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2014-01-21
I'm trying to fit a multilevel model to data from an experiment. In the experiment, I have 125 participants. There are three experimental factors, each with two levels. Two factors, power (high vs low) and audience (public vs private) are manipulated between participants; one factor conspicuousness (conspicuous vs inconspicuous) is manipulated within participants. All participants respond to the same set of ten different stimuli, 5 stimuli are conspicuous, 5 are inconspicuous. So, participants are nested within power*audience combination, stimuli are nested within conspicuous/inconspicuous.

I've got 5 potential sources of variation: power, audience and conspicuousness will be fixed effects ; participants and stimuli are random effects. I want to include stimuli as a random effect because I want to avoid the 'traditional' approach employed by psychologists facing such a design. In this approach, for each participant an average is formed over the participant's responses within each cell of the within-subjects factor (in my case: for each participant, there would be an average of the participant's responses on the conspicuous stimuli and an average of the participant's responses on the inconspicuous stimuli), and then an analysis is performed on two data points per participant. This ignores the fact that some stimuli will systematically elicit higher/lower responses than other stimuli.

I've read about this approach in Judd et al: treating stimuli as a random factor in social psychology (jpsp, 2012), but they only give the model for a design with a within subjects factor but no between subjects factors.

I'm trying to build a model, but I'm not sure if I am doing it correctly. Any corrections / comments / ... would be greatly appreciated!

So I start from level-1 where a response by participant j on stimulus i is modelled as:
Y_ij = b0_i + b1_i * CONSPICUOUSNESS_ij + error_ij

Where:
b0_i = b0 + stimulus_i
b1_i = b1

Substitution: Y_ij = b0 + b1 * CONSPICUOUSNESS_ij + stimulus_i + error_ij
I guess it does not make sense to introduce an interaction between stimulus and conspicuousness because stimulus is nested within conspicuousness.


Now, intercepts and b1's can vary randomly between participants, so I include a subscript j:
Y_ij = b0_j + b1_j * CONSPICUOUSNESS_ij + stimulus_i + error_ij

Where:
b0_j = b00 + b01 * POWER_ij + b02 * AUDIENCE_ij + b03 * POWER_ij * AUDIENCE_ij + participant0_j
b1_j = b10 + b11 * POWER_ij + b12 * AUDIENCE_ij + b13 * POWER_ij * AUDIENCE_ij + participant1_j

Substitution:
Y_ij = (b00 + participant0_j + stimulus_i)
+ b01 * POWER_ij + b02 * AUDIENCE_ij + b03 * POWER_ij * AUDIENCE_ij
+ (b10 + participant1_j) * CONSPICUOUSNESS_ij
+ b11 * POWER_ij * CONSPICUOUSNESS_ij + b12 * AUDIENCE_ij * CONSPICUOUSNESS_ij + b13 * POWER_ij * AUDIENCE_ij * CONSPICUOUSNESS_ij
+ error_ij
Thus far, I think this makes sense: there is a random effect of stimulus and a random effect of participant. Also, the effect of conspicuousness is random with regards to participants (some participants can show a larger effect of conspicuousness than other participants).

Now, the thing I am unsure about is whether it makes sense to include the following element:
Stimulus_i = stimulus0_i + stimulus1_i * POWER_ij + stimulus2_i * AUDIENCE_ij + stimulus3_i * POWER * AUDIENCE
Substitution:
Y_ij = (b00 + participant0_j + stimulus0_i)
+ (b01 + stimulus1_i) * POWER_ij + (b02 + stimulus2_i) * AUDIENCE_ij + (b03 + stimulus3_i) * POWER_ij * AUDIENCE_ij
+ (b10 + participant1_j) * CONSPICUOUSNESS_ij
+ b11 * POWER_ij * CONSPICUOUSNESS_ij + b12 * AUDIENCE_ij * CONSPICUOUSNESS_ij + b13 * POWER_ij * AUDIENCE_ij * CONSPICUOUSNESS_ij
+ error_ij
On the one hand, I guess it makes sense to allow the power, audience, power*audience effects to vary randomly with regards to stimuli. On the other hand, it feels strange to explain variation in random stimuli effects (level 1) by level 2 explanatory variables. Or should I understand that last line as: stimulus_ij = stimulus0_i + stimulus1_i * POWER_ij + stimulus2_i * AUDIENCE_ij + stimulus3_i * POWER * AUDIENCE + participant2_j ; so that we are explaining variance in stimulus effects between participants by level 2 explanatory variables (power, audience, power*audience) ; But then after substitution participant2_j and participant0_j would be inseparable / participant-specific stimulus effects would be inestimable because of only 1 observation per participant*stimulus combination ?

I would estimate such a model in R with following code:
lmer (y ~  power*audience*conspicuousness + (power|stimulus) + (audience|stimulus) + (power*audience|stimulus) + (conspicuousness|participant))

I am also interested two planned contrasts: 1. between power: high vs low within audience = public ; 2: between power: high vs low within audience = private. I can get the estimates by filling the the coefficients in the model, but I am not sure which error term and df's to use ... Is there an easy way to do this in R ? Any references on contrasts in multilevel models?
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2014-1-21 10:56:42

Treating Stimuli as a Random Factor in Social Psychology:


A New and Comprehensive Solution to a Pervasive but Largely Ignored Problem



Throughout social and cognitive psychology, participants are routinely asked to respond in some way to experimental stimuli that are thought to represent categories of theoretical interest. For instance, in measures of implicit attitudes, participants are primed with pictures of specific African American and White stimulus persons sampled in some way from possible stimuli that might have been used. Yet seldom is the sampling of stimuli taken into account in the analysis of the resulting data, in spite of
numerous warnings about the perils of ignoring stimulus variation. Part of this failure to attend to stimulus variation is due to the demands imposed by traditional analysis of variance procedures for the analysis of data when both participants and stimuli are
treated as random factors. In this article, we present a comprehensive solution using mixed models for the analysis of data with crossed random factors (e.g., participants and stimuli). We show the substantial biases inherent in analyses that ignore one or the other of the random factors, and we illustrate the substantial advantages of the mixed models approach with both hypothetical and actual, well-known data sets in social psychology.

http://webcom.upmf-grenoble.fr/LIP/Perso/DMuller/M2R/R_et_Mixed/documents/Journal%20of%20Personality%20and%20Social%20Psychology%202012%20Judd.pdf
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