Personality study EGG analysis for ABCT 2018
Saren Seeley
11-10-2018
If you want to use any of these plots for the presentation, let me know and I’ll make them into high-res .pptx slides (with axes appropriately labeled).
Importing SPSS dataset and converting it to long format
library(tidyverse)
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library(afex)Loading required package: lme4
Loading required package: Matrix
Attaching package: ‘Matrix’
The following object is masked from ‘package:tidyr’:
expand
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Welcome to afex. For support visit: http://afex.singmann.science/
- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'KR', 'S', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- NEWS: library('emmeans') now needs to be called explicitly!
- Get and set global package options with: afex_options()
- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
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Attaching package: ‘afex’
The following object is masked from ‘package:lme4’:
lmerlibrary(emmeans)NOTE: As of emmeans versions > 1.2.3,
The 'cld' function will be deprecated in favor of 'CLD'.
You may use 'cld' only if you have package:multcomp attached.library(jtools)
library(psych)
Attaching package: ‘psych’
The following objects are masked from ‘package:ggplot2’:
%+%, alphalibrary(bestNormalize)Check out the dataset
# overview the entire [wide] dataset
# this will look like TOTAL crap in the notebook output (i.e., what you are probably looking at right now)
# from the R console, use view(dfSummary(all.data)) to see a much more readable and attractive output
library(summarytools)
dfSummary(all.data)unable to identify var names: all.dataData Frame Summary
all.data
N: 240
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Text Graph Valid Missing
----- ------------------------------------------ ------------------------------------- --------------------- ---------------------------------------- ---------- ----------
1 egg_n mean (sd) : 0.56 (0.39) 13 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
0 < 0.67 < 1 : . :
IQR (CV) : 0.67 (0.69) : : : :
: . : : . : : :
2 cond 1. BASELINE 48 (20.0%) IIIIIIIIIIIIIIII 240 0
[factor] 2. DIS 48 (20.0%) IIIIIIIIIIIIIIII (100%) (0%)
3. FE 48 (20.0%) IIIIIIIIIIIIIIII
4. NE 48 (20.0%) IIIIIIIIIIIIIIII
5. SA 48 (20.0%) IIIIIIIIIIIIIIII
3 ID 1. 1004 5 ( 2.1%) 240 0
[factor] 2. 1006 5 ( 2.1%) (100%) (0%)
3. 1010 5 ( 2.1%)
4. 1011 5 ( 2.1%)
5. 1013 5 ( 2.1%)
6. 1016 5 ( 2.1%)
7. 1017 5 ( 2.1%)
8. 1024 5 ( 2.1%)
9. 1028 5 ( 2.1%)
10. 1031 5 ( 2.1%)
[ 38 others ] 190 (79.0%) IIIIIIIIIIIIIIII
4 egg_b mean (sd) : 0.04 (0.15) 0.00 : 215 (89.6%) IIIIIIIIIIIIIIII 240 0
[numeric] min < med < max : 0.20 : 2 ( 0.8%) (100%) (0%)
0 < 0 < 1 0.25 : 6 ( 2.5%)
IQR (CV) : 0 (3.45) 0.33 : 7 ( 2.9%)
0.33!: 3 ( 1.2%)
0.40 : 1 ( 0.4%)
0.50 : 1 ( 0.4%)
0.67!: 1 ( 0.4%)
0.67 : 1 ( 0.4%)
1.00 : 3 ( 1.2%)
! rounded
5 egg_t mean (sd) : 0.23 (0.34) 13 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
0 < 0 < 1 :
IQR (CV) : 0.33 (1.48) :
: . : . :
6 hrv.msd mean (sd) : 43.5 (22.54) 238 distinct val. : . 240 0
[numeric] min < med < max : : : : (100%) (0%)
9.29 < 40.72 < 122.68 : : : : .
IQR (CV) : 28.83 (0.52) : : : : :
: : : : : : : . .
7 panas_NA mean (sd) : 14.55 (4.99) 24 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
9 < 13 < 41 :
IQR (CV) : 5 (0.34) . : .
: : : .
8 panas_PA mean (sd) : 19.46 (7.08) 28 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
10 < 18 < 37 : : .
IQR (CV) : 10 (0.36) : : : .
: : : : : .
9 age mean (sd) : 21.63 (4.52) 11 distinct val. : 215 25
[numeric] min < med < max : : (89.58%) (10.42%)
18 < 20 < 36 :
IQR (CV) : 7 (0.21) : .
: : . : .
10 gender 1. male 80 (34.0%) IIIIIIII 235 5
[factor] 2. female 155 (66.0%) IIIIIIIIIIIIIIII (97.92%) (2.08%)
11 hrs_ate mean (sd) : 9.61 (4.43) 16 distinct val. : 240 0
[numeric] min < med < max : : . : (100%) (0%)
2 < 10.5 < 20 : : : .
IQR (CV) : 6.25 (0.46) : : : :
: : : : : : : . .
12 hrs_coff mean (sd) : 10.72 (12.22) 12 distinct val. : 100 140
[numeric] min < med < max : : (41.67%) (58.33%)
0 < 4 < 48 :
IQR (CV) : 16.5 (1.14) : . :
: . : : .
13 hrs_soda mean (sd) : 11.93 (8.05) 0.00 : 15 (20.0%) IIIIIIIIIIIIIIII 75 165
[numeric] min < med < max : 3.00 : 5 ( 6.7%) IIIII (31.25%) (68.75%)
0 < 12 < 26 10.00 : 5 ( 6.7%) IIIII
IQR (CV) : 13 (0.67) 12.00 : 15 (20.0%) IIIIIIIIIIIIIIII
14.00 : 5 ( 6.7%) IIIII
15.00 : 10 (13.3%) IIIIIIIIII
16.00 : 5 ( 6.7%) IIIII
20.00 : 5 ( 6.7%) IIIII
24.00 : 5 ( 6.7%) IIIII
26.00 : 5 ( 6.7%) IIIII
14 hrs_tea mean (sd) : 8.46 (11.07) 0.00 : 25 (38.5%) IIIIIIIIIIIIIIII 65 175
[numeric] min < med < max : 2.00 : 5 ( 7.7%) III (27.08%) (72.92%)
0 < 3 < 40 3.00 : 5 ( 7.7%) III
IQR (CV) : 12 (1.31) 10.00 : 5 ( 7.7%) III
11.00 : 5 ( 7.7%) III
12.00 : 5 ( 7.7%) III
14.00 : 5 ( 7.7%) III
18.00 : 5 ( 7.7%) III
40.00 : 5 ( 7.7%) III
15 race_cat 1. "prefer not to answer" 5 ( 2.1%) 235 5
[factor] 2. Asian-American, Asian 60 (25.5%) IIIIIIIIII (97.92%) (2.08%)
3. African-American 10 ( 4.3%) I
4. Caucasian/White 90 (38.3%) IIIIIIIIIIIIIIII
5. Hispanic/Latin@ 35 (14.9%) IIIIII
6. Other (see specifier) 35 (14.9%) IIIIII
16 tot_AIM_NegativeIntensity.b mean (sd) : 19.29 (4.56) 16 distinct val. . : . 240 0
[numeric] min < med < max : . : : : (100%) (0%)
9 < 20 < 34 : : : :
IQR (CV) : 6 (0.24) : . : : :
: : : : : : . : .
17 tot_AIM_NegativeIntensity.w mean (sd) : 33.06 (7.01) 24 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
19 < 33 < 49 . : :
IQR (CV) : 9.25 (0.21) : : : : .
. : : : : : .
18 tot_AIM_NegativeReactivity.b mean (sd) : 23.67 (4.73) 19 distinct val. : 240 0
[numeric] min < med < max : . : (100%) (0%)
14 < 23 < 36 . : : : .
IQR (CV) : 6.25 (0.2) : . : : : : : :
: : : : : : : : .
19 tot_AIM_NegativeReactivity.w mean (sd) : 23.38 (5.04) 16 distinct val. : : . : . 240 0
[numeric] min < med < max : : : : : : (100%) (0%)
13 < 23.5 < 34 : : : : : : :
IQR (CV) : 7 (0.22) : : . : : : : : : .
: : : : : : : : : :
20 tot_AIM_PositiveAffect.b mean (sd) : 59.02 (10.25) 29 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
40 < 57 < 85 : . :
IQR (CV) : 13 (0.17) . . : : : . .
: : : : : : : . :
21 tot_AIM_PositiveAffect.w mean (sd) : 67.35 (11.74) 32 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
47 < 65 < 97 . : : . :
IQR (CV) : 13.5 (0.17) : . : : : : . .
: : : : : : : : . :
22 tot_AIM_Serenity.w mean (sd) : 22.79 (5.26) 21 distinct val. : . . 240 0
[numeric] min < med < max : : : : : (100%) (0%)
13 < 23 < 34 : . . : : : .
IQR (CV) : 7 (0.23) : : : : : : : : :
: : : : : : : : : :
23 tot_BFI_Extraversion mean (sd) : 3.19 (0.8) 22 distinct val. : . 240 0
[numeric] min < med < max : : : : . (100%) (0%)
1.5 < 3.25 < 4.38 . : : : :
IQR (CV) : 1.25 (0.25) : : : : : :
: : : : : :
24 tot_BFI_Neuroticism mean (sd) : 2.99 (0.84) 24 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
1.25 < 2.94 < 4.62 : : :
IQR (CV) : 1.25 (0.28) . : : : . :
: : : : : : : .
