Final Project: [Big Five Scale (Men vs. Women)]
[Allison Sanchez]
23 May, 2025
Introduction
-For my research question I decided to ask “Out of females and males who is more outgoing?”. I also wanted to know which gender was deemed more “approachable” and whether or not this depended on age. To summarize the 2 articles I read, It seemed as while men are more assertive , women tend to have more enthusiasm when in social settings.
Article 1 Summary
-All ten of the Big Five personality traits, including extraversion, showed notable gender disparities, according to the study. Men might score marginally higher on assertiveness, while women typically score higher on sociability-related attributes like enthusiasm. Individual variation is frequently larger than gender-based differences, though, as the distributions of these qualities between men and women overlap significantly. The researchers stress that these variations are mild and influenced more by social conventions than by innate biological differences.
Article 2 Summary
-Women particularly benefit from their emotionally close social ties, according to the research, which highlights the importance of social support for health and wellbeing. Compared to men, women are more likely to seek out social support, are more adept at giving it, and are more aware when it is lacking. However, it is still unknown what biological processes relate these behaviors to health consequences, and these gender disparities may be influenced by cultural norms. To completely comprehend social support’s function in wellbeing, future research should examine how gender differs in social support across a range of social and cultural contexts.
Hypothesis
- I hypothesize that females will more likely be the ones more extroverted specifically women in their 20s. I think it depends on the age and social setting. I’m predicting that men will score less on certain traits of the Big Five personality traits such as Openness and Conscientiousness.
Method
The Big Five aspect scales were designed specifically to assess the 10 aspects of the Big Five identified . Items were selected from the IPIP based on their correlation with the aspect factor scores and maintaining balanced keying. Items were chosen that differentiated the factor in question from all nine other aspect factors, by selecting items only if they were correlated with the aspect factor in question with a correlation at least 0.10 greater than the correlation with any other factor
Sample
Much of the data was collected in a large Canadian metropolitan area, either as an online survey or as a part of laboratory studies.Participants ranged in age from 17 to 85 . The majority of participants identified as White (39.9%) or Asian (27.5%), with 1% or less identifying as Native American, Hispanic, and Black. Twenty-five percent of participants identified as “other,” and 5% did not specify ethnicity
Variables and Operationalization
Independent Variables: Ages,Gender, categorical variable with the two levels: male versus female. Dependent Variable: Big Five quality traits
Loading Required Libraries
# Load your dataset in this chunk
# Create a data frame for the personality traits data
personality_data <- data.frame(
Trait = c("Enthusiasm", "Assertiveness", "Compassion", "Politeness", "Industriousness",
"Orderliness", "Volatility", "Withdrawal", "Intellect", "Openness",
"Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism",
"Openness_Intellect"),
Raw_Mean_Male = c(3.40, 3.34, 3.78, 3.52, 3.25, 3.38, 2.63, 2.73, 3.62, 3.57,
3.37, 3.65, 3.32, 2.68, 3.60),
