Statistics for Social Research Final Project Guide

Introduction: Your Final Project Roadmap

Welcome to the final group project for Statistics for Social Research Fall 2025. This project challenges you to apply the R skills and statistical methods learned throughout the semester to investigate a relationship between an outcome variable (Y) and a key predictor (X) using a dataset of your choice. Whether you use the Future of Families dataset or find your own, this guide will walk you through each milestone: from selecting a research question and citing academic literature, to performing ordinary least squares (OLS) regression in R, and finally writing a polished report. By the end, you'll have a clear, step-by-step plan to earn full points and produce a compelling analysis.

Step 1: Choosing Your Research Question and Dataset

Your first task is to identify a testable research question about the relationship between one interval/ratio outcome variable Y and one key predictor X. For example, you might ask: How does household income (X) affect student test scores (Y) in the Future of Families dataset? Or, using a dataset from FiveThirtyEight, Does a team's payroll (X) predict its win percentage (Y) in the NBA? Trend-inspired examples like analyzing the impact of social media usage on academic performance or the effect of AI tool adoption on productivity are timely and engaging.

Ensure your dataset has at least 100 observations and includes both X and Y. Good sources include the Future of Families dataset (provided on Brightspace), Pew Research opinion polls, FiveThirtyEight sports/politics data, or Data is Plural. For this tutorial, we'll assume you've chosen the Future of Families dataset and want to explore how maternal education (X) predicts child's reading score (Y).

Step 2: Literature Review and Hypothesis Formation

Before running any code, find at least two academic articles that motivate your research question. For our example, you might cite a study showing that maternal education positively correlates with children's cognitive development. Based on the literature, state your hypothesis: We hypothesize that higher maternal education is associated with higher child reading scores, even after controlling for household income and parental involvement. This step is worth 10 points and sets the stage for your statistical analysis.

Step 3: Data Preparation in R

Load your dataset into R. For a CSV file, use read.csv(). For other formats like .dta, use the haven package:

install.packages("haven")
library(haven)
fragile_families <- read_dta("fragile_families.dta")

Subset your focal variables and handle missing values. For example, keep only rows where maternal education and reading score are not NA:

library(dplyr)
my_data <- fragile_families %>%
  select(momeduc, read_score, income, parent_involve) %>%
  filter(!is.na(momeduc), !is.na(read_score))

Always check for outliers and recode variables if needed. Use summary() and hist() to explore distributions.

Step 4: Descriptive Statistics and Visualization

Produce summary statistics for Y and X:

summary(my_data$read_score)
sd(my_data$read_score, na.rm = TRUE)
table(my_data$momeduc)

Create a scatterplot to visualize the relationship:

plot(my_data$momeduc, my_data$read_score,
     xlab = "Maternal Education (years)",
     ylab = "Child Reading Score",
     main = "Scatterplot of Reading Score vs. Maternal Education")

Add a simple linear regression line:

abline(lm(read_score ~ momeduc, data = my_data), col = "blue")

This step helps you and your reader see the pattern before formal modeling.

Step 5: Ordinary Least Squares (OLS) Regression

Run a bivariate regression first:

model1 <- lm(read_score ~ momeduc, data = my_data)
summary(model1)

Interpret the coefficient: a one-year increase in maternal education is associated with a change of [coefficient] points in reading score. If the p-value is less than 0.05, the relationship is statistically significant.

Now add control variables to address alternative explanations. For example, include household income and parental involvement:

model2 <- lm(read_score ~ momeduc + income + parent_involve, data = my_data)
summary(model2)

Compare the R-squared and adjusted R-squared to see if the model fit improves. Check for multicollinearity using variance inflation factor (VIF) if needed.

Step 6: Model Diagnostics

Validate regression assumptions:

Linearity: Plot residuals vs. fitted values. No obvious pattern.
Normality of residuals: Q-Q plot or Shapiro-Wilk test.
Homoscedasticity: Breusch-Pagan test or residual plot.
Independence: Durbin-Watson test for autocorrelation.

If assumptions are violated, consider transforming variables (e.g., log of income) or using robust standard errors.

Step 7: Reporting Results

In your final report, present a table of regression results. You can use the stargazer package to create well-formatted tables in LaTeX or HTML:

install.packages("stargazer")
library(stargazer)
stargazer(model1, model2, type = "text",
          title = "OLS Regression Results",
          dep.var.labels = "Child Reading Score")

Interpret the key findings in plain language. For example: After controlling for household income and parental involvement, maternal education remains a significant predictor of child reading scores (β = 2.1, p < 0.001). A one-year increase in maternal education is associated with a 2.1-point increase in reading score.

Step 8: Writing the Report Sections

Your final report should include:

Introduction (5 points): Describe the phenomenon, why it matters, preview data/methods, and highlight central findings.
Literature Review, Research Question, and Hypothesis (10 points): Summarize at least two academic sources, state your research question, and justify your hypothesis.
Data, Sample, and Key Variables: Describe the dataset, sample size, variable definitions, and any data cleaning steps.
Methods: Explain that you used OLS regression and list control variables.
Results: Present descriptive statistics, plots, regression tables, and interpretation.
Discussion: Relate findings to literature, acknowledge limitations, and suggest future research.

Use R Markdown to knit your report to PDF. Ensure your code chunks are well-commented and output is clean.

Final Tips for Success

Start early and divide tasks among group members. Use version control (e.g., GitHub) to collaborate. Double-check variable coding and missing values. If you choose your own dataset, avoid files larger than 50 MB to keep R responsive. Remember, the goal is to demonstrate your ability to apply regression analysis to a meaningful social research question. Good luck!