Thank you Steve. It is not a contradiction, because they find the interaction effect in experiment 1, but not in experiment 2, which is the experiment for which I have the data.  But that is a detail. I appreciate your further suggestion! Eman ... View more

In the dataset above, I forgot that 13 participants should be excluded, as per the article. This is the syntax to exclude them: if participant_id in ("R_1OPSLlJlqe3kiC4", "R_1LGxs9MJDOpC4cW", "R_1onI3J1yaENBuV5", "R_2cjUAsUP9KOJMuf", "R_ahE2LFATHw6jfxv", "R_3PhnubUyhv5TJwC", "R_2le1UgRGD0H6B6p", "R_2trjDfHePcgEJtO", "R_1i4HIHYXq68MaPG", "R_1n7xJ3q8CHDfXpo" "R_22F9xpFhoQWmYjZ", "R_1PbpFkFxoK1xFk7", "R_wRg4AXfUfegZ8tz") then delete; ... View more

Dear Steve,   that is brillant, thank you for this!   Emotion_type, with values "complex" or "simple", depends on which of the specific emotion (out of 14 emotions) were displayed in each of the 42 videos: some of the emotions are considered complex, while others are considered simple. Each of the 14 emotions was presented in three different videos, and the order of presentation of the 42 video was randomized - hence (in principle) different for each participant.     I run the syntax you sent on the dataset, which I add in attachment here (exportx). I attach also the article. The description of the model is in Experiment 1, but the data that I am working on are those of Experiment 2.   A few things:   1. This PROC Glimmix takes 30 minutes on my SAS studio to run!   2. I get an interaction between the fixed factors (emotion_type and cent_trans_ART), while the authors report not finding an interaction: "Participants were not better at recognizing one type of emotion (simple/complex) over another, as indicated by similar rates of recognition for simple and complex emotions and a non-significant effect of emotion type, X2(1) = 0.49, p = .48. Furthermore, participants who had higher ART scores were not better at recognizing emotions, overall, as indicated by a non-significant effect of ART score, X2(1) = 2.34, p = .13. However, the interaction between the emotion type and ART score was significant, b = 0.18, X2(1) = 4.24, p < .05, indicating that participants who had higher ART scores were better able to recognize complex emotions."   3. From what they write in the paper (which I mentioned earlier) it looks like they included four, not 2 random factors. This might be the reason for the different results.     ... View more

hello Steve,   you're right about asking for more clarity on the design. It is a cognitive science study, in which 400 participants watched 42 very short videos in which an emotion was depicted, and their task was to indicate what the emotion was. The dichotomous criterion is the var correct_recognized, coded -1 vs 1. The emotions vary and are a-priori categorized as simple or complex, which is the repeated-measure factor represented by the variable emotion_type. The other predictor is the continuous variable, cent_trans_ART. In addition to their main effects and interaction, the researchers added these random factors, which are target_video_num (which is categorical and has 42 different values) and the participant_id (also categorical, of course). The researchers themselves describe the model tested in the article by saying   "Accuracy on each trial of the GERT-S (0 = incorrect, 1 = correct) was regressed on the emotion type presented in the video (− 0.5 = simple, 0.5 = complex), participant ART score (square rooted and mean centered), and the interaction between the two. By-item and by-participant random intercepts as well as a by-item random slope for participant ART score and a by- participant random slope for emotion type were included in the reported model."   Since I am doing additional analyses on the dataset that they kindly shared with me, I wanted to first replicate their findings, but I am a SAS user and not very familiar with random effects in general. Hence my post.   Thank you for your time.   Eman     ... View more

Thank you, Rick, as always ... View more