Introduction

This report analyzes survey data on investor demographics and crowdfunding behavior. The dataset includes age, gender, education, professional experience, and investing activity. The goal is to identify trends among those who have participated in or supported equity-based crowdfunding campaigns. This report includes descriptive statistics and five distinct visualizations, each accompanied by interpretation and implications.

Load and Prepare Data

data <- read_excel("/Users/jasoncherubini/Dropbox (Personal)/7 - Wasteland/2025.03/DATA_For_JASP.xlsx", sheet = 1, skip = 1)

# Rename key variables for clarity
names(data)[names(data) == "What is your age?"] <- "Age"
names(data)[names(data) == "What is your gender?"] <- "Gender"
names(data)[names(data) == "What is your highest level of education?"] <- "Education"
names(data)[5] <- "Accredited"
names(data)[6] <- "Professional_Investor"
names(data)[7] <- "Entrepreneur"
names(data)[8] <- "Raised_CF"
names(data)[9] <- "Invested_Outside_CF"
names(data)[10] <- "Investing_Experience"

# Convert selected variables to numeric
vars <- c("Age", "Gender", "Education", "Accredited", "Professional_Investor",
          "Entrepreneur", "Raised_CF", "Invested_Outside_CF", "Investing_Experience")
data[vars] <- lapply(data[vars], as.numeric)

Descriptive Statistics

psych::describe(data[vars])
##                       vars   n mean   sd median trimmed  mad min max range
## Age                      1 320 2.97 0.93      3    2.86 0.00   1   6     5
## Gender                   2 320 0.54 0.50      1    0.55 0.00   0   1     1
## Education                3 320 3.98 1.12      4    4.14 0.00   1   6     5
## Accredited               4 320 0.58 0.49      1    0.60 0.00   0   1     1
## Professional_Investor    5 320 0.42 0.50      0    0.41 0.00   0   1     1
## Entrepreneur             6 320 0.63 0.48      1    0.67 0.00   0   1     1
## Raised_CF                7 320 1.50 1.00      2    1.50 1.48   0   3     3
## Invested_Outside_CF      8 320 0.75 0.43      1    0.81 0.00   0   1     1
## Investing_Experience     9 320 3.58 0.97      4    3.62 1.48   1   5     4
##                        skew kurtosis   se
## Age                    1.05     1.62 0.05
## Gender                -0.15    -1.98 0.03
## Education             -1.17     1.62 0.06
## Accredited            -0.31    -1.91 0.03
## Professional_Investor  0.30    -1.91 0.03
## Entrepreneur          -0.56    -1.70 0.03
## Raised_CF             -0.15    -1.08 0.06
## Invested_Outside_CF   -1.15    -0.68 0.02
## Investing_Experience  -0.20    -0.53 0.05

These statistics summarize the central tendencies and distributions of each key variable. We observe that most respondents cluster in the middle categories of age, education, and investment experience. Standard deviations suggest moderate variability across responses, particularly in investing behavior. This sets the stage for a more detailed visual exploration.

Visualization 1: Age Distribution

ggplot(data, aes(x = Age)) +
  geom_histogram(binwidth = 1, fill = "skyblue", color = "white") +
  labs(title = "Participant Age Distribution",
       x = "Age Group (Ordinal Code)",
       y = "Number of Participants")

Narrative:
The distribution of participant age shows a concentration in the mid-range categories. This suggests that individuals engaging in crowdfunding activities tend to fall within a specific age bracket—likely working professionals in their 30s or 40s. There are fewer younger and older respondents, possibly reflecting experience requirements or familiarity with digital investment platforms. Understanding this age concentration can inform targeted outreach strategies for future campaigns.

Visualization 2: Gender Distribution

ggplot(data, aes(x = factor(Gender))) +
  geom_bar(fill = "#FDC086") +
  labs(title = "Gender Distribution of Respondents",
       x = "Gender (0 = Female, 1 = Male)",
       y = "Count")

Narrative:
The gender distribution appears approximately balanced, with a slight overrepresentation of male respondents. This aligns with common trends in early-stage investing and entrepreneurial ecosystems, which often report higher male participation. However, the presence of female investors remains meaningful and should not be overlooked. A balanced perspective can help platform designers ensure inclusive access and messaging.

Visualization 3: Investing Experience by Gender

ggplot(data, aes(x = factor(Gender), y = Investing_Experience)) +
  geom_boxplot(fill = "#BEAED4") +
  labs(title = "Investing Experience by Gender",
       x = "Gender (0 = Female, 1 = Male)",
       y = "Self-Rated Investing Experience (1–5)")

Narrative:
The boxplot reveals that male respondents report slightly higher investing experience compared to female participants. While both groups show overlapping ranges, the median experience level is marginally higher for men. This could reflect differences in access to investing opportunities or confidence in self-assessing financial knowledge. Programs aimed at financial education and inclusion could help reduce this experience gap.

Visualization 4: Raised vs. Invested

ggplot(data, aes(x = Raised_CF, y = Invested_Outside_CF)) +
  geom_jitter(width = 0.2, height = 0.1, alpha = 0.6, color = "#7FC97F") +
  geom_smooth(method = "lm", color = "darkred", se = FALSE) +
  labs(title = "Raised vs Invested in Startups",
       x = "Raised via Crowdfunding (0 = No, 1+ = Yes/Multiple)",
       y = "Invested Outside Crowdfunding (0 = No, 1 = Yes)")

Narrative:
There is a weak positive trend between individuals who have raised funds via crowdfunding and those who have also invested in startups outside that channel. This may suggest a shared entrepreneurial mindset or network overlap. However, the variability in responses and binary coding limits the strength of this relationship. Further exploration using richer investment data could validate this connection.

Visualization 5: Correlation Heatmap

corr_data <- na.omit(data[vars])
corr_matrix <- cor(corr_data)
corrplot(corr_matrix, method = "color", type = "lower", tl.cex = 0.8)

Narrative:
The heatmap illustrates moderate positive correlations between education, accreditation status, and investing experience. Individuals who are accredited or better educated are more likely to report higher investment experience. Additionally, entrepreneurship shows some association with external investing and crowdfunding activity. These relationships suggest that investor profiles tend to cluster around experience, education, and professional engagement.

Summary of Findings