Exp. 2 - Vignettes and Emotions

This notebook will extract the emotion ratings for Experiment 2, where about half the participants rated HAAS adjectives and the others chosen 0 to 15 GEMIAC emotion categories (specific N reported below for both subsets). The purpose here is to offer some convergent validity measures (two common emotion measures) that can be compared to sub-constructs.

Get HAAS data

[1] "Number of rows in the long data: 43365"
[1] "Number of unique participants in the long data: 1239"
[1] "Number of unique items in the long data: 35"

HAAS

Visualise

Correlation of HAAS emotion ratings with the factor scores (Convergent Validity)

# Extract factor scores from your CFA
#factor_scores <- lavPredict(fit1, method = "regression")
fs0 <- read.csv(file = here("exp2/data","factor_scores_exp2.csv"))

# HAAS: Correlate with emotion ratings
df_haas_wide <- df_haas %>%
  pivot_wider(names_from = EmotionQ, values_from = Rating)
S <- summarise(group_by(df_haas_wide,ProlificID),pos_high_M=mean(pos_high,na.rm=TRUE),pos_low_M=mean(pos_low,na.rm=TRUE),neg_high_M=mean(neg_high,na.rm=TRUE),neg_low_M=mean(neg_low,na.rm=TRUE))

print(paste("N =",length(unique(S$ProlificID))))

[1] “N = 639”