25 tot_bisbas_BAS.Drive mean (sd) : 11.56 (2.14) 5.00 : 5 ( 2.1%) I 240 0
[numeric] min < med < max : 8.00 : 15 ( 6.2%) IIII (100%) (0%)
5 < 12 < 15 9.00 : 25 (10.4%) IIIIIIII
IQR (CV) : 2.25 (0.19) 10.00 : 15 ( 6.2%) IIII
11.00 : 50 (20.8%) IIIIIIIIIIIIIIII
12.00 : 50 (20.8%) IIIIIIIIIIIIIIII
13.00 : 40 (16.7%) IIIIIIIIIIII
14.00 : 15 ( 6.2%) IIII
15.00 : 25 (10.4%) IIIIIIII
26 tot_bisbas_BAS.Fun mean (sd) : 12.27 (2.26) 8.00 : 10 ( 4.2%) III 240 0
[numeric] min < med < max : 9.00 : 20 ( 8.3%) IIIIIII (100%) (0%)
8 < 12 < 16 10.00 : 25 (10.4%) IIIIIIII
IQR (CV) : 3 (0.18) 11.00 : 45 (18.8%) IIIIIIIIIIIIIIII
12.00 : 30 (12.5%) IIIIIIIIII
13.00 : 30 (12.5%) IIIIIIIIII
14.00 : 35 (14.6%) IIIIIIIIIIII
15.00 : 20 ( 8.3%) IIIIIII
16.00 : 25 (10.4%) IIIIIIII
27 tot_bisbas_BAS.Reward mean (sd) : 16.52 (1.86) 12.00 : 5 ( 2.1%) I 240 0
[numeric] min < med < max : 13.00 : 10 ( 4.2%) III (100%) (0%)
12 < 17 < 20 14.00 : 20 ( 8.3%) IIIIIII
IQR (CV) : 3 (0.11) 15.00 : 35 (14.6%) IIIIIIIIIIII
16.00 : 45 (18.8%) IIIIIIIIIIIIIIII
17.00 : 45 (18.8%) IIIIIIIIIIIIIIII
18.00 : 45 (18.8%) IIIIIIIIIIIIIIII
19.00 : 25 (10.4%) IIIIIIII
20.00 : 10 ( 4.2%) III
28 tot_bisbas_BIS.Total mean (sd) : 21 (3.95) 16 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
8 < 21 < 28 . : .
IQR (CV) : 4 (0.19) . : : . :
. . . : : : : : :
29 tot_DERS_Awareness mean (sd) : 15.25 (4.01) 16 distinct val. : 240 0
[numeric] min < med < max : . : (100%) (0%)
6 < 15 < 28 : : : .
IQR (CV) : 6 (0.26) : : : : :
. : : : : : : . .
30 tot_DERS_Clarity mean (sd) : 13.92 (4.65) 15 distinct val. : . . : 240 0
[numeric] min < med < max : : : : : : (100%) (0%)
5 < 14.5 < 22 : : : : : :
IQR (CV) : 8 (0.33) : : : : : : : :
. : : : : : : : :
31 tot_DERS_Goals mean (sd) : 16.46 (4.11) 17 distinct val. . . . : 240 0
[numeric] min < med < max : : : . : : (100%) (0%)
6 < 17 < 25 : : : : :
IQR (CV) : 7 (0.25) . : : : : : .
: : : : : : : : .
32 tot_DERS_Impulse mean (sd) : 14.46 (6.59) 20 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
6 < 13.5 < 29 :
IQR (CV) : 12 (0.46) : : : : :
: : : : : . : . : .
33 tot_DERS_Nonacceptance mean (sd) : 14.96 (6.71) 18 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
6 < 16 < 28 : .
IQR (CV) : 12 (0.45) : . : : : :
: : . : : : : : :
34 tot_DERS_Strategies mean (sd) : 20.79 (7.03) 23 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
8 < 21 < 34 : : :
IQR (CV) : 12 (0.34) : : : : :
: : : : : :
35 tot_DERS_Total mean (sd) : 95.83 (21.24) 38 distinct val. : 240 0
[numeric] min < med < max : . : : (100%) (0%)
38 < 96.5 < 136 : : : : . .
IQR (CV) : 28.75 (0.22) : : : : : : :
. . . : : : : : : :
36 tot_ERQ_Reappraisal mean (sd) : 27.42 (8.42) 25 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
6 < 28 < 42 : : . .
IQR (CV) : 11.25 (0.31) . . : : : :
. : : : : : : :
37 tot_ERQ_Suppression mean (sd) : 15.71 (5.68) 20 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
4 < 16 < 28 : . : . . .
IQR (CV) : 7.5 (0.36) . : : : : . : :
: : : : : : : : : .
38 tot_ERS_Intensity mean (sd) : 9.75 (6.25) 19 distinct val. . . . . : . 240 0
[numeric] min < med < max : : : : : : : . (100%) (0%)
0 < 9 < 22 : : : : : : :
IQR (CV) : 10.25 (0.64) : : : : : : : : :
: : : : : : : : :
39 tot_ERS_Persistence mean (sd) : 5.94 (3.65) 15 distinct val. : . 240 0
[numeric] min < med < max : : : : : : (100%) (0%)
0 < 5.5 < 14 : : : : :
IQR (CV) : 6 (0.61) : : : : : .
: : : : : : :
40 tot_ERS_Sensitivity mean (sd) : 12.46 (7.74) 24 distinct val. : 240 0
[numeric] min < med < max : : : : (100%) (0%)
0 < 10.5 < 30 : : : .
IQR (CV) : 13.25 (0.62) : : : : :
: : : : : :
41 tot_IUS_Negativity mean (sd) : 27.73 (9.39) 23 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
16 < 25 < 46 : : :
IQR (CV) : 17 (0.34) : : : :
: : : : : : .
42 tot_IUS_Total mean (sd) : 56.35 (17.9) 32 distinct val. . : . 240 0
[numeric] min < med < max : : : : : (100%) (0%)
30 < 53 < 88 : : : . . :
IQR (CV) : 34.25 (0.32) : : : : : : : : : :
: : : : : : : : : :
43 tot_ius_Unfair mean (sd) : 28.62 (9.17) 27 distinct val. : 240 0
[numeric] min < med < max : : : . . : (100%) (0%)
12 < 27.5 < 45 : : : : : :
IQR (CV) : 15.75 (0.32) : : : : : :
: : : : : : :
44 tot_MASQ_AnhedonicDepression mean (sd) : 63.23 (12.72) 34 distinct val. : . . 240 0
[numeric] min < med < max : : : : : (100%) (0%)
34 < 63 < 90 : : : : :
IQR (CV) : 14.5 (0.2) : : : : . :
: : : : : : : : : :
45 tot_MASQ_AnxiousArousal mean (sd) : 26.06 (8.2) 21 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
17 < 24 < 48 : : :
IQR (CV) : 10.25 (0.31) : : :
: : : . : : .
46 tot_MASQ_GeneralDistressAnxiety mean (sd) : 20.08 (7.39) 24 distinct val. : . 240 0
[numeric] min < med < max : : : (100%) (0%)
11 < 18 < 41 : : :
IQR (CV) : 9.25 (0.37) : : : .
: : : : . . .
47 tot_MASQ_GeneralDistressDepression mean (sd) : 23.83 (9.47) 24 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
13 < 19 < 50 :
IQR (CV) : 13.25 (0.4) . :
: : : . : : .
48 tot_PANAS_Trait.NA mean (sd) : 23.33 (6.48) 20 distinct val. . : 240 0
[numeric] min < med < max : : : . (100%) (0%)
12 < 23.5 < 38 . : : :
IQR (CV) : 7.25 (0.28) : : : : :
: : : : : :
49 tot_PANAS_Trait.PA mean (sd) : 32.06 (5.6) 19 distinct val. . : 240 0
[numeric] min < med < max : : : (100%) (0%)
15 < 32 < 45 : : .
IQR (CV) : 6.5 (0.17) : : :
. : : : : :
50 tot_PSWQ_Total mean (sd) : 48.02 (13.38) 29 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
16 < 46 < 79 : . .
IQR (CV) : 18.5 (0.28) : : : : .
. . . : : : : : : .
51 tot_RPA_Dampening mean (sd) : 14.79 (5.37) 19 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
8 < 13.5 < 29 : . .
IQR (CV) : 9 (0.36) : : : : :
: : : : : : : : . .
52 tot_RPA_EmotionFocus mean (sd) : 13.27 (3.03) 13 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
6 < 13 < 20 : : : :
IQR (CV) : 4 (0.23) . . : : : :
: : : : : : .
53 tot_RPA_SelfFocus mean (sd) : 10.08 (2.59) 11 distinct val. : 240 0
[numeric] min < med < max : . . : : (100%) (0%)
5 < 10 < 16 : : : . :
IQR (CV) : 4 (0.26) : : : : : : :
: : : : : : : . :
54 tot_RS_Brooding mean (sd) : 10.42 (2.8) 12 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
5 < 10 < 16 : : : : :
IQR (CV) : 3.25 (0.27) : : : : : : : : :
: : : : : : : : : :
55 tot_SPSRQ_Punishment mean (sd) : 12.31 (5.65) 18 distinct val. : 240 0
[numeric] min < med < max : : : . (100%) (0%)
1 < 12.5 < 21 : : : : : : :
IQR (CV) : 9.25 (0.46) : : . : : : : :
: : : : : : . : : :
56 tot_SPSRQ_Reward mean (sd) : 12.46 (4.63) 16 distinct val. : . : 240 0
[numeric] min < med < max : : : : : (100%) (0%)
4 < 13.5 < 22 : : : : :
IQR (CV) : 8 (0.37) : : : : : : :
: : : : : : : :
57 tot_STAI_Trait mean (sd) : 43.81 (9.25) 23 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
26 < 40 < 63 : .
IQR (CV) : 14 (0.21) : . . :
: : : : : : : .
58 tot_TEPS_Anticipatory mean (sd) : 4.55 (0.64) 22 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
2.7 < 4.7 < 5.7 : : .
IQR (CV) : 0.75 (0.14) : : : :
. . : : : : .
