I'm working with data from an experiment that I plan to analyze using a mixed-effects logistic model. In this study, 200 participants (identified by the variable Participant) were randomly assigned to one of four experimental conditions (variable: Condition). Within each condition, participants were tasked with determining whether a given word (variable: Word) could appropriately conclude a sentence (variable: Sentence).

We used a set of 30 unique words, and each participant had to evaluate all of them. For each word, participants were presented with two sentences: one where the word could correctly be used at the end (variable: Appropriateness = 'Yes') and another where it could not (variable: Appropriateness = 'No'). Participants earned a point (variable: score) for each correct judgment they made.

I would appreciate any advice on how to best approach the statistical analysis of this data using mixed-effects models.

Here's what I've been considering:

Model 1:

score ~ Condition * Appropriateness + (1|Participant) + (1|Word)

Model 2:

score ~ Condition * Appropriateness + (1|Participant) + (1|Sentence/Word)

Model 3:

score ~ Condition * Appropriateness + (Appropriateness|Participant) + 
                 (Appropriateness|Sentence/Word)

As you can see, my particular concern is related to specifying the random effects.

Please share your thoughts or suggestions on which model might be the most appropriate for this type of data.