combined_HAAS <- merge(fs0, S, by="ProlificID")
#dim(combined_HAAS) #639
vars<-c("E","D","R","M","F","G","L","C","X","S","I","B","pos_high_M","pos_low_M","neg_high_M","neg_low_M")
matrix <- dplyr::select(combined_HAAS, all_of(vars))
cm <- cor(matrix, use="pairwise.complete.obs")
print(knitr::kable(cm, digits = 2, caption = "Correlation Matrix between Factor Scores and HAAS Emotion Ratings."))
Correlation Matrix between Factor Scores and HAAS Emotion Ratings.
E D R M F G L C X S I B pos_high_M pos_low_M neg_high_M neg_low_M
E 1.00 0.52 0.61 0.53 0.46 0.57 0.50 0.66 0.63 0.43 0.62 0.88 0.56 0.45 -0.30 -0.27
D 0.52 1.00 0.63 0.57 0.48 0.32 0.69 0.84 0.74 0.53 0.38 0.51 0.31 0.31 0.12 0.14
R 0.61 0.63 1.00 0.37 0.46 0.35 0.51 0.82 0.54 0.49 0.44 0.65 0.30 0.61 -0.12 -0.05
M 0.53 0.57 0.37 1.00 0.79 0.33 0.48 0.50 0.68 0.35 0.45 0.44 0.55 0.13 0.00 -0.06
F 0.46 0.48 0.46 0.79 1.00 0.32 0.46 0.52 0.56 0.37 0.44 0.44 0.48 0.24 -0.08 -0.13
G 0.57 0.32 0.35 0.33 0.32 1.00 0.53 0.47 0.63 0.50 0.59 0.66 0.45 0.25 -0.19 -0.25
L 0.50 0.69 0.51 0.48 0.46 0.53 1.00 0.74 0.65 0.47 0.45 0.53 0.35 0.33 0.00 0.02
C 0.66 0.84 0.82 0.50 0.52 0.47 0.74 1.00 0.74 0.58 0.50 0.73 0.39 0.48 -0.04 0.01
X 0.63 0.74 0.54 0.68 0.56 0.63 0.65 0.74 1.00 0.59 0.60 0.68 0.50 0.26 0.02 -0.03
S 0.43 0.53 0.49 0.35 0.37 0.50 0.47 0.58 0.59 1.00 0.56 0.68 0.38 0.35 -0.03 -0.03
I 0.62 0.38 0.44 0.45 0.44 0.59 0.45 0.50 0.60 0.56 1.00 0.82 0.54 0.31 -0.18 -0.24
B 0.88 0.51 0.65 0.44 0.44 0.66 0.53 0.73 0.68 0.68 0.82 1.00 0.58 0.50 -0.26 -0.25
pos_high_M 0.56 0.31 0.30 0.55 0.48 0.45 0.35 0.39 0.50 0.38 0.54 0.58 1.00 0.34 -0.24 -0.30
pos_low_M 0.45 0.31 0.61 0.13 0.24 0.25 0.33 0.48 0.26 0.35 0.31 0.50 0.34 1.00 -0.27 -0.12
neg_high_M -0.30 0.12 -0.12 0.00 -0.08 -0.19 0.00 -0.04 0.02 -0.03 -0.18 -0.26 -0.24 -0.27 1.00 0.71
neg_low_M -0.27 0.14 -0.05 -0.06 -0.13 -0.25 0.02 0.01 -0.03 -0.03 -0.24 -0.25 -0.30 -0.12 0.71 1.00 
# Just arousal and valence
combined_HAAS2 <- merge(fs0, haas_summary3, by="ProlificID")
vars<-c("E","D","R","M","F","G","L","C","X","S","I","B","Arousal","Valence")
matrix <- dplyr::select(combined_HAAS2, all_of(vars))
cm <- cor(matrix, use="pairwise.complete.obs")
print(knitr::kable(cm, digits = 2, caption = "Correlation Matrix between Factor Scores and HAAS Valence and Arousal Ratings."))
Correlation Matrix between Factor Scores and HAAS Valence and Arousal Ratings.
E D R M F G L C X S I B Arousal Valence
E 1.00 0.52 0.61 0.53 0.46 0.57 0.50 0.66 0.63 0.43 0.62 0.88 0.07 0.51
D 0.52 1.00 0.63 0.57 0.48 0.32 0.69 0.84 0.74 0.53 0.38 0.51 -0.01 0.05
R 0.61 0.63 1.00 0.37 0.46 0.35 0.51 0.82 0.54 0.49 0.44 0.65 -0.26 0.32
M 0.53 0.57 0.37 1.00 0.79 0.33 0.48 0.50 0.68 0.35 0.45 0.44 0.34 0.22
F 0.46 0.48 0.46 0.79 1.00 0.32 0.46 0.52 0.56 0.37 0.44 0.44 0.19 0.29
G 0.57 0.32 0.35 0.33 0.32 1.00 0.53 0.47 0.63 0.50 0.59 0.66 0.17 0.37
L 0.50 0.69 0.51 0.48 0.46 0.53 1.00 0.74 0.65 0.47 0.45 0.53 0.01 0.17
C 0.66 0.84 0.82 0.50 0.52 0.47 0.74 1.00 0.74 0.58 0.50 0.73 -0.09 0.25
X 0.63 0.74 0.54 0.68 0.56 0.63 0.65 0.74 1.00 0.59 0.60 0.68 0.20 0.21
S 0.43 0.53 0.49 0.35 0.37 0.50 0.47 0.58 0.59 1.00 0.56 0.68 0.03 0.23
I 0.62 0.38 0.44 0.45 0.44 0.59 0.45 0.50 0.60 0.56 1.00 0.82 0.21 0.41
B 0.88 0.51 0.65 0.44 0.44 0.66 0.53 0.73 0.68 0.68 0.82 1.00 0.06 0.51
Arousal 0.07 -0.01 -0.26 0.34 0.19 0.17 0.01 -0.09 0.20 0.03 0.21 0.06 1.00 0.05
Valence 0.51 0.05 0.32 0.22 0.29 0.37 0.17 0.25 0.21 0.23 0.41 0.51 0.05 1.00

Get GEMIAC data

[1] “Number of rows in the long data: 20265” [1] “Number of unique participants in the long data: 579” [1] “Number of unique items in the long data: 35”