59 tot_TEPS_Consummatory mean (sd) : 4.73 (0.86) 20 distinct val. : 240 0
[numeric] min < med < max : : . : (100%) (0%)
3 < 4.75 < 6 . : : :
IQR (CV) : 1.38 (0.18) : : : . : :
: : : : : :
60 tot_TPQ_HarmAvoidance mean (sd) : 17.46 (7.07) 26 distinct val. : : : 240 0
[numeric] min < med < max : : : : (100%) (0%)
4 < 18 < 30 . : : : :
IQR (CV) : 11.25 (0.4) : : : : :
: : : : : :
61 tot_TPQ_HarmAvoidance.fatigability mean (sd) : 5.5 (2.91) 11 distinct val. . . . : 240 0
[numeric] min < med < max : : : : : (100%) (0%)
0 < 5 < 10 . : : : : :
IQR (CV) : 5 (0.53) : : : : : : : :
: : : : : : : : : :
62 tot_TPQ_HarmAvoidance.shyness mean (sd) : 3.6 (2.17) 0.00 : 20 ( 8.3%) IIIII 240 0
[numeric] min < med < max : 1.00 : 15 ( 6.2%) IIII (100%) (0%)
0 < 3 < 7 2.00 : 60 (25.0%) IIIIIIIIIIIIIIII
IQR (CV) : 3.25 (0.6) 3.00 : 30 (12.5%) IIIIIIII
4.00 : 30 (12.5%) IIIIIIII
5.00 : 25 (10.4%) IIIIII
6.00 : 25 (10.4%) IIIIII
7.00 : 35 (14.6%) IIIIIIIII
63 tot_TPQ_HarmAvoidance.uncertainty mean (sd) : 3.42 (1.92) 0.00 : 10 ( 4.2%) III 240 0
[numeric] min < med < max : 1.00 : 45 (18.8%) IIIIIIIIIIIIIIII (100%) (0%)
0 < 3 < 7 2.00 : 25 (10.4%) IIIIIIII
IQR (CV) : 3 (0.56) 3.00 : 45 (18.8%) IIIIIIIIIIIIIIII
4.00 : 35 (14.6%) IIIIIIIIIIII
5.00 : 40 (16.7%) IIIIIIIIIIIIII
6.00 : 30 (12.5%) IIIIIIIIII
7.00 : 10 ( 4.2%) III
64 tot_TPQ_HarmAvoidance.worry mean (sd) : 4.94 (2.48) 0.00 : 10 ( 4.2%) III 240 0
[numeric] min < med < max : 1.00 : 20 ( 8.3%) IIIIIII (100%) (0%)
0 < 5 < 9 2.00 : 20 ( 8.3%) IIIIIII
IQR (CV) : 4 (0.5) 3.00 : 15 ( 6.2%) IIIII
4.00 : 35 (14.6%) IIIIIIIIIIII
5.00 : 25 (10.4%) IIIIIIII
6.00 : 45 (18.8%) IIIIIIIIIIIIIIII
7.00 : 25 (10.4%) IIIIIIII
8.00 : 35 (14.6%) IIIIIIIIIIII
9.00 : 10 ( 4.2%) III
65 tot_TPQ_NoveltySeeking mean (sd) : 17.33 (4.53) 18 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
7 < 17.5 < 26 . : : : : .
IQR (CV) : 6.25 (0.26) : : : : : : :
. : : : : : : : : :
66 tot_TPQ_NoveltySeeking.disorderliness mean (sd) : 4.73 (1.79) 0.00 : 5 ( 2.1%) I 240 0
[numeric] min < med < max : 2.00 : 5 ( 2.1%) I (100%) (0%)
0 < 4 < 9 3.00 : 50 (20.8%) IIIIIIIIIII
IQR (CV) : 2.25 (0.38) 4.00 : 70 (29.2%) IIIIIIIIIIIIIIII
5.00 : 30 (12.5%) IIIIII
6.00 : 45 (18.8%) IIIIIIIIII
7.00 : 10 ( 4.2%) II
8.00 : 20 ( 8.3%) IIII
9.00 : 5 ( 2.1%) I
67 tot_TPQ_NoveltySeeking.extravagance mean (sd) : 3.46 (1.56) 0.00 : 10 ( 4.2%) II 240 0
[numeric] min < med < max : 1.00 : 20 ( 8.3%) IIII (100%) (0%)
0 < 4 < 6 2.00 : 35 (14.6%) IIIIIII
IQR (CV) : 2 (0.45) 3.00 : 40 (16.7%) IIIIIIII
4.00 : 80 (33.3%) IIIIIIIIIIIIIIII
5.00 : 30 (12.5%) IIIIII
6.00 : 25 (10.4%) IIIII
68 tot_TPQ_NoveltySeeking.impulsiveness mean (sd) : 4.69 (1.96) 0.00 : 5 ( 2.1%) I 240 0
[numeric] min < med < max : 1.00 : 15 ( 6.2%) IIII (100%) (0%)
0 < 5 < 8 2.00 : 10 ( 4.2%) II
IQR (CV) : 3.25 (0.42) 3.00 : 40 (16.7%) IIIIIIIIIII
4.00 : 35 (14.6%) IIIIIIIIII
5.00 : 45 (18.8%) IIIIIIIIIIIII
6.00 : 30 (12.5%) IIIIIIII
7.00 : 55 (22.9%) IIIIIIIIIIIIIIII
8.00 : 5 ( 2.1%) I
69 tot_TPQ_NovelySeeking.excitability mean (sd) : 4.46 (1.83) 1.00 : 15 ( 6.2%) IIII 240 0
[numeric] min < med < max : 2.00 : 20 ( 8.3%) IIIII (100%) (0%)
1 < 4 < 8 3.00 : 30 (12.5%) IIIIIIII
IQR (CV) : 2 (0.41) 4.00 : 60 (25.0%) IIIIIIIIIIIIIIII
5.00 : 60 (25.0%) IIIIIIIIIIIIIIII
6.00 : 25 (10.4%) IIIIII
7.00 : 5 ( 2.1%) I
8.00 : 25 (10.4%) IIIIII
70 tot_TPQ_RewardDependence mean (sd) : 13.38 (4.84) 20 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
3 < 14 < 24 . : : .
IQR (CV) : 5.5 (0.36) . : : : : : . : :
: : : : : : : : : :
71 tot_TPQ_RewardDependence.attachment mean (sd) : 5.06 (2.74) 11 distinct val. : . : 240 0
[numeric] min < med < max : . : : : . (100%) (0%)
0 < 5 < 10 : : : : : : : :
IQR (CV) : 4 (0.54) : : : : : : : : : :
: : : : : : : : : :
72 tot_TPQ_RewardDependence.dependence mean (sd) : 2.62 (1.2) 0.00 : 10 ( 4.2%) II 240 0
[numeric] min < med < max : 1.00 : 35 (14.6%) IIIIIII (100%) (0%)
0 < 3 < 5 2.00 : 60 (25.0%) IIIIIIIIIIII
IQR (CV) : 1.25 (0.46) 3.00 : 75 (31.2%) IIIIIIIIIIIIIIII
4.00 : 50 (20.8%) IIIIIIIIII
5.00 : 10 ( 4.2%) II
73 tot_TPQ_RewardDependence.persistence mean (sd) : 3.94 (2.07) 0.00 : 5 ( 2.1%) II 240 0
[numeric] min < med < max : 1.00 : 35 (14.6%) IIIIIIIIIIIIII (100%) (0%)
0 < 4 < 7 2.00 : 25 (10.4%) IIIIIIIIII
IQR (CV) : 4 (0.53) 3.00 : 40 (16.7%) IIIIIIIIIIIIIIII
4.00 : 40 (16.7%) IIIIIIIIIIIIIIII
5.00 : 30 (12.5%) IIIIIIIIIIII
6.00 : 25 (10.4%) IIIIIIIIII
7.00 : 40 (16.7%) IIIIIIIIIIIIIIII
74 tot_TPQ_RewardDependence.sentimentality mean (sd) : 1.75 (1.5) 0.00 : 65 (27.1%) IIIIIIIIIIIIIIII 240 0
[numeric] min < med < max : 1.00 : 55 (22.9%) IIIIIIIIIIIII (100%) (0%)
0 < 1.5 < 5 2.00 : 45 (18.8%) IIIIIIIIIII
IQR (CV) : 3 (0.85) 3.00 : 30 (12.5%) IIIIIII
4.00 : 40 (16.7%) IIIIIIIII
5.00 : 5 ( 2.1%) I
75 tot_VIS_Total mean (sd) : 56.62 (19.08) 28 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
18 < 64.5 < 75 :
IQR (CV) : 28.5 (0.34) . . : :
: : : . . . : : : :
76 tot_aftervid.AMUSE_PANAS_State.PA mean (sd) : 21.69 (7.35) 23 distinct val. : . 240 0
[numeric] min < med < max : : : (100%) (0%)
11 < 22 < 41 : . : :
IQR (CV) : 11.5 (0.34) : : : : .
: : : : : . .
77 tot_aftervid.AMUSE_PANAS_State.NA mean (sd) : 12.23 (2.56) 10.00 : 60 (25.0%) IIIIIIIIIIIIIIII 240 0
[numeric] min < med < max : 11.00 : 45 (18.8%) IIIIIIIIIIII (100%) (0%)
10 < 12 < 25 12.00 : 55 (22.9%) IIIIIIIIIIIIII
IQR (CV) : 2.25 (0.21) 13.00 : 40 (16.7%) IIIIIIIIII
14.00 : 10 ( 4.2%) II
15.00 : 10 ( 4.2%) II
16.00 : 10 ( 4.2%) II
17.00 : 5 ( 2.1%) I
25.00 : 5 ( 2.1%) I
78 tot_aftervid.DISGUST_PANAS_State.PA mean (sd) : 17.85 (6.66) 18 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
10 < 16 < 37 : :
IQR (CV) : 8 (0.37) : : .
: : : . .
79 tot_aftervid.DISGUST_PANAS_State.NA mean (sd) : 15.79 (6.21) 16 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
10 < 14 < 41 : .
IQR (CV) : 7 (0.39) : :
: : .
80 tot_aftervid.FEAR_PANAS_State.PA mean (sd) : 21.19 (6.47) 20 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
11 < 19.5 < 36 . : : . .
IQR (CV) : 9.5 (0.31) : : : : : : . : .
