Code
library(sjPlot)
library(ggplot2)Alysen - Days of Discovery
Load Libraries
Load Data
Code
df <- read.csv(file="data.csv", header=T)Hypothesis 1
We tested the hypothesis that if there is increased levels of social support from significant others, then there will be less feelings of helplessness.
Code
reg1 <- lm(stress_helpless ~ supp_person, data=df)
summary(reg1)
Call:
lm(formula = stress_helpless ~ supp_person, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.63462 -0.58737 -0.00269 0.60980 1.85422
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.85672 0.23063 12.39 <2e-16 ***
supp_person -0.07775 0.03926 -1.98 0.0492 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8147 on 182 degrees of freedom
Multiple R-squared: 0.02109, Adjusted R-squared: 0.01571
F-statistic: 3.921 on 1 and 182 DF, p-value: 0.04918Plot
Code
ggplot(df, aes(x = supp_person, y = stress_helpless)) +
# Points
geom_point(
color = "#2E86AB",
alpha = 0.65,
size = 3,
shape = 16
) +
# Regression line with confidence interval ribbon
geom_smooth(
method = "lm",
se = TRUE,
color = "#E84855",
fill = "#E84855",
alpha = 0.15,
linewidth = 1.2
) +
# Labels
labs(
title = "Social Support and Perceived Helplessness",
subtitle = "Adj. R² = .02, F(1,182) = 4.74, p = .031.",
x = "Perceived Social Support (Significant Other)",
y = "Perceived Stress -- Helplessness"
) +
# Clean theme
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", size = 15, margin = margin(b = 4)),
plot.subtitle = element_text(color = "grey50", size = 11, margin = margin(b = 12)),
plot.caption = element_text(color = "grey60", size = 9),
axis.title = element_text(face = "bold", color = "grey30"),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "grey92"),
plot.background = element_rect(fill = "white", color = NA),
plot.margin = margin(20, 20, 20, 20)
)`geom_smooth()` using formula = 'y ~ x'
Hypothesis 2
I tested the hypothesis that individuals who score higher on self-efficacy, will have lower feelings of non-acceptance.
Code
reg2 <- lm(ders_nonacceptance ~ stress_se, data=df)
summary(reg2)
Call:
lm(formula = ders_nonacceptance ~ stress_se, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.82382 -0.60865 -0.07955 0.77655 2.74602
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.52152 0.27327 16.546 < 2e-16 ***
stress_se -0.69770 0.08143 -8.568 4.4e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9969 on 182 degrees of freedom
Multiple R-squared: 0.2874, Adjusted R-squared: 0.2835
F-statistic: 73.42 on 1 and 182 DF, p-value: 4.395e-15Plot
Code
ggplot(df, aes(x = stress_se, y = ders_nonacceptance)) +
# Points
geom_point(
color = "#2E86AB",
alpha = 0.65,
size = 3,
shape = 16
) +
# Regression line with confidence interval ribbon
geom_smooth(
method = "lm",
se = TRUE,
color = "#E84855",
fill = "#E84855",
alpha = 0.15,
linewidth = 1.2
) +
# Labels
labs(
title = "Perceived Self-Efficacy and Emotion Dysregulation (Nonacceptance)",
subtitle = "Adj. R² = .28, F(1, 182) = 73.42, p < .001",
x = "Perceived Self-Efficacy",
y = "Emotion Dysregulation (Nonacceptance)"
) +
# Clean theme
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", size = 15, margin = margin(b = 4)),
plot.subtitle = element_text(color = "grey50", size = 11, margin = margin(b = 12)),
plot.caption = element_text(color = "grey60", size = 9),
axis.title = element_text(face = "bold", color = "grey30"),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "grey92"),
plot.background = element_rect(fill = "white", color = NA),
plot.margin = margin(20, 20, 20, 20)
)`geom_smooth()` using formula = 'y ~ x'
Hypothesis 3
I tested the hypothesis that more social support from family would predict more emotional clarity.
Code
reg3 <- lm(ders_clarity ~ supp_family, data=df)
summary(reg3)
Call:
lm(formula = ders_clarity ~ supp_family, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.4583 -0.9095 -0.1340 0.4907 2.9658
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.55808 0.28895 8.853 7.47e-16 ***
supp_family -0.09978 0.05155 -1.936 0.0545 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.111 on 182 degrees of freedom
Multiple R-squared: 0.02017, Adjusted R-squared: 0.01479
F-statistic: 3.747 on 1 and 182 DF, p-value: 0.05446Plot
Code
ggplot(df, aes(x = supp_family, y = ders_clarity)) +
# Points
geom_point(
color = "#2E86AB",
alpha = 0.65,
size = 3,
shape = 16
) +
# Regression line with confidence interval ribbon
geom_smooth(
method = "lm",
se = TRUE,
color = "#E84855",
fill = "#E84855",
alpha = 0.15,
linewidth = 1.2
) +
# Labels
labs(
title = "Social Support (Family) and Emotion Dysregulation (Clarity)",
subtitle = "Adj. R² = .02, F(1, 182) = 3.75, p = .054",
x = "Social Support (Family)",
y = "Emotion Dysregulation (Clarity)"
) +
# Clean theme
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", size = 15, margin = margin(b = 4)),
plot.subtitle = element_text(color = "grey50", size = 11, margin = margin(b = 12)),
plot.caption = element_text(color = "grey60", size = 9),
axis.title = element_text(face = "bold", color = "grey30"),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "grey92"),
plot.background = element_rect(fill = "white", color = NA),
plot.margin = margin(20, 20, 20, 20)
)`geom_smooth()` using formula = 'y ~ x'