We are focusing here on caring action/prosocial behavior, rather than on self and other compassion.
(not in final order)
Does caring action lead to well-being?
The literature suggests that people who engage in caring actions more than average tend to have higher well-being than average. This conclusion is typically seen between individuals.
Can we make conclusions within person based on these between person effrects? Giving has been described as one of the five “ways to well-being.” However, this is a within person claim based on between person effects.
To address this hypothesis, you can look at the ** pooled correlations** initially.
care_for_other_num
) and well-being variables, with all
correlations under 0.06.The between and within analyses thus present different conclusions.
The null result for within-person effects is interesting because, as we will see, the variable is equasyncratic (a new term coined by Steve).
In the table depicting variability for the link between caring and unhappiness, we observe that for some individuals, caring brings more unhappiness, while for others, it brings more happiness. The pooled effect (middle blue bars) shows that the positive and negative effects cancel out, concealing the significant impact caring can have on well-being for many people. The pooled effect makes it seem as if caring has no effect.
The rest of the analysis attempted to find differences between the two groups—those who experience happiness and those who experience unhappiness when caring. We did not find any significant differences, so more research is needed here
CareForOther = V_19
CAT since the last scheduled prompt: Did you help, care for,
support, or do anything for another person?
CareForAltruistic = V_19_1
CAT: Did this happen without self-interest or profit for yourself
and without compensation?
WhoCaredFor = V_19a
CAT: Whom did you help?
0
- spouse, 1
- children, 2
-
other family, 4
- work colleague, 5
- fellow
student, 6
- medical staff, 7
- fellow
patients, 8
- other
** Important. the reason for caring items were reverse coded. I will reverse back so higher numbers mean more of the state or more “bad” feelings **
This was original but then programmer reversed it (andrew email may
29,2024, 1049 am)
Slider from 0 – 100: 0 = not true 100 = true
CareVoluntary = V_19_2b
Why did you help this person? I did it voluntarily.
CareObliged = V_19_2c
I felt obliged to do so. Slider from 0 – 100:0 = not true
100 = true
CareGuilt = V_19_2d
I had a bad conscience or felt guilty.
CareAvoidProblem = V_19_2e
I wanted to avoid problems.
CareFeelings = V_19_2f
How did you feel doing it? Slider from 0 – 100: 0 = very bad 50
= okay 100 = very good
HowUnhappy = V_10_1
Since last prompt: 1 to 100 (very much)
WithoutEnergy = V_10_2
Distracted = V_10_3
Stressed = V_10_4
AtEase = `V_11_1’ Slider from 0 – 100:0 = not at all 50= somewhat 100 = very much’
Optimistic = V_11_2
Delighted = V_11_3
Satisfied = V_11_4
Grateful = V_11_5
SelfCompassion = V_14_1g
I looked at myself with tolerance, goodwill, and care.
OtherCompassion = V_14_1h
I looked at others with tolerance, goodwill, and care.
Skewness values between -0.5 and 0.5 indicate an approximately symmetrical distribution. Values within -1 to -0.5 or 0.5 to 1 suggest a slightly skewed distribution. Values beyond -1 or above 1 are considered highly skewed. Kurtosis quantifies distribution “tailedness.” A kurtosis greater than 3 (excess kurtosis > 0) indicates heavy tails and a more peaked distribution, while less than 3 (excess kurtosis < 0) suggests lighter tails and a flatter distribution
##
## Female Male
## 59 70
##
## Inpatient Outpatient
## 59 70
## # A tibble: 1 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Age 36.2 11.5 0.443 -0.809
## # A tibble: 2 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 OtherCompassion 76.6 19.8 -1.33 2.05
## 2 SelfCompassion 59.6 24.7 -0.453 -0.494
##
## cared for another Did not care for another
## 1050 2271
##
## Child Fellow Patient Fellow student Friend Medical Staff
## 104 271 15 133 27
## Other Other Family spouse Work Colleague
## 90 115 181 95
## # A tibble: 1 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CareForOther_num 0.316 0.465 0.790 -1.38
## # A tibble: 5 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CareAvoidProblemr 20.1 27.1 1.53 1.20
## 2 CareFeelingsr 23.6 16.6 0.850 1.57
## 3 CareGuiltr 14.9 22.3 2.18 4.32
## 4 CareObligedr 40.0 34.0 0.350 -1.37
## 5 CareVoluntaryr 83.0 23.5 -2.02 3.42
## # A tibble: 1 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 CareForAltruistic_num 0.871 0.335 -2.22 2.91
## # A tibble: 9 × 5
## Variable Mean SD Skewness Kurtosis
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AtEase 52.9 25.1 -0.277 -0.631
## 2 Delighted 54.5 25.9 -0.333 -0.669
## 3 Distracted 36.9 26.5 0.348 -0.900
## 4 Grateful 59.8 26.8 -0.505 -0.585
## 5 HowUnhappy 38.7 26.8 0.291 -1.01
## 6 Optimistic 57.9 24.1 -0.433 -0.391
## 7 Satisfied 58.3 25.0 -0.444 -0.499
## 8 Stressed 40.8 28.1 0.275 -1.04
## 9 WithoutEnergy 37.6 27.0 0.349 -0.960
Nomethetic (group level)
This shows you the simple correlations and significant levels
Within are correlations within person Between are correlations between people
These are pooled effects…they are averages, and hide individual variability.
