BB3 Q of H_Analyses

Data

dat <- readxl::read_excel("BB3.QofH_wrangled.xlsx")

Effect size difference scores

dat$BREG_diff <- dat$BREG_T2 - dat$BREG_T1

dat$pEM_diff <- dat$pEM_T2-dat$pEM_T1

dat$nEM_diff <- dat$nEM_T2-dat$nEM_T1

dat$EREG_diff <- dat$EREG_T2- dat$EREG_T1

Convert to long data

#From S21 grad stats Ch 12 slide 32
#Body regulation
dat.long1 <- dat %>% 
  pivot_longer(
    cols = c("BREG_T1","BREG_T2"),  # the columns we want to pivot
    names_to = ("Time"), # put the column names in a var called day
    names_prefix = "BREG_", # trim “con” off the front of names
    values_to = "BREG"  # put the values in a variable called con 
    ) 


#Positive emotions
dat.long3 <- dat %>% 
  pivot_longer(
    cols = c("pEM_T1","pEM_T2"),  # the columns we want to pivot
    names_to = ("Time"), # put the column names in a var called day
    names_prefix = "pEM_", # trim “con” off the front of names
    values_to = "pEM"  # put the values in a variable called con 
    ) 

#Negative emotions
dat.long4 <- dat %>% 
  pivot_longer(
    cols = c("nEM_T1","nEM_T2"),  # the columns we want to pivot
    names_to = ("Time"), # put the column names in a var called day
    names_prefix = "nEM_", # trim “con” off the front of names
    values_to = "nEM"  # put the values in a variable called con 
    ) 

#Emotional regulation
dat.long5 <- dat %>% 
  pivot_longer(
    cols = c("EREG_T1","EREG_T2"),  # the columns we want to pivot
    names_to = ("Time"), # put the column names in a var called day
    names_prefix = "EREG_", # trim “con” off the front of names
    values_to = "EREG"  # put the values in a variable called con 
    ) 


b_longsub1 <- left_join(dat.long1, dat.long3)
b_longsub3<- left_join(b_longsub1, dat.long4)

BB3_long<- left_join(b_longsub3, dat.long5)


longnames <- as.data.frame(colnames(BB3_long))

Power

With N = 28, with α = .05, G*power 3.1 calculates that there is a 1-β = .80 power to detect an effect size of Cohen’s dz = .43 (small/medium effect).

Effect size

From: https://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/tdunpaired#:~:text=dz%3A%20Standardized%20difference%20scores,as%20the%20denominator%20of%20d.

dz: Standardized difference scores

A third way to compute a d-like effect size is to reduce each subject’s data to a single difference score—the mean difference between their responses in each condition—and then use the standard deviation of these difference scores as the denominator of d. Cohen actually discusses this statistic in his power analysis textbook (Cohen, 1988, p. 48), where he carefully distinguishes it from the classical Cohen’s d by calling it d_z. In R, we can compute this as:

(d_z <- md / sd(sub_means[,2] - sub_means[,1]))

dz = Mean of difference scores / sd of difference scores

small = .2, medium = .5, large ≥ .8

Descriptives

descripts.T1 <- dat[,c("RES.T1_av","BREG_T1", "pEM_T1","nEM_T1","EREG_T1")]

descripts.T2 <- dat[,c("RES.T1_av","BREG_T2", "pEM_T2","nEM_T2","EREG_T2")]

T1

modelsummary::datasummary_skim(descripts.T1)
Unique Missing Pct. Mean SD Min Median Max Histogram
RES.T1_av 11 0 3.5 0.5 2.7 3.3 5.0
BREG_T1 12 0 3.6 0.6 2.2 3.6 5.0
pEM_T1 12 0 2.9 0.7 2.0 2.8 4.7
nEM_T1 13 0 1.6 0.4 1.0 1.5 2.4
EREG_T1 8 0 3.4 0.9 1.0 3.5 5.0

T2

modelsummary::datasummary_skim(descripts.T2)
Unique Missing Pct. Mean SD Min Median Max Histogram
RES.T1_av 11 0 3.5 0.5 2.7 3.3 5.0
BREG_T2 14 0 3.5 0.8 1.4 3.5 4.8
pEM_T2 12 0 3.5 0.7 2.5 3.5 5.0
nEM_T2 6 0 1.2 0.2 1.0 1.1 1.8
EREG_T2 7 4 4.1 0.8 1.5 4.0 5.0

Time compare

ds<- BB3_long[,c("Time","BREG", "pEM", "nEM","EREG")]
vtable::sumtable(ds, group = c("Time"), digits = 3)
Summary Statistics
Time
T1
T2
Variable N Mean SD N Mean SD
BREG 28 3.58 0.582 28 3.46 0.788
pEM 28 2.95 0.673 28 3.52 0.728
nEM 28 1.56 0.407 28 1.16 0.209
EREG 28 3.43 0.868 27 4.09 0.821

