Code
knitr::include_graphics("alysen poster.jpg")
Days of Discovery 2026 - The Interplay of Perceived Stress, Emotional Dysregulation, and Social Support
Poster
Poster Text
INTRODUCTION
Social support, emotional dysregulation, and perceived stress are all fundamental concepts in understanding psychological well-being and how individuals cope with life’s challenges. The impact of social support can be utilized as a protective barrier against feelings of emotional dysregulation (Carbone et al., 2025). Additionally, there is also evidence of social support acting as a buffer against perceived stress (Cohen & Wills, 1985). Although the shielding effects of social support are noticed with feelings of stress and emotional dysregulation, not every individual may have the shield of social support. In fact, individuals who experienced increased stress experienced increased feelings of emotional dysregulation (Strizhitskaya et al., 2019).
In the current study, we seek to discover the whole picture of how these variables interact with one another. We examined more specific aspects of these variables to get a more detailed picture about what distinct facets of each of these variables influences the others. We hypothesized that increased social support from significant others would decrease feelings of helplessness linked to stress, that individuals who report higher self-efficacy regarding stress will report lower feelings of nonacceptance due to emotional dysregulation, and that increased social support from family would predict increased emotional clarity from emotional dysregulation.
METHOD
We collected data from a sample of students and adults (N = 183). The participants from our sample answered questions from multiple self-report likert scales including: The Perceived Stress Scale (PSS), Difficulties in Emotion Regulation Scale (DERS-16), and Multidimensional Scale of Perceived Social Support (MPSS). We analyzed the data collected by these likert scales using several simple linear regressions.
RESULTS
For our first hypothesis, there was statistical significance, Adj. R² = .02, F(1,182) = 4.74, p = .031. The predictor, social support from a significant other (ꞵ = -.12, t(182) = -2.18, p = .031) was weakly predictive of perceived helplessness.
Regarding the second hypothesis, there was statistical significance predicted perceived stress, adj. R² = .28, F(1, 182) = 73.42, p < .001. Stress self-efficacy (ꞵ = −0.54, t(1, 182) = -8.57, p < .001) was a moderate predictor of nonacceptance.
For the third hypothesis, there was little to no statistical significance, R² = .02, F(1, 182) = 3.75, p = .054. Social support from family, (ꞵ = -.14, t(182) = -1.94, p = .054) was not a predictor of emotional clarity.
DISCUSSION
These findings suggest that social support from a significant other has only a weak association with perceived helplessness. While these findings show statistical significance, the effect size is relatively small. While future research should keep these findings in mind, the small effect suggests that other variables should also be examined.
Additionally, our results also suggest that higher self-efficacy is moderately associated with lower feelings of nonacceptance. Nonacceptance of emotions is generally positively associated with poor outcomes, but here we see something different. This suggests that even when people appear to be coping well with stress, there may be problematic or harmful mechanisms occurring beneath the surface. Our third set of results are borderline significant. This suggests that more social support from family members is not strongly associated with higher emotional clarity. In future research, examining the relationship between support from friends or significant others and emotional clarity may have more significant results.
REFERENCES
Carbone, G. A., Adenzato, M., Dell’Acqua, C., Imperatori, C., Lo Presti, A., De Rossi, E., Allegrini, G., Messerotti Benvenuti, S., Farina, B., & Ardito, R. B. (2026). Breaking the cycle: The protective role of perceived social support in the relationship between adverse childhood experiences, emotional dysregulation and depression in university students. Journal of Affective Disorders, 395, 120669. https://doi.org/10.1016/j.jad.2025.120669
Cohen, S., & Wills, T. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98, 310–357.
Strizhitskaya, O., Petrash, M., Savenysheva, S., Murtazina, I., & Golovey, L. (2019). Perceived stress and psychological well-being: the role of the emotional stability. European Proceedings of Social and Behavioural Sciences, 56.
Code
Load Libraries
Code
library(haven)
library(sjPlot)
library(ggplot2)Load Data
Code
df <- read_sav("alysen data_study2_clean (1).sav")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(pss_help ~ soc_so, data=df)
summary(reg1)
Call:
lm(formula = pss_help ~ soc_so, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.95656 -0.72943 -0.00577 0.68180 2.12949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.50156 0.30548 11.463 <2e-16 ***
soc_so -0.11474 0.05272 -2.176 0.0308 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9672 on 182 degrees of freedom
Multiple R-squared: 0.02537, Adjusted R-squared: 0.02001
F-statistic: 4.737 on 1 and 182 DF, p-value: 0.03081Plot
Code
ggplot(df, aes(x = soc_so, y = pss_help)) +
# 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
df$ders_nonacceptance_std <- scale(df$ders_nonacceptance, center=T, scale=T)
df$pss_eff_std <- scale(df$pss_eff, center=T, scale=T)
reg2 <- lm(ders_nonacceptance_std ~ pss_eff_std, data=df)
options(scipen = 999)
summary(reg2)
Call:
lm(formula = ders_nonacceptance_std ~ pss_eff_std, data = df)
Residuals:
Min 1Q Median 3Q Max
-2.39769 -0.51681 -0.06755 0.65937 2.33164
Coefficients:
Estimate Std. Error t value
(Intercept) -0.00000000000000003254 0.06240091431132209487 0.000
pss_eff_std -0.53613647184266699508 0.06257117633122338818 -8.568
Pr(>|t|)
(Intercept) 1
pss_eff_std 0.0000000000000044 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8464 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: 0.000000000000004395Code
options(scipen = 0)Plot
Code
p <- ggplot(df, aes(x = pss_eff, y = ders_nonacceptance)) +
# Points
geom_point(
color = "#45818E",
alpha = 0.65,
size = 3,
shape = 16
) +
# Regression line with confidence interval ribbon
geom_smooth(
method = "lm",
se = TRUE,
color = "#126082",
fill = "#126082",
alpha = 0.15,
linewidth = 1.2
) +
# Labels
labs(
title = "Perceived Self-Efficacy and Non-Acceptance",
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", margin = margin(b = 4)),
plot.subtitle = element_text(color = "grey50", margin = margin(b = 12)),
plot.caption = element_text(color = "grey60"),
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)
)
p`geom_smooth()` using formula = 'y ~ x'
Code
# ggsave("alysen_plot.svg", plot = p, width = 12.7, height = 13.7, bg = "transparent")Hypothesis 3
I tested the hypothesis that more social support from family would predict more emotional clarity.
Code
reg3 <- lm(ders_clarity ~ soc_fam, data=df)
summary(reg3)
Call:
lm(formula = ders_clarity ~ soc_fam, 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 ***
soc_fam -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 = soc_fam, 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'