“CareVoluntary”
[11] “CareObliged” “CareGuilt”
[13] “CareAvoidProblem” “CareFeelings”
[15] “HowUnhappy” “WithoutEnergy”
[17] “Distracted” “Stressed”
[19] “AtEase” “Optimistic”
[21] “Delighted” “Satisfied”
[23] “Grateful”
## These are the within-person correlations:
## These are the within-person p-values:
## These are the between-person correlations:
## These are the between-person p-values:
## These are the within-person correlations :
## These are the within-person p-values :
## These are the between-person correlations :
## These are the between-person p-values :
## Df Sum Sq Mean Sq F value Pr(>F)
## CareForAltruistic 1 15782 15782 29.62 6.56e-08 ***
## Residuals 1046 557373 533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4370 observations deleted due to missingness
## Tables of means
## Grand mean
##
## 16.93798
##
## CareForAltruistic
## care with self interest cared without self interest
## 27.03 15.45
## rep 135.00 913.00
## Df Sum Sq Mean Sq F value Pr(>F)
## CareForAltruistic 1 1924 1923.7 6.997 0.00829 **
## Residuals 1046 287578 274.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4370 observations deleted due to missingness
## Tables of means
## Grand mean
##
## 76.41221
##
## CareForAltruistic
## care with self interest cared without self interest
## 72.89 76.93
## rep 135.00 913.00
ivs <- c(“CareForOther_num”) dvs <- c(“HowUnhappy”, “WithoutEnergy”, “Distracted”, “Stressed”, “AtEase”, “Optimistic”, “Delighted”, “Satisfied”, “Grateful”)
What is the link between caring for others (=1 on
careforother_num
) and different indices of well-being?
Summary of Findings:
p < .00001
, also associated with I² above 50%.
In conclusion, for most variables, there is no significant pooled effect but there is significant variability, indicating that effects may exist for some people. Further investigation is needed to explore these effects.
## variable b se pval ci_lb ci_ub I2 QE QEp
## HowUnhappy -0.09 0.05 0.07 -0.19 0.01 72.46 521.00 0
## WithoutEnergy -0.05 0.04 0.22 -0.14 0.03 63.83 294.30 0
## Distracted -0.04 0.04 0.38 -0.11 0.04 57.66 239.08 0
## Stressed 0.05 0.04 0.14 -0.02 0.12 30.96 204.58 0
## AtEase 0.05 0.04 0.20 -0.03 0.13 64.56 391.89 0
## Optimistic 0.08 0.04 0.08 -0.01 0.16 59.70 316.27 0
## Delighted 0.10 0.04 0.01 0.03 0.17 35.59 206.30 0
## Satisfied 0.09 0.04 0.05 0.00 0.17 61.54 402.59 0
## Grateful 0.07 0.04 0.06 0.00 0.14 37.73 212.26 0
ICC1 (Single-Raters Absolute Agreement)
ICC1 measures the proportion of total variance in the measurements that is attributable to the differences among subjects. It can be thought of as the consistency of scores within a particular participant when measured multiple times. Higher ICC1 values indicate that individual scores are more consistent for each participant, meaning that the variation within participants is low compared to the variation between participants. Conversely, lower ICC1 values indicate higher within-participant variance.
ICC2 (Average-Raters Agreement)
ICC2 assesses the reliability of the average of multiple measurements (e.g., multiple raters or multiple time points). It assumes that the measurements are a random sample from a larger population. Essentially, ICC2 reflects the reliability of the measure if all items nested within each participant (ID) were used to form an average. Higher ICC2 values indicate that the average scores are more consistent and reliable across different participants.