: : : : : : : : : .
81 tot_aftervid.FEAR_PANAS_State.NA mean (sd) : 15.69 (5.13) 18 distinct val. : . 240 0
[numeric] min < med < max : : : (100%) (0%)
10 < 14 < 31 : :
IQR (CV) : 5.25 (0.33) : : : .
: : : : : : . . . .
82 tot_aftervid.NEUTRAL_PANAS_State.PA mean (sd) : 14.85 (5.48) 14 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
10 < 14 < 35 : :
IQR (CV) : 6.25 (0.37) : : . .
: : : : . . . .
83 tot_aftervid.NEUTRAL_PANAS_State.NA mean (sd) : 12.4 (3.43) 13 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
9 < 11 < 24 : :
IQR (CV) : 3 (0.28) : : .
: : : . : .
84 tot_aftervid.SAD_PANAS_State.PA mean (sd) : 17.71 (5.02) 17 distinct val. . : 240 0
[numeric] min < med < max : : : (100%) (0%)
10 < 17.5 < 35 : :
IQR (CV) : 6 (0.28) : : :
: : : : : . . .
85 tot_aftervid.SAD_PANAS_State.NA mean (sd) : 15.15 (5.06) 16 distinct val. : 240 0
[numeric] min < med < max : : : (100%) (0%)
10 < 13.5 < 34 : : .
IQR (CV) : 4 (0.33) : : :
: : : : . . . . .
86 tot_PANAS_State.PA mean (sd) : 25.71 (6.33) 22 distinct val. : : 240 0
[numeric] min < med < max : : : : : (100%) (0%)
14 < 25 < 37 : . : : . : :
IQR (CV) : 8.5 (0.25) : : : : : : : : :
: . : : : : : : : :
87 tot_PANAS_State.NA mean (sd) : 13.73 (3.71) 11 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
10 < 12.5 < 32 :
IQR (CV) : 3 (0.27) : . .
: : : :
88 cen_tot_SPSRQ_Punishment mean (sd) : 0 (5.65) 18 distinct val. : 240 0
[numeric] min < med < max : : : . (100%) (0%)
-11.31 < 0.19 < 8.69 : : : : : : :
IQR (CV) : 9.25 (Inf) : : . : : : : :
: : : : : : . : : :
89 cen_tot_SPSRQ_Reward mean (sd) : 0 (4.63) 16 distinct val. . : 240 0
[numeric] min < med < max : : : : (100%) (0%)
-8.46 < 1.04 < 9.54 : : : : : :
IQR (CV) : 8 (-7819215259437784) . : : : : : : :
: : : : : : : : :
90 cen_tot_BIS mean (sd) : 0 (3.95) 16 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
-13 < 0 < 7 . : .
IQR (CV) : 4 (Inf) . : : . :
. . . : : : : : :
91 cen_tot_BAS_Rew mean (sd) : 0 (1.86) -4.52!: 5 ( 2.1%) I 240 0
[numeric] min < med < max : -3.52!: 10 ( 4.2%) III (100%) (0%)
-4.52 < 0.48 < 3.48 -2.52!: 20 ( 8.3%) IIIIIII
IQR (CV) : 3 (1573575695177501) -1.52!: 35 (14.6%) IIIIIIIIIIII
-0.52!: 45 (18.8%) IIIIIIIIIIIIIIII
0.48!: 45 (18.8%) IIIIIIIIIIIIIIII
1.48!: 45 (18.8%) IIIIIIIIIIIIIIII
2.48!: 25 (10.4%) IIIIIIII
3.48!: 10 ( 4.2%) III
! rounded
92 cen_tot_PSWQ mean (sd) : 0 (13.38) 29 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
-32.02 < -2.02 < 30.98 : . .
IQR (CV) : 18.5 (-5655205783658562) : : : : .
. . . : : : : : : .
93 cen_tot_RS_Brooding mean (sd) : 0 (2.8) 12 distinct val. : 240 0
[numeric] min < med < max : : . (100%) (0%)
-5.42 < -0.42 < 5.58 : : : : :
IQR (CV) : 3.25 (4725330298614217) : : : : : : : : :
: : : : : : : : : :
94 cen_age All NA's 0 240
[numeric] (0%) (100%)
95 cen_hrs_ate mean (sd) : 0 (4.43) 16 distinct val. : 240 0
[numeric] min < med < max : : (100%) (0%)
-7.61 < 0.89 < 10.39 . : : .
IQR (CV) : 6.25 (-7470164046795687) : : : : :
: : : . : : : : . .
96 med_split_BIS 1. high 115 (47.9%) IIIIIIIIIIIIII 240 0
[factor] 2. low 125 (52.1%) IIIIIIIIIIIIIIII (100%) (0%)
97 med_split_BAS_Rew 1. high 125 (52.1%) IIIIIIIIIIIIIIII 240 0
[factor] 2. low 115 (47.9%) IIIIIIIIIIIIII (100%) (0%)
98 med_split_PSWQ 1. high 115 (47.9%) IIIIIIIIIIIIII 240 0
[factor] 2. low 125 (52.1%) IIIIIIIIIIIIIIII (100%) (0%)
99 med_split_RS_Brood 1. high 115 (47.9%) IIIIIIIIIIIIII 240 0
[factor] 2. low 125 (52.1%) IIIIIIIIIIIIIIII (100%) (0%)
100 med_split_BAS_Rew <= All NA's 0 240
as.factor(ifelse(tot_bisbas_BAS.Reward < (0%) (100%)
median(tot_bisbas_BAS.Reward),
"low", "high"))
[logical]
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------# view(dfSummary(all.data))The EGG variables look pretty bimodal, which has me kinda worried. I did attempt transformation using the bestNormalize package (picks the transformation that gets your data closest to normal) on egg_n but it didn’t really improve matters - data are still bimodal, as shown below. So I just went with the non-transformed values.
Note missing observations for age, gender, and race.
Transformation
library(bestNormalize)
egg_n_transf <- bestNormalize(all.data$egg_n)boxcox did not work; Error in estimate_boxcox_lambda(x, ...) : x must be positive
Ties in data, Normal distribution not guaranteed
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|============================================================================================================================================ | 98% ~0 s remaining egg_n_transf$x.t [1] 0.33372218 0.96190050 -1.21578617 -0.23619932 -0.15665257 0.33372218 -0.75853164 -0.71084801 0.33372218 -0.15665257 -0.06244624 -0.15665257 -0.06244624
[14] -0.15665257 -0.71084801 0.33372218 -0.06244624 -0.15665257 0.40612970 0.33372218 0.40612970 0.33372218 -0.15665257 -0.15665257 -0.06244624 0.33372218
[27] 0.40612970 0.33372218 0.96190050 0.96190050 -0.75853164 0.96190050 0.96190050 -0.06244624 0.33372218 -0.53997885 -0.15665257 -0.15665257 -0.71084801
[40] 0.96190050 -1.21578617 -0.15665257 0.33372218 -0.32822891 -0.71084801 -0.71084801 -0.15665257 -0.23619932 -1.21578617 0.96190050 -1.21578617 -1.21578617
[53] -1.21578617 0.96190050 0.96190050 0.96190050 0.96190050 0.96190050 -1.21578617 0.96190050 0.96190050 -1.21578617 -1.21578617 0.96190050 0.96190050
[66] -1.21578617 0.96190050 0.96190050 0.96190050 0.96190050 0.96190050 0.96190050 -1.21578617 -1.21578617 0.96190050 0.96190050 -1.21578617 0.96190050
[79] 0.96190050 0.96190050 0.96190050 0.96190050 0.96190050 -1.21578617 0.96190050 -1.21578617 0.96190050 -1.21578617 -1.21578617 -1.21578617 -1.21578617
[92] 0.96190050 -1.21578617 -1.21578617 -1.21578617 0.96190050 -0.53997885 0.02600530 -1.21578617 -1.21578617 0.96190050 0.96190050 -0.32822891 -0.53997885
[105] 0.96190050 -1.21578617 -1.21578617 0.18303344 0.18303344 -0.32822891 -0.32822891 0.96190050 -0.32822891 -0.32822891 -0.53997885 0.18303344 -0.32822891
[118] 0.18303344 -0.32822891 0.96190050 -1.21578617 0.96190050 -0.53997885 0.96190050 -0.32822891 0.96190050 -0.53997885 -0.53997885 -0.53997885 0.96190050
[131] -0.53997885 -0.53997885 -0.32822891 0.96190050 -0.32822891 0.02600530 -1.21578617 0.18303344 0.96190050 0.96190050 -1.21578617 -0.53997885 -0.53997885
[144] -0.53997885 0.96190050 0.96190050 -1.21578617 -1.21578617 -1.21578617 0.96190050 -1.21578617 -1.21578617 0.96190050 -1.21578617 0.96190050 -1.21578617
[157] -1.21578617 0.96190050 0.96190050 -1.21578617 0.96190050 -1.21578617 0.96190050 0.96190050 0.96190050 -1.21578617 -1.21578617 -1.21578617 -1.21578617
[170] 0.96190050 0.96190050 0.96190050 0.96190050 0.96190050 -1.21578617 0.96190050 0.96190050 0.96190050 -1.21578617 -1.21578617 0.96190050 0.96190050
[183] 0.96190050 -1.21578617 0.96190050 0.96190050 0.96190050 -1.21578617 0.96190050 -1.21578617 -1.21578617 0.96190050 -1.21578617 0.96190050 -1.21578617
[196] 0.02600530 -0.53997885 0.18303344 -0.32822891 -0.53997885 0.18303344 -0.32822891 0.96190050 0.18303344 0.96190050 -0.32822891 0.02600530 0.18303344
[209] -0.32822891 0.96190050 0.18303344 0.18303344 0.96190050 0.18303344 0.96190050 0.18303344 0.02600530 0.02600530 0.02600530 0.18303344 0.02600530
[222] 0.96190050 0.18303344 -0.53997885 -0.53997885 0.02600530 0.96190050 0.18303344 0.02600530 -0.15665257 0.02600530 0.02600530 -0.53997885 0.18303344
[235] -0.53997885 -0.15665257 -0.53997885 -0.53997885 -0.53997885 0.18303344psych::describe(egg_n_transf$x) #non-transformed vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 240 0.56 0.39 0.67 0.58 0.49 0 1 1 -0.23 -1.43 0.02psych::describe(egg_n_transf$x.t) #transformed vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 240 -0.01 0.83 0.03 0.02 1.39 -1.22 0.96 2.18 -0.17 -1.39 0.05hist(egg_n_transf$x) #non-transformed
hist(egg_n_transf$x.t) #transformed
qqnorm(egg_n_transf$x) #non-transformed
qqnorm(egg_n_transf$x.t) #transformed
Correlations (within-condition; BASELINE, NE, FE, SA only)
Because of the aforementioned non-normality of the EGG variables, I used both Spearman’s rho as well as Pearson’s r.