Pairwise Correlations

T1

rcorr(as.matrix(descripts.T1),type="pearson")
##           RES.T1_av BREG_T1 pEM_T1 nEM_T1 EREG_T1
## RES.T1_av      1.00    0.38   0.18  -0.37    0.15
## BREG_T1        0.38    1.00   0.60  -0.21    0.28
## pEM_T1         0.18    0.60   1.00  -0.53    0.02
## nEM_T1        -0.37   -0.21  -0.53   1.00    0.14
## EREG_T1        0.15    0.28   0.02   0.14    1.00
## 
## n= 28 
## 
## 
## P
##           RES.T1_av BREG_T1 pEM_T1 nEM_T1 EREG_T1
## RES.T1_av           0.0469  0.3512 0.0553 0.4558 
## BREG_T1   0.0469            0.0007 0.2823 0.1562 
## pEM_T1    0.3512    0.0007         0.0034 0.8999 
## nEM_T1    0.0553    0.2823  0.0034        0.4806 
## EREG_T1   0.4558    0.1562  0.8999 0.4806

T2

rcorr(as.matrix(descripts.T2),type="pearson")
##           RES.T1_av BREG_T2 pEM_T2 nEM_T2 EREG_T2
## RES.T1_av      1.00    0.33   0.02  -0.17    0.11
## BREG_T2        0.33    1.00   0.59  -0.32   -0.05
## pEM_T2         0.02    0.59   1.00  -0.35   -0.13
## nEM_T2        -0.17   -0.32  -0.35   1.00   -0.08
## EREG_T2        0.11   -0.05  -0.13  -0.08    1.00
## 
## n
##           RES.T1_av BREG_T2 pEM_T2 nEM_T2 EREG_T2
## RES.T1_av        28      28     28     28      27
## BREG_T2          28      28     28     28      27
## pEM_T2           28      28     28     28      27
## nEM_T2           28      28     28     28      27
## EREG_T2          27      27     27     27      27
## 
## P
##           RES.T1_av BREG_T2 pEM_T2 nEM_T2 EREG_T2
## RES.T1_av           0.0890  0.9084 0.3990 0.5993 
## BREG_T2   0.0890            0.0010 0.0955 0.8095 
## pEM_T2    0.9084    0.0010         0.0679 0.5071 
## nEM_T2    0.3990    0.0955  0.0679        0.6964 
## EREG_T2   0.5993    0.8095  0.5071 0.6964

Body Regulation (n.s.)

Items

T1 prompt: How often do the following statements apply to you? (1=never, 5 = always)

T2 & 3 prompt: How much do each of the following statements apply to you right now?

BREG1_T1: I notice how my body changes when I feel happy / joyful.

BREG1_T2: I am noticing how my body changes when I feel happy / joyful.

BREG2_T1: When I feel overwhelmed, I can find a calm place inside.

BREG2_T2: I notice that my body feels different while viewing dance.

BREG3_T1: When I bring awareness to my body, I feel a sense of calm.

BREG3_T2: When I bring awareness to my body, I feel a sense of calm.

BREG4_T1: I can use my breath to reduce tension.

BREG4_T2: I can use my breath to reduce tension.

BREG5_T1: When I am caught up in thoughts, I can calm my mind by focusing on my body/breathing.

BREG5_T2: When I am caught up in thoughts, I can calm my mind by focusing on my body/breathing.

Paired t model (n.s.)

t.test(dat$BREG_T2, dat$BREG_T1, paired = T)
## 
##  Paired t-test
## 
## data:  dat$BREG_T2 and dat$BREG_T1
## t = -1.179, df = 27, p-value = 0.2487
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.3376527  0.0912241
## sample estimates:
## mean difference 
##      -0.1232143

Effect size

abs(mean(dat$BREG_diff,na.rm=T)/sd(dat$BREG_diff,na.rm = T))
## [1] 0.222803

Conclusion

There is not a significant positive effect of show (with time as proxy) on body regulation (t = 0.52, p = .249).

Graph

tapply(BB3_long$BREG,BB3_long$Time,mean,na.rm=T);tapply(BB3_long$BREG,BB3_long$Time,sd,na.rm=T)
##       T1       T2 
## 3.578571 3.455357
##        T1        T2 
## 0.5820507 0.7877934

!Positive Emotions (SIGNIFICANT)!