## Statistics within and between groups
## Call: statsBy(data = new_data_frame, group = "ID")
## Intraclass Correlation 1 (Percentage of variance due to groups)
## ID CareForOther_num HowUnhappy WithoutEnergy
## 1.00 0.14 0.35 0.45
## Distracted Stressed AtEase Optimistic
## 0.52 0.41 0.44 0.53
## Delighted Satisfied Grateful
## 0.56 0.46 0.56
## Intraclass Correlation 2 (Reliability of group differences)
## ID CareForOther_num HowUnhappy WithoutEnergy
## 1.00 0.81 0.93 0.96
## Distracted Stressed AtEase Optimistic
## 0.97 0.95 0.95 0.97
## Delighted Satisfied Grateful
## 0.97 0.96 0.97
## eta^2 between groups
## CareForOther_num.bg HowUnhappy.bg WithoutEnergy.bg Distracted.bg
## 0.17 0.37 0.47 0.53
## Stressed.bg AtEase.bg Optimistic.bg Delighted.bg
## 0.43 0.46 0.54 0.57
## Satisfied.bg Grateful.bg
## 0.48 0.58
##
## To see the correlations between and within groups, use the short=FALSE option in your print statement.
## Many results are not shown directly. To see specific objects select from the following list:
## mean sd n F ICC1 ICC2 ci1 ci2 raw rbg ci.bg pbg rwg nw ci.wg pwg etabg etawg nwg nG Call
Interpreting the Silhouette Plot
The silhouette plot produced by the fviz_silhouette
function provides a graphical representation of how well each data point
fits within its assigned cluster. Here is how to interpret the plot:
By analyzing the silhouette plot, we can assess the quality of clustering and identify if any data points have been misclassified.
## [1] 2
## Here are the silhouette scores for 2 to 10 clusters:
## [1] 0.27054196 0.16265428 0.06877118 0.08229655 0.09808085 0.10599966 0.11716224
## [8] 0.11480569 0.10581597
## We want to choose the number of clusters with the highest value.
## The highest silhouette score is 0.270542 for 2 clusters.
## cluster size ave.sil.width
## 1 1 79 0.34
## 2 2 50 0.17
##
## 1 2
## 79 50
The table below represents the medoids of the clusters obtained from the Partitioning Around Medoids (PAM) clustering algorithm. Medoids are the representative points of each cluster, similar to centroids in k-means clustering, but medoids are actual data points from the dataset.
By analyzing the medoids, we can understand the characteristics of each cluster and how the representative points compare across different clusters.
Cluster 1 seems to involve caring having a positive effect oun well-being; cluster 2 suggests that carring had a negative effect
## HowUnhappy WithoutEnergy AtEase Optimistic Satisfied
## 1 -0.20 -0.25 0.14 0.22 0.13
## 2 0.23 0.23 -0.20 -0.30 -0.23
We saw earlier that some people did not fit well into either cluster
A silhouette score of 0.10 for an individual data point can be interpreted as follows:
Poor Fit: The data point is poorly matched to its assigned cluster. Ambiguous Position: The data point is very close to the boundary between its assigned cluster and a neighboring cluster, indicating that it does not fit well with either. Possible Misclassification: The low score suggests that the data point might be misclassified and may belong to a different cluster. Low Confidence: There is low confidence in the clustering assignment for this data point, and it indicates that the clustering structure might not be optimal.
## # A tibble: 5 × 7
## variable cluster0_avg cluster0_se cluster1_avg cluster1_se cluster2_avg
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Unhappy 0.00635 0.143 -0.296 0.0470 0.485
## 2 NoEnergy 0.140 0.106 -0.270 0.0516 0.371
## 3 AtEase -0.0688 0.0811 0.256 0.0442 -0.372
## 4 Optimistic 0.00865 0.0650 0.297 0.0426 -0.476
## 5 Satisfied -0.203 0.130 0.337 0.0481 -0.386
## cluster2_se
## <dbl>
## 1 0.0935
## 2 0.115
## 3 0.0864
## 4 0.0831
## 5 0.0748
## Number in each group is:
##
## 0 1 2
## 21 75 33
## Observed Expected WhoCaredFor Cluster3
## 1 38 41.72 Kin HelpBad
## 2 167 163.28 NotKin HelpBad
## 3 135 131.28 Kin HelpGood
## 4 510 513.72 NotKin HelpGood
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: kin ~ cluster3 + (1 | ID)
## Data: long_data_with_cluster_noZero
##
## AIC BIC logLik deviance df.resid
## 696.4 710.7 -345.2 690.4 847
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2529 0.1470 0.1877 0.3575 2.2765
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.326 2.08
## Number of obs: 850, groups: ID, 108
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.42472 0.51380 4.719 2.37e-06 ***
## cluster3HelpGood 0.03696 0.56869 0.065 0.948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## clstr3HlpGd -0.779
##
## Interpretation: The analysis found no significant differences between the groups in terms of helping kin versus non-kin.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: CareForOther_num ~ cluster3 + (1 | ID)
## Data: long_data_with_cluster_noZero
##
## AIC BIC logLik deviance df.resid
## 3219.0 3236.7 -1606.5 3213.0 2753
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5120 -0.6583 -0.4259 0.9521 3.3221
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7564 0.8697
## Number of obs: 2756, groups: ID, 108
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1467 0.1787 -6.416 1.4e-10 ***
## cluster3HelpGood 0.3632 0.2115 1.718 0.0859 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## clstr3HlpGd -0.843
##
## Interpretation: According to the model, the HelpGood group tended to show a marginal tendency to help more than the other groups.
