I collected data from an experiment where I showed one of four videos (
condition) to a person and asked them to predict how it ended / assign one of three labels to the video (
prediction). I asked each person to make a prediction after they had completed watching 25%, 50%, 75%, and 100% of the video (
completed). I collected 330 responses total.
From a more technical perspective, I have a dataset with two independent variables, and one dependent variable.
condition is categorical and has 4 levels measured between subject;
completion is ordinal with 4 levels (although the underlying construct is continuous - should I model it as a continuous variable?) and measured within-subject. The dependent variable
prediction is categorical with 3 levels. I could make it dichotomous, since I have ground truth on the correct prediction, so I could code it as
My Hypothesis is that slope of correct predictions over time differs significantly between conditions, i.e., for some videos people make better predictions earlier compared to other videos.
Since this is a repeated measures experiment with a categorical dependent variable, I am using a Generalized Linear Mixed-Effects Model (
glmer in R) to fit my data. Here are the models I'm currently fitting:
interaction <- glmer("prediction ~ condition*completion + (1|id)", data=data, family="binomial") main_effects <- glmer("prediction ~ condition + completion + (1|id)", data=data, family="binomial") baseline <- glmer("prediction ~ completion + (1|id)", data=data, family="binomial")
I then compare
anova(). Am I actually testing my hypothesis like this? (I still get confused easily with linear models)
My main question, however, is: I am getting the error:
boundary (singular) fit: see ?isSingular, for
interaction. Am I interpreting this correctly as: The data doesn't support participants as a random factor, and I should fit a