Charitable Giving Pilot – Factor Analysis and EDA
2023-05-10
Summarizing Emotions by Valence
# Positive Affect
pa_pre_df <- select(fa, contains("pos_"), -contains("post"), -contains("mean"))
pa_pre_df$pre_1_pos_inspired <- as.numeric(pa_pre_df$pre_1_pos_inspired)
pa_pre_df$pre_2_pos_hopeful <- as.numeric(pa_pre_df$pre_2_pos_hopeful)
pa_post_df <- select(fa, contains("pos_") & contains("post"))
psych::alpha(pa_pre_df)##
## Reliability analysis
## Call: psych::alpha(x = pa_pre_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.95 0.57 18 0.0021 3 0.95 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.95 0.95
## Duhachek 0.94 0.95 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## pre_1_pos_inspired 0.94 0.94 0.95 0.57 16 0.0023
## pre_2_pos_hopeful 0.94 0.94 0.95 0.57 16 0.0023
## pre_3_pos_purposeful 0.94 0.94 0.95 0.57 16 0.0023
## pre_4_pos_happy 0.94 0.94 0.95 0.57 16 0.0023
## pre_9_pos_interested 0.94 0.94 0.95 0.57 16 0.0023
## pre_10_pos_excited 0.94 0.94 0.95 0.58 16 0.0022
## pre_12_pos_strong 0.94 0.94 0.95 0.57 16 0.0023
## pre_15_pos_enthusiastic 0.94 0.94 0.94 0.56 16 0.0023
## pre_16_pos_proud 0.94 0.94 0.95 0.57 16 0.0023
## pre_18_pos_alert 0.95 0.95 0.95 0.60 18 0.0020
## pre_21_pos_determined 0.94 0.94 0.95 0.57 16 0.0022
## pre_22_pos_attentive 0.95 0.95 0.95 0.60 18 0.0021
## pre_24_pos_active 0.94 0.94 0.95 0.57 16 0.0022
## var.r med.r
## pre_1_pos_inspired 0.0122 0.60
## pre_2_pos_hopeful 0.0126 0.60
## pre_3_pos_purposeful 0.0140 0.60
## pre_4_pos_happy 0.0126 0.60
## pre_9_pos_interested 0.0145 0.61
## pre_10_pos_excited 0.0115 0.60
## pre_12_pos_strong 0.0130 0.60
## pre_15_pos_enthusiastic 0.0122 0.60
## pre_16_pos_proud 0.0121 0.60
## pre_18_pos_alert 0.0083 0.63
## pre_21_pos_determined 0.0143 0.61
## pre_22_pos_attentive 0.0105 0.63
## pre_24_pos_active 0.0143 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pre_1_pos_inspired 1399 0.83 0.82 0.81 0.79 2.6 1.3
## pre_2_pos_hopeful 1399 0.83 0.82 0.81 0.79 3.0 1.3
## pre_3_pos_purposeful 1399 0.80 0.81 0.79 0.77 3.1 1.2
## pre_4_pos_happy 1399 0.81 0.81 0.80 0.77 3.1 1.2
## pre_9_pos_interested 1399 0.80 0.81 0.79 0.77 3.4 1.1
## pre_10_pos_excited 1399 0.78 0.77 0.76 0.73 2.3 1.3
## pre_12_pos_strong 1399 0.83 0.83 0.82 0.80 2.8 1.3
## pre_15_pos_enthusiastic 1399 0.85 0.84 0.84 0.82 2.7 1.3
## pre_16_pos_proud 1399 0.82 0.82 0.80 0.78 2.6 1.3
## pre_18_pos_alert 1399 0.58 0.59 0.55 0.52 3.5 1.2
## pre_21_pos_determined 1399 0.79 0.79 0.77 0.75 3.3 1.2
## pre_22_pos_attentive 1399 0.62 0.64 0.60 0.57 3.9 1.0
## pre_24_pos_active 1399 0.78 0.78 0.76 0.74 2.9 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pre_1_pos_inspired 0.25 0.23 0.25 0.17 0.10 0
## pre_2_pos_hopeful 0.15 0.20 0.28 0.23 0.14 0
## pre_3_pos_purposeful 0.12 0.18 0.30 0.26 0.14 0
## pre_4_pos_happy 0.12 0.21 0.28 0.26 0.13 0
## pre_9_pos_interested 0.06 0.14 0.31 0.32 0.18 0
## pre_10_pos_excited 0.35 0.27 0.19 0.13 0.07 0
## pre_12_pos_strong 0.18 0.21 0.29 0.20 0.11 0
## pre_15_pos_enthusiastic 0.21 0.25 0.26 0.18 0.10 0
## pre_16_pos_proud 0.30 0.19 0.26 0.16 0.09 0
## pre_18_pos_alert 0.08 0.11 0.26 0.35 0.20 0
## pre_21_pos_determined 0.11 0.17 0.25 0.28 0.18 0
## pre_22_pos_attentive 0.03 0.07 0.21 0.38 0.30 0
## pre_24_pos_active 0.18 0.21 0.29 0.20 0.12 0psych::alpha(pa_post_df)##
## Reliability analysis
## Call: psych::alpha(x = pa_post_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.96 0.62 22 0.0017 3.1 1 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.96 0.96
## Duhachek 0.95 0.96 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## post_1_pos_inspired 0.95 0.95 0.96 0.62 19 0.0019
## post_2_pos_hopeful 0.95 0.95 0.96 0.62 19 0.0019
## post_3_pos_purposeful 0.95 0.95 0.96 0.62 19 0.0019
## post_4_pos_happy 0.95 0.95 0.96 0.62 20 0.0019
## post_9_pos_interested 0.95 0.95 0.96 0.62 20 0.0019
## post_10_pos_excited 0.95 0.95 0.96 0.62 20 0.0019
## post_12_pos_strong 0.95 0.95 0.96 0.62 20 0.0019
## post_15_pos_enthusiastic 0.95 0.95 0.96 0.62 19 0.0019
## post_16_pos_proud 0.95 0.95 0.96 0.62 20 0.0019
## post_18_pos_alert 0.96 0.96 0.96 0.65 23 0.0016
## post_21_pos_determined 0.95 0.95 0.96 0.62 20 0.0019
## post_22_pos_attentive 0.96 0.96 0.96 0.64 22 0.0017
## post_24_pos_active 0.95 0.95 0.96 0.63 20 0.0018
## var.r med.r
## post_1_pos_inspired 0.012 0.64
## post_2_pos_hopeful 0.012 0.64
## post_3_pos_purposeful 0.013 0.65
## post_4_pos_happy 0.012 0.65
## post_9_pos_interested 0.014 0.66
## post_10_pos_excited 0.011 0.65
## post_12_pos_strong 0.013 0.64
## post_15_pos_enthusiastic 0.012 0.64
## post_16_pos_proud 0.011 0.65
## post_18_pos_alert 0.007 0.68
## post_21_pos_determined 0.014 0.66
## post_22_pos_attentive 0.011 0.68
## post_24_pos_active 0.014 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## post_1_pos_inspired 1399 0.86 0.86 0.85 0.84 2.9 1.3
## post_2_pos_hopeful 1399 0.86 0.86 0.85 0.83 3.1 1.3
## post_3_pos_purposeful 1399 0.85 0.85 0.84 0.82 3.2 1.2
## post_4_pos_happy 1399 0.84 0.83 0.82 0.81 3.1 1.3
## post_9_pos_interested 1399 0.83 0.83 0.82 0.80 3.4 1.1
## post_10_pos_excited 1399 0.82 0.82 0.81 0.79 2.5 1.3
## post_12_pos_strong 1399 0.84 0.84 0.82 0.81 2.8 1.3
## post_15_pos_enthusiastic 1399 0.86 0.85 0.85 0.83 2.8 1.3
## post_16_pos_proud 1399 0.83 0.83 0.81 0.80 2.7 1.3
## post_18_pos_alert 1399 0.61 0.62 0.58 0.55 3.5 1.2
## post_21_pos_determined 1399 0.82 0.82 0.80 0.79 3.3 1.2
## post_22_pos_attentive 1399 0.68 0.69 0.66 0.63 3.8 1.1
## post_24_pos_active 1399 0.80 0.80 0.77 0.76 2.9 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## post_1_pos_inspired 0.19 0.22 0.26 0.22 0.12 0
## post_2_pos_hopeful 0.14 0.20 0.25 0.25 0.16 0
## post_3_pos_purposeful 0.12 0.17 0.29 0.26 0.17 0
## post_4_pos_happy 0.14 0.20 0.27 0.24 0.15 0
## post_9_pos_interested 0.07 0.13 0.31 0.31 0.18 0
## post_10_pos_excited 0.30 0.24 0.22 0.16 0.08 0
## post_12_pos_strong 0.20 0.21 0.28 0.20 0.12 0
## post_15_pos_enthusiastic 0.22 0.21 0.25 0.19 0.11 0
## post_16_pos_proud 0.27 0.21 0.24 0.17 0.11 0
## post_18_pos_alert 0.08 0.11 0.27 0.33 0.21 0
## post_21_pos_determined 0.10 0.16 0.27 0.28 0.19 0
## post_22_pos_attentive 0.04 0.08 0.25 0.36 0.27 0
## post_24_pos_active 0.17 0.19 0.30 0.22 0.12 0fa$pos_affect_pre_mean <- rowMeans(pa_pre_df)
fa$pos_affect_post_mean <- rowMeans(pa_post_df)
#Negative Affect
na_pre_df <- select(fa, contains("neg_"), -contains("post"), -contains("mean"))
na_post_df <- select(fa, contains("neg_") & contains("post"), -contains("mean"))
psych::alpha(na_pre_df)##
## Reliability analysis
## Call: psych::alpha(x = na_pre_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.94 0.54 14 0.0026 1.3 0.58 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.93 0.94
## Duhachek 0.93 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## pre_5_neg_distressed 0.92 0.93 0.93 0.53 13 0.0029 0.0080
## pre_6_neg_sad 0.93 0.93 0.94 0.54 13 0.0028 0.0083
## pre_7_neg_angry 0.93 0.93 0.93 0.54 13 0.0028 0.0087
## pre_8_neg_guilty 0.93 0.93 0.94 0.55 14 0.0027 0.0082
## pre_11_neg_upset 0.92 0.93 0.93 0.53 12 0.0030 0.0079
## pre_13_neg_scared 0.92 0.93 0.93 0.53 13 0.0029 0.0073
## pre_14_neg_hostile 0.93 0.93 0.94 0.55 14 0.0027 0.0083
## pre_17_neg_irritable 0.93 0.93 0.94 0.55 13 0.0027 0.0093
## pre_19_neg_ashamed 0.93 0.93 0.93 0.55 13 0.0028 0.0090
## pre_20_neg_nervous 0.93 0.93 0.94 0.54 13 0.0028 0.0087
## pre_23_neg_jittery 0.93 0.93 0.94 0.56 14 0.0026 0.0073
## pre_25_neg_afraid 0.92 0.93 0.93 0.53 13 0.0029 0.0074
## med.r
## pre_5_neg_distressed 0.53
## pre_6_neg_sad 0.54
## pre_7_neg_angry 0.54
## pre_8_neg_guilty 0.56
## pre_11_neg_upset 0.51
## pre_13_neg_scared 0.54
## pre_14_neg_hostile 0.55
## pre_17_neg_irritable 0.55
## pre_19_neg_ashamed 0.54
## pre_20_neg_nervous 0.54
## pre_23_neg_jittery 0.56
## pre_25_neg_afraid 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pre_5_neg_distressed 1399 0.83 0.82 0.81 0.79 1.4 0.86
## pre_6_neg_sad 1399 0.79 0.78 0.75 0.73 1.5 0.91
## pre_7_neg_angry 1399 0.75 0.76 0.74 0.71 1.2 0.64
## pre_8_neg_guilty 1399 0.69 0.70 0.67 0.63 1.2 0.66
## pre_11_neg_upset 1399 0.84 0.84 0.84 0.81 1.3 0.79
## pre_13_neg_scared 1399 0.82 0.82 0.82 0.78 1.3 0.73
## pre_14_neg_hostile 1399 0.68 0.70 0.66 0.63 1.2 0.61
## pre_17_neg_irritable 1399 0.75 0.74 0.71 0.68 1.5 0.83
## pre_19_neg_ashamed 1399 0.74 0.74 0.72 0.69 1.2 0.69
## pre_20_neg_nervous 1399 0.78 0.76 0.74 0.72 1.5 0.90
## pre_23_neg_jittery 1399 0.66 0.65 0.60 0.58 1.4 0.79
## pre_25_neg_afraid 1399 0.82 0.82 0.81 0.78 1.3 0.73
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pre_5_neg_distressed 0.74 0.14 0.08 0.03 0.01 0
## pre_6_neg_sad 0.72 0.16 0.07 0.04 0.02 0
## pre_7_neg_angry 0.86 0.09 0.03 0.01 0.01 0
## pre_8_neg_guilty 0.84 0.11 0.03 0.02 0.01 0
## pre_11_neg_upset 0.80 0.12 0.05 0.03 0.01 0
## pre_13_neg_scared 0.85 0.09 0.04 0.02 0.01 0
## pre_14_neg_hostile 0.87 0.08 0.04 0.01 0.01 0
## pre_17_neg_irritable 0.69 0.20 0.08 0.03 0.01 0
## pre_19_neg_ashamed 0.85 0.09 0.04 0.01 0.01 0
## pre_20_neg_nervous 0.70 0.18 0.07 0.03 0.02 0
## pre_23_neg_jittery 0.74 0.17 0.06 0.03 0.01 0
## pre_25_neg_afraid 0.83 0.10 0.04 0.02 0.01 0psych::alpha(na_post_df)##
## Reliability analysis
## Call: psych::alpha(x = na_post_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.95 0.56 15 0.0024 1.3 0.58 0.55
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.94
## Duhachek 0.93 0.94 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## post_5_neg_distressed 0.93 0.93 0.94 0.55 14 0.0028 0.0082
## post_6_neg_sad 0.93 0.93 0.94 0.56 14 0.0026 0.0083
## post_7_neg_angry 0.93 0.93 0.94 0.56 14 0.0026 0.0078
## post_8_neg_guilty 0.93 0.94 0.94 0.57 15 0.0025 0.0080
## post_11_neg_upset 0.93 0.93 0.94 0.55 14 0.0027 0.0082
## post_13_neg_scared 0.93 0.93 0.94 0.55 14 0.0027 0.0070
## post_14_neg_hostile 0.93 0.93 0.94 0.56 14 0.0026 0.0085
## post_17_neg_irritable 0.93 0.93 0.94 0.56 14 0.0026 0.0087
## post_19_neg_ashamed 0.93 0.94 0.94 0.57 15 0.0026 0.0085
## post_20_neg_nervous 0.93 0.93 0.94 0.56 14 0.0026 0.0086
## post_23_neg_jittery 0.94 0.94 0.95 0.58 15 0.0025 0.0077
## post_25_neg_afraid 0.93 0.93 0.94 0.55 13 0.0027 0.0072
## med.r
## post_5_neg_distressed 0.53
## post_6_neg_sad 0.55
## post_7_neg_angry 0.55
## post_8_neg_guilty 0.56
## post_11_neg_upset 0.53
## post_13_neg_scared 0.54
## post_14_neg_hostile 0.56
## post_17_neg_irritable 0.55
## post_19_neg_ashamed 0.55
## post_20_neg_nervous 0.55
## post_23_neg_jittery 0.56
## post_25_neg_afraid 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## post_5_neg_distressed 1399 0.84 0.83 0.82 0.80 1.4 0.83
## post_6_neg_sad 1399 0.78 0.77 0.74 0.72 1.4 0.89
## post_7_neg_angry 1399 0.75 0.76 0.73 0.70 1.2 0.64
## post_8_neg_guilty 1399 0.70 0.71 0.67 0.64 1.3 0.68
## post_11_neg_upset 1399 0.84 0.83 0.82 0.80 1.3 0.80
## post_13_neg_scared 1399 0.83 0.83 0.83 0.80 1.2 0.71
## post_14_neg_hostile 1399 0.75 0.76 0.74 0.70 1.2 0.60
## post_17_neg_irritable 1399 0.77 0.76 0.74 0.71 1.4 0.84
## post_19_neg_ashamed 1399 0.72 0.73 0.70 0.67 1.2 0.64
## post_20_neg_nervous 1399 0.79 0.78 0.76 0.73 1.4 0.87
## post_23_neg_jittery 1399 0.68 0.68 0.64 0.62 1.3 0.75
## post_25_neg_afraid 1399 0.84 0.84 0.84 0.80 1.2 0.71
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## post_5_neg_distressed 0.78 0.12 0.06 0.03 0.02 0
## post_6_neg_sad 0.75 0.14 0.06 0.03 0.02 0
## post_7_neg_angry 0.86 0.08 0.03 0.02 0.01 0
## post_8_neg_guilty 0.83 0.11 0.04 0.02 0.01 0
## post_11_neg_upset 0.81 0.11 0.04 0.02 0.02 0
## post_13_neg_scared 0.86 0.07 0.04 0.01 0.01 0
## post_14_neg_hostile 0.88 0.07 0.04 0.01 0.01 0
## post_17_neg_irritable 0.74 0.16 0.06 0.03 0.01 0
## post_19_neg_ashamed 0.85 0.09 0.03 0.01 0.01 0
## post_20_neg_nervous 0.74 0.16 0.06 0.03 0.02 0
## post_23_neg_jittery 0.78 0.14 0.04 0.03 0.01 0
## post_25_neg_afraid 0.86 0.08 0.03 0.02 0.01 0fa$neg_affect_pre_mean <- rowMeans(na_pre_df)
fa$neg_affect_post_mean <- rowMeans(na_post_df)General Affect Change
fa$neg_emotion_change <- fa$neg_affect_post_mean - fa$neg_affect_pre_mean
fa$pos_emotion_change <- fa$pos_affect_post_mean - fa$pos_affect_pre_mean
hist(fa$neg_emotion_change)
hist(fa$pos_emotion_change)
test <- lm(fa$neg_emotion_change ~ fa$treat)
summary(test)##
## Call:
## lm(formula = fa$neg_emotion_change ~ fa$treat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.46785 -0.05257 0.02938 0.03215 1.69743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.032154 0.011591 -2.774 0.00561 **
## fa$treat 0.001386 0.008700 0.159 0.87346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2601 on 1397 degrees of freedom
## Multiple R-squared: 1.816e-05, Adjusted R-squared: -0.0006976
## F-statistic: 0.02537 on 1 and 1397 DF, p-value: 0.8735test <- lm(fa$pos_emotion_change ~ fa$treat)
summary(test)##