##
## Female Male
## HelpBad 18 15
## HelpGood 32 43
## Results of the Chi-Square Test:
## Chi-Squared Statistic: 0.87
## Degrees of Freedom: 1
## P-Value: 0.3519
## Conclusion: There are no significant gender differences. (p > 0.05)
## $avg_CareFeelings
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 10 10.5 0.079 0.779
## Residuals 106 14028 132.3
## 21 observations deleted due to missingness
##
## $avg_CareVoluntary
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 97 96.92 0.481 0.489
## Residuals 106 21355 201.46
## 21 observations deleted due to missingness
##
## $avg_CareObliged
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 378 377.9 0.629 0.43
## Residuals 106 63715 601.1
## 21 observations deleted due to missingness
##
## $avg_CareGuilt
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 40 39.91 0.147 0.703
## Residuals 106 28869 272.35
## 21 observations deleted due to missingness
##
## $avg_CareAvoidProblem
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 27 26.7 0.082 0.775
## Residuals 106 34617 326.6
## 21 observations deleted due to missingness
##
## $avg_HowUnhappy
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 8 7.81 0.032 0.858
## Residuals 106 25563 241.16
## 21 observations deleted due to missingness
##
## $avg_WithoutEnergy
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 7 7.4 0.023 0.88
## Residuals 106 34344 324.0
## 21 observations deleted due to missingness
##
## $avg_Distracted
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 30 30.2 0.086 0.77
## Residuals 106 37270 351.6
## 21 observations deleted due to missingness
##
## $avg_Stressed
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 47 46.96 0.151 0.698
## Residuals 106 32867 310.06
## 21 observations deleted due to missingness
##
## $avg_AtEase
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 403 402.6 1.736 0.191
## Residuals 106 24585 231.9
## 21 observations deleted due to missingness
##
## $avg_Optimistic
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 347 347.3 1.21 0.274
## Residuals 106 30424 287.0
## 21 observations deleted due to missingness
##
## $avg_Delighted
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 495 495.4 1.354 0.247
## Residuals 106 38794 366.0
## 21 observations deleted due to missingness
##
## $avg_Satisfied
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 195 195.3 0.743 0.391
## Residuals 106 27859 262.8
## 21 observations deleted due to missingness
##
## $avg_Grateful
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 1 0.8 0.002 0.965
## Residuals 106 44325 418.2
## 21 observations deleted due to missingness
##
## $avg_careforAltruistic
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster3 1 0.006 0.00622 0.147 0.702
## Residuals 106 4.492 0.04237
## 21 observations deleted due to missingness
##
## Conclusion: Based on the ANOVA analyses, there are no significant differences between groups in terms of well-being or motivation to care.
Effect size differences between caring good and caring bad
## Variable Cluster1_Mean Cluster2_Mean Cohen_d
## 1 Unhappy -0.296 0.485 -1.638918
## 2 NoEnergy -0.270 0.371 -1.136592
## 3 AtEase 0.256 -0.372 1.416942
## 4 Optimistic 0.297 -0.476 1.811960
## 5 Satisfied 0.337 -0.386 1.708506
Variable | Cluster1_Mean | Cluster2_Mean | Cohen_d |
---|---|---|---|
Unhappy | -0.296 | 0.485 | -1.638918 |
NoEnergy | -0.270 | 0.371 | -1.136592 |
AtEase | 0.256 | -0.372 | 1.416942 |
Optimistic | 0.297 | -0.476 | 1.811959 |
Satisfied | 0.337 | -0.386 | 1.708506 |