EGG normogastria
# HRV and EGG normo @ baseline
baseline <- all.data %>% filter(cond=="BASELINE")
cor.test(baseline$egg_n, baseline$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: baseline$egg_n and baseline$hrv.msd
t = -0.175, df = 46, p-value = 0.8618
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3076736 0.2602482
sample estimates:
cor
-0.02579378 cor.test(baseline$egg_n, baseline$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: baseline$egg_n and baseline$hrv.msd
S = 17667, p-value = 0.7817
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.04106877 # HRV and EGG normo @ fear video
fe <- all.data %>% filter(cond=="FE")
cor.test(fe$egg_n, fe$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: fe$egg_n and fe$hrv.msd
t = -3.1494, df = 46, p-value = 0.002873
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.6299199 -0.1556592
sample estimates:
cor
-0.4211665 cor.test(fe$egg_n, fe$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: fe$egg_n and fe$hrv.msd
S = 26751, p-value = 0.00126
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.4519664 # HRV and EGG normo @ sad video
sa <- all.data %>% filter(cond=="SA")
cor.test(sa$egg_n, sa$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: sa$egg_n and sa$hrv.msd
t = -0.65518, df = 46, p-value = 0.5156
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3701746 0.1932615
sample estimates:
cor
-0.09615324 cor.test(sa$egg_n, sa$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: sa$egg_n and sa$hrv.msd
S = 20005, p-value = 0.5619
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.08583065 # HRV and EGG normo @ neutral video
ne <- all.data %>% filter(cond=="NE")
cor.test(ne$egg_n, ne$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: ne$egg_n and ne$hrv.msd
t = -0.61873, df = 46, p-value = 0.5391
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3655483 0.1984066
sample estimates:
cor
-0.09084973 cor.test(ne$egg_n, ne$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: ne$egg_n and ne$hrv.msd
S = 18396, p-value = 0.9918
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.001515731 Luckily, the results match: HRV and EGG normogastria for the sample as a whole are uncorrelated except in the Fear condition, where they are negatively correlated based either on Spearman or Pearson ( p < .003).
EGG tachygastria
# HRV and EGG tachy @ baseline
baseline <- all.data %>% filter(cond=="BASELINE")
cor.test(baseline$egg_t, baseline$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: baseline$egg_t and baseline$hrv.msd
t = -0.13687, df = 46, p-value = 0.8917
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3025768 0.2654799
sample estimates:
cor
-0.02017674 cor.test(baseline$egg_t, baseline$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: baseline$egg_t and baseline$hrv.msd
S = 20593, p-value = 0.4255
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.1177177 # HRV and EGG tachy @ fear video
fe <- all.data %>% filter(cond=="FE")
cor.test(fe$egg_t, fe$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: fe$egg_t and fe$hrv.msd
t = 2.2804, df = 46, p-value = 0.02726
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.0380050 0.5527772
sample estimates:
cor
0.3186982 cor.test(fe$egg_t, fe$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: fe$egg_t and fe$hrv.msd
S = 10424, p-value = 0.002044
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.434238 # HRV and EGG tachy @ sad video
sa <- all.data %>% filter(cond=="SA")
cor.test(sa$egg_t, sa$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: sa$egg_t and sa$hrv.msd
t = 2.0598, df = 46, p-value = 0.0451
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.007039263 0.530892951
sample estimates:
cor
0.2905927 cor.test(sa$egg_t, sa$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: sa$egg_t and sa$hrv.msd
S = 13588, p-value = 0.07148
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.2624902 # HRV and EGG tachy @ neutral video
ne <- all.data %>% filter(cond=="NE")
cor.test(ne$egg_t, ne$hrv.msd, method=c("pearson"))
Pearson's product-moment correlation
data: ne$egg_t and ne$hrv.msd
t = 3.3413, df = 46, p-value = 0.001663
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1804545 0.6450653
sample estimates:
cor
0.4419299 cor.test(ne$egg_t, ne$hrv.msd, method=c("spearman"))Cannot compute exact p-value with ties
Spearman's rank correlation rho
data: ne$egg_t and ne$hrv.msd
S = 12079, p-value = 0.01654
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.3443775 For tachygastria, we see that EGG and HRV are positively correlated during both Fear ( p < .03) and Neutral ( p < .02). The correlation during Sadness is no-significant if using Spearman ( p < .075) but significant if using Pearson ( p = .045).
Scatterplots
Let’s visualize these relationships.
Across conditions
all.data %>% ggplot(aes(x=egg_n, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # normo
all.data %>% ggplot(aes(x=egg_t, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # tachy
Baseline
baseline %>% ggplot(aes(x=egg_n, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # normo
baseline %>% ggplot(aes(x=egg_t, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # tachy
Fear
fe %>% ggplot(aes(x=egg_n, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # normo
fe %>% ggplot(aes(x=egg_t, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # tachy
Sadness
sa %>% ggplot(aes(x=egg_n, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # normo
sa %>% ggplot(aes(x=egg_t, y=hrv.msd)) + geom_point() + geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") # tachy
Based on these plots, you can see the problem with assuming that these data are continuous (EGG values from Acqknowledge are really closer to ordinal) and normally distributed, which I think supports the use of Spearman’s rho over Pearson’s r, though in most cases the general pattern is in agreement.
EGG/HRV by condition: RM-ANOVAs
anovaData <- filter(all.data, !grepl("DIS",cond)) # filtered out data in DIS condition, we dropped AMU earlier in the data cleaning
# repeated-measures ANOVA
a1 <- aov_ez(id = "ID", dv = "egg_n", anovaData, within = "cond", fun_aggregate = mean)
a1 Anova Table (Type 3 tests)
Response: egg_n
Effect df MSE F ges p.value
1 cond 2.10, 98.85 0.15 0.81 .01 .45
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a2 <- aov_ez(id = "ID", dv = "egg_t", anovaData, within = "cond", fun_aggregate = mean)
a2 Anova Table (Type 3 tests)
Response: egg_t
Effect df MSE F ges p.value
1 cond 2.32, 109.19 0.11 0.60 .009 .57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a3 <- aov_ez(id = "ID", dv = "hrv.msd", anovaData, within = "cond", fun_aggregate = mean)
a3 Anova Table (Type 3 tests)
Response: hrv.msd
Effect df MSE F ges p.value
1 cond 2.38, 111.69 55.10 0.86 .001 .44
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a5 <- aov_ez(id = "ID", dv = "panas_NA", anovaData, within = "cond", fun_aggregate = mean)
a5 Anova Table (Type 3 tests)
Response: panas_NA
Effect df MSE F ges p.value
1 cond 2.61, 122.75 12.56 9.62 *** .08 <.0001
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a6 <- aov_ez(id = "ID", dv = "panas_PA", anovaData, within = "cond", fun_aggregate = mean)
a6 Anova Table (Type 3 tests)
Response: panas_PA
Effect df MSE F ges p.value
1 cond 2.82, 132.53 16.99 65.77 *** .32 <.0001
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG There is no main effect of condition alone on egg_n or egg_t or hrv.msd. However, there is a main effect of condition on PANAS, both NA and PA.
