I would be very grateful for any suggestions and help regarding the creation of a linear mixed model with lmer4 package and the lmer function.
I have created an experiment which randomly assigned participants into 8 different conditions. Each condition presented a short video with an actor. The actors were varying in two conditions Sex and weight. So basically there were
- 2 x normal weight males,
- 2 x normal weight females,
- 2 x obese weight males, and
- 2 x obese weight females.
I have measured participants on multiple scales but essentially the response variable is whether they liked the video or not, expressed as a numeric score with highers scores meaning participants favoured video more (i.e., VideoLiking). I also collected additional control variables about participants, however, to simplify let's say I have only measured their Sex (factor, 2 levels) and Weight (presented as Body Mass Index).
What is important to understand here is that each participant really viewed only one video, that means that they have seen only normal weight male video for example but not the other 7 videos. So that means participants are nested within condition; however, not crossed across conditions.
The research questions are:
- How does the VideoLiking variable differ across videos?
- Is it mediated by gender and weight of actors?
- Is it mediated by gender and weight of participants?
Simplified data structure would look like this:
RespondentID Condition SexParticipans WeightParticipans VideoLiking numeric factor (8levels) factor (2levels) numeric (BMI) numeric
Jumping into lmer, I believe that this is the formula that could measure questions that I am looking for:
VideoLiking ~ 1 + SexParticipant + WeightParticipant + (1 + SexParticipant + WeightParticipant | Condition)
Is this a correct model specification? Is mixed model suitable for this task or should I go with some other method?
Based on comments and answer below I have tried to model the data with separate factors for Participant and Target (Video) using linear model (linear mixed effect and multilevel modelling does not apply here according to the answer), it seemed to produce sensible results while working in 2x2 design:
model = lm(VideoLiking ~ Weight_Target*Sex_Target*Sex_Participant*BMI_Participant, data)
However, when I included an additional independent variable called Script_Target (Level: Negative / Positive) the results had strange estimates. See below, (note Video_Liking scale has a minimum score of 4 and a maximum score of 20).
Condition Sex_Target Weight_Target Script_Read VideoLiking 7 Male Overweight Positive 14 5 Male Overweight Positive 13 8 Male Non-Overweight Positive 15 5 Male Overweight Negative 9
model = lm(VideoLiking ~ Weight_Target*Sex_Target*Sex_Participant*BMI_Participant*Script_Target, data)
Coefficients: Estimate (Intercept) 23.2873 Weight_TargetOverweight 131.6127 <-- ! Sex_TargetMale -5.8691 Sex_ParticipantMale -18.7204 BMI_Participant -0.4091 Advice_TargetPositive Script -7.7320
Considering that DV Video_Liking scale can only have scores up to 20, estimate with 131 does not make sense. I am not exactly sure why is this happening; however it only happened with additional IV.