How Does Social Support and Family Relationships Impact Your Perceived Level of Freedom?
Iris Williamson
5/21/25
Introduction
My research question focuses on how your social support system impacts your perceived level of freedom. I would like to explore the results of this potential correlation, as an individual’s perceived levels of freedom are integral to human happiness, and knowing if your social support system or family relationships impact that positively could be beneficial information to have.
Literature Review
Article 1 Summary
In an article from 2012, researched by Fausta Petito and Robert Cummins, they found that adult levels of subjective quality of life are stable on a population basis. However, this homestasis comes under severe challenge during adolescence. In this fluctuating state, factors such as perceived social support and interactions with parents had the power to influence stability. This study tested the influence of these variables with teenagers, and found that more social support and adolescents with authoritarian parents experienced a higher SQOL (Subjective Quality of Life) than those with unengaged parents.
Article 2 Summary
In a literature study conducted in 2023, data was collected on the basis of the importance of parenting patterns to introduce social-emotional influences that greatly influenced children’s ability to interact with their environment. It was found that a democratic parenting style where children have the freedom to do things but are still responsible was the parenting style that best suited children’s social and emotional development. This is because democratic parenting gives children the opportunity and freedom to choose.
Hypothesis
IV1 - Social Support: Individuals with higher levels of social support will report higher perceived freedom, as supportive relationships can provide emotional validation and encouragement.
IV2 - Healthy Life Expectancy: Individuals with higher life expectancy will perceive greater freedom.
Interaction: The positive relationship between social support and perceived freedom will be stronger in countries with higher healthy life expectancy, suggesting that personal support and societal health conditions jointly enhance individuals’ sense of autonomy.
Method
Sample
My sample was taken from a global report of global human happiness scores, taken from 143 different countries in all different continents around the world.
Variables and Operationalization
Dependent Variable - Freedom to make life choices: The national average of responses to the question about satisfaction with freedom to choose what to do with one’s life. IV1 - Social Support: The national average of binary responses (either 0 or 1 representing No/Yes) to the question about having relatives or friends to count on in times of trouble. IV2 - Healthy life expectancy: The average number of years a newborn infant would live in good health, based on mortality rates and life expectancy at different ages.
Descriptive Statistics
Present the descriptive statistics for your variables. Include appropriate measures of central tendency (mean, median), variability (standard deviation, range), and frequency distributions where applicable. Use R code chunks to generate and display your results.
## vars n mean sd median trimmed mad min
## Country.name* 1 143 72.00 41.42 72.00 72.00 53.37 1.00
## Regional.indicator* 2 143 6.08 3.15 6.00 6.22 4.45 1.00
## Ladder.score 3 143 5.53 1.17 5.78 5.59 1.21 1.72
## upperwhisker 4 143 5.64 1.16 5.89 5.71 1.19 1.77
## lowerwhisker 5 143 5.41 1.19 5.67 5.48 1.24 1.67
## Log.GDP.per.capita 6 140 1.38 0.43 1.43 1.40 0.50 0.00
## Social.support 7 140 1.13 0.33 1.24 1.17 0.30 0.00
## Healthy.life.expectancy 8 140 0.52 0.16 0.55 0.53 0.17 0.00
## Freedom.to.make.life.choices 9 140 0.62 0.16 0.64 0.64 0.15 0.00
## Generosity 10 140 0.15 0.07 0.14 0.14 0.07 0.00
## Perceptions.of.corruption 11 140 0.15 0.13 0.12 0.13 0.09 0.00
## Dystopia...residual 12 140 1.58 0.54 1.64 1.60 0.39 -0.07
## max range skew kurtosis se
## Country.name* 143.00 142.00 0.00 -1.23 3.46
## Regional.indicator* 10.00 9.00 -0.25 -1.41 0.26
## Ladder.score 7.74 6.02 -0.51 -0.26 0.10
## upperwhisker 7.82 6.04 -0.54 -0.18 0.10
## lowerwhisker 7.67 6.00 -0.49 -0.32 0.10
## Log.GDP.per.capita 2.14 2.14 -0.50 -0.42 0.04
## Social.support 1.62 1.62 -0.97 0.40 0.03
## Healthy.life.expectancy 0.86 0.86 -0.53 -0.44 0.01
## Freedom.to.make.life.choices 0.86 0.86 -1.00 1.16 0.01
## Generosity 0.40 0.40 0.65 0.72 0.01
## Perceptions.of.corruption 0.58 0.58 1.49 1.83 0.01
## Dystopia...residual 3.00 3.07 -0.59 0.68 0.05Statistical Analysis
Analysis
Perform your chosen analysis. Make sure your output shows.
