Based on the way you described it, indeed it sounds like a mixed model. Assuming you have many investors from many countries i, you would make both investor and country of origin random.

It’s hard to say what should be random or fixed without knowing the exact data structure and research questions.

Karen

I’m not sure I understand your question. Are you trying to see which questions don’t load reliably with the others? And if your data aren’t measured on likert scales, how are they measured?

Karen

You could run a two-way anova as is without the interaction on this. The problem subcategories are the ones with 1 and 0 people in them.

The other alternative, if the interaction seems necessary, is to collapse the experience variable into fewer categories.

I would suggest graphing the means to see if the interaction is important, and if not, leave it out. If it is, you’d be better of collapsing.

Karen

If you’re registered for the webinar, you’ll get an email letting you know when the recording is ready, with a link. It generally takes us about a day to get it converted and uploaded.

You can get free recordings from our other webinars here: http://www.theanalysisfactor.com/webinars/recordings/

Karen

Thank you!

I just want to know if i could actually use two way factorial anova for this.

I have two groups of DEvice 1 (n=27) and Device 2 (n=28). in each group, I have 5 sub categories of participants (very low, low, moderate, high and very high experience of playing games). For the Device 1 group I have 9, 8, 5, 2, 2 and 1 for ach sub category. For the Device 2, I have 7, 4, 7, 8, 2, 0 for each sub category. Can I use two way ANOVA for this? Or should I just provide descriptive analysis? The main objective of the experiment is to see if there is any difference on the participants total score when playing games in Device 1 or 2.

It might, although if you have a lot of zeros, a log transform won’t result in normal residual plots, the way it would with a high mean and not a lot of zeros.

I’m not sure where the mixed model is coming into it–I’m guessing you have a design with clustered count data.

It’s always difficult to advise on what’s an appropriate analysis without all the details–for example, if you’re submitting to a high level journal, you ought to use the more sophisticated technique, even if you’re getting the exact same results and reasonable residuals from the linear model. If the answer is more important than how you get to it, then it would be fine. Unfortunately, you probably can’t tell if the linear mixed model is good enough without running the GLMM.

Karen

Thank you for this excellent blog post. I look forward to listening to the webinar (couldn’t be tune-in in real time). I was what do do if you have count data that has a “large” mean (~10-12) but still many zeros? It could be considered zero inflated if there are “too many” zeros for a poisson distribution with a mean of 10 or it could be considered over dispersed (too many high values for the rest of the distribution). I have heard that some people don’t like NB regressions and quasi-poisson has fallen out of favor for mixed models (even removed from the lme4 package in R). If a log transformed regression resulted in reasonable residual plots, is that a case when it would be better than trying to deal with a GLMM?

Thanks,
Dan