Pairwise comparisons for effect of cond on state affect
a5$lm[1] # coefficients for PANAS NA (use a5$lm[2] to get residuals)$coefficients
BASELINE FE NE SA
(Intercept) 13.72917 15.6875 12.39583 15.14583# negative affect estimated marginal means and pairwise comparisons
emm <- emmeans(a5, ~c(cond))
emm cond emmean SE df lower.CL upper.CL
BASELINE 13.72917 0.6404344 118.02 12.46093 14.99740
FE 15.68750 0.6404344 118.02 14.41927 16.95573
NE 12.39583 0.6404344 118.02 11.12760 13.66407
SA 15.14583 0.6404344 118.02 13.87760 16.41407
Confidence level used: 0.95 update(pairs(emm), by=NULL, adjust = "holm") contrast estimate SE df t.ratio p.value
BASELINE - FE -1.9583333 0.674986 141 -2.901 0.0173
BASELINE - NE 1.3333333 0.674986 141 1.975 0.1128
BASELINE - SA -1.4166667 0.674986 141 -2.099 0.1128
FE - NE 3.2916667 0.674986 141 4.877 <.0001
FE - SA 0.5416667 0.674986 141 0.802 0.4236
NE - SA -2.7500000 0.674986 141 -4.074 0.0004
P value adjustment: holm method for 6 tests In terms of negative affect, NA is significantly higher after the fear video compared to when it was measured pre-baseline recording. NA is also significantly higher after the fear video compared to after the neutral video, and after the sad video compared to after the neutral video. These patterns are shown in the boxplot below:
a6$lm[1] # coefficients for PANAS PA (use a6$lm[2] to get residuals)$coefficients
BASELINE FE NE SA
(Intercept) 25.70833 21.1875 14.85417 17.70833# negative affect estimated marginal means and pairwise comparisons
emm <- emmeans(a6, ~c(cond))
emm cond emmean SE df lower.CL upper.CL
BASELINE 25.70833 0.8526869 99.88 24.01660 27.40007
FE 21.18750 0.8526869 99.88 19.49577 22.87923
NE 14.85417 0.8526869 99.88 13.16243 16.54590
SA 17.70833 0.8526869 99.88 16.01660 19.40007
Confidence level used: 0.95 update(pairs(emm), by=NULL, adjust = "holm") contrast estimate SE df t.ratio p.value
BASELINE - FE 4.520833 0.8157988 141 5.542 <.0001
BASELINE - NE 10.854167 0.8157988 141 13.305 <.0001
BASELINE - SA 8.000000 0.8157988 141 9.806 <.0001
FE - NE 6.333333 0.8157988 141 7.763 <.0001
FE - SA 3.479167 0.8157988 141 4.265 0.0001
NE - SA -2.854167 0.8157988 141 -3.499 0.0006
P value adjustment: holm method for 6 tests In terms of positive affect, PA is significantly higher pre-baseline recording, compared to the neutral, fear, and sadness videos. Interesting, PA is significantly lower after neutral than after both sadness and fear (so maybe people REALLY hate being bored by the colorbars?? hmmm). It’s also significantly lower for sadness relative to fear. This is interesting! I guess that last one fits with the idea that many undergrads enjoy horror movies… These patterns are shown in the boxplot below:
pa_box <- ggplot(anovaData, aes(y=panas_PA, x=as.factor(cond))) +
geom_boxplot(aes(fill=cond),outlier.alpha = 0.2) + xlab("Condition") + ylab("PANAS Positive Affect")
pa_box
EGG and HRV models with covariates
# repeated-measures ANOVA with covariates
a7 <- aov_ez(id = "ID", dv = "egg_n", anovaData, within = "cond", covariate = c("hrs_ate","gender","age"), fun_aggregate = mean)Numerical variables NOT centered on 0 (i.e., likely bogus results): hrs_ate, NAMissing values for following ID(s):
1004, 1016, 1024, 1031, 1118
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: gendera7 Anova Table (Type 3 tests)
Response: egg_n
Effect df MSE F ges p.value
1 hrs_ate 1, 39 0.21 0.83 .009 .37
2 gender 1, 39 0.21 1.42 .01 .24
3 age 1, 39 0.21 0.31 .003 .58
4 cond 2.14, 83.41 0.14 0.73 .01 .50
5 hrs_ate:cond 2.14, 83.41 0.14 0.29 .004 .77
6 gender:cond 2.14, 83.41 0.14 1.88 .03 .16
7 age:cond 2.14, 83.41 0.14 0.87 .01 .43
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a8 <- aov_ez(id = "ID", dv = "egg_t", anovaData, within = "cond", covariate = c("hrs_ate","gender","age"), fun_aggregate = mean)Numerical variables NOT centered on 0 (i.e., likely bogus results): hrs_ate, NAMissing values for following ID(s):
1004, 1016, 1024, 1031, 1118
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: gendera8 Anova Table (Type 3 tests)
Response: egg_t
Effect df MSE F ges p.value
1 hrs_ate 1, 39 0.11 0.01 <.0001 .92
2 gender 1, 39 0.11 0.54 .004 .47
3 age 1, 39 0.11 0.13 .001 .72
4 cond 2.13, 82.98 0.11 0.34 .006 .72
5 hrs_ate:cond 2.13, 82.98 0.11 0.72 .01 .50
6 gender:cond 2.13, 82.98 0.11 1.07 .02 .35
7 age:cond 2.13, 82.98 0.11 1.19 .02 .31
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a9 <- aov_ez(id = "ID", dv = "hrv.msd", anovaData, within = "cond", covariate = c("gender","age"), fun_aggregate = mean)Numerical variables NOT centered on 0 (i.e., likely bogus results): NAMissing values for following ID(s):
1004, 1016, 1024, 1031, 1118
Removing those cases from the analysis.Contrasts set to contr.sum for the following variables: gendera9 Anova Table (Type 3 tests)
Response: hrv.msd
Effect df MSE F ges p.value
1 gender 1, 40 1993.84 1.14 .03 .29
2 age 1, 40 1993.84 3.71 + .08 .06
3 cond 2.34, 93.78 59.25 0.98 .002 .39
4 gender:cond 2.34, 93.78 59.25 0.10 .0002 .93
5 age:cond 2.34, 93.78 59.25 0.86 .001 .44
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG The package tells me I need to center age and hrs_ate, but I’m not sure that I do since zero is a perfectly reasonable value for both of those, so I would think we’d want to preserve the actual units for interpretability…??
No significant effects when covariates are included, either. There is a non-significant effect of age on hrv.msd, p = .06.
Lattice plots
The lack of main effects suggests that either that people’s physiology isn’t affected by the videos, or alternatively, that people just vary a lot in their response. Let’s check it out using lattice plots:
library(lattice)
xyplot(egg_n ~ cond | ID, data = anovaData, as.table=T)
xyplot(egg_t ~ cond | ID, data = anovaData, as.table=T)
xyplot(hrv.msd ~ cond | ID, data = anovaData, as.table=T)
Too many people/hard to see patterns with the whole sample, so look at a random subset of IDs:
# too many people/hard to see, try random subset
ids <- sample(unique(anovaData$ID), 18) # random subset of IDs (n=18)
temp <- anovaData[anovaData$ID %in% ids, ]
xyplot(egg_n ~ cond | ID, data = temp, as.table=T) 
xyplot(egg_t ~ cond | ID, data = temp, as.table=T) 
xyplot(hrv.msd ~ cond | ID, data = temp, as.table=T) 
What this shows is that people vary pretty widely in their response to the videos. This could explain some of the lack of overall effects of cond.
Effects of emotionality and regulation (continuous)
afex stop giving me every possible interaction under the sun.
a10 <- aov_ez(id = "ID", dv = "egg_n", anovaData, within = "cond", between = c("cen_tot_BIS","cen_tot_BAS_Rew","cen_tot_PSWQ","cen_tot_RS_Brooding"), fun_aggregate = mean)
a10 Anova Table (Type 3 tests)
Response: egg_n
Effect df MSE F ges p.value
1 cen_tot_BIS 1, 32 0.16 4.32 * .04 .05
2 cen_tot_BAS_Rew 1, 32 0.16 1.16 .01 .29
3 cen_tot_PSWQ 1, 32 0.16 2.04 .02 .16
4 cen_tot_RS_Brooding 1, 32 0.16 0.97 .010 .33
5 cen_tot_BIS:cen_tot_BAS_Rew 1, 32 0.16 0.45 .004 .51
6 cen_tot_BIS:cen_tot_PSWQ 1, 32 0.16 0.03 .0003 .87
7 cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 0.16 2.35 .02 .14
8 cen_tot_BIS:cen_tot_RS_Brooding 1, 32 0.16 1.13 .01 .30
9 cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 0.16 0.08 .0008 .78
10 cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.16 0.25 .002 .62
11 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 0.16 0.30 .003 .59
12 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 0.16 1.29 .01 .26
13 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.16 0.77 .008 .39
14 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.16 1.23 .01 .28
15 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.16 0.26 .003 .61
16 cond 2.01, 64.47 0.16 0.22 .005 .81
17 cen_tot_BIS:cond 2.01, 64.47 0.16 1.48 .03 .24
18 cen_tot_BAS_Rew:cond 2.01, 64.47 0.16 0.88 .02 .42
19 cen_tot_PSWQ:cond 2.01, 64.47 0.16 0.60 .01 .55
20 cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 0.29 .006 .75
21 cen_tot_BIS:cen_tot_BAS_Rew:cond 2.01, 64.47 0.16 0.78 .02 .46
22 cen_tot_BIS:cen_tot_PSWQ:cond 2.01, 64.47 0.16 0.28 .006 .76
23 cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.01, 64.47 0.16 0.22 .005 .80
24 cen_tot_BIS:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 0.63 .01 .54
25 cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 1.59 .03 .21
26 cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 1.11 .02 .34
27 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.01, 64.47 0.16 0.34 .007 .72
28 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 1.88 .04 .16
29 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 0.02 .0004 .98
30 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 0.50 .01 .61
31 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.01, 64.47 0.16 0.24 .005 .79
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a11 <- aov_ez(id = "ID", dv = "egg_t", anovaData, within = "cond", between = c("cen_tot_BIS","cen_tot_BAS_Rew","cen_tot_PSWQ","cen_tot_RS_Brooding"), fun_aggregate = mean)
a11Anova Table (Type 3 tests)
Response: egg_t
Effect df MSE F ges p.value
1 cen_tot_BIS 1, 32 0.11 3.34 + .03 .08
2 cen_tot_BAS_Rew 1, 32 0.11 0.07 .0007 .79
3 cen_tot_PSWQ 1, 32 0.11 0.71 .006 .41
4 cen_tot_RS_Brooding 1, 32 0.11 0.34 .003 .56
5 cen_tot_BIS:cen_tot_BAS_Rew 1, 32 0.11 0.01 <.0001 .93
6 cen_tot_BIS:cen_tot_PSWQ 1, 32 0.11 0.29 .003 .60
7 cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 0.11 0.57 .005 .45
8 cen_tot_BIS:cen_tot_RS_Brooding 1, 32 0.11 1.07 .010 .31