## Call:
## lm(formula = fa$pos_emotion_change ~ fa$treat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.22600 -0.20109 -0.02233 0.18353 2.31246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02233 0.01737 1.285 0.1989
## fa$treat 0.02491 0.01304 1.911 0.0563 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3899 on 1397 degrees of freedom
## Multiple R-squared: 0.002606, Adjusted R-squared: 0.001892
## F-statistic: 3.65 on 1 and 1397 DF, p-value: 0.05627Loading Packages
library(psych)
library(corrplot)
library(psych)
library(ggplot2)
library(readr)
library(here)
library(tidyverse)
library(GPArotation)Creating dataframe with regulatory focus items
Creating dataframe with regulatory focus items for exploratory factor analysis
rf <- select(fa, starts_with("regulatory_focus")) Examining Data Fitness for Factor Analysis
Kaiser-Meyer Test – “The test measures sampling adequacy for each variable in the model and the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. The higher the proportion, the higher the KMO-value, the more suited the data is to factor analysis”
Values above .60 considered adequate, above .9 considered “marvelous” :)
psych::KMO(rf) ## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = rf)
## Overall MSA = 0.93
## MSA for each item =
## regulatory_focus_1 regulatory_focus_2 regulatory_focus_3 regulatory_focus_4
## 0.93 0.94 0.96 0.95
## regulatory_focus_5 regulatory_focus_6 regulatory_focus_7 regulatory_focus_8
## 0.93 0.93 0.94 0.94
## regulatory_focus_9 regulatory_focus_10 regulatory_focus_11 regulatory_focus_12
## 0.94 0.96 0.84 0.84
## regulatory_focus_13 regulatory_focus_14 regulatory_focus_15 regulatory_focus_16
## 0.91 0.91 0.89 0.89
## regulatory_focus_17 regulatory_focus_18 regulatory_focus_19 regulatory_focus_20
## 0.96 0.96 0.96 0.95
## regulatory_focus_21 regulatory_focus_22 regulatory_focus_23
## 0.95 0.96 0.96Scree Plot
A scree plot is a visualization of the analysis performed to evaluate how many factors or dimensions best account for the data – the rule of thumb here is to look for the “elbow” – essentially where you are no longer gaining much in explanatory strength with an additional dimension or where eigenvalues fall below a certain threshold
ncol(rf)## [1] 23library(ggplot2)
fafitfree <- fa(rf,nfactors = ncol(rf), rotate = "none")
n_factors <- length(fafitfree$e.values)
scree <- data.frame(
Factor_n = as.factor(1:n_factors),
Eigenvalue = fafitfree$e.values)
ggplot(scree, aes(x = Factor_n, y = Eigenvalue, group = 1)) +
geom_point() + geom_line() +
xlab("Number of factors") +
ylab("Initial eigenvalue") +
labs( title = "Scree Plot",
subtitle = "(Based on the unreduced correlation matrix)")
Parellel
parallel <- fa.parallel(rf) # Suggests three components and 5 factors
## Parallel analysis suggests that the number of factors = 5 and the number of components = 3Parallel analysis suggests 3 components (largest majority explained by 3 clusters and 5 factors – data suggests five unique dimensions to the data)
Factor Analysis
Now we examine the item loadings – which items (questions being evaluated for the scale) load most heavily on each of these factors – what do these grouped items tell us about latent variables?
Items that do not load strongly onto any factors can be dicarded along with items that cross-load – have roughly equal weighting across multiple factors
rf_clean <- filter(rf, complete.cases(rf))
# Fitting data to 2 factors
factanal.3 <- factanal(rf_clean, factors=3, scores = c("regression"), rotation = "promax")
print(factanal.3, cutoff=0.2)##
## Call:
## factanal(x = rf_clean, factors = 3, scores = c("regression"), rotation = "promax")
##
## Uniquenesses:
## regulatory_focus_1 regulatory_focus_2 regulatory_focus_3 regulatory_focus_4
## 0.481 0.431 0.530 0.320
## regulatory_focus_5 regulatory_focus_6 regulatory_focus_7 regulatory_focus_8
## 0.336 0.372 0.421 0.372
## regulatory_focus_9 regulatory_focus_10 regulatory_focus_11 regulatory_focus_12
## 0.508 0.479 0.130 0.110
## regulatory_focus_13 regulatory_focus_14 regulatory_focus_15 regulatory_focus_16
## 0.372 0.396 0.585 0.577
## regulatory_focus_17 regulatory_focus_18 regulatory_focus_19 regulatory_focus_20
## 0.514 0.548 0.298 0.482
## regulatory_focus_21 regulatory_focus_22 regulatory_focus_23
## 0.355 0.444 0.746
##
## Loadings:
## Factor1 Factor2 Factor3
## regulatory_focus_1 0.652 -0.243
## regulatory_focus_2 0.656 -0.240 0.244
## regulatory_focus_3 0.563
## regulatory_focus_4 0.760
## regulatory_focus_5 0.772
## regulatory_focus_6 0.798
## regulatory_focus_7 0.756
## regulatory_focus_8 0.729
## regulatory_focus_9 0.231 0.574
## regulatory_focus_10 0.231 0.499
## regulatory_focus_11 0.948
## regulatory_focus_12 0.969
## regulatory_focus_13 0.795
## regulatory_focus_14 0.804
## regulatory_focus_15 0.496
## regulatory_focus_16 0.256 0.487
## regulatory_focus_17 0.702 0.213
## regulatory_focus_18 0.646 0.221
## regulatory_focus_19 0.897
## regulatory_focus_20 0.652 0.209
## regulatory_focus_21 0.861
## regulatory_focus_22 0.774
## regulatory_focus_23 0.551
##
## Factor1 Factor2 Factor3
## SS loadings 6.309 4.051 2.600
## Proportion Var 0.274 0.176 0.113
## Cumulative Var 0.274 0.450 0.563
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 -0.357 0.479
## Factor2 -0.357 1.000 -0.596
## Factor3 0.479 -0.596 1.000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 2858.02 on 187 degrees of freedom.
## The p-value is 0factanal.5 <- factanal(rf_clean, factors=5, scores = c("regression"), rotation = "varimax")
print(factanal.5, cutoff=0.2)##
## Call:
## factanal(x = rf_clean, factors = 5, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## regulatory_focus_1 regulatory_focus_2 regulatory_focus_3 regulatory_focus_4
## 0.386 0.314 0.503 0.249
## regulatory_focus_5 regulatory_focus_6 regulatory_focus_7 regulatory_focus_8
## 0.275 0.337 0.395 0.363
## regulatory_focus_9 regulatory_focus_10 regulatory_focus_11 regulatory_focus_12
## 0.498 0.467 0.113 0.069
## regulatory_focus_13 regulatory_focus_14 regulatory_focus_15 regulatory_focus_16
## 0.375 0.405 0.229 0.228
## regulatory_focus_17 regulatory_focus_18 regulatory_focus_19 regulatory_focus_20
## 0.471 0.499 0.277 0.430
## regulatory_focus_21 regulatory_focus_22 regulatory_focus_23
## 0.333 0.395 0.708
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## regulatory_focus_1 0.338 0.677
## regulatory_focus_2 0.335 0.720 0.231
## regulatory_focus_3 0.366 0.492 0.253 0.217
## regulatory_focus_4 0.456 0.693 0.226
## regulatory_focus_5 0.469 0.675 0.212
## regulatory_focus_6 0.205 0.220 0.743
## regulatory_focus_7 0.251 0.689
## regulatory_focus_8 0.254 0.314 0.677
## regulatory_focus_9 0.322 0.572
## regulatory_focus_10 0.280 0.330 0.550
## regulatory_focus_11 0.910 0.211
## regulatory_focus_12 0.938
## regulatory_focus_13 0.715 0.251
## regulatory_focus_14 0.712 0.231
## regulatory_focus_15 0.421 0.224 0.707
## regulatory_focus_16 0.245 0.409 0.704
## regulatory_focus_17 0.642 0.204
## regulatory_focus_18 0.579 0.279
## regulatory_focus_19 0.739 0.358
## regulatory_focus_20 0.641 0.216 0.225
## regulatory_focus_21 0.711 0.354
## regulatory_focus_22 0.692 0.283
## regulatory_focus_23 0.467 0.222
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 4.047 3.545 2.877 2.777 1.435
## Proportion Var 0.176 0.154 0.125 0.121 0.062
## Cumulative Var 0.176 0.330 0.455 0.576 0.638
##
## Test of the hypothesis that 5 factors are sufficient.
## The chi square statistic is 1019.35 on 148 degrees of freedom.
## The p-value is 5.01e-130Three Factor Solution does not distinguish between other-focused and self-focused promotion motives, but does distinguish between a general sense of responsibility towards others – more neutral valence about reducing generalized guilt for being priviledged not helping enough vs. an emphasis about feeling terrible
Five Factor solution dimension descriptions:
1 Other-focused promotion – create something good/beautiful 2 Self-focused promotion – feel sense of meaning/warm glow 3 Other-focused prevention – prevent harm to others 4 Self-focused prevention – ego protection 5 Self-focused prevention – responsibility maintenance
Creating Composite Scores based on Factor Analysis Results
Using the clusters identified in the analyses above (taking highest factor loadings while dropping items with high cross-loadings)
other_focused_positive_df <- select(rf, regulatory_focus_1, regulatory_focus_2, regulatory_focus_4, regulatory_focus_5) # 3 crossloads
psych::alpha(other_focused_positive_df)##
## Reliability analysis
## Call: psych::alpha(x = other_focused_positive_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.68 8.7 0.0045 7.5 1.5 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.90
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_1 0.88 0.88 0.83 0.71 7.3 0.0055 0.00267
## regulatory_focus_2 0.87 0.86 0.82 0.68 6.4 0.0063 0.00654
## regulatory_focus_4 0.86 0.86 0.80 0.67 6.0 0.0067 0.00245
## regulatory_focus_5 0.86 0.86 0.81 0.68 6.4 0.0063 0.00052
## med.r
## regulatory_focus_1 0.69
## regulatory_focus_2 0.66
## regulatory_focus_4 0.69
## regulatory_focus_5 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_1 1399 0.85 0.85 0.78 0.73 7.5 1.6
## regulatory_focus_2 1399 0.87 0.88 0.82 0.77 7.6 1.6
## regulatory_focus_4 1399 0.89 0.89 0.85 0.80 7.2 1.8
## regulatory_focus_5 1399 0.88 0.88 0.83 0.78 7.5 1.7
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_1 0.01 0.01 0.02 0.06 0.10 0.19 0.23 0.37 0
## regulatory_focus_2 0.01 0.01 0.01 0.07 0.11 0.17 0.23 0.38 0
## regulatory_focus_4 0.02 0.02 0.03 0.08 0.13 0.22 0.20 0.31 0
## regulatory_focus_5 0.02 0.01 0.02 0.08 0.11 0.18 0.22 0.36 0fa$other_focused_pos_mean <- rowMeans(other_focused_positive_df) # e.g. "My major goal for my charitable giving is to bring about something good in the world."
other_focused_negative_df <- select(rf, regulatory_focus_6, regulatory_focus_7, regulatory_focus_9, regulatory_focus_10) # 8 crossloads
psych::alpha(other_focused_negative_df)##
## Reliability analysis
## Call: psych::alpha(x = other_focused_negative_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.79 0.55 5 0.0073 5.4 2.1 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.83 0.85
## Duhachek 0.82 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_6 0.79 0.79 0.72 0.56 3.8 0.0097 0.0028
## regulatory_focus_7 0.79 0.79 0.72 0.55 3.7 0.0097 0.0028
## regulatory_focus_9 0.78 0.78 0.71 0.55 3.6 0.0101 0.0022
## regulatory_focus_10 0.79 0.79 0.72 0.56 3.8 0.0097 0.0016
## med.r
## regulatory_focus_6 0.56
## regulatory_focus_7 0.53
## regulatory_focus_9 0.53
## regulatory_focus_10 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_6 1399 0.80 0.81 0.72 0.65 6.0 2.4
## regulatory_focus_7 1399 0.82 0.82 0.73 0.66 5.4 2.6
## regulatory_focus_9 1399 0.83 0.82 0.74 0.68 4.5 2.6
## regulatory_focus_10 1399 0.81 0.81 0.72 0.66 5.6 2.6
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_6 0.10 0.06 0.07 0.14 0.15 0.18 0.14 0.17 0
## regulatory_focus_7 0.17 0.08 0.08 0.15 0.13 0.13 0.12 0.14 0
## regulatory_focus_9 0.23 0.13 0.11 0.17 0.10 0.10 0.08 0.08 0
## regulatory_focus_10 0.14 0.09 0.08 0.13 0.14 0.14 0.13 0.15 0fa$other_focused_neg_mean <- rowMeans(other_focused_negative_df) # e.g. "When I give to charity, it’s because I am focused on preventing potential harm to others."
self_focused_negative_df <- select(rf, regulatory_focus_11, regulatory_focus_12)
psych::alpha(self_focused_negative_df) ##
## Reliability analysis
## Call: psych::alpha(x = self_focused_negative_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.91 0.91 21 0.0025 3.5 2.5 0.91
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.95 0.96
## Duhachek 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_11 0.91 0.91 0.83 0.91 10 NA 0
## regulatory_focus_12 0.91 0.91 0.83 0.91 10 NA 0
## med.r
## regulatory_focus_11 0.91
## regulatory_focus_12 0.91
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_11 1399 0.98 0.98 0.93 0.91 3.5 2.6
## regulatory_focus_12 1399 0.98 0.98 0.93 0.91 3.5 2.6
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_11 0.42 0.12 0.09 0.10 0.10 0.08 0.04 0.05 0
## regulatory_focus_12 0.43 0.13 0.09 0.11 0.09 0.08 0.04 0.05 0fa$self_focused_neg_mean <- rowMeans(self_focused_negative_df) #e.g., "I give to charity because I feel terrible about myself when I don’t give."