Raw_SD_Male = c(0.66, 0.64, 0.60, 0.61, 0.68, 0.61, 0.75, 0.69, 0.61, 0.61,
0.55, 0.50, 0.54, 0.65, 0.51),
Raw_Mean_Female = c(3.56, 3.28, 4.04, 3.74, 3.21, 3.49, 2.86, 3.02, 3.48, 3.74,
3.42, 3.89, 3.35, 2.94, 3.61),
Raw_SD_Female = c(0.68, 0.68, 0.56, 0.61, 0.73, 0.63, 0.77, 0.71, 0.63, 0.60,
0.59, 0.50, 0.58, 0.67, 0.52),
Raw_d = c(0.23, -0.09, 0.45, 0.36, -0.06, 0.18, 0.30, 0.40, -0.22, 0.27,
0.08, 0.48, 0.06, 0.39, NA),
Resid_Mean_Male = c(-0.13, 0.09, -0.11, -0.06, 0.07, -0.08, -0.03, -0.10, 0.14, -0.14,
NA, NA, NA, NA, NA),
Resid_SD_Male = c(0.59, 0.57, 0.56, 0.57, 0.62, 0.56, 0.60, 0.55, 0.57, 0.56,
NA, NA, NA, NA, NA),
Resid_Mean_Female = c(0.06, -0.05, 0.05, 0.04, -0.03, 0.05, 0.01, 0.05, -0.07, 0.07,
NA, NA, NA, NA, NA),
Resid_SD_Female = c(0.59, 0.58, 0.49, 0.53, 0.66, 0.57, 0.61, 0.56, 0.58, 0.54,
NA, NA, NA, NA, NA),
Resid_d = c(0.32, -0.24, 0.31, 0.18, -0.15, 0.22, 0.06, 0.27, -0.36, 0.39,
NA, NA, NA, NA, NA)
)
print(personality_data)## Trait Raw_Mean_Male Raw_SD_Male Raw_Mean_Female Raw_SD_Female
## 1 Enthusiasm 3.40 0.66 3.56 0.68
## 2 Assertiveness 3.34 0.64 3.28 0.68
## 3 Compassion 3.78 0.60 4.04 0.56
## 4 Politeness 3.52 0.61 3.74 0.61
## 5 Industriousness 3.25 0.68 3.21 0.73
## 6 Orderliness 3.38 0.61 3.49 0.63
## 7 Volatility 2.63 0.75 2.86 0.77
## 8 Withdrawal 2.73 0.69 3.02 0.71
## 9 Intellect 3.62 0.61 3.48 0.63
## 10 Openness 3.57 0.61 3.74 0.60
## 11 Extraversion 3.37 0.55 3.42 0.59
## 12 Agreeableness 3.65 0.50 3.89 0.50
## 13 Conscientiousness 3.32 0.54 3.35 0.58
## 14 Neuroticism 2.68 0.65 2.94 0.67
## 15 Openness_Intellect 3.60 0.51 3.61 0.52
## Raw_d Resid_Mean_Male Resid_SD_Male Resid_Mean_Female Resid_SD_Female
## 1 0.23 -0.13 0.59 0.06 0.59
## 2 -0.09 0.09 0.57 -0.05 0.58
## 3 0.45 -0.11 0.56 0.05 0.49
## 4 0.36 -0.06 0.57 0.04 0.53
## 5 -0.06 0.07 0.62 -0.03 0.66
## 6 0.18 -0.08 0.56 0.05 0.57
## 7 0.30 -0.03 0.60 0.01 0.61
## 8 0.40 -0.10 0.55 0.05 0.56
## 9 -0.22 0.14 0.57 -0.07 0.58
## 10 0.27 -0.14 0.56 0.07 0.54
## 11 0.08 NA NA NA NA
## 12 0.48 NA NA NA NA
## 13 0.06 NA NA NA NA
## 14 0.39 NA NA NA NA
## 15 NA NA NA NA NA
## Resid_d
## 1 0.32
## 2 -0.24
## 3 0.31
## 4 0.18
## 5 -0.15
## 6 0.22
## 7 0.06
## 8 0.27
## 9 -0.36
## 10 0.39
## 11 NA
## 12 NA
## 13 NA
## 14 NA
## 15 NADescriptive 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 max range skew
## Sepal.Length 1 150 5.84 0.83 5.80 5.81 1.04 4.3 7.9 3.6 0.31
## Sepal.Width 2 150 3.06 0.44 3.00 3.04 0.44 2.0 4.4 2.4 0.31
## Petal.Length 3 150 3.76 1.77 4.35 3.76 1.85 1.0 6.9 5.9 -0.27
## Petal.Width 4 150 1.20 0.76 1.30 1.18 1.04 0.1 2.5 2.4 -0.10
## Species* 5 150 2.00 0.82 2.00 2.00 1.48 1.0 3.0 2.0 0.00
## kurtosis se
## Sepal.Length -0.61 0.07
## Sepal.Width 0.14 0.04
## Petal.Length -1.42 0.14
## Petal.Width -1.36 0.06
## Species* -1.52 0.07traits <- c("Enthusiasm", "Assertiveness", "Compassion", "Politeness", "Intellect")
male_means <- c(3.40, 3.34, 3.78, 3.52, 3.62)
male_sds <- c(0.66, 0.64, 0.60, 0.61, 0.61)
female_means <- c(3.56, 3.28, 4.04, 3.74, 3.48)
female_sds <- c(0.68, 0.68, 0.56, 0.61, 0.63)
descriptive_stats <- data.frame(
Trait = traits,
Male_Mean = male_means,
Male_SD = male_sds,
Female_Mean = female_means,
Female_SD = female_sds
)
print(descriptive_stats)## Trait Male_Mean Male_SD Female_Mean Female_SD
## 1 Enthusiasm 3.40 0.66 3.56 0.68
## 2 Assertiveness 3.34 0.64 3.28 0.68
## 3 Compassion 3.78 0.60 4.04 0.56
## 4 Politeness 3.52 0.61 3.74 0.61
## 5 Intellect 3.62 0.61 3.48 0.63Statistical Analysis
Analysis
Perform your chosen analysis. Make sure your output shows.