Items

T1-2 prompt: How much are you feeling each of the following emotions right now? (1 = none at all; 5 = A great deal)

pEM1: amused, fun-loving, silly.

pEM2: content, serene, peaceful.

pEM3: glad, happy, joyful.

pEM4: grateful, appreciative, thankful.

pEM5: hopeful, optimistic, encouraged.

pEM6: love, closeness, trust.

Paired t model

t.test(dat$pEM_T2, dat$pEM_T1, paired = T)
## 
##  Paired t-test
## 
## data:  dat$pEM_T2 and dat$pEM_T1
## t = 4.1893, df = 27, p-value = 0.0002677
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.2945938 0.8601681
## sample estimates:
## mean difference 
##        0.577381

Effect size

abs(mean(dat$pEM_diff,na.rm=T)/sd(dat$pEM_diff,na.rm = T))
## [1] 0.7917085

Conclusion

There is a medium/large significant positive effect of show (with time as proxy) on positive emotions, t(27) = 4.19, p < .001, dz = .79. Positive emotions increase throughout the show.

Graph

tapply(BB3_long$pEM,BB3_long$Time,mean,na.rm=T);tapply(BB3_long$pEM,BB3_long$Time,sd,na.rm=T)
##       T1       T2 
## 2.946429 3.523810
##        T1        T2 
## 0.6728931 0.7282007

!Negative Emotions (SIGNIFICANT)!

Items

T1-2 prompt: How much are you feeling each of the following emotions right now? (1 = none at all; 5 = A great deal)

nEM1: angry, irritated, annoyed.

nEM2: sad, downhearted, unhappy,

nEM3: scared, fearful, afraid.

nEM4: disgust, distaste, revulsion.

nEM5: repentant, guilty, blameworthy.

nEM6: ashamed, humiliated, disgraced.

nEM7: contemptuous, scornful, disdainful.

nEM8: anxious, nervous, pressured.

Paired t model

t.test(dat$nEM_T2, dat$nEM_T1, paired = T)
## 
##  Paired t-test
## 
## data:  dat$nEM_T2 and dat$nEM_T1
## t = -5.0987, df = 27, p-value = 2.337e-05
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.5536362 -0.2359046
## sample estimates:
## mean difference 
##      -0.3947704

Effect size

abs(mean(dat$nEM_diff,na.rm=T)/sd(dat$nEM_diff,na.rm = T))
## [1] 0.9635551

Conclusion

There is a large significant negative effect of show (with time as proxy) on negative emotions (t(27) = -5.10, p < .001, dz = 0.96).

Graph

tapply(BB3_long$nEM,BB3_long$Time,mean,na.rm=T);tapply(BB3_long$nEM,BB3_long$Time,sd,na.rm=T)
##       T1       T2 
## 1.555485 1.160714
##       T1       T2 
## 0.406693 0.209323
p <- ggplot(BB3_long, aes(Time, nEM, fill = Time)) +
        geom_violinhalf(position = position_nudge(x = 0.1, y = 0)) +
  geom_boxplot(width=0.4, color="black", alpha=0.75) +
        geom_jitter(color="black", size=0.4, alpha=0.5)+ 
        coord_flip()+
theme(legend.position = "none", 
    panel.grid.minor = element_blank(), 
    panel.border = element_rect(color = "black", fill = NA, size = 1),
    axis.text.x = element_text(size = 14,face = "bold"))

p = p + scale_fill_paletteer_d("fishualize::Halichoeres_garnoti") + ylab("") + xlab("")
p

! Emotion Regulation

Items

T1

How much do you agree with each statement? (1 = strongly disagree; 5 = strongly agree)

EREG1: I am good at managing my emotions.

EREG2: I am always successful at regulating my emotions when the need arises.

T2 & 3

EREG1: I think I did pretty well at managing my emotions

EREG2: I was successful at regulating my emotions

EREG3: I tried to manage my emotions

Paired t model (significant)

t.test(dat$EREG_T2, dat$EREG_T1, paired = T)
## 
##  Paired t-test
## 
## data:  dat$EREG_T2 and dat$EREG_T1
## t = 3.9756, df = 26, p-value = 0.0004981
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.3488114 1.0956331
## sample estimates:
## mean difference 
##       0.7222222

Effect size

abs(mean(dat$EREG_diff,na.rm=T)/sd(dat$EREG_diff,na.rm = T))
## [1] 0.7651133

Conclusion

There is a medium/large significant positive effect of show (with time as proxy) on emotional regulation (t (27) = 3.98, p < .001, dz = 0.77).

EREG mean

tapply(BB3_long$EREG,BB3_long$Time,mean,na.rm=T);tapply(BB3_long$EREG,BB3_long$Time,sd,na.rm=T)
##       T1       T2 
## 3.428571 4.092593
##        T1        T2 
## 0.8683135 0.8208466

Graph