# not giving as a signal of one's character -- integrity threat -- ego protection motive; although stronger correlation with other-focused prevention makes me wonder if this is more about feeling morally responsible for others in a way that makes one feel terrible, whereas the other may be more about alleviating guilt about privilege.
self_focused_positive_df <- select(rf, regulatory_focus_17, regulatory_focus_18, regulatory_focus_19) #
psych::alpha(self_focused_positive_df)##
## Reliability analysis
## Call: psych::alpha(x = self_focused_positive_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.8 0.73 0.57 3.9 0.0097 6.7 1.7 0.6
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.77 0.79 0.81
## Duhachek 0.77 0.79 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_17 0.73 0.75 0.60 0.60 3.0 0.014 NA
## regulatory_focus_18 0.74 0.75 0.60 0.60 3.0 0.014 NA
## regulatory_focus_19 0.67 0.68 0.51 0.51 2.1 0.017 NA
## med.r
## regulatory_focus_17 0.60
## regulatory_focus_18 0.60
## regulatory_focus_19 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_17 1399 0.83 0.83 0.69 0.61 6.7 2.1
## regulatory_focus_18 1399 0.85 0.83 0.69 0.62 6.3 2.3
## regulatory_focus_19 1399 0.85 0.87 0.77 0.69 7.0 1.8
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_17 0.05 0.03 0.05 0.10 0.17 0.21 0.18 0.22 0
## regulatory_focus_18 0.09 0.04 0.06 0.11 0.17 0.20 0.14 0.20 0
## regulatory_focus_19 0.03 0.02 0.03 0.09 0.17 0.22 0.20 0.25 0fa$self_focused_pos_mean <- rowMeans(self_focused_positive_df) # e.g. "I feel very privileged to be able to give to others."
self_focused_negative_guilt_df <- select(rf, regulatory_focus_13, regulatory_focus_14)
psych::alpha(self_focused_negative_guilt_df) ##
## Reliability analysis
## Call: psych::alpha(x = self_focused_negative_guilt_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.76 0.76 6.4 0.0073 3.2 2.4 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.86 0.88
## Duhachek 0.85 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_13 0.80 0.76 0.58 0.76 3.2 NA 0
## regulatory_focus_14 0.72 0.76 0.58 0.76 3.2 NA 0
## med.r
## regulatory_focus_13 0.76
## regulatory_focus_14 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_13 1399 0.94 0.94 0.82 0.76 3.5 2.6
## regulatory_focus_14 1399 0.94 0.94 0.82 0.76 3.0 2.5
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_13 0.44 0.11 0.09 0.11 0.10 0.07 0.05 0.04 0
## regulatory_focus_14 0.52 0.11 0.09 0.08 0.08 0.06 0.04 0.03 0fa$self_focused_negative_guilt_mean <- rowMeans(self_focused_negative_guilt_df) # # Reduce Guilt e.g. "I often give to charity to reduce the guilt I feel about not helping." -- "I give to charity because I feel guilty about my privileges. I owe it to others to give to charity."
# not giving as a signal of unmet goal -- goal/value motive# Self-Focus vs. Other-Focus
self_focus_df <- select(fa, regulatory_focus_13, regulatory_focus_14, regulatory_focus_17, regulatory_focus_18, regulatory_focus_19, regulatory_focus_11, regulatory_focus_12)
psych::alpha(self_focus_df)##
## Reliability analysis
## Call: psych::alpha(x = self_focus_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.9 0.44 5.5 0.006 4.8 1.7 0.3
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.85 0.86
## Duhachek 0.84 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_13 0.81 0.81 0.86 0.41 4.2 0.0079 0.055
## regulatory_focus_14 0.82 0.82 0.87 0.43 4.5 0.0075 0.051
## regulatory_focus_17 0.85 0.84 0.89 0.47 5.3 0.0060 0.069
## regulatory_focus_18 0.85 0.84 0.89 0.46 5.2 0.0059 0.071
## regulatory_focus_19 0.86 0.85 0.88 0.49 5.7 0.0058 0.056
## regulatory_focus_11 0.81 0.81 0.85 0.41 4.2 0.0080 0.047
## regulatory_focus_12 0.81 0.80 0.85 0.41 4.1 0.0081 0.045
## med.r
## regulatory_focus_13 0.29
## regulatory_focus_14 0.30
## regulatory_focus_17 0.37
## regulatory_focus_18 0.30
## regulatory_focus_19 0.37
## regulatory_focus_11 0.30
## regulatory_focus_12 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_13 1399 0.83 0.80 0.78 0.74 3.5 2.6
## regulatory_focus_14 1399 0.78 0.75 0.72 0.68 3.0 2.5
## regulatory_focus_17 1399 0.59 0.63 0.55 0.46 6.7 2.1
## regulatory_focus_18 1399 0.62 0.65 0.57 0.48 6.3 2.3
## regulatory_focus_19 1399 0.53 0.59 0.51 0.41 7.0 1.8
## regulatory_focus_11 1399 0.84 0.81 0.82 0.75 3.5 2.6
## regulatory_focus_12 1399 0.85 0.82 0.84 0.77 3.5 2.6
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_13 0.44 0.11 0.09 0.11 0.10 0.07 0.05 0.04 0
## regulatory_focus_14 0.52 0.11 0.09 0.08 0.08 0.06 0.04 0.03 0
## regulatory_focus_17 0.05 0.03 0.05 0.10 0.17 0.21 0.18 0.22 0
## regulatory_focus_18 0.09 0.04 0.06 0.11 0.17 0.20 0.14 0.20 0
## regulatory_focus_19 0.03 0.02 0.03 0.09 0.17 0.22 0.20 0.25 0
## regulatory_focus_11 0.42 0.12 0.09 0.10 0.10 0.08 0.04 0.05 0
## regulatory_focus_12 0.43 0.13 0.09 0.11 0.09 0.08 0.04 0.05 0other_focus_df <- select(fa, regulatory_focus_1:regulatory_focus_10)
other_focus_df <- select(other_focus_df, -regulatory_focus_3, -regulatory_focus_8)
psych::alpha(other_focus_df)##
## Reliability analysis
## Call: psych::alpha(x = other_focus_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.87 0.89 0.46 6.7 0.0057 6.4 1.5 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.86 0.87
## Duhachek 0.85 0.86 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regulatory_focus_1 0.85 0.86 0.87 0.46 5.9 0.0062 0.022
## regulatory_focus_2 0.84 0.85 0.87 0.45 5.7 0.0063 0.022
## regulatory_focus_4 0.84 0.84 0.86 0.44 5.4 0.0065 0.023
## regulatory_focus_5 0.84 0.85 0.87 0.44 5.6 0.0064 0.023
## regulatory_focus_6 0.83 0.85 0.88 0.45 5.8 0.0069 0.032
## regulatory_focus_7 0.84 0.86 0.88 0.47 6.3 0.0064 0.027
## regulatory_focus_9 0.84 0.86 0.88 0.47 6.3 0.0065 0.026
## regulatory_focus_10 0.84 0.86 0.88 0.46 6.0 0.0068 0.031
## med.r
## regulatory_focus_1 0.41
## regulatory_focus_2 0.41
## regulatory_focus_4 0.38
## regulatory_focus_5 0.41
## regulatory_focus_6 0.37
## regulatory_focus_7 0.41
## regulatory_focus_9 0.41
## regulatory_focus_10 0.40
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regulatory_focus_1 1399 0.65 0.72 0.68 0.56 7.5 1.6
## regulatory_focus_2 1399 0.69 0.75 0.72 0.60 7.6 1.6
## regulatory_focus_4 1399 0.74 0.80 0.78 0.67 7.2 1.8
## regulatory_focus_5 1399 0.71 0.76 0.74 0.62 7.5 1.7
## regulatory_focus_6 1399 0.77 0.73 0.68 0.66 6.0 2.4
## regulatory_focus_7 1399 0.72 0.66 0.60 0.58 5.4 2.6
## regulatory_focus_9 1399 0.72 0.66 0.61 0.59 4.5 2.6
## regulatory_focus_10 1399 0.75 0.71 0.65 0.64 5.6 2.6
##
## Non missing response frequency for each item
## 1 3 4 5 6 7 8 9 miss
## regulatory_focus_1 0.01 0.01 0.02 0.06 0.10 0.19 0.23 0.37 0
## regulatory_focus_2 0.01 0.01 0.01 0.07 0.11 0.17 0.23 0.38 0
## regulatory_focus_4 0.02 0.02 0.03 0.08 0.13 0.22 0.20 0.31 0
## regulatory_focus_5 0.02 0.01 0.02 0.08 0.11 0.18 0.22 0.36 0
## regulatory_focus_6 0.10 0.06 0.07 0.14 0.15 0.18 0.14 0.17 0
## regulatory_focus_7 0.17 0.08 0.08 0.15 0.13 0.13 0.12 0.14 0
## regulatory_focus_9 0.23 0.13 0.11 0.17 0.10 0.10 0.08 0.08 0
## regulatory_focus_10 0.14 0.09 0.08 0.13 0.14 0.14 0.13 0.15 0fa$other_focus_mean <- rowMeans(other_focus_df)
fa$self_focus_mean <- rowMeans(self_focus_df)Response Bias Index
Here we are creating an index of Response Bias (a measure of people’s general tendency to agree or disagree with survey items) by using a previously validated approach in which we compute a mean score using a selection of items with matched pairs of statements with conceptually opposite meanings such that on a 1-5 scale, a perfectly consistent responding would have a mean of 3 – the midpoint on the scale, a tendency to disagree > 3 and a tendency to disagree will be representend by a mean < 3 – or when standardized – mean of 0 etc.
Since this scale uses validated personality measure scales we also get Big Five personality trait measures so we’ll start with creating these variables.
extraversion_df <- select(fa, contains("bfi_e"))
psych::alpha(extraversion_df)##
## Reliability analysis
## Call: psych::alpha(x = extraversion_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.75 0.46 3.5 0.0097 2.9 1 0.43
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.78 0.8
## Duhachek 0.76 0.78 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## bfi_e_social_Recoded 0.68 0.68 0.59 0.41 2.1 0.015 0.00047
## bfi_e_assertive 0.76 0.76 0.72 0.51 3.1 0.011 0.03898
## bfi_e_energy 0.78 0.77 0.73 0.53 3.4 0.011 0.03037
## bfi_e_social_acq 0.67 0.67 0.57 0.40 2.0 0.015 0.00119
## med.r
## bfi_e_social_Recoded 0.42
## bfi_e_assertive 0.42
## bfi_e_energy 0.44
## bfi_e_social_acq 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_e_social_Recoded 1399 0.83 0.82 0.78 0.66 2.5 1.3
## bfi_e_assertive 1399 0.73 0.73 0.57 0.51 2.9 1.3
## bfi_e_energy 1399 0.69 0.71 0.53 0.47 3.2 1.2
## bfi_e_social_acq 1399 0.84 0.84 0.81 0.69 2.9 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_e_social_Recoded 0.29 0.32 0.13 0.15 0.11 0
## bfi_e_assertive 0.18 0.23 0.20 0.27 0.12 0
## bfi_e_energy 0.11 0.23 0.21 0.31 0.15 0
## bfi_e_social_acq 0.18 0.26 0.17 0.25 0.13 0fa$bfi_extraversion <- rowMeans(extraversion_df)
openness_df <- select(fa, contains("bfi_o"))
psych::alpha(openness_df)##
## Reliability analysis
## Call: psych::alpha(x = openness_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.61 0.51 0.34 1.5 0.018 3.9 0.82 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.57 0.6 0.64
## Duhachek 0.57 0.6 0.64
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## bfi_o_aesthetic 0.45 0.46 0.30 0.30 0.84 0.029 NA 0.30
## bfi_o_curious 0.50 0.50 0.34 0.34 1.02 0.026 NA 0.34
## bfi_o_creative 0.55 0.56 0.39 0.39 1.25 0.024 NA 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_o_aesthetic 1399 0.76 0.77 0.58 0.45 4.1 1.1
## bfi_o_curious 1399 0.78 0.75 0.54 0.42 3.8 1.2
## bfi_o_creative 1399 0.70 0.73 0.49 0.38 3.9 1.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_o_aesthetic 0.03 0.09 0.12 0.32 0.45 0
## bfi_o_curious 0.05 0.12 0.14 0.32 0.37 0
## bfi_o_creative 0.03 0.09 0.15 0.45 0.28 0fa$bfi_openness <- rowMeans(openness_df)
agreeableness_df <- select(fa, contains("bfi_a"))
psych::alpha(agreeableness_df)## Warning in psych::alpha(agreeableness_df): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( bfi_a_trust_R_acq ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = agreeableness_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.52 -0.27 0.14 -0.057 -0.21 0.064 3.5 0.48 -0.017
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.65 -0.52 -0.39
## Duhachek -0.65 -0.52 -0.40
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## bfi_a_compassion -1.42 -1.32 -0.337 -0.23 -0.57 0.110
## bfi_a_respect_Recode -0.87 -0.60 -0.069 -0.14 -0.38 0.091
## bfi_a_trust -1.06 -0.77 -0.171 -0.17 -0.44 0.100
## bfi_a_trust_R_acq 0.57 0.59 0.487 0.32 1.41 0.020
## var.r med.r
## bfi_a_compassion 0.1997 -0.49
## bfi_a_respect_Recode 0.2068 -0.32
## bfi_a_trust 0.1773 -0.32
## bfi_a_trust_R_acq 0.0022 0.30
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_a_compassion 1399 0.659 0.75 0.82 0.271 4.3 0.86
## bfi_a_respect_Recode 1399 0.602 0.60 0.54 -0.019 3.9 1.19
## bfi_a_trust 1399 0.641 0.64 0.68 0.030 3.5 1.19
## bfi_a_trust_R_acq 1399 -0.095 -0.16 -1.37 -0.601 2.5 1.26
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_a_compassion 0.01 0.04 0.10 0.38 0.47 0
## bfi_a_respect_Recode 0.02 0.18 0.11 0.28 0.41 0
## bfi_a_trust 0.08 0.15 0.19 0.38 0.20 0
## bfi_a_trust_R_acq 0.26 0.31 0.15 0.22 0.06 0fa$bfi_agreeableness <- rowMeans(agreeableness_df)
neuroticism_df <- select(fa, contains("bfi_n"))
psych::alpha(neuroticism_df)##
## Reliability analysis
## Call: psych::alpha(x = neuroticism_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.74 0.59 4.3 0.0086 2.7 1.2 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.81 0.83
## Duhachek 0.79 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## bfi_n_vol_Recoded 0.76 0.76 0.62 0.62 3.2 0.013 NA
## bfi_n_anxiety 0.73 0.74 0.58 0.58 2.8 0.014 NA
## bfi_n_depression 0.72 0.73 0.57 0.57 2.7 0.015 NA
## med.r
## bfi_n_vol_Recoded 0.62
## bfi_n_anxiety 0.58
## bfi_n_depression 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_n_vol_Recoded 1399 0.82 0.84 0.71 0.64 2.3 1.2
## bfi_n_anxiety 1399 0.87 0.86 0.74 0.67 3.2 1.5
## bfi_n_depression 1399 0.87 0.86 0.75 0.68 2.6 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_n_vol_Recoded 0.31 0.33 0.16 0.14 0.06 0
## bfi_n_anxiety 0.18 0.19 0.10 0.29 0.23 0
## bfi_n_depression 0.32 0.22 0.11 0.24 0.12 0fa$bfi_neuroticism <- rowMeans(neuroticism_df)
conscientiousness_df <- select(fa, contains("bfi_c"))
psych::alpha(conscientiousness_df)##
## Reliability analysis
## Call: psych::alpha(x = conscientiousness_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.79 0.5 4 0.0079 3.7 0.95 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.8 0.82
## Duhachek 0.79 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## bfi_c_org_Recoded 0.68 0.69 0.60 0.42 2.2 0.0138 0.0066
## bfi_c_productive_Rec 0.75 0.74 0.71 0.49 2.9 0.0103 0.0540
## bfi_c_responsibility 0.83 0.83 0.79 0.62 4.9 0.0081 0.0170
## bfi_c_org_acq 0.72 0.73 0.66 0.47 2.7 0.0114 0.0151
## med.r
## bfi_c_org_Recoded 0.43
## bfi_c_productive_Rec 0.37
## bfi_c_responsibility 0.61
## bfi_c_org_acq 0.43
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_c_org_Recoded 1399 0.89 0.87 0.85 0.76 3.7 1.32
## bfi_c_productive_Rec 1399 0.82 0.80 0.70 0.63 3.3 1.36
## bfi_c_responsibility 1399 0.61 0.68 0.48 0.44 4.3 0.85
## bfi_c_org_acq 1399 0.83 0.82 0.77 0.68 3.6 1.21
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_c_org_Recoded 0.07 0.18 0.10 0.27 0.38 0
## bfi_c_productive_Rec 0.10 0.27 0.11 0.28 0.24 0
## bfi_c_responsibility 0.01 0.04 0.07 0.36 0.52 0
## bfi_c_org_acq 0.06 0.16 0.15 0.35 0.28 0fa$bfi_conscientiousness <- rowMeans(conscientiousness_df)
acquiescence_items_df <- select(fa, contains("acq"))