Summary
VigNro n
1 43
2 49
3 45
4 48
5 46
6 47
7 49
8 46
9 48
10 46
11 45
12 49

[1] 561 [1] 561

GEMIAC plot

Top GEMIAC Emotions for Convergent Validity Summary

Add top 3 GEMIAC emotions to the convergent validity table.

gemiac_summary_top3 <- GEMIAC_summary %>%
  group_by(VigNro) %>%
  slice_max(order_by = N, n = 3) %>%
  ungroup()

print(knitr::kable(gemiac_summary_top3, digits = 2, caption = "GEMIAC Emotion Ratings Summary (Top 3 for each Vignette)."))
GEMIAC Emotion Ratings Summary (Top 3 for each Vignette).
VigNro Construct Emotion_New N Total Proportion valence
Enjoyment EDR Energetic, lively 34 170 0.20 Positive
Enjoyment EDR Joyful, wanting to dance 32 170 0.19 Positive
Enjoyment EDR Inspired, enthusiastic 25 170 0.15 Positive
Distraction EDR Inspired, enthusiastic 21 129 0.16 Positive
Distraction EDR Tense, uneasy 16 129 0.12 Negative
Distraction EDR Relaxed, peaceful 15 129 0.12 Positive
Relaxation EDR Relaxed, peaceful 38 150 0.25 Positive
Relaxation EDR Inspired, enthusiastic 20 150 0.13 Positive
Relaxation EDR Moved, touched 18 150 0.12 Positive
Relaxation EDR Nostalgic, sentimental 18 150 0.12 Positive
Motivation FM Energetic, lively 35 133 0.26 Positive
Motivation FM Powerful, strong 31 133 0.23 Positive
Motivation FM Inspired, enthusiastic 28 133 0.21 Positive
Focus FM Inspired, enthusiastic 28 146 0.19 Positive
Focus FM Relaxed, peaceful 28 146 0.19 Positive
Focus FM Powerful, strong 20 146 0.14 Positive
Group Bonding CB Nostalgic, sentimental 37 171 0.22 Positive
Group Bonding CB Inspired, enthusiastic 24 171 0.14 Positive
Group Bonding CB Energetic, lively 20 171 0.12 Positive
Group Bonding CB Relaxed, peaceful 20 171 0.12 Positive
Reduce Loneliness CB Relaxed, peaceful 34 191 0.18 Positive
Reduce Loneliness CB Full of tenderness, warmhearted 33 191 0.17 Positive
Reduce Loneliness CB Moved, touched 31 191 0.16 Positive
Reduce Loneliness CB Nostalgic, sentimental 31 191 0.16 Positive
Comforting PEP Melancholic, sad 26 112 0.23 Negative
Comforting PEP Agitated, aggressive 14 112 0.12 Negative
Comforting PEP Tense, uneasy 14 112 0.12 Negative
Expression PEP Moved, touched 37 194 0.19 Positive
Expression PEP Nostalgic, sentimental 29 194 0.15 Positive
Expression PEP Powerful, strong 24 194 0.12 Positive
Expression PEP Relaxed, peaceful 24 194 0.12 Positive
Spirituality AIA Filled with wonder, amazed 31 215 0.14 Positive
Spirituality AIA Moved, touched 29 215 0.13 Positive
Spirituality AIA Enchanted, in awe 26 215 0.12 Positive
Curiosity AIA Moved, touched 28 180 0.16 Positive
Curiosity AIA Inspired, enthusiastic 26 180 0.14 Positive
Curiosity AIA Enchanted, in awe 21 180 0.12 Positive
Curiosity AIA Filled with wonder, amazed 21 180 0.12 Positive
Curiosity AIA Relaxed, peaceful 21 180 0.12 Positive
Beauty AIA Relaxed, peaceful 37 187 0.20 Positive
Beauty AIA Moved, touched 26 187 0.14 Positive
Beauty AIA Inspired, enthusiastic 22 187 0.12 Positive
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