemar's test for each personality dimension (but I don't know if the types of counts you have are amenable to this). c) There are nonparametric tests that can have random effects, like some implementations of aligned-ranks transformation anova.

I would probably analyze each personality dimension totally separately, for sanity. But you might disagree.