acquiescence_items_df <- 6 -acquiescence_items_df # Reverse-coding conceptual opposites#Response Bias Dataframe – both acquiescence items reversecoded and their positive key opposites
rb_df <- select(fa, bfi_c_org_Recoded, bfi_a_trust, bfi_e_social_Recoded)
rb_df <- bind_cols(rb_df, acquiescence_items_df)
psych::alpha(rb_df) # A negative alpha here is exactly what we would want to see## Warning in psych::alpha(rb_df): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( bfi_c_org_acq bfi_e_social_acq ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = rb_df)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.58 -0.53 0.24 -0.061 -0.35 0.062 3.1 0.43 -0.044
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.71 -0.58 -0.45
## Duhachek -0.70 -0.58 -0.46
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## bfi_c_org_Recoded -0.452 -0.375 0.14 -0.0577 -0.273 0.060
## bfi_a_trust -1.440 -1.451 -0.16 -0.1343 -0.592 0.102
## bfi_e_social_Recoded -0.304 -0.294 0.20 -0.0476 -0.227 0.054
## bfi_c_org_acq 0.042 0.085 0.39 0.0183 0.093 0.039
## bfi_e_social_acq 0.027 0.020 0.36 0.0042 0.021 0.038
## bfi_a_trust_R_acq -1.974 -1.888 -0.35 -0.1504 -0.654 0.126
## var.r med.r
## bfi_c_org_Recoded 0.10 -0.090
## bfi_a_trust 0.12 -0.065
## bfi_e_social_Recoded 0.11 -0.065
## bfi_c_org_acq 0.11 0.102
## bfi_e_social_acq 0.11 0.102
## bfi_a_trust_R_acq 0.12 -0.110
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## bfi_c_org_Recoded 1399 0.361 0.322 0.25 -0.16 3.7 1.3
## bfi_a_trust 1399 0.675 0.698 0.70 0.28 3.5 1.2
## bfi_e_social_Recoded 1399 0.287 0.273 0.11 -0.24 2.5 1.3
## bfi_c_org_acq 1399 -0.085 -0.050 -0.53 -0.49 2.4 1.2
## bfi_e_social_acq 1399 0.024 0.019 -0.41 -0.44 3.1 1.3
## bfi_a_trust_R_acq 1399 0.774 0.777 0.87 0.41 3.5 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## bfi_c_org_Recoded 0.07 0.18 0.10 0.27 0.38 0
## bfi_a_trust 0.08 0.15 0.19 0.38 0.20 0
## bfi_e_social_Recoded 0.29 0.32 0.13 0.15 0.11 0
## bfi_c_org_acq 0.28 0.35 0.15 0.16 0.06 0
## bfi_e_social_acq 0.13 0.25 0.17 0.26 0.18 0
## bfi_a_trust_R_acq 0.06 0.22 0.15 0.31 0.26 0fa$response_bias_index <- rowMeans(rb_df)
hist(fa$response_bias_index) # I would almost describe this distribution as bimodal -- that there is a spike -- large group of people who respond in logically consistent ways using the the range of response options-- and also a slight skew -- another cluster with a slight acquiescence bias -- tendency to slightly agree
Other Focus vs. Self-Focus Motives as Predictors of Outcomes
test <- lm(fa$complete_vol ~ fa$other_focus_mean + fa$self_focus_mean + fa$bfi_agreeableness + fa$pos_affect_pre_mean + fa$neg_affect_pre_mean + fa$bfi_neuroticism + fa$bfi_conscientiousness + fa$bfi_openness + fa$response_bias_index)
summary(test)##
## Call:
## lm(formula = fa$complete_vol ~ fa$other_focus_mean + fa$self_focus_mean +
## fa$bfi_agreeableness + fa$pos_affect_pre_mean + fa$neg_affect_pre_mean +
## fa$bfi_neuroticism + fa$bfi_conscientiousness + fa$bfi_openness +
## fa$response_bias_index)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7323 -0.4369 -0.3036 0.5272 0.9278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.130182 0.167214 -0.779 0.436385
## fa$other_focus_mean 0.028456 0.010771 2.642 0.008339 **
## fa$self_focus_mean -0.005926 0.009648 -0.614 0.539140
## fa$bfi_agreeableness 0.035602 0.029680 1.200 0.230536
## fa$pos_affect_pre_mean -0.018379 0.017250 -1.065 0.286859
## fa$neg_affect_pre_mean -0.092290 0.025607 -3.604 0.000324 ***
## fa$bfi_neuroticism 0.051085 0.015577 3.280 0.001065 **
## fa$bfi_conscientiousness -0.038559 0.016940 -2.276 0.022987 *
## fa$bfi_openness 0.013878 0.016561 0.838 0.402160
## fa$response_bias_index 0.134615 0.035520 3.790 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4874 on 1389 degrees of freedom
## Multiple R-squared: 0.04096, Adjusted R-squared: 0.03475
## F-statistic: 6.592 on 9 and 1389 DF, p-value: 2.922e-09test <- lm(fa$link ~ fa$other_focus_mean + fa$self_focus_mean)
summary(test)##
## Call:
## lm(formula = fa$link ~ fa$other_focus_mean + fa$self_focus_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20138 -0.11612 -0.08871 -0.05311 0.98507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.061445 0.033680 -1.824 0.0683 .
## fa$other_focus_mean 0.032046 0.006164 5.199 2.3e-07 ***
## fa$self_focus_mean -0.010552 0.005444 -1.938 0.0528 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2887 on 1396 degrees of freedom
## Multiple R-squared: 0.02003, Adjusted R-squared: 0.01862
## F-statistic: 14.27 on 2 and 1396 DF, p-value: 7.36e-07test <- lm(fa$recontact ~ fa$other_focus_mean + fa$self_focus_mean)
summary(test)##
## Call:
## lm(formula = fa$recontact ~ fa$other_focus_mean + fa$self_focus_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8535 0.1550 0.2074 0.2430 0.3739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.597670 0.048705 12.271 < 2e-16 ***
## fa$other_focus_mean 0.023894 0.008913 2.681 0.00744 **
## fa$self_focus_mean 0.004534 0.007872 0.576 0.56475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4175 on 1396 degrees of freedom
## Multiple R-squared: 0.009688, Adjusted R-squared: 0.008269
## F-statistic: 6.828 on 2 and 1396 DF, p-value: 0.001119Correlation Table of Subscales
reg_focus_measures_df <- select(fa, complete_vol, donation, recontact, link, contains("focused"), -self_focused_neg_mean, self_focused_neg_mean, pos_emotion_change, neg_emotion_change, response_bias_index)
datamatrix <- cor(reg_focus_measures_df, use = "complete.obs")
corrplot(datamatrix, method="color", width = 1200, height = 800)## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "width" is not a graphical parameter## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "height" is not a graphical parameter## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "width" is not a graphical parameter## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "height" is not a graphical parameter## Warning in title(title, ...): "width" is not a graphical parameter## Warning in title(title, ...): "height" is not a graphical parameter
apaTables::apa.cor.table(reg_focus_measures_df)##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2
## 1. complete_vol 0.44 0.50
##
## 2. donation 0.69 0.46 .56**
## [.52, .59]
##
## 3. recontact 0.77 0.42 .20** .20**
## [.15, .25] [.15, .25]
##
## 4. link 0.09 0.29 .13** .19**
## [.08, .18] [.14, .24]
##
## 5. other_focused_pos_mean 7.45 1.46 .14** .20**
## [.09, .19] [.15, .25]
##
## 6. other_focused_neg_mean 5.38 2.07 .02 .13**
## [-.03, .08] [.08, .18]
##
## 7. self_focused_pos_mean 6.66 1.74 .10** .20**
## [.05, .16] [.15, .25]
##
## 8. self_focused_negative_guilt_mean 3.25 2.38 -.05 .01
## [-.10, .01] [-.04, .06]
##
## 9. self_focused_neg_mean 3.52 2.52 -.01 .06*
## [-.07, .04] [.01, .11]
##
## 10. pos_emotion_change 0.05 0.39 .18** .25**
## [.13, .23] [.20, .30]
##
## 11. neg_emotion_change -0.03 0.26 -.10** -.17**
## [-.15, -.04] [-.22, -.12]
##
## 12. response_bias_index 3.10 0.43 .08** .08**
## [.03, .14] [.03, .13]
##
## 3 4 5 6 7 8
##
##
##
##
##
##
##
##
## .13**
## [.08, .18]
##
## .11** .11**
## [.06, .17] [.06, .17]
##
## .06* .11** .47**
## [.01, .11] [.06, .16] [.42, .51]
##
## .11** .11** .62** .45**
## [.06, .16] [.06, .16] [.59, .66] [.40, .49]
##
## .02 -.05 .09** .45** .32**
## [-.03, .07] [-.10, .00] [.04, .14] [.41, .49] [.27, .37]
##
## .03 .01 .12** .50** .32** .77**
## [-.02, .08] [-.04, .06] [.07, .17] [.46, .54] [.27, .37] [.75, .79]
##
## .10** .05* .07** .04 .09** -.02
## [.05, .16] [.00, .11] [.02, .12] [-.02, .09] [.04, .14] [-.07, .03]
##
## .04 -.03 .01 -.01 -.02 -.02
## [-.02, .09] [-.09, .02] [-.04, .06] [-.06, .04] [-.07, .03] [-.07, .04]
##
## .08** .08** .17** .08** .19** -.02
## [.03, .13] [.02, .13] [.12, .22] [.03, .13] [.14, .24] [-.07, .03]
##
## 9 10 11
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .01
## [-.04, .07]
##
## -.03 -.15**
## [-.08, .02] [-.21, -.10]
##
## -.01 .04 .07**
## [-.06, .04] [-.01, .10] [.02, .12]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## Exploratory
# Predicting SAYING you will donate by from regulatory focus measures -- that is, what people SAY motivates them to give to charity
test <- glm(fa$donation ~ + fa$self_focused_neg_mean + fa$self_focused_negative_guilt_mean + fa$other_focused_neg_mean + fa$other_focused_pos_mean + fa$self_focused_pos_mean + fa$response_bias_index , family = binomial())
summary(test)##
## Call:
## glm(formula = fa$donation ~ +fa$self_focused_neg_mean + fa$self_focused_negative_guilt_mean +
## fa$other_focused_neg_mean + fa$other_focused_pos_mean + fa$self_focused_pos_mean +
## fa$response_bias_index, family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9733 -1.2761 0.7206 0.8768 1.7122
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.00429 0.49923 -4.015 5.95e-05 ***
## fa$self_focused_neg_mean 0.08146 0.04089 1.992 0.046359 *
## fa$self_focused_negative_guilt_mean -0.12078 0.04177 -2.892 0.003833 **
## fa$other_focused_neg_mean 0.04463 0.03827 1.166 0.243570
## fa$other_focused_pos_mean 0.13677 0.05456 2.507 0.012180 *
## fa$self_focused_pos_mean 0.16529 0.04608 3.587 0.000335 ***
## fa$response_bias_index 0.18511 0.14223 1.302 0.193073
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1742.2 on 1398 degrees of freedom
## Residual deviance: 1662.1 on 1392 degrees of freedom
## AIC: 1676.1
##
## Number of Fisher Scoring iterations: 4exp((test$coefficients))## (Intercept) fa$self_focused_neg_mean
## 0.1347560 1.0848691
## fa$self_focused_negative_guilt_mean fa$other_focused_neg_mean
## 0.8862299 1.0456385
## fa$other_focused_pos_mean fa$self_focused_pos_mean
## 1.1465606 1.1797408
## fa$response_bias_index
## 1.2033538# Predicting Actual Donation Behavior from regulatory focus measures -- that is, what people SAY motivates them to give to charity
test <- glm(fa$complete_vol ~ + fa$self_focused_neg_mean + fa$self_focused_negative_guilt_mean + fa$other_focused_neg_mean + fa$other_focused_pos_mean + fa$self_focused_pos_mean + fa$response_bias_index , family = binomial())
summary(test)##
## Call:
## glm(formula = fa$complete_vol ~ +fa$self_focused_neg_mean + fa$self_focused_negative_guilt_mean +
## fa$other_focused_neg_mean + fa$other_focused_pos_mean + fa$self_focused_pos_mean +
## fa$response_bias_index, family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4418 -1.0825 -0.8623 1.2271 1.8789
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.49827 0.47247 -5.288 1.24e-07 ***
## fa$self_focused_neg_mean 0.03879 0.03554 1.092 0.27504
## fa$self_focused_negative_guilt_mean -0.07659 0.03697 -2.072 0.03830 *
## fa$other_focused_neg_mean -0.04398 0.03490 -1.260 0.20760
## fa$other_focused_pos_mean 0.18297 0.05360 3.414 0.00064 ***
## fa$self_focused_pos_mean 0.05753 0.04363 1.319 0.18732
## fa$response_bias_index 0.26899 0.13102 2.053 0.04006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1916.5 on 1398 degrees of freedom
## Residual deviance: 1873.8 on 1392 degrees of freedom
## AIC: 1887.8
##
## Number of Fisher Scoring iterations: 4# People who say they give to do something good in the world were also actually more likely to finish the donation survey
# People who say they give out of guilt were actually LESS likely to complete the donation survey
# Separating by Pilot
p2 <- filter(fa, fa$pilot_one == 0)
p1 <- filter(fa, fa$pilot_one == 1)
test <- glm(fa$complete_vol ~ fa$treat , family = binomial())
summary(test)##
## Call:
## glm(formula = fa$complete_vol ~ fa$treat, family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.120 -1.073 -1.027 1.285 1.336
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.13603 0.08945 -1.521 0.1283
## fa$treat -0.11429 0.06751 -1.693 0.0905 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1916.5 on 1398 degrees of freedom
## Residual deviance: 1913.6 on 1397 degrees of freedom
## AIC: 1917.6
##
## Number of Fisher Scoring iterations: 4test <- lm(p2$donation ~ as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$donation ~ as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6720 -0.6468 0.3280 0.3480 0.3532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.646766 0.033532 19.288 <2e-16 ***
## as.factor(p2$treat)1 0.025234 0.045038 0.560 0.575
## as.factor(p2$treat)2 0.005234 0.045038 0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4754 on 698 degrees of freedom
## Multiple R-squared: 0.0005276, Adjusted R-squared: -0.002336
## F-statistic: 0.1842 on 2 and 698 DF, p-value: 0.8318test <- lm(p2$complete_vol ~ as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4400 -0.4229 -0.3960 0.5771 0.6040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42289 0.03486 12.132 <2e-16 ***
## as.factor(p2$treat)1 -0.02689 0.04682 -0.574 0.566
## as.factor(p2$treat)2 0.01711 0.04682 0.366 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4942 on 698 degrees of freedom
## Multiple R-squared: 0.001438, Adjusted R-squared: -0.001423
## F-statistic: 0.5025 on 2 and 698 DF, p-value: 0.6052test <- lm(p1$donation ~ as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$donation ~ as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7635 -0.6748 0.2364 0.2892 0.3252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.76355 0.03170 24.083 <2e-16 ***
## as.factor(p1$treat)1 -0.05270 0.04272 -1.234 0.2177
## as.factor(p1$treat)2 -0.08875 0.04283 -2.072 0.0386 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4517 on 695 degrees of freedom
## Multiple R-squared: 0.006158, Adjusted R-squared: 0.003298
## F-statistic: 2.153 on 2 and 695 DF, p-value: 0.1169test <- lm(p1$complete_vol ~ as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$complete_vol ~ as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5271 -0.4498 -0.3943 0.5502 0.6057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.52709 0.03481 15.141 < 2e-16 ***