happiness_data <- read.csv("World-happiness-report-2024.csv")
model <- lm(Freedom.to.make.life.choices ~ Social.support, data = happiness_data)
model.2 <- lm(Freedom.to.make.life.choices ~ Healthy.life.expectancy, data = happiness_data)
model.3 <- lm(Freedom.to.make.life.choices ~ Social.support + Healthy.life.expectancy, data = happiness_data)
summary(model.3)##
## Call:
## lm(formula = Freedom.to.make.life.choices ~ Social.support +
## Healthy.life.expectancy, data = happiness_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.43927 -0.07217 0.02076 0.09216 0.32879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33833 0.04476 7.559 5.25e-12 ***
## Social.support 0.19598 0.05128 3.822 0.0002 ***
## Healthy.life.expectancy 0.11517 0.10364 1.111 0.2684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1425 on 137 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2417, Adjusted R-squared: 0.2306
## F-statistic: 21.84 on 2 and 137 DF, p-value: 5.87e-09Post-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
##
## Two-sample t test power calculation
##
## n = 154.2643
## d = 0.32
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* groupResults Interpretation
A power analysis using a pwr.t.test() indicated that a sample size of approximately 154 participants per group is required to detect a small-to-medium effect size of d = .32 with 80^ power at a 5% significance level. It is important because we want to have enough power to detect a meaningful difference between the three groups. Based on the size of the data, it can be assumed that there were more than 154 participants studied in order to capture the data, meaning that there is enough power in the data to detect a meaningful difference.
Graph and Table
Include at least one table and one graph that effectively summarize your analysis and findings. Use R code chunks to generate these visualizations.
happiness_data <- happiness_data %>%
mutate(SocialSupportGroup = ifelse(Social.support > median(Social.support), "High Support", "Low Support"))
ggplot(happiness_data, aes(x = Healthy.life.expectancy, y = Freedom.to.make.life.choices, color = SocialSupportGroup)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(
title = "Freedom vs. Healthy Life Expectancy by Social Support Group",
x = "Healthy Life Expectancy",
y = "Perceived Freedom"
) +
scale_color_manual(values = c("High Support" = "blue", "Low Support" = "red")) +
theme_apa()
happiness_data <- happiness_data %>%
mutate(Support_Group = ifelse(Social.support > median(Social.support), "High Support", "Low Support"))
summary_table <- happiness_data %>%
group_by(Support_Group) %>%
dplyr::summarise(
Freedom.Mean = mean(Freedom.to.make.life.choices, na.rm = TRUE),
Freedom.SD = sd(Freedom.to.make.life.choices, na.rm = TRUE),
Freedom.Min = min(Freedom.to.make.life.choices, na.rm = TRUE),
Freedom.Max = max(Freedom.to.make.life.choices, na.rm = TRUE))# Display the table using knitr::kable()
kable(summary_table, caption = "Descriptive Statistics for Perceived Freedom Data")| Support_Group | Freedom.Mean | Freedom.SD | Freedom.Min | Freedom.Max |
|---|---|---|---|---|
| NA | 0.6206214 | 0.1624918 | 0 | 0.863 |
Discussion
- Implications: These findings suggest that the higher social support you have, the greater perceived freedom you have. This supports other psychological theories that emphasize the role of interpersonal relationships when it comes to your well-being. Healthy life expectancy did not significantly predict freedom, which may indicate that structural or health-related factors are less directly felt in people’s perceived levels of freedom.
- Limitations: One limitation with this study is that it is cross-sectional and correlational, which makes it difficult to draw any causal conclusions.
- Future Directions: Future research could explore longitudinal effects or examine whether specific types of social support have stronger impacts on perceived freedom. It would also be helpful to see how parent and child relationships, namely parenting styles, impact perceived freedom in tandem with social support in a more specific study.
References
Petito F, Cummins RA. Quality of Life in Adolescence: The Role of Perceived Control, Parenting Style, and Social Support. Behaviour Change. 2000;17(3):196-207. doi:10.1375/bech.17.3.196
Anggi Nursahara, Haliatun Nisa, & Risnaeni Ainunsyah. (2023). The Influence of Parenting Patterns on Early Childhood Social Development. Feelings: Journal of Counseling and Psychology, 1(1), 23-33. https://doi.org/10.61166/feelings.viii.3