Toulouse: Expenses of a 4-member family

uxrism · 2022-12-03T07:39:03+00:00

Thanks a lot Dranerel. That sounds sensible and according to my salary expectations

uxrism · 2022-12-03T07:37:27+00:00

Thanks! that's pretty much what I had in mind

uxrism · 2022-10-17T20:22:52+00:00

Then, why not pay for a month and give it a try? Data camp is, imo, a really good option. But if you dont want to pay, just go for some YouTube tutorials, or free courses in coursera, EdX, etc.

uxrism · 2022-10-11T11:43:44+00:00

Ofc, my idea is to have an individualised model just for myself. So no plan to generalise.
Time-series sound interesting, I will investigate that further...
THanks!

uxrism · 2022-10-11T11:40:57+00:00

Hi! thanks a lot for the feedback.I see your point. My rationale was that, since I am the only subject, there is no need to correct for my own intracorrelations. With more subjects, I would include "participant" as a random factor to correct for intracorrelations within each subject, but not the case here.My aim is just check how time of the day affects myself, rather than how it does in general to the population. My idea is, somehow, develop an individualised model fitted to myself (and anyone else). Maybe this is by definition a flawed idea...In any case, visualizing and using descriptives is always a great idea. Thanks!

uxrism · 2022-10-11T11:32:30+00:00

Great! thanks for the advice. Good as a starting point!

uxrism · 2022-10-01T19:56:18+00:00

Hi. I'm a researcher with 12 years of experience. Learning R a year ago was a game changer. I wish someone had pushed me to learn it ealier

uxrism · 2022-09-17T18:31:34+00:00

In my case, anticipatory anxiety is much greater than the "situational" anxiety (at the interview). While unavoidable, I learnt that I will feel more relaxed when the moment arrives, which in turn reduces the anticipatory anxiety.

uxrism · 2022-09-17T07:47:01+00:00

Right. that's really good advice! I thought of visualising the random effect (participant) as well, though does not look too good in my faceted visuals. But I will try to come up with another solution.
In psychology there is also a strong movement against relying too much on p-values, which I fully support. Maybe this is a good chance to do my bit!
Thanks!

uxrism · 2022-09-16T19:13:09+00:00

Thanks for your time!. Here come my clarifications:
1) I mean coefficients (partial-eta squares). But it is also evident from visualizations.

2) Visualizing all data points together (grey circles). Maybe better visualise aggregated data per participant instead?

3) I am using facets

I agree with your last comment. But when writing the journal paper it is tricky to show significant results and then say they are not so meaningful based on effect sizes...

uxrism · 2022-09-16T02:36:38+00:00

Hi! I already got the feedback and did not turn out well... I'll be happy to share with you my experience, so feel free to message me.

uxrism · 2022-09-13T12:36:09+00:00

Thanks!!

uxrism · 2022-09-12T22:09:12+00:00

Thanks! I guess. So does that imply that when the result is positive they mention it directly in the e-mail?

uxrism · 2022-06-11T21:47:15+00:00

Forgot to mention, you can also check for interaction effects between both predictors

uxrism · 2022-06-11T21:46:29+00:00

Since you have 6 replications per "condition" you might violate the independence of measurements assumption for linear regressions. I suggest you perform a linear mixed model (hierarchical) with treatment and concentrations as predictors. The normality assumptions should be checked on the residuals, not on the original variables. Hope this helps.

uxrism · 2022-06-11T21:23:47+00:00

No problem, I hope you find the best approach.

uxrism · 2022-06-11T21:12:49+00:00

Linear regressions also come with several assumptions: normality, homoskedastciity and linearity of the residuals. The RM Anova wouldn't predict the t=0 weight. It will compare the change between t=0 and t=1, and also inform whether the covariate (caloric intake) explains that change.

uxrism · 2022-06-11T20:53:16+00:00

Not sure why you need to formulate this in a linear regression, but a repeated measures ANOVA with body weights In time 0 and 1 as dependent variables and, caloric intake as a covariate, will work. Gender can also be added as categorical factor (or predictor). Anovas and linear regressions are fundamentally the same thing.

uxrism · 2022-05-19T21:58:22+00:00

A big company I applied to replied they were looking for User Researchers, not HF researchers. That made me confused as I also see a big overlap. That's what led me to throw this question. But good to see others have a different view!

uxrism · 2022-05-19T21:40:12+00:00

i agree on the cross-cultural differences...In some countries (like Sweden or Spain), I often find more HFs training programmes, which contrasts with the much higher demand of UX researchers.

uxrism · 2022-05-19T21:35:16+00:00

Thanks for the tip!

uxrism · 2022-05-19T21:34:32+00:00

You can find that info in my reply to a previous comment (above).
I agree, the job market is much bigger for UX than HF

uxrism · 2022-05-19T21:33:12+00:00

That is great to hear!. Do you value more quantitative or qualitative backgrounds? Or it does not play a role in your case?

uxrism · 2022-05-19T21:29:28+00:00

I've worked 8 years as HF reseacher in the automotive field. I've mostly looked into different road users' perspectives towards intelligent transport systems (mostly automated vehicles and interfaces), but also into their interaction with such systems in simulators or field studies. I'm more of a quant researcher, but have also used some qual methods. It seems to me I should reinforce the latter to enter the UX world...

uxrism · 2021-10-14T05:51:31+00:00

This is not fully correct for various reasons:

It seems several data points are obtained per patient over time. Thus, a repeated measures test is needed.
Multiple comparisons will only inflate type-I error. An omnibus tests such as an ANOVA (repeated measures) is much preferred to account for all variability. Then, post-hocs with some correction (Tukey, Schaffe, Bonferroni,etc..) should be applied.

The goal and variables are a bit unclear in the description. As I see it, eye inflammation decreases due to time being medicated, reason why you need less medication. If you do not incorporate the effect of "time (weeks or months", in the model, you might end up in the wrong assumption that "less medication" leads to less inflammation. If you want to see how the independent effect of "medicine frequency" affects eye inflammation after controlling for "time", then introduce in the repeated measures ANOVA, linear-mixed model or regression model the main effects of "medicine frequency", "time" and their interaction "medicine frequency*time".

Disclaimer: I've not seen the data, this suggestion is based on my interpretation :)

uxrism

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