## as.factor(p1$treat)1 -0.07729 0.04690 -1.648 0.09981 .
## as.factor(p1$treat)2 -0.13278 0.04703 -2.823 0.00489 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.496 on 695 degrees of freedom
## Multiple R-squared: 0.01136, Adjusted R-squared: 0.008513
## F-statistic: 3.992 on 2 and 695 DF, p-value: 0.01888exp(test$coefficients)## (Intercept) as.factor(p1$treat)1 as.factor(p1$treat)2
## 1.6940017 0.9256173 0.8756536test <- lm(p2$change_guilty ~ as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$change_guilty ~ as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.024 -0.024 0.000 0.016 3.000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.409e-16 4.117e-02 0.000 1.000
## as.factor(p2$treat)1 2.400e-02 5.530e-02 0.434 0.664
## as.factor(p2$treat)2 -1.600e-02 5.530e-02 -0.289 0.772
##
## Residual standard error: 0.5837 on 698 degrees of freedom
## Multiple R-squared: 0.00085, Adjusted R-squared: -0.002013
## F-statistic: 0.2969 on 2 and 698 DF, p-value: 0.7432test <- lm(p2$donation ~ p2$change_guilty)
summary(test)##
## Call:
## lm(formula = p2$donation ~ p2$change_guilty)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0498 -0.6579 0.3421 0.3421 0.6360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.65791 0.01782 36.926 < 2e-16 ***
## p2$change_guilty -0.09797 0.03058 -3.204 0.00142 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4717 on 699 degrees of freedom
## Multiple R-squared: 0.01447, Adjusted R-squared: 0.01306
## F-statistic: 10.26 on 1 and 699 DF, p-value: 0.001418test <- lm(p2$donation ~ p2$change_inspired)
summary(test)##
## Call:
## lm(formula = p2$donation ~ p2$change_inspired)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8483 -0.6406 0.2556 0.3594 0.6709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.64060 0.01789 35.803 < 2e-16 ***
## p2$change_inspired 0.10384 0.01965 5.284 1.69e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.466 on 699 degrees of freedom
## Multiple R-squared: 0.0384, Adjusted R-squared: 0.03703
## F-statistic: 27.92 on 1 and 699 DF, p-value: 1.694e-07test <- lm(p2$complete_vol ~ p2$change_guilty * as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$change_guilty * as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7485 -0.4229 -0.3981 0.5630 0.7177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42289 0.03465 12.203 <2e-16 ***
## p2$change_guilty -0.04688 0.06141 -0.763 0.446
## as.factor(p2$treat)1 -0.02478 0.04656 -0.532 0.595
## as.factor(p2$treat)2 0.01411 0.04656 0.303 0.762
## p2$change_guilty:as.factor(p2$treat)1 -0.04073 0.07504 -0.543 0.587
## p2$change_guilty:as.factor(p2$treat)2 -0.14067 0.09626 -1.461 0.144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4913 on 695 degrees of freedom
## Multiple R-squared: 0.01715, Adjusted R-squared: 0.01008
## F-statistic: 2.426 on 5 and 695 DF, p-value: 0.0341test <- lm(p2$complete_vol ~ p2$change_hopeful * as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$change_hopeful * as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6915 -0.4153 -0.3956 0.5686 0.8608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41533 0.03478 11.942 < 2e-16 ***
## p2$change_hopeful 0.13808 0.04520 3.055 0.00234 **
## as.factor(p2$treat)1 -0.01969 0.04667 -0.422 0.67326
## as.factor(p2$treat)2 0.02583 0.04684 0.551 0.58160
## p2$change_hopeful:as.factor(p2$treat)1 -0.12020 0.05895 -2.039 0.04184 *
## p2$change_hopeful:as.factor(p2$treat)2 -0.15011 0.06252 -2.401 0.01661 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4918 on 695 degrees of freedom
## Multiple R-squared: 0.01509, Adjusted R-squared: 0.008003
## F-statistic: 2.129 on 5 and 695 DF, p-value: 0.06016test <- lm(p1$complete_vol ~ p1$change_distressed * as.factor(p1$treat))
test <- lm(p2$complete_vol ~ p2$self_focused_negative_guilt_mean + p2$other_focused_pos_mean + p2$self_focused_pos_mean)
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$self_focused_negative_guilt_mean +
## p2$other_focused_pos_mean + p2$self_focused_pos_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5819 -0.4337 -0.2934 0.5363 0.9103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.049199 0.095837 0.513 0.6079
## p2$self_focused_negative_guilt_mean -0.021039 0.008079 -2.604 0.0094 **
## p2$other_focused_pos_mean 0.039460 0.016049 2.459 0.0142 *
## p2$self_focused_pos_mean 0.022068 0.013944 1.583 0.1140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4862 on 697 degrees of freedom
## Multiple R-squared: 0.03468, Adjusted R-squared: 0.03052
## F-statistic: 8.346 on 3 and 697 DF, p-value: 1.858e-05p2$treat <- as.factor(p2$treat)
test <- lm(p2$complete_vol ~ p2$other_focused_pos_mean*p2$change_inspired)
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$other_focused_pos_mean * p2$change_inspired)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7392 -0.4219 -0.2880 0.5200 0.9046
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.047333 0.094587 0.500
## p2$other_focused_pos_mean 0.048074 0.012495 3.847
## p2$change_inspired 0.042953 0.110394 0.389
## p2$other_focused_pos_mean:p2$change_inspired 0.004827 0.014634 0.330
## Pr(>|t|)
## (Intercept) 0.61694
## p2$other_focused_pos_mean 0.00013 ***
## p2$change_inspired 0.69733
## p2$other_focused_pos_mean:p2$change_inspired 0.74160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4836 on 697 degrees of freedom
## Multiple R-squared: 0.04508, Adjusted R-squared: 0.04097
## F-statistic: 10.97 on 3 and 697 DF, p-value: 4.817e-07summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$other_focused_pos_mean * p2$change_inspired)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7392 -0.4219 -0.2880 0.5200 0.9046
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.047333 0.094587 0.500
## p2$other_focused_pos_mean 0.048074 0.012495 3.847
## p2$change_inspired 0.042953 0.110394 0.389
## p2$other_focused_pos_mean:p2$change_inspired 0.004827 0.014634 0.330
## Pr(>|t|)
## (Intercept) 0.61694
## p2$other_focused_pos_mean 0.00013 ***
## p2$change_inspired 0.69733
## p2$other_focused_pos_mean:p2$change_inspired 0.74160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4836 on 697 degrees of freedom
## Multiple R-squared: 0.04508, Adjusted R-squared: 0.04097
## F-statistic: 10.97 on 3 and 697 DF, p-value: 4.817e-07Tests of “Background Factors” like mood and personality on heterogenaity of outcomes
test <- lm(fa$complete_vol ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset + fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile + fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud +fa$pre_8_neg_guilty + fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested + fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired + fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry + fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy + fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive + fa$pre_23_neg_jittery)
# Effects of Baseline mood and personality traits on eventual "donation" behavior
test <- lm(fa$complete_vol ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset + fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile + fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud +fa$pre_8_neg_guilty + fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested + fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired + fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry + fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy + fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive + fa$pre_23_neg_jittery + fa$bfi_neuroticism + fa$bfi_agreeableness + fa$bfi_openness + fa$bfi_conscientiousness + fa$bfi_extraversion)
summary(test) # Baseline interest and distress predict completing the survey as well as neuroticism and agreeableness##
## Call:
## lm(formula = fa$complete_vol ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset +
## fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile +
## fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud + fa$pre_8_neg_guilty +
## fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested +
## fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired +
## fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry +
## fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy +
## fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive +
## fa$pre_23_neg_jittery + fa$bfi_neuroticism + fa$bfi_agreeableness +
## fa$bfi_openness + fa$bfi_conscientiousness + fa$bfi_extraversion)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7744 -0.4272 -0.2928 0.5218 0.9931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.192425 0.152287 1.264 0.2066
## fa$pre_10_pos_excited -0.026480 0.017729 -1.494 0.1355
## fa$pre_11_neg_upset -0.053971 0.030286 -1.782 0.0750 .
## fa$pre_12_pos_strong 0.002929 0.018116 0.162 0.8716
## fa$pre_13_neg_scared -0.032079 0.028937 -1.109 0.2678
## fa$pre_14_neg_hostile -0.010756 0.030986 -0.347 0.7285
## fa$pre_15_pos_enthusiastic 0.008031 0.019047 0.422 0.6734
## fa$pre_16_pos_proud -0.016019 0.017509 -0.915 0.3604
## fa$pre_8_neg_guilty 0.007317 0.028589 0.256 0.7980
## fa$pre_17_neg_irritable -0.047794 0.022923 -2.085 0.0373 *
## fa$pre_2_pos_hopeful 0.017185 0.018035 0.953 0.3408
## fa$pre_9_pos_interested 0.041172 0.019622 2.098 0.0361 *
## fa$pre_18_pos_alert -0.004261 0.014721 -0.289 0.7723
## fa$pre_19_neg_ashamed 0.027219 0.029053 0.937 0.3490
## fa$pre_1_pos_inspired -0.017967 0.017845 -1.007 0.3142
## fa$pre_20_neg_nervous 0.014514 0.022499 0.645 0.5190
## fa$pre_21_pos_determined -0.012444 0.016835 -0.739 0.4599
## fa$pre_7_neg_angry 0.002007 0.032401 0.062 0.9506
## fa$pre_6_neg_sad 0.004481 0.023513 0.191 0.8489
## fa$pre_3_pos_purposeful 0.004325 0.017348 0.249 0.8032
## fa$pre_4_pos_happy -0.001094 0.020005 -0.055 0.9564
## fa$pre_5_neg_distressed 0.057083 0.026312 2.170 0.0302 *
## fa$pre_22_pos_attentive 0.014437 0.018201 0.793 0.4278
## fa$pre_23_neg_jittery -0.054995 0.021458 -2.563 0.0105 *
## fa$bfi_neuroticism 0.033610 0.015894 2.115 0.0346 *
## fa$bfi_agreeableness 0.073233 0.028950 2.530 0.0115 *
## fa$bfi_openness 0.022874 0.016843 1.358 0.1747
## fa$bfi_conscientiousness -0.030205 0.017453 -1.731 0.0837 .
## fa$bfi_extraversion -0.017086 0.015562 -1.098 0.2724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4884 on 1370 degrees of freedom
## Multiple R-squared: 0.05015, Adjusted R-squared: 0.03073
## F-statistic: 2.583 on 28 and 1370 DF, p-value: 1.284e-05test <- lm(fa$donation ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset + fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile + fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud +fa$pre_8_neg_guilty + fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested + fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired + fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry + fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy + fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive + fa$pre_23_neg_jittery)
summary(test) # Baseline mood on saying you'll "donate"##
## Call:
## lm(formula = fa$donation ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset +
## fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile +
## fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud + fa$pre_8_neg_guilty +
## fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested +
## fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired +
## fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry +
## fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy +
## fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive +
## fa$pre_23_neg_jittery)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9866 -0.5968 0.2599 0.3229 0.6532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.693902 0.069474 9.988 < 2e-16 ***