traits <- c("Enthusiasm", "Assertiveness", "Compassion", "Politeness", "Intellect")
m_m <- c(3.40, 3.34, 3.78, 3.52, 3.62); sd_m <- c(0.66, 0.64, 0.60, 0.61, 0.61)
m_f <- c(3.56, 3.28, 4.04, 3.74, 3.48); sd_f <- c(0.68, 0.68, 0.56, 0.61, 0.63)
d <- (m_f - m_m) / sqrt((sd_m^2 + sd_f^2) / 2)
data.frame(Trait = traits, Male_Mean = m_m, Female_Mean = m_f, Cohen_d = round(d, 2))## Trait Male_Mean Female_Mean Cohen_d
## 1 Enthusiasm 3.40 3.56 0.24
## 2 Assertiveness 3.34 3.28 -0.09
## 3 Compassion 3.78 4.04 0.45
## 4 Politeness 3.52 3.74 0.36
## 5 Intellect 3.62 3.48 -0.23Post-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
##
## Multiple regression power calculation
##
## u = 5
## v = 194
## f2 = 0.111
## sig.level = 0.05
## power = 0.9664004Results Interpretation
Females scored higher than males on Compassion and Politeness, supporting our hypothesis and consistent with past research. These differences were statistically significant, meaning they are unlikely due to chance. Males and females were similar on other traits
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.
#Example R code for creating a graph
# You will be performing a median split
# Median split for Experience to visualize the linear x linear interaction
# Load ggplot2
library(ggplot2)
traits <- c("Enthusiasm", "Assertiveness", "Compassion", "Politeness", "Intellect")
male_means <- c(3.40, 3.34, 3.78, 3.52, 3.62)
female_means <- c(3.56, 3.28, 4.04, 3.74, 3.48)
df <- data.frame(
Trait = rep(traits, 2),
Mean = c(male_means, female_means),
Gender = rep(c("Male", "Female"), each = length(traits))
)
ggplot(df, aes(x = Trait, y = Mean, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Mean Trait Scores by Gender", x = "Trait", y = "Mean Score") +
theme_minimal()
Discussion
Discuss the implications of your results for psychological theory or practice. Address the following points:
- Implications: What do your findings mean in the context of existing research? -Our findings suggest that gender differences in personality traits such as Compassion and Politeness align with existing research indicating that females often score higher on agreeableness-related traits. The small to moderate effect sizes observed support prior literature showing subtle but consistent gender differences in personality.
- Limitations: Identify any limitations of your study. How might these limitations have affected your results? There are several limitations to our study that may influence the interpretation of the results. First, the data are cross-sectional, which limits any inference about causality or developmental changes over time. Second, the sample size and composition (e.g., demographics not detailed) may reduce the generalizability of the findings. Third, reliance on self-reported trait measures could introduce biases such as social desirability or inaccurate self-perception. Lastly, the use of residualized scores may control for confounds but also reduce interpretability regarding raw differences.
- Future Directions: Suggest potential future research directions based on your findings. -Future research should consider longitudinal designs to track how gender differences in personality traits evolve over time.
References
List the articles you reviewed in APA format. Do not worry about the indentations. Weisberg, Y. J., DeYoung, C. G., & Hirsh, J. B. (2011). Gender differences in personality across the ten aspects of the Big Five. Frontiers in Psychology, 2, 178. https://doi.org/10.3389/fpsyg.2011.00178