9 cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 0.11 1.11 .01 .30
10 cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.11 0.01 <.0001 .94
11 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 0.11 0.38 .003 .54
12 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 0.11 1.15 .01 .29
13 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.11 0.16 .001 .70
14 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.11 0.00 <.0001 .97
15 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 0.11 0.24 .002 .63
16 cond 2.47, 79.17 0.11 1.04 .02 .37
17 cen_tot_BIS:cond 2.47, 79.17 0.11 0.55 .01 .62
18 cen_tot_BAS_Rew:cond 2.47, 79.17 0.11 0.15 .003 .90
19 cen_tot_PSWQ:cond 2.47, 79.17 0.11 0.13 .003 .91
20 cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 0.29 .006 .79
21 cen_tot_BIS:cen_tot_BAS_Rew:cond 2.47, 79.17 0.11 0.65 .01 .56
22 cen_tot_BIS:cen_tot_PSWQ:cond 2.47, 79.17 0.11 0.28 .006 .80
23 cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.47, 79.17 0.11 0.04 .0009 .98
24 cen_tot_BIS:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 0.79 .02 .48
25 cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 0.12 .003 .92
26 cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 0.78 .02 .49
27 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.47, 79.17 0.11 0.21 .005 .85
28 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 3.37 * .07 .03
29 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 1.06 .02 .36
30 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 1.21 .03 .31
31 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.47, 79.17 0.11 0.53 .01 .63
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a12 <- aov_ez(id = "ID", dv = "hrv.msd", anovaData, within = "cond", between = c("cen_tot_BIS","cen_tot_BAS_Rew","cen_tot_PSWQ","cen_tot_RS_Brooding"), fun_aggregate = mean)
a12Anova Table (Type 3 tests)
Response: hrv.msd
Effect df MSE F ges p.value
1 cen_tot_BIS 1, 32 1679.82 7.18 * .17 .01
2 cen_tot_BAS_Rew 1, 32 1679.82 3.22 + .08 .08
3 cen_tot_PSWQ 1, 32 1679.82 1.62 .04 .21
4 cen_tot_RS_Brooding 1, 32 1679.82 0.17 .005 .68
5 cen_tot_BIS:cen_tot_BAS_Rew 1, 32 1679.82 0.01 .0002 .94
6 cen_tot_BIS:cen_tot_PSWQ 1, 32 1679.82 0.17 .005 .68
7 cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 1679.82 0.03 .0008 .87
8 cen_tot_BIS:cen_tot_RS_Brooding 1, 32 1679.82 1.47 .04 .23
9 cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 1679.82 0.26 .007 .61
10 cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 1679.82 1.39 .04 .25
11 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 1679.82 0.01 .0003 .92
12 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 1679.82 2.58 .07 .12
13 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 1679.82 1.32 .04 .26
14 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 1679.82 2.54 .07 .12
15 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 1679.82 0.02 .0005 .90
16 cond 2.32, 74.40 68.95 0.50 .001 .64
17 cen_tot_BIS:cond 2.32, 74.40 68.95 0.04 .0001 .97
18 cen_tot_BAS_Rew:cond 2.32, 74.40 68.95 0.30 .0008 .78
19 cen_tot_PSWQ:cond 2.32, 74.40 68.95 0.07 .0002 .95
20 cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.03 <.0001 .98
21 cen_tot_BIS:cen_tot_BAS_Rew:cond 2.32, 74.40 68.95 0.13 .0004 .90
22 cen_tot_BIS:cen_tot_PSWQ:cond 2.32, 74.40 68.95 0.20 .0005 .85
23 cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.32, 74.40 68.95 0.44 .001 .68
24 cen_tot_BIS:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 1.44 .004 .24
25 cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.43 .001 .68
26 cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.99 .003 .39
27 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.32, 74.40 68.95 0.29 .0008 .78
28 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.71 .002 .52
29 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.15 .0004 .89
30 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 1.52 .004 .22
31 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.32, 74.40 68.95 0.02 <.0001 .99
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a13 <- aov_ez(id = "ID", dv = "panas_NA", anovaData, within = "cond", between = c("cen_tot_BIS","cen_tot_BAS_Rew","cen_tot_PSWQ","cen_tot_RS_Brooding"), fun_aggregate = mean)
a13Anova Table (Type 3 tests)
Response: panas_NA
Effect df MSE F ges p.value
1 cen_tot_BIS 1, 32 35.42 1.90 .03 .18
2 cen_tot_BAS_Rew 1, 32 35.42 0.04 .0006 .84
3 cen_tot_PSWQ 1, 32 35.42 7.78 ** .11 .009
4 cen_tot_RS_Brooding 1, 32 35.42 2.51 .04 .12
5 cen_tot_BIS:cen_tot_BAS_Rew 1, 32 35.42 1.63 .03 .21
6 cen_tot_BIS:cen_tot_PSWQ 1, 32 35.42 0.40 .006 .53
7 cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 35.42 0.12 .002 .73
8 cen_tot_BIS:cen_tot_RS_Brooding 1, 32 35.42 0.69 .01 .41
9 cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 35.42 0.29 .005 .59
10 cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 35.42 3.14 + .05 .09
11 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 35.42 2.50 .04 .12
12 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 35.42 3.69 + .05 .06
13 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 35.42 0.70 .01 .41
14 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 35.42 0.05 .0008 .83
15 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 35.42 0.82 .01 .37
16 cond 2.63, 84.01 13.23 8.52 *** .12 .0001
17 cen_tot_BIS:cond 2.63, 84.01 13.23 0.69 .01 .54
18 cen_tot_BAS_Rew:cond 2.63, 84.01 13.23 0.11 .002 .94
19 cen_tot_PSWQ:cond 2.63, 84.01 13.23 0.45 .007 .69
20 cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.51 .008 .65
21 cen_tot_BIS:cen_tot_BAS_Rew:cond 2.63, 84.01 13.23 1.48 .02 .23
22 cen_tot_BIS:cen_tot_PSWQ:cond 2.63, 84.01 13.23 1.61 .02 .20
23 cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.63, 84.01 13.23 0.20 .003 .87
24 cen_tot_BIS:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.43 .007 .71
25 cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 1.50 .02 .22
26 cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.07 .001 .97
27 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.63, 84.01 13.23 0.46 .007 .69
28 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.14 .002 .92
29 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.62 .010 .58
30 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.14 .002 .92
31 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.63, 84.01 13.23 0.79 .01 .49
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG a13 <- aov_ez(id = "ID", dv = "panas_PA", anovaData, within = "cond", between = c("cen_tot_BIS","cen_tot_BAS_Rew","cen_tot_PSWQ","cen_tot_RS_Brooding"), fun_aggregate = mean)
a13Anova Table (Type 3 tests)
Response: panas_PA
Effect df MSE F ges p.value
1 cen_tot_BIS 1, 32 69.16 10.76 ** .17 .003
2 cen_tot_BAS_Rew 1, 32 69.16 0.16 .003 .69
3 cen_tot_PSWQ 1, 32 69.16 1.15 .02 .29
4 cen_tot_RS_Brooding 1, 32 69.16 1.07 .02 .31
5 cen_tot_BIS:cen_tot_BAS_Rew 1, 32 69.16 1.23 .02 .28
6 cen_tot_BIS:cen_tot_PSWQ 1, 32 69.16 0.98 .02 .33
7 cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 69.16 0.56 .01 .46
8 cen_tot_BIS:cen_tot_RS_Brooding 1, 32 69.16 0.07 .001 .79
9 cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 69.16 2.29 .04 .14
10 cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 69.16 0.11 .002 .74
11 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ 1, 32 69.16 0.63 .01 .43
12 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding 1, 32 69.16 0.00 <.0001 .95
13 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 69.16 1.38 .03 .25
14 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 69.16 0.93 .02 .34
15 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding 1, 32 69.16 1.00 .02 .32
16 cond 2.87, 91.86 14.54 47.07 *** .36 <.0001
17 cen_tot_BIS:cond 2.87, 91.86 14.54 0.64 .007 .59
18 cen_tot_BAS_Rew:cond 2.87, 91.86 14.54 0.36 .004 .77
19 cen_tot_PSWQ:cond 2.87, 91.86 14.54 1.14 .01 .33
20 cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.29 .003 .82
21 cen_tot_BIS:cen_tot_BAS_Rew:cond 2.87, 91.86 14.54 0.72 .008 .54
22 cen_tot_BIS:cen_tot_PSWQ:cond 2.87, 91.86 14.54 2.34 + .03 .08
23 cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.87, 91.86 14.54 2.88 * .03 .04
24 cen_tot_BIS:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.66 .008 .57
25 cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.66 .008 .57
26 cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.97 .01 .41
27 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cond 2.87, 91.86 14.54 0.90 .01 .44
28 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.58 .007 .62
29 cen_tot_BIS:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 1.14 .01 .34
30 cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.57 .007 .63
31 cen_tot_BIS:cen_tot_BAS_Rew:cen_tot_PSWQ:cen_tot_RS_Brooding:cond 2.87, 91.86 14.54 0.33 .004 .80
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Sphericity correction method: GG There is a significant(ish) effect of BIS on EGG normogastria, F = 4.32, p = .05, and of BIS on HRV, F = 7.18, p = .01.
There is a significant effect of PSWQ on PANAS NA, F = 7.78, p = .009, as well as a main effect of condition (as seen above in the model without between-subjects effects), F = 8.52, p = .0001.
There is a significant effect of PSWQ on PANAS PA, F = 10.76, p = .003, as well as a main effect of condition (as seen above in the model without between-subjects effects), F = 47.07, p < .0001.
So that’s kind of interesting! …maybe suggests that physiological response is more a function of emotionality, whereas subjective response is more a function of regulation?