## fa$pre_10_pos_excited -0.006841 0.016666 -0.411 0.68150
## fa$pre_11_neg_upset -0.049800 0.028460 -1.750 0.08037 .
## fa$pre_12_pos_strong 0.011311 0.016786 0.674 0.50053
## fa$pre_13_neg_scared -0.017229 0.027237 -0.633 0.52711
## fa$pre_14_neg_hostile -0.010621 0.028938 -0.367 0.71366
## fa$pre_15_pos_enthusiastic -0.011515 0.017861 -0.645 0.51922
## fa$pre_16_pos_proud -0.020144 0.016337 -1.233 0.21777
## fa$pre_8_neg_guilty -0.001630 0.026703 -0.061 0.95135
## fa$pre_17_neg_irritable -0.057856 0.021391 -2.705 0.00692 **
## fa$pre_2_pos_hopeful 0.040611 0.016929 2.399 0.01658 *
## fa$pre_9_pos_interested 0.037932 0.018484 2.052 0.04035 *
## fa$pre_18_pos_alert 0.005489 0.013842 0.397 0.69179
## fa$pre_19_neg_ashamed 0.032334 0.027348 1.182 0.23728
## fa$pre_1_pos_inspired -0.011679 0.016792 -0.696 0.48686
## fa$pre_20_neg_nervous 0.009171 0.020954 0.438 0.66169
## fa$pre_21_pos_determined -0.043291 0.015698 -2.758 0.00590 **
## fa$pre_7_neg_angry 0.024180 0.030474 0.793 0.42764
## fa$pre_6_neg_sad 0.003069 0.021886 0.140 0.88852
## fa$pre_3_pos_purposeful 0.036763 0.016294 2.256 0.02421 *
## fa$pre_4_pos_happy -0.011461 0.018540 -0.618 0.53656
## fa$pre_5_neg_distressed 0.071823 0.024737 2.904 0.00375 **
## fa$pre_22_pos_attentive -0.020267 0.017102 -1.185 0.23619
## fa$pre_23_neg_jittery -0.032604 0.020019 -1.629 0.10361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4603 on 1375 degrees of freedom
## Multiple R-squared: 0.03391, Adjusted R-squared: 0.01775
## F-statistic: 2.099 on 23 and 1375 DF, p-value: 0.001773test <- lm(fa$donation ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset + fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile + fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud +fa$pre_8_neg_guilty + fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested + fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired + fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry + fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy + fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive + fa$pre_23_neg_jittery +
fa$post_10_pos_excited + fa$post_11_neg_upset + fa$post_12_pos_strong + fa$post_13_neg_scared + fa$post_14_neg_hostile + fa$post_15_pos_enthusiastic + fa$post_16_pos_proud +fa$post_8_neg_guilty + fa$post_17_neg_irritable + fa$post_2_pos_hopeful + fa$post_9_pos_interested + fa$post_18_pos_alert + fa$post_19_neg_ashamed + fa$post_1_pos_inspired + fa$post_20_neg_nervous + fa$post_21_pos_determined + fa$post_7_neg_angry + fa$post_6_neg_sad + fa$post_3_pos_purposeful + fa$post_4_pos_happy + fa$post_5_neg_distressed + fa$post_18_pos_alert + fa$post_22_pos_attentive + fa$post_23_neg_jittery)
test <- lm(fa$complete_vol ~ fa$pre_10_pos_excited + fa$pre_11_neg_upset + fa$pre_12_pos_strong + fa$pre_13_neg_scared + fa$pre_14_neg_hostile + fa$pre_15_pos_enthusiastic + fa$pre_16_pos_proud +fa$pre_8_neg_guilty + fa$pre_17_neg_irritable + fa$pre_2_pos_hopeful + fa$pre_9_pos_interested + fa$pre_18_pos_alert + fa$pre_19_neg_ashamed + fa$pre_1_pos_inspired + fa$pre_20_neg_nervous + fa$pre_21_pos_determined + fa$pre_7_neg_angry + fa$pre_6_neg_sad + fa$pre_3_pos_purposeful + fa$pre_4_pos_happy + fa$pre_5_neg_distressed + fa$pre_18_pos_alert + fa$pre_22_pos_attentive + fa$pre_23_neg_jittery +
fa$post_10_pos_excited + fa$post_11_neg_upset + fa$post_12_pos_strong + fa$post_13_neg_scared + fa$post_14_neg_hostile + fa$post_15_pos_enthusiastic + fa$post_16_pos_proud +fa$post_8_neg_guilty + fa$post_17_neg_irritable + fa$post_2_pos_hopeful + fa$post_9_pos_interested + fa$post_18_pos_alert + fa$post_19_neg_ashamed + fa$post_1_pos_inspired + fa$post_20_neg_nervous + fa$post_21_pos_determined + fa$post_7_neg_angry + fa$post_6_neg_sad + fa$post_3_pos_purposeful + fa$post_4_pos_happy + fa$post_5_neg_distressed + fa$post_18_pos_alert + fa$post_22_pos_attentive + fa$post_23_neg_jittery)
######## Pilot 1
test <- lm(p1$complete_vol ~ p1$pre_10_pos_excited + p1$pre_11_neg_upset + p1$pre_12_pos_strong + p1$pre_13_neg_scared + p1$pre_14_neg_hostile + p1$pre_15_pos_enthusiastic + p1$pre_16_pos_proud +p1$pre_8_neg_guilty + p1$pre_17_neg_irritable + p1$pre_2_pos_hopeful + p1$pre_9_pos_interested + p1$pre_18_pos_alert + p1$pre_19_neg_ashamed + p1$pre_1_pos_inspired + p1$pre_20_neg_nervous + p1$pre_21_pos_determined + p1$pre_7_neg_angry + p1$pre_6_neg_sad + p1$pre_3_pos_purposeful + p1$pre_4_pos_happy + p1$pre_5_neg_distressed + p1$pre_18_pos_alert + p1$pre_22_pos_attentive + p1$pre_23_neg_jittery +
p1$post_10_pos_excited + p1$post_11_neg_upset + p1$post_12_pos_strong + p1$post_13_neg_scared + p1$post_14_neg_hostile + p1$post_15_pos_enthusiastic + p1$post_16_pos_proud +p1$post_8_neg_guilty + p1$post_17_neg_irritable + p1$post_2_pos_hopeful + p1$post_9_pos_interested + p1$post_18_pos_alert + p1$post_19_neg_ashamed + p1$post_1_pos_inspired + p1$post_20_neg_nervous + p1$post_21_pos_determined + p1$post_7_neg_angry + p1$post_6_neg_sad + p1$post_3_pos_purposeful + p1$post_4_pos_happy + p1$post_5_neg_distressed + p1$post_18_pos_alert + p1$post_22_pos_attentive + p1$post_23_neg_jittery)
summary(test)##
## Call:
## lm(formula = p1$complete_vol ~ p1$pre_10_pos_excited + p1$pre_11_neg_upset +
## p1$pre_12_pos_strong + p1$pre_13_neg_scared + p1$pre_14_neg_hostile +
## p1$pre_15_pos_enthusiastic + p1$pre_16_pos_proud + p1$pre_8_neg_guilty +
## p1$pre_17_neg_irritable + p1$pre_2_pos_hopeful + p1$pre_9_pos_interested +
## p1$pre_18_pos_alert + p1$pre_19_neg_ashamed + p1$pre_1_pos_inspired +
## p1$pre_20_neg_nervous + p1$pre_21_pos_determined + p1$pre_7_neg_angry +
## p1$pre_6_neg_sad + p1$pre_3_pos_purposeful + p1$pre_4_pos_happy +
## p1$pre_5_neg_distressed + p1$pre_18_pos_alert + p1$pre_22_pos_attentive +
## p1$pre_23_neg_jittery + p1$post_10_pos_excited + p1$post_11_neg_upset +
## p1$post_12_pos_strong + p1$post_13_neg_scared + p1$post_14_neg_hostile +
## p1$post_15_pos_enthusiastic + p1$post_16_pos_proud + p1$post_8_neg_guilty +
## p1$post_17_neg_irritable + p1$post_2_pos_hopeful + p1$post_9_pos_interested +
## p1$post_18_pos_alert + p1$post_19_neg_ashamed + p1$post_1_pos_inspired +
## p1$post_20_neg_nervous + p1$post_21_pos_determined + p1$post_7_neg_angry +
## p1$post_6_neg_sad + p1$post_3_pos_purposeful + p1$post_4_pos_happy +
## p1$post_5_neg_distressed + p1$post_18_pos_alert + p1$post_22_pos_attentive +
## p1$post_23_neg_jittery)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0480 -0.4132 -0.1735 0.4594 0.9775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.438497 0.104606 4.192 3.15e-05 ***
## p1$pre_10_pos_excited -0.048647 0.028883 -1.684 0.09261 .
## p1$pre_11_neg_upset -0.047659 0.047817 -0.997 0.31928
## p1$pre_12_pos_strong -0.008741 0.029917 -0.292 0.77024
## p1$pre_13_neg_scared -0.086435 0.051425 -1.681 0.09328 .
## p1$pre_14_neg_hostile 0.093387 0.055438 1.685 0.09256 .
## p1$pre_15_pos_enthusiastic -0.008383 0.029493 -0.284 0.77633
## p1$pre_16_pos_proud -0.077429 0.028763 -2.692 0.00729 **
## p1$pre_8_neg_guilty -0.040712 0.048684 -0.836 0.40332
## p1$pre_17_neg_irritable -0.022071 0.037094 -0.595 0.55204
## p1$pre_2_pos_hopeful -0.013521 0.027889 -0.485 0.62798
## p1$pre_9_pos_interested 0.035097 0.028973 1.211 0.22619
## p1$pre_18_pos_alert 0.004856 0.024561 0.198 0.84332
## p1$pre_19_neg_ashamed 0.054741 0.047296 1.157 0.24753
## p1$pre_1_pos_inspired -0.028710 0.027941 -1.028 0.30455
## p1$pre_20_neg_nervous 0.052423 0.036874 1.422 0.15560
## p1$pre_21_pos_determined -0.028710 0.026892 -1.068 0.28610
## p1$pre_7_neg_angry 0.029779 0.055577 0.536 0.59227
## p1$pre_6_neg_sad 0.050215 0.037951 1.323 0.18625
## p1$pre_3_pos_purposeful 0.029653 0.026327 1.126 0.26044
## p1$pre_4_pos_happy 0.019064 0.033672 0.566 0.57147
## p1$pre_5_neg_distressed 0.002442 0.041893 0.058 0.95354
## p1$pre_22_pos_attentive -0.014527 0.030221 -0.481 0.63089
## p1$pre_23_neg_jittery 0.051851 0.039918 1.299 0.19442
## p1$post_10_pos_excited -0.001435 0.030603 -0.047 0.96261
## p1$post_11_neg_upset 0.038615 0.046430 0.832 0.40590
## p1$post_12_pos_strong -0.036509 0.030061 -1.215 0.22499
## p1$post_13_neg_scared 0.053304 0.058283 0.915 0.36076
## p1$post_14_neg_hostile -0.110640 0.061366 -1.803 0.07186 .
## p1$post_15_pos_enthusiastic 0.044178 0.028475 1.551 0.12128
## p1$post_16_pos_proud 0.031018 0.030013 1.033 0.30176
## p1$post_8_neg_guilty -0.047686 0.044245 -1.078 0.28154
## p1$post_17_neg_irritable 0.002693 0.042594 0.063 0.94961
## p1$post_2_pos_hopeful 0.047043 0.031161 1.510 0.13161
## p1$post_9_pos_interested 0.005939 0.030402 0.195 0.84517
## p1$post_18_pos_alert -0.020618 0.025881 -0.797 0.42595
## p1$post_19_neg_ashamed -0.083458 0.049372 -1.690 0.09143 .
## p1$post_1_pos_inspired 0.048821 0.030476 1.602 0.10965
## p1$post_20_neg_nervous -0.042558 0.039058 -1.090 0.27629
## p1$post_21_pos_determined -0.005219 0.029402 -0.178 0.85917
## p1$post_7_neg_angry -0.012607 0.050713 -0.249 0.80375
## p1$post_6_neg_sad 0.018989 0.038576 0.492 0.62270
## p1$post_3_pos_purposeful 0.034799 0.028472 1.222 0.22208
## p1$post_4_pos_happy -0.032841 0.033220 -0.989 0.32323
## p1$post_5_neg_distressed 0.050671 0.041906 1.209 0.22704
## p1$post_22_pos_attentive 0.024233 0.031072 0.780 0.43575
## p1$post_23_neg_jittery -0.087425 0.042915 -2.037 0.04204 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4828 on 651 degrees of freedom
## Multiple R-squared: 0.1227, Adjusted R-squared: 0.0607
## F-statistic: 1.979 on 46 and 651 DF, p-value: 0.0001888######## Pilot 2
# Pre AND Post emotions predicting actual donation behavior
test <- lm(p2$complete_vol ~ p2$pre_10_pos_excited + p2$pre_11_neg_upset + p2$pre_12_pos_strong + p2$pre_13_neg_scared + p2$pre_14_neg_hostile + p2$pre_15_pos_enthusiastic + p2$pre_16_pos_proud +p2$pre_8_neg_guilty + p2$pre_17_neg_irritable + p2$pre_2_pos_hopeful + p2$pre_9_pos_interested + p2$pre_18_pos_alert + p2$pre_19_neg_ashamed + p2$pre_1_pos_inspired + p2$pre_20_neg_nervous + p2$pre_21_pos_determined + p2$pre_7_neg_angry + p2$pre_6_neg_sad + p2$pre_3_pos_purposeful + p2$pre_4_pos_happy + p2$pre_5_neg_distressed + p2$pre_18_pos_alert + p2$pre_22_pos_attentive + p2$pre_23_neg_jittery +
p2$post_10_pos_excited + p2$post_11_neg_upset + p2$post_12_pos_strong + p2$post_13_neg_scared + p2$post_14_neg_hostile + p2$post_15_pos_enthusiastic + p2$post_16_pos_proud +p2$post_8_neg_guilty + p2$post_17_neg_irritable + p2$post_2_pos_hopeful + p2$post_9_pos_interested + p2$post_18_pos_alert + p2$post_19_neg_ashamed + p2$post_1_pos_inspired + p2$post_20_neg_nervous + p2$post_21_pos_determined + p2$post_7_neg_angry + p2$post_6_neg_sad + p2$post_3_pos_purposeful + p2$post_4_pos_happy + p2$post_5_neg_distressed + p2$post_18_pos_alert + p2$post_22_pos_attentive + p2$post_23_neg_jittery)
summary(test) # Baseline guilt (maybe related to neuroticism), increase in distress, increase in purpose and interest (curiosity) predict actual giving##
## Call:
## lm(formula = p2$complete_vol ~ p2$pre_10_pos_excited + p2$pre_11_neg_upset +
## p2$pre_12_pos_strong + p2$pre_13_neg_scared + p2$pre_14_neg_hostile +
## p2$pre_15_pos_enthusiastic + p2$pre_16_pos_proud + p2$pre_8_neg_guilty +
## p2$pre_17_neg_irritable + p2$pre_2_pos_hopeful + p2$pre_9_pos_interested +
## p2$pre_18_pos_alert + p2$pre_19_neg_ashamed + p2$pre_1_pos_inspired +
## p2$pre_20_neg_nervous + p2$pre_21_pos_determined + p2$pre_7_neg_angry +
## p2$pre_6_neg_sad + p2$pre_3_pos_purposeful + p2$pre_4_pos_happy +
## p2$pre_5_neg_distressed + p2$pre_18_pos_alert + p2$pre_22_pos_attentive +
## p2$pre_23_neg_jittery + p2$post_10_pos_excited + p2$post_11_neg_upset +
## p2$post_12_pos_strong + p2$post_13_neg_scared + p2$post_14_neg_hostile +
## p2$post_15_pos_enthusiastic + p2$post_16_pos_proud + p2$post_8_neg_guilty +
## p2$post_17_neg_irritable + p2$post_2_pos_hopeful + p2$post_9_pos_interested +
## p2$post_18_pos_alert + p2$post_19_neg_ashamed + p2$post_1_pos_inspired +
## p2$post_20_neg_nervous + p2$post_21_pos_determined + p2$post_7_neg_angry +
## p2$post_6_neg_sad + p2$post_3_pos_purposeful + p2$post_4_pos_happy +
## p2$post_5_neg_distressed + p2$post_18_pos_alert + p2$post_22_pos_attentive +
## p2$post_23_neg_jittery)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9778 -0.4134 -0.1765 0.4796 0.9186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.464927 0.112782 4.122 4.23e-05 ***
