Hi Gabriella,
I'm not sure you really have zero inflation here. 0 inflation usually occurs when you have counts or a yes/no response. The 0 means a 'lack of a response'. One way to think of this is that the 0's represent when the 'thing' you are measuring didn?t occur, and the count is when it did. For example if you had random samples from all over the planet, you wouldn?t find fish in the desert, but you do find them in water. And when you do find them there are lots of different variables that might affect how many you see. So you would fit 2 models:
1) model 1: are fish present i.e. is the sample a water or land sample?
2) model 2: how many fish did you find, assuming they were there.
So in yr example your 0's don?t really mean an absence of data, they mean a neutral score.
1 way to analyse this is to use at least 2 logistic regressions. 1 that explains the difference between negative vs neutral, and then another neutral vs positive. You might also want to have a 3rd model that shows negative vs positive.
Chris Howden B.Sc. (Hons)
Founding Partner
Data Analysis, Modelling and Training
Evidence Based Strategy/Policy Development, IP Commercialisation and Innovation
(mobile) +61 (0) 410 689 945 | (skype) chris at trickysolutions.com.au
-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> On Behalf Of Gabriella Kountourides
Sent: Monday, 30 November 2020 11:24 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] What to do with zero inflated, negative skewed, negative data: a question about GLMMs
Hello everyone,
This is my first question to this list :) I hope this email finds you all well.
I have been struggling for the past few weeks to set an appropriate model for my data. I have read Prof Bolker's practical guide for ecology and evolution paper, as well as the GLMM FAQs which have been immensely helpful. I am only just beginning my stats journey (and R!) and although I am really enjoying it, I have found myself completely stumped with my dataset. I will describe the data set below, and below that the various attempts I have made to analyse it. I would be incredibly grateful to hear your thoughts.
All the very best
Data:
I want to look whether there is a relationship between the phrasing used when a question is asked (positive, negative, neutral wording) and the polarity of the response from the individual.
2638 people were asked a question about medical symptoms.
1/3 of the people were asked it with a negative wording, 1/3 with a neutral one, 1/3 with a positive one.
The big question is: does the way the question is asked affect the polarity of the response