NOTE: There is probably a more correct test to use given the non-normality of the data, but I haven’t been able to find a clear good equivalent to RM-ANOVA, at least so far…
T-tests for PANAS
# add the median split vars to the wide dataset
tots <- tots %>% mutate(med_split_BIS = as.factor(ifelse(tot_bisbas_BIS.Total <= median(tot_bisbas_BIS.Total), "low", "high")),
med_split_BAS_Rew = as.factor(ifelse(tot_bisbas_BAS.Reward < median(tot_bisbas_BAS.Reward), "low", "high")),
med_split_PSWQ = as.factor(ifelse(tot_PSWQ_Total <= median(tot_PSWQ_Total), "low", "high")),
med_split_RS_Brood = as.factor(ifelse(tot_RS_Brooding <= median(tot_RS_Brooding), "low", "high"))
)
data <- left_join(data, tots, by="ID")BIS
# BIS
t.test(data$tot_PANAS_State.NA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_PANAS_State.NA by data$med_split_BIS
t = 1.7034, df = 30.13, p-value = 0.09878
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3687091 4.0800135
sample estimates:
mean in group high mean in group low
14.69565 12.84000 t.test(data$tot_PANAS_State.PA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_PANAS_State.PA by data$med_split_BIS
t = -0.73473, df = 45.888, p-value = 0.4662
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.086144 2.366144
sample estimates:
mean in group high mean in group low
25.00 26.36 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.NA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.NA by data$med_split_BIS
t = 0.81359, df = 39.907, p-value = 0.4207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.226169 2.878342
sample estimates:
mean in group high mean in group low
12.82609 12.00000 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.PA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.PA by data$med_split_BIS
t = -1.5539, df = 37.469, p-value = 0.1286
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.5080456 0.7254369
sample estimates:
mean in group high mean in group low
13.6087 16.0000 t.test(data$tot_aftervid.FEAR_PANAS_State.NA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.NA by data$med_split_BIS
t = 2.2951, df = 32.58, p-value = 0.02831
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.3794805 6.3300847
sample estimates:
mean in group high mean in group low
17.43478 14.08000 t.test(data$tot_aftervid.FEAR_PANAS_State.PA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.PA by data$med_split_BIS
t = -1.3977, df = 45.337, p-value = 0.169
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-6.379890 1.152064
sample estimates:
mean in group high mean in group low
19.82609 22.44000 t.test(data$tot_aftervid.SAD_PANAS_State.NA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.NA by data$med_split_BIS
t = 1.7191, df = 27.949, p-value = 0.09665
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4902641 5.6067859
sample estimates:
mean in group high mean in group low
16.47826 13.92000 t.test(data$tot_aftervid.SAD_PANAS_State.PA ~ data$med_split_BIS)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.PA by data$med_split_BIS
t = -0.53023, df = 45.459, p-value = 0.5985
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.721157 2.169853
sample estimates:
mean in group high mean in group low
17.30435 18.08000 The high BIS group had significantly higher negative affect after the fear video than the low group, t = 2.30, p = .028.
bis <- ggplot(anovaData, aes(fill=cond, y=panas_NA, x=med_split_BIS)) +
geom_boxplot(aes(fill=cond), outlier.alpha = 0.3)
bis
BAS-Reward
# BAS_Rew
t.test(data$tot_PANAS_State.NA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_PANAS_State.NA by data$med_split_BAS_Rew
t = 0.20785, df = 37.058, p-value = 0.8365
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.023455 2.486064
sample estimates:
mean in group high mean in group low
13.8400 13.6087 t.test(data$tot_PANAS_State.PA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_PANAS_State.PA by data$med_split_BAS_Rew
t = 0.19242, df = 45.834, p-value = 0.8483
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.389948 4.106470
sample estimates:
mean in group high mean in group low
25.88000 25.52174 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.NA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.NA by data$med_split_BAS_Rew
t = 0.090463, df = 42.09, p-value = 0.9283
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.963941 2.148289
sample estimates:
mean in group high mean in group low
12.44000 12.34783 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.PA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.PA by data$med_split_BAS_Rew
t = 0.98847, df = 43.015, p-value = 0.3284
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.619079 4.732123
sample estimates:
mean in group high mean in group low
15.60000 14.04348 t.test(data$tot_aftervid.FEAR_PANAS_State.NA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.NA by data$med_split_BAS_Rew
t = -0.11997, df = 42.757, p-value = 0.9051
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.252666 2.887448
sample estimates:
mean in group high mean in group low
15.60000 15.78261 t.test(data$tot_aftervid.FEAR_PANAS_State.PA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.PA by data$med_split_BAS_Rew
t = 0.67812, df = 45.781, p-value = 0.5011
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.516538 5.073060
sample estimates:
mean in group high mean in group low
21.80000 20.52174 t.test(data$tot_aftervid.SAD_PANAS_State.NA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.NA by data$med_split_BAS_Rew
t = -0.7594, df = 40.112, p-value = 0.4521
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.170562 1.892301
sample estimates:
mean in group high mean in group low
14.60000 15.73913 t.test(data$tot_aftervid.SAD_PANAS_State.PA ~ data$med_split_BAS_Rew)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.PA by data$med_split_BAS_Rew
t = 2.0505, df = 42.574, p-value = 0.04651
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.04634305 5.67887434
sample estimates:
mean in group high mean in group low
19.08000 16.21739 The high BAS-Reward Responsiveness group had significantly(ish) higher positive affect after the sad video than the low group, t = 2.05, p = .047.
basrr <- ggplot(anovaData, aes(fill=cond, y=panas_PA, x=med_split_BAS_Rew)) +
geom_boxplot(aes(fill=cond), outlier.alpha = 0.3)
basrr
PSWQ
# PSWQ
t.test(data$tot_PANAS_State.NA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_PANAS_State.NA by data$med_split_PSWQ
t = 3.4503, df = 29.632, p-value = 0.001703
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.403447 5.480031
sample estimates:
mean in group high mean in group low
15.52174 12.08000 t.test(data$tot_PANAS_State.PA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_PANAS_State.PA by data$med_split_PSWQ
t = 0.12272, df = 44.262, p-value = 0.9029
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.486074 3.938248
sample estimates:
mean in group high mean in group low
25.82609 25.60000 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.NA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.NA by data$med_split_PSWQ
t = 2.2301, df = 40.63, p-value = 0.03133
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.2035568 4.1199214
sample estimates:
mean in group high mean in group low
13.52174 11.36000 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.PA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.PA by data$med_split_PSWQ
t = -0.24454, df = 41.65, p-value = 0.808
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.589137 2.813485
sample estimates:
mean in group high mean in group low
14.65217 15.04000 t.test(data$tot_aftervid.FEAR_PANAS_State.NA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.NA by data$med_split_PSWQ
t = 2.3668, df = 34.665, p-value = 0.02366
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.4880615 6.3884602
sample estimates:
mean in group high mean in group low
17.47826 14.04000 t.test(data$tot_aftervid.FEAR_PANAS_State.PA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.PA by data$med_split_PSWQ
t = 0.51167, df = 44.942, p-value = 0.6114
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.864998 4.816303
sample estimates:
mean in group high mean in group low
21.69565 20.72000 t.test(data$tot_aftervid.SAD_PANAS_State.NA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.NA by data$med_split_PSWQ
t = 1.5491, df = 31.214, p-value = 0.1314
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.7297752 5.3454274
sample estimates:
mean in group high mean in group low
16.34783 14.04000 t.test(data$tot_aftervid.SAD_PANAS_State.PA ~ data$med_split_PSWQ)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.PA by data$med_split_PSWQ
t = 0.72754, df = 45.314, p-value = 0.4706
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.875456 3.997195
sample estimates:
mean in group high mean in group low
18.26087 17.20000 The high PSWQ group had significantly higher NA at baseline (t = 3.45, p = .002), following the neutral video (t = 2.23, p = .03), and following the fear video (t = 2.37, p = .024) than the low PSWQ group.
pswq <- ggplot(anovaData, aes(fill=cond, y=panas_NA, x=med_split_PSWQ)) +
geom_boxplot(aes(fill=cond), outlier.alpha = 0.3)
pswq
RS-Brood
# RS_Brood
t.test(data$tot_PANAS_State.NA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_PANAS_State.NA by data$med_split_RS_Brood
t = 0.41093, df = 34.348, p-value = 0.6837
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.721490 2.594533
sample estimates:
mean in group high mean in group low
13.95652 13.52000 t.test(data$tot_PANAS_State.PA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_PANAS_State.PA by data$med_split_RS_Brood
t = -0.60193, df = 45.588, p-value = 0.5502
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.820932 2.601802
sample estimates:
mean in group high mean in group low
25.13043 26.24000 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.NA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.NA by data$med_split_RS_Brood
t = 1.5075, df = 43.799, p-value = 0.1389
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5035468 3.4913729
sample estimates:
mean in group high mean in group low
13.17391 11.68000 t.test(data$tot_aftervid.NEUTRAL_PANAS_State.PA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.NEUTRAL_PANAS_State.PA by data$med_split_RS_Brood
t = -0.56536, df = 37.54, p-value = 0.5752
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.072123 2.294732
sample estimates:
mean in group high mean in group low
14.3913 15.2800 t.test(data$tot_aftervid.FEAR_PANAS_State.NA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.NA by data$med_split_RS_Brood
t = -0.5553, df = 39.747, p-value = 0.5818
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.801034 2.162773
sample estimates:
mean in group high mean in group low
15.26087 16.08000 t.test(data$tot_aftervid.FEAR_PANAS_State.PA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.FEAR_PANAS_State.PA by data$med_split_RS_Brood
t = -0.18795, df = 44.218, p-value = 0.8518
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.219718 3.499718
sample estimates:
mean in group high mean in group low
21.00 21.36 t.test(data$tot_aftervid.SAD_PANAS_State.NA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.NA by data$med_split_RS_Brood
t = -1.3061, df = 36.415, p-value = 0.1997
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.762658 1.030484
sample estimates:
mean in group high mean in group low
14.17391 16.04000 t.test(data$tot_aftervid.SAD_PANAS_State.PA ~ data$med_split_RS_Brood)
Welch Two Sample t-test
data: data$tot_aftervid.SAD_PANAS_State.PA by data$med_split_RS_Brood
t = 0.1519, df = 43.607, p-value = 0.88
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.774427 3.226601
sample estimates:
mean in group high mean in group low
17.82609 17.60000 There is no different in PANAS (NA or PA) by RS-Brood median split.
Comparing HRV-EGG correlations by median splits
Same above in the earlier section, except now looking at the low/high groups separately. Using Jeannie’s output from SPSS here. Jeannie, you could re-run using Spearman’s rho (https://statistics.laerd.com/spss-tutorials/spearmans-rank-order-correlation-using-spss-statistics.php) to double-check but based on the correlations above, they’re similar enough that using the Pearson ones you did already are fine. May want to ultimately use Spearman’s for the publication, but for the presentation next week, I think this is okay.
library(cocor)
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