## p2$pre_10_pos_excited -0.002709 0.027961 -0.097 0.9228
## p2$pre_11_neg_upset -0.051492 0.043432 -1.186 0.2362
## p2$pre_12_pos_strong 0.012953 0.029550 0.438 0.6613
## p2$pre_13_neg_scared 0.020892 0.051432 0.406 0.6847
## p2$pre_14_neg_hostile -0.040300 0.044733 -0.901 0.3680
## p2$pre_15_pos_enthusiastic -0.009465 0.028624 -0.331 0.7410
## p2$pre_16_pos_proud -0.025447 0.031412 -0.810 0.4182
## p2$pre_8_neg_guilty 0.083257 0.041319 2.015 0.0443 *
## p2$pre_17_neg_irritable -0.031085 0.035209 -0.883 0.3776
## p2$pre_2_pos_hopeful 0.017435 0.029227 0.597 0.5510
## p2$pre_9_pos_interested -0.017182 0.032085 -0.536 0.5925
## p2$pre_18_pos_alert -0.009953 0.025046 -0.397 0.6912
## p2$pre_19_neg_ashamed 0.048883 0.045528 1.074 0.2834
## p2$pre_1_pos_inspired -0.040019 0.026397 -1.516 0.1300
## p2$pre_20_neg_nervous 0.031364 0.040272 0.779 0.4364
## p2$pre_21_pos_determined -0.020925 0.026382 -0.793 0.4280
## p2$pre_7_neg_angry 0.003855 0.046378 0.083 0.9338
## p2$pre_6_neg_sad -0.020133 0.037357 -0.539 0.5901
## p2$pre_3_pos_purposeful -0.062848 0.025455 -2.469 0.0138 *
## p2$pre_4_pos_happy -0.056645 0.033033 -1.715 0.0869 .
## p2$pre_5_neg_distressed 0.024224 0.041098 0.589 0.5558
## p2$pre_22_pos_attentive 0.014724 0.032844 0.448 0.6541
## p2$pre_23_neg_jittery -0.025450 0.036862 -0.690 0.4902
## p2$post_10_pos_excited -0.001002 0.030318 -0.033 0.9736
## p2$post_11_neg_upset -0.038517 0.040621 -0.948 0.3434
## p2$post_12_pos_strong -0.024759 0.030546 -0.811 0.4179
## p2$post_13_neg_scared -0.036496 0.051718 -0.706 0.4806
## p2$post_14_neg_hostile 0.017786 0.052360 0.340 0.7342
## p2$post_15_pos_enthusiastic 0.042426 0.029993 1.415 0.1577
## p2$post_16_pos_proud 0.040708 0.032563 1.250 0.2117
## p2$post_8_neg_guilty -0.030929 0.044803 -0.690 0.4902
## p2$post_17_neg_irritable -0.063556 0.037515 -1.694 0.0907 .
## p2$post_2_pos_hopeful 0.011672 0.031123 0.375 0.7078
## p2$post_9_pos_interested 0.065038 0.032177 2.021 0.0437 *
## p2$post_18_pos_alert -0.020785 0.027370 -0.759 0.4479
## p2$post_19_neg_ashamed -0.002479 0.051453 -0.048 0.9616
## p2$post_1_pos_inspired -0.010549 0.030823 -0.342 0.7323
## p2$post_20_neg_nervous -0.045220 0.043955 -1.029 0.3040
## p2$post_21_pos_determined 0.001739 0.027889 0.062 0.9503
## p2$post_7_neg_angry -0.030222 0.050327 -0.601 0.5484
## p2$post_6_neg_sad 0.010691 0.035645 0.300 0.7643
## p2$post_3_pos_purposeful 0.073746 0.029735 2.480 0.0134 *
## p2$post_4_pos_happy -0.012129 0.034051 -0.356 0.7218
## p2$post_5_neg_distressed 0.114187 0.047019 2.429 0.0154 *
## p2$post_22_pos_attentive 0.044671 0.034272 1.303 0.1929
## p2$post_23_neg_jittery -0.033526 0.042582 -0.787 0.4314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4741 on 654 degrees of freedom
## Multiple R-squared: 0.1389, Adjusted R-squared: 0.07836
## F-statistic: 2.294 on 46 and 654 DF, p-value: 5.269e-06# Pre AND Post emotions predicting saying they will donate
test <- lm(p2$donation ~ p2$pre_10_pos_excited + p2$pre_11_neg_upset + p2$pre_12_pos_strong + p2$pre_13_neg_scared + p2$pre_14_neg_hostile + p2$pre_15_pos_enthusiastic + p2$pre_16_pos_proud +p2$pre_8_neg_guilty + p2$pre_17_neg_irritable + p2$pre_2_pos_hopeful + p2$pre_9_pos_interested + p2$pre_18_pos_alert + p2$pre_19_neg_ashamed + p2$pre_1_pos_inspired + p2$pre_20_neg_nervous + p2$pre_21_pos_determined + p2$pre_7_neg_angry + p2$pre_6_neg_sad + p2$pre_3_pos_purposeful + p2$pre_4_pos_happy + p2$pre_5_neg_distressed + p2$pre_18_pos_alert + p2$pre_22_pos_attentive + p2$pre_23_neg_jittery +
p2$post_10_pos_excited + p2$post_11_neg_upset + p2$post_12_pos_strong + p2$post_13_neg_scared + p2$post_14_neg_hostile + p2$post_15_pos_enthusiastic + p2$post_16_pos_proud +p2$post_8_neg_guilty + p2$post_17_neg_irritable + p2$post_2_pos_hopeful + p2$post_9_pos_interested + p2$post_18_pos_alert + p2$post_19_neg_ashamed + p2$post_1_pos_inspired + p2$post_20_neg_nervous + p2$post_21_pos_determined + p2$post_7_neg_angry + p2$post_6_neg_sad + p2$post_3_pos_purposeful + p2$post_4_pos_happy + p2$post_5_neg_distressed + p2$post_18_pos_alert + p2$post_22_pos_attentive + p2$post_23_neg_jittery)
summary(test) # Baseline levels of pride slightly predict lower likelihood of saying they'll donate whereas baseline levels of shame slightly predict saying you will give --- the looks like an ego repair story -- Saying I'll give as a way to feel better about myself but if I already feel fine then I don't need this; Feeling proud more correlated with saying "yes I'll give"##
## Call:
## lm(formula = p2$donation ~ p2$pre_10_pos_excited + p2$pre_11_neg_upset +
## p2$pre_12_pos_strong + p2$pre_13_neg_scared + p2$pre_14_neg_hostile +
## p2$pre_15_pos_enthusiastic + p2$pre_16_pos_proud + p2$pre_8_neg_guilty +
## p2$pre_17_neg_irritable + p2$pre_2_pos_hopeful + p2$pre_9_pos_interested +
## p2$pre_18_pos_alert + p2$pre_19_neg_ashamed + p2$pre_1_pos_inspired +
## p2$pre_20_neg_nervous + p2$pre_21_pos_determined + p2$pre_7_neg_angry +
## p2$pre_6_neg_sad + p2$pre_3_pos_purposeful + p2$pre_4_pos_happy +
## p2$pre_5_neg_distressed + p2$pre_18_pos_alert + p2$pre_22_pos_attentive +
## p2$pre_23_neg_jittery + p2$post_10_pos_excited + p2$post_11_neg_upset +
## p2$post_12_pos_strong + p2$post_13_neg_scared + p2$post_14_neg_hostile +
## p2$post_15_pos_enthusiastic + p2$post_16_pos_proud + p2$post_8_neg_guilty +
## p2$post_17_neg_irritable + p2$post_2_pos_hopeful + p2$post_9_pos_interested +
## p2$post_18_pos_alert + p2$post_19_neg_ashamed + p2$post_1_pos_inspired +
## p2$post_20_neg_nervous + p2$post_21_pos_determined + p2$post_7_neg_angry +
## p2$post_6_neg_sad + p2$post_3_pos_purposeful + p2$post_4_pos_happy +
## p2$post_5_neg_distressed + p2$post_18_pos_alert + p2$post_22_pos_attentive +
## p2$post_23_neg_jittery)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1002 -0.4441 0.1719 0.3290 0.7591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.581314 0.105795 5.495 5.61e-08 ***
## p2$pre_10_pos_excited -0.016719 0.026229 -0.637 0.5241
## p2$pre_11_neg_upset -0.049607 0.040741 -1.218 0.2238
## p2$pre_12_pos_strong 0.021421 0.027720 0.773 0.4399
## p2$pre_13_neg_scared 0.047763 0.048246 0.990 0.3225
## p2$pre_14_neg_hostile 0.011570 0.041962 0.276 0.7829
## p2$pre_15_pos_enthusiastic -0.046121 0.026851 -1.718 0.0863 .
## p2$pre_16_pos_proud -0.052722 0.029466 -1.789 0.0740 .
## p2$pre_8_neg_guilty 0.036428 0.038760 0.940 0.3476
## p2$pre_17_neg_irritable -0.023069 0.033028 -0.698 0.4851
## p2$pre_2_pos_hopeful 0.033710 0.027417 1.230 0.2193
## p2$pre_9_pos_interested -0.018127 0.030097 -0.602 0.5472
## p2$pre_18_pos_alert -0.004708 0.023495 -0.200 0.8413
## p2$pre_19_neg_ashamed 0.078609 0.042708 1.841 0.0661 .
## p2$pre_1_pos_inspired -0.044822 0.024762 -1.810 0.0707 .
## p2$pre_20_neg_nervous 0.040073 0.037777 1.061 0.2892
## p2$pre_21_pos_determined -0.022166 0.024748 -0.896 0.3708
## p2$pre_7_neg_angry -0.040063 0.043505 -0.921 0.3575
## p2$pre_6_neg_sad 0.048324 0.035043 1.379 0.1684
## p2$pre_3_pos_purposeful 0.016817 0.023878 0.704 0.4815
## p2$pre_4_pos_happy -0.030229 0.030987 -0.976 0.3297
## p2$pre_5_neg_distressed 0.030276 0.038552 0.785 0.4325
## p2$pre_22_pos_attentive -0.027611 0.030809 -0.896 0.3705
## p2$pre_23_neg_jittery -0.027260 0.034578 -0.788 0.4308
## p2$post_10_pos_excited 0.042401 0.028439 1.491 0.1365
## p2$post_11_neg_upset -0.068383 0.038105 -1.795 0.0732 .
## p2$post_12_pos_strong -0.051803 0.028654 -1.808 0.0711 .
## p2$post_13_neg_scared -0.105360 0.048514 -2.172 0.0302 *
## p2$post_14_neg_hostile -0.009582 0.049117 -0.195 0.8454
## p2$post_15_pos_enthusiastic 0.014040 0.028135 0.499 0.6179
## p2$post_16_pos_proud 0.063640 0.030546 2.083 0.0376 *
## p2$post_8_neg_guilty -0.052176 0.042028 -1.241 0.2149
## p2$post_17_neg_irritable -0.104459 0.035191 -2.968 0.0031 **
## p2$post_2_pos_hopeful 0.040432 0.029195 1.385 0.1666
## p2$post_9_pos_interested 0.077341 0.030184 2.562 0.0106 *
## p2$post_18_pos_alert 0.002309 0.025674 0.090 0.9284
## p2$post_19_neg_ashamed -0.021205 0.048266 -0.439 0.6606
## p2$post_1_pos_inspired 0.028005 0.028914 0.969 0.3331
## p2$post_20_neg_nervous -0.058798 0.041232 -1.426 0.1543
## p2$post_21_pos_determined -0.022829 0.026162 -0.873 0.3832
## p2$post_7_neg_angry 0.077977 0.047209 1.652 0.0991 .
## p2$post_6_neg_sad 0.020943 0.033437 0.626 0.5313
## p2$post_3_pos_purposeful 0.051521 0.027893 1.847 0.0652 .
## p2$post_4_pos_happy -0.041995 0.031941 -1.315 0.1891
## p2$post_5_neg_distressed 0.065105 0.044106 1.476 0.1404
## p2$post_22_pos_attentive 0.024144 0.032149 0.751 0.4529
## p2$post_23_neg_jittery 0.060649 0.039944 1.518 0.1294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4447 on 654 degrees of freedom
## Multiple R-squared: 0.1805, Adjusted R-squared: 0.1229
## F-statistic: 3.132 on 46 and 654 DF, p-value: 1.318e-10Correlation Table
cor_df <- select(fa, donation, complete_vol, link, recontact, self_focused_neg_mean, self_focused_negative_guilt_mean, self_focused_pos_mean, other_focused_pos_mean, pos_emotion_change, neg_emotion_change)
apaTables::apa.cor.table(cor_df, filename = "Ch_Giving_Pilot_Correlation_Table.doc")##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2
## 1. donation 0.69 0.46
##
## 2. complete_vol 0.44 0.50 .56**
## [.52, .59]
##
## 3. link 0.09 0.29 .19** .13**
## [.14, .24] [.08, .18]
##
## 4. recontact 0.77 0.42 .20** .20**
## [.15, .25] [.15, .25]
##
## 5. self_focused_neg_mean 3.52 2.52 .06* -.01
## [.01, .11] [-.07, .04]
##
## 6. self_focused_negative_guilt_mean 3.25 2.38 .01 -.05
## [-.04, .06] [-.10, .01]
##
## 7. self_focused_pos_mean 6.66 1.74 .20** .10**
## [.15, .25] [.05, .16]
##
## 8. other_focused_pos_mean 7.45 1.46 .20** .14**
## [.15, .25] [.09, .19]
##
## 9. pos_emotion_change 0.05 0.39 .25** .18**
## [.20, .30] [.13, .23]
##
## 10. neg_emotion_change -0.03 0.26 -.17** -.10**
## [-.22, -.12] [-.15, -.04]
##
## 3 4 5 6 7 8
##
##
##
##
##
##
##
##
## .13**
## [.08, .18]
##
## .01 .03
## [-.04, .06] [-.02, .08]
##
## -.05 .02 .77**
## [-.10, .00] [-.03, .07] [.75, .79]
##
## .11** .11** .32** .32**
## [.06, .16] [.06, .16] [.27, .37] [.27, .37]
##
## .11** .11** .12** .09** .62**
## [.06, .17] [.06, .17] [.07, .17] [.04, .14] [.59, .66]
##
## .05* .10** .01 -.02 .09** .07**
## [.00, .11] [.05, .16] [-.04, .07] [-.07, .03] [.04, .14] [.02, .12]
##
## -.03 .04 -.03 -.02 -.02 .01
## [-.09, .02] [-.02, .09] [-.08, .02] [-.07, .04] [-.07, .03] [-.04, .06]
##
## 9
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## -.15**
## [-.21, -.10]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## Interesting pattern of results – Interactions between regulatory focus and treatment condition
Pilot 2
###########
test <- glm(p2$donation ~ p2$self_focused_negative_guilt_mean * as.factor(p2$treat), family = binomial())
summary(test)##
## Call:
## glm(formula = p2$donation ~ p2$self_focused_negative_guilt_mean *
## as.factor(p2$treat), family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7118 -1.4243 0.8878 0.9061 1.0256
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.26389 0.24885
## p2$self_focused_negative_guilt_mean 0.10428 0.06298
## as.factor(p2$treat)1 0.39762 0.33649
## as.factor(p2$treat)2 0.49809 0.33767
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)1 -0.08776 0.08312
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)2 -0.14388 0.08312
## z value Pr(>|z|)
## (Intercept) 1.060 0.2890
## p2$self_focused_negative_guilt_mean 1.656 0.0978 .
## as.factor(p2$treat)1 1.182 0.2373
## as.factor(p2$treat)2 1.475 0.1402
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)1 -1.056 0.2911
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)2 -1.731 0.0834 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 900.92 on 700 degrees of freedom
## Residual deviance: 897.09 on 695 degrees of freedom
## AIC: 909.09
##
## Number of Fisher Scoring iterations: 4# A motivation to reduce guilt predicts saying they will donate -- and this effect is slightly weaker in the Opportunity condition
test <- glm(p2$complete_vol ~ p2$self_focused_negative_guilt_mean * as.factor(p2$treat), family = binomial())
summary(test)##
## Call:
## glm(formula = p2$complete_vol ~ p2$self_focused_negative_guilt_mean *
## as.factor(p2$treat), family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1900 -1.0612 -0.9304 1.2869 1.5811
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.48428 0.24550
## p2$self_focused_negative_guilt_mean 0.05117 0.05856
## as.factor(p2$treat)1 0.30235 0.32768
## as.factor(p2$treat)2 0.63171 0.32817
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)1 -0.12306 0.07891
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)2 -0.16892 0.07982
## z value Pr(>|z|)
## (Intercept) -1.973 0.0485 *
## p2$self_focused_negative_guilt_mean 0.874 0.3822
## as.factor(p2$treat)1 0.923 0.3562
## as.factor(p2$treat)2 1.925 0.0542 .
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)1 -1.559 0.1189
## p2$self_focused_negative_guilt_mean:as.factor(p2$treat)2 -2.116 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 953.50 on 700 degrees of freedom
## Residual deviance: 944.99 on 695 degrees of freedom
## AIC: 956.99
##
## Number of Fisher Scoring iterations: 4# When it comes to actual donation behavior -- Opportunity condition performs slightly better in general but NOT for those who say they are motivated by reducing guilt
# This pattern is similar but weaker for people who say they give just to avoid feeling terrible in general (not specifically to reduce guilt for priviledge etc.)
test <- lm(p2$donation ~ p2$self_focused_neg_mean * as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$donation ~ p2$self_focused_neg_mean * as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7728 -0.6263 0.3067 0.3635 0.4089
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.568408 0.056757 10.015
## p2$self_focused_neg_mean 0.022711 0.013286 1.709
## as.factor(p2$treat)1 0.040682 0.076214 0.534
## as.factor(p2$treat)2 0.056591 0.077706 0.728
## p2$self_focused_neg_mean:as.factor(p2$treat)1 -0.005466 0.017414 -0.314
## p2$self_focused_neg_mean:as.factor(p2$treat)2 -0.015433 0.017769 -0.869
## Pr(>|t|)
## (Intercept) <2e-16 ***
## p2$self_focused_neg_mean 0.0878 .
## as.factor(p2$treat)1 0.5937
## as.factor(p2$treat)2 0.4667
## p2$self_focused_neg_mean:as.factor(p2$treat)1 0.7537
## p2$self_focused_neg_mean:as.factor(p2$treat)2 0.3854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4745 on 695 degrees of freedom
## Multiple R-squared: 0.008586, Adjusted R-squared: 0.001453
## F-statistic: 1.204 on 5 and 695 DF, p-value: 0.3056test <- lm(p2$complete_vol ~ p2$self_focused_neg_mean * as.factor(p2$treat), family = binomial())## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'family' will be disregardedsummary(test) # Slight main effect of opportunity performing better and is slightly worse for people who say the give to reduce avoid feeling terrible##
## Call:
## lm(formula = p2$complete_vol ~ p2$self_focused_neg_mean * as.factor(p2$treat),
## family = binomial())
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5038 -0.4071 -0.3919 0.5800 0.6846
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.4006264 0.0590718 6.782
## p2$self_focused_neg_mean 0.0064515 0.0138277 0.467
## as.factor(p2$treat)1 -0.0002074 0.0793215 -0.003
## as.factor(p2$treat)2 0.1267770 0.0808743 1.568
## p2$self_focused_neg_mean:as.factor(p2$treat)1 -0.0076628 0.0181241 -0.423
## p2$self_focused_neg_mean:as.factor(p2$treat)2 -0.0300103 0.0184934 -1.623
## Pr(>|t|)
## (Intercept) 2.54e-11 ***
## p2$self_focused_neg_mean 0.641
## as.factor(p2$treat)1 0.998
## as.factor(p2$treat)2 0.117
## p2$self_focused_neg_mean:as.factor(p2$treat)1 0.673
## p2$self_focused_neg_mean:as.factor(p2$treat)2 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4938 on 695 degrees of freedom
## Multiple R-squared: 0.007022, Adjusted R-squared: -0.0001212
## F-statistic: 0.983 on 5 and 695 DF, p-value: 0.4272# anticipated negative feeling vs. guilt as baseline
test <- glm(p2$donation ~ p2$other_focused_pos_mean * as.factor(p2$treat),family = binomial())
summary(test)##
## Call:
## glm(formula = p2$donation ~ p2$other_focused_pos_mean * as.factor(p2$treat),
## family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8441 -1.3287 0.7957 0.9126 1.5309
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -1.24555 0.74665 -1.668
## p2$other_focused_pos_mean 0.25138 0.09986 2.517
## as.factor(p2$treat)1 -1.85528 1.13077 -1.641
## as.factor(p2$treat)2 0.56052 0.96456 0.581
## p2$other_focused_pos_mean:as.factor(p2$treat)1 0.25970 0.15088 1.721
## p2$other_focused_pos_mean:as.factor(p2$treat)2 -0.07194 0.12915 -0.557
## Pr(>|z|)
## (Intercept) 0.0953 .
## p2$other_focused_pos_mean 0.0118 *
## as.factor(p2$treat)1 0.1009
## as.factor(p2$treat)2 0.5612
## p2$other_focused_pos_mean:as.factor(p2$treat)1 0.0852 .
## p2$other_focused_pos_mean:as.factor(p2$treat)2 0.5775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 900.92 on 700 degrees of freedom
## Residual deviance: 866.01 on 695 degrees of freedom
## AIC: 878.01
##
## Number of Fisher Scoring iterations: 4# Changes in Emotion
test <- lm(p2$complete_vol ~ p2$change_pos * as.factor(p2$treat), family = binomial())## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'family' will be disregardedsummary(test) ##
## Call:
## lm(formula = p2$complete_vol ~ p2$change_pos * as.factor(p2$treat),
## family = binomial())
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9228 -0.4188 -0.3018 0.5343 0.8620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.411971 0.034160 12.060 < 2e-16 ***
## p2$change_pos 0.390672 0.102255 3.821 0.000145 ***
## as.factor(p2$treat)1 -0.016533 0.045810 -0.361 0.718283
## as.factor(p2$treat)2 0.007006 0.046220 0.152 0.879567
## p2$change_pos:as.factor(p2$treat)1 -0.086387 0.138788 -0.622 0.533859
## p2$change_pos:as.factor(p2$treat)2 -0.137618 0.126179 -1.091 0.275804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4826 on 695 degrees of freedom
## Multiple R-squared: 0.05169, Adjusted R-squared: 0.04487
## F-statistic: 7.576 on 5 and 695 DF, p-value: 6.087e-07test <- glm(p2$donation ~ p2$change_pos * as.factor(p2$treat),family = binomial())
summary(test)##
## Call:
## glm(formula = p2$donation ~ p2$change_pos * as.factor(p2$treat),
## family = binomial())
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6769 -1.2921 0.7659 0.9126 1.6923
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.61180 0.15592 3.924 8.71e-05 ***
## p2$change_pos 2.25044 0.55527 4.053 5.06e-05 ***
## as.factor(p2$treat)1 0.11813 0.20723 0.570 0.5687
## as.factor(p2$treat)2 -0.07746 0.20975 -0.369 0.7119
## p2$change_pos:as.factor(p2$treat)1 -1.34984 0.70457 -1.916 0.0554 .
## p2$change_pos:as.factor(p2$treat)2 -0.24916 0.71769 -0.347 0.7285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 900.92 on 700 degrees of freedom
## Residual deviance: 849.56 on 695 degrees of freedom
## AIC: 861.56
##
## Number of Fisher Scoring iterations: 4test <- lm(p2$pos_emotion_change ~ p2$treat) #Might call that trending increase of positive in opportunity
summary(test)##
## Call:
## lm(formula = p2$pos_emotion_change ~ p2$treat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.23692 -0.18178 -0.00615 0.15200 1.99385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02794 0.02552 1.095 0.274
## p2$treat1 -0.02609 0.03427 -0.761 0.447
## p2$treat2 0.05514 0.03427 1.609 0.108
##
## Residual standard error: 0.3617 on 698 degrees of freedom
## Multiple R-squared: 0.009274, Adjusted R-squared: 0.006435
## F-statistic: 3.267 on 2 and 698 DF, p-value: 0.0387test <- lm(p2$neg_emotion_change ~ p2$treat) # Treatment 1 is not necessarily increasing negative emotion
summary(test)##
## Call:
## lm(formula = p2$neg_emotion_change ~ p2$treat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47844 -0.04700 0.03633 0.04233 1.54233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02156 0.01711 -1.260 0.208
## p2$treat1 -0.01477 0.02298 -0.643 0.521
## p2$treat2 -0.02077 0.02298 -0.904 0.366
##
## Residual standard error: 0.2426 on 698 degrees of freedom
## Multiple R-squared: 0.001211, Adjusted R-squared: -0.001651
## F-statistic: 0.4231 on 2 and 698 DF, p-value: 0.6552test <- lm(p2$neg_emotion_change ~ p2$donation)
summary(test) # Saying they##
## Call:
## lm(formula = p2$neg_emotion_change ~ p2$donation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51528 -0.02332 0.06001 0.06001 1.48472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01528 0.01549 0.987 0.324
## p2$donation -0.07529 0.01910 -3.943 8.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2399 on 699 degrees of freedom
## Multiple R-squared: 0.02176, Adjusted R-squared: 0.02036
## F-statistic: 15.55 on 1 and 699 DF, p-value: 8.859e-05test <- lm(p2$pos_emotion_change ~ p2$donation)
summary(test)##
## Call:
## lm(formula = p2$pos_emotion_change ~ p2$donation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.06891 -0.17938 0.00801 0.16186 1.97447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08494 0.02273 -3.737 0.000201 ***
## p2$donation 0.18739 0.02803 6.686 4.69e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3521 on 699 degrees of freedom
## Multiple R-squared: 0.06011, Adjusted R-squared: 0.05877
## F-statistic: 44.71 on 1 and 699 DF, p-value: 4.687e-11test <- lm(p2$complete_vol ~ p2$pos_emotion_change * p2$donation)
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$pos_emotion_change * p2$donation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86943 -0.02463 -0.02021 0.36393 0.97949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.020508 0.026488 0.774 0.4391
## p2$pos_emotion_change -0.003829 0.073174 -0.052 0.9583
## p2$donation 0.588107 0.032805 17.928 <2e-16 ***
## p2$pos_emotion_change:p2$donation 0.182280 0.090273 2.019 0.0438 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3989 on 697 degrees of freedom
## Multiple R-squared: 0.3503, Adjusted R-squared: 0.3475
## F-statistic: 125.2 on 3 and 697 DF, p-value: < 2.2e-16test <- lm(p2$complete_vol ~ p2$neg_emotion_change * p2$donation)
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$neg_emotion_change * p2$donation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68464 -0.04959 -0.01737 0.37649 0.98263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02030 0.02599 0.781 0.435
## p2$neg_emotion_change 0.03515 0.09810 0.358 0.720
## p2$donation 0.60322 0.03242 18.608 <2e-16 ***
## p2$neg_emotion_change:p2$donation -0.09158 0.12852 -0.713 0.476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.402 on 697 degrees of freedom
## Multiple R-squared: 0.3402, Adjusted R-squared: 0.3374
## F-statistic: 119.8 on 3 and 697 DF, p-value: < 2.2e-16p2$treat <- as.factor(p2$treat)
#Plotting Interactions Between Regulatory Focus and Treatment Condition on Completing Volunteer Survey
ggplot(p2, aes(other_focused_pos_mean, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(other_focused_neg_mean, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_negative_guilt_mean, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_neg_mean, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_pos_mean, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
#Plotting Interactions Between Regulatory Focus and Treatment Condition on Saying they plan to donate
ggplot(p2, aes(other_focused_pos_mean, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(other_focused_neg_mean, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_negative_guilt_mean, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_neg_mean, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(self_focused_pos_mean, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
# Plotting Interactions Between Emotion Change and Condition
ggplot(p2, aes(p2$pos_emotion_change, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$neg_emotion_change, donation, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$pos_emotion_change, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$neg_emotion_change, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_guilty, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_purposeful, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_inspired, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_hopeful, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_distressed, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_sad, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_ashamed, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_happy, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(p2, aes(p2$change_proud, complete_vol, color = p2$treat)) + geom_smooth(method = glm)## `geom_smooth()` using formula = 'y ~ x'
Pilot 1
test <- lm(p1$donation ~ p1$self_focused_negative_guilt_mean * as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$donation ~ p1$self_focused_negative_guilt_mean *
## as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7953 -0.6381 0.2708 0.2926 0.3619
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.80936 0.05549
## p1$self_focused_negative_guilt_mean -0.01403 0.01394
## as.factor(p1$treat)1 -0.08992 0.07247
## as.factor(p1$treat)2 -0.18726 0.07433
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)1 0.01101 0.01900
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)2 0.03003 0.01853
## t value Pr(>|t|)
## (Intercept) 14.587 <2e-16 ***
## p1$self_focused_negative_guilt_mean -1.006 0.315
## as.factor(p1$treat)1 -1.241 0.215
## as.factor(p1$treat)2 -2.519 0.012 *
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)1 0.580 0.562
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)2 1.621 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4518 on 692 degrees of freedom
## Multiple R-squared: 0.01014, Adjusted R-squared: 0.00299
## F-statistic: 1.418 on 5 and 692 DF, p-value: 0.2155test <- lm(p1$complete_vol ~ p1$self_focused_negative_guilt_mean * as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$complete_vol ~ p1$self_focused_negative_guilt_mean *
## as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5745 -0.4490 -0.3615 0.5232 0.6398
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.595401 0.060869
## p1$self_focused_negative_guilt_mean -0.020914 0.015294
## as.factor(p1$treat)1 -0.104069 0.079501
## as.factor(p1$treat)2 -0.248242 0.081536
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)1 0.006349 0.020839
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)2 0.035234 0.020329
## t value Pr(>|t|)
## (Intercept) 9.782 < 2e-16 ***
## p1$self_focused_negative_guilt_mean -1.368 0.17191
## as.factor(p1$treat)1 -1.309 0.19096
## as.factor(p1$treat)2 -3.045 0.00242 **
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)1 0.305 0.76071
## p1$self_focused_negative_guilt_mean:as.factor(p1$treat)2 1.733 0.08351 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4956 on 692 degrees of freedom
## Multiple R-squared: 0.01714, Adjusted R-squared: 0.01004
## F-statistic: 2.414 on 5 and 692 DF, p-value: 0.03491test <- lm(p1$donation ~ p1$self_focused_neg_mean * as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$donation ~ p1$self_focused_neg_mean * as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7847 -0.6463 0.2426 0.3088 0.3537
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.771874 0.054515 14.159
## p1$self_focused_neg_mean -0.002415 0.012858 -0.188
## as.factor(p1$treat)1 -0.108245 0.072801 -1.487
## as.factor(p1$treat)2 -0.136789 0.074270 -1.842
## p1$self_focused_neg_mean:as.factor(p1$treat)1 0.016656 0.017393 0.958
## p1$self_focused_neg_mean:as.factor(p1$treat)2 0.013631 0.017380 0.784
## Pr(>|t|)
## (Intercept) <2e-16 ***
## p1$self_focused_neg_mean 0.8511
## as.factor(p1$treat)1 0.1375
## as.factor(p1$treat)2 0.0659 .
## p1$self_focused_neg_mean:as.factor(p1$treat)1 0.3386
## p1$self_focused_neg_mean:as.factor(p1$treat)2 0.4331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4519 on 692 degrees of freedom
## Multiple R-squared: 0.009641, Adjusted R-squared: 0.002485
## F-statistic: 1.347 on 5 and 692 DF, p-value: 0.2424test <- lm(p1$donation ~ p1$other_focused_pos_mean * as.factor(p1$treat))
summary(test)##
## Call:
## lm(formula = p1$donation ~ p1$other_focused_pos_mean * as.factor(p1$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8493 -0.5654 0.2284 0.2943 0.7805
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.360260 0.160363 2.247
## p1$other_focused_pos_mean 0.054334 0.021195 2.564
## as.factor(p1$treat)1 -0.216959 0.221605 -0.979
## as.factor(p1$treat)2 -0.103706 0.216344 -0.479
## p1$other_focused_pos_mean:as.factor(p1$treat)1 0.021828 0.029262 0.746
## p1$other_focused_pos_mean:as.factor(p1$treat)2 0.001812 0.028546 0.063
## Pr(>|t|)
## (Intercept) 0.0250 *
## p1$other_focused_pos_mean 0.0106 *
## as.factor(p1$treat)1 0.3279
## as.factor(p1$treat)2 0.6318
## p1$other_focused_pos_mean:as.factor(p1$treat)1 0.4559
## p1$other_focused_pos_mean:as.factor(p1$treat)2 0.9494
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4434 on 692 degrees of freedom
## Multiple R-squared: 0.04672, Adjusted R-squared: 0.03983
## F-statistic: 6.783 on 5 and 692 DF, p-value: 3.465e-06test <- lm(p2$complete_vol ~ p2$other_focused_pos_mean * as.factor(p2$treat))
summary(test)##
## Call:
## lm(formula = p2$complete_vol ~ p2$other_focused_pos_mean * as.factor(p2$treat))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5397 -0.4388 -0.2944 0.5379 0.8135
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.170872 0.173065 0.987
## p2$other_focused_pos_mean 0.033957 0.022855 1.486
## as.factor(p2$treat)1 -0.539243 0.250317 -2.154
## as.factor(p2$treat)2 0.008801 0.225404 0.039
## p2$other_focused_pos_mean:as.factor(p2$treat)1 0.066937 0.032798 2.041
## p2$other_focused_pos_mean:as.factor(p2$treat)2 0.001347 0.029807 0.045
## Pr(>|t|)
## (Intercept) 0.3238
## p2$other_focused_pos_mean 0.1378
## as.factor(p2$treat)1 0.0316 *
## as.factor(p2$treat)2 0.9689
## p2$other_focused_pos_mean:as.factor(p2$treat)1 0.0416 *
## p2$other_focused_pos_mean:as.factor(p2$treat)2 0.9640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4869 on 695 degrees of freedom
## Multiple R-squared: 0.03478, Adjusted R-squared: 0.02784
## F-statistic: 5.009 on 5 and 695 DF, p-value: 0.0001623Writing Data to CSV
#write_csv(fa, here("Desktop","pilot_data_paf.csv"))