What Makes a Strong Proposal?
A good proposal demonstrates that you've thought carefully about what you want to learn and how you'll learn it.
Specificity is key. Instead of asking "How does childcare affect women?", ask "Do mothers with children under age 5 work fewer hours than mothers with school-age children, and does this gap differ by education level?" The more precise your question, the clearer your path to answering it.
Know your data. Strong proposals name specific datasets (like "IPUMS-ATUS 2018-2023") and identify the actual variables you'll use. This shows you've explored what's available and that your question is actually answerable with real data.
Make comparisons clear. The best research questions compare groups or test relationships: caregivers vs. non-caregivers, Bay Area vs. national averages, before vs. after, high-income vs. low-income families. Comparison gives your analysis direction.
Sketch your approach. You don't need a full methodology, but indicate what you'll do: "I'll calculate average eldercare hours by gender and income quartile" or "I'll compare wage levels for parents and non-parents controlling for education and age."
Connect to something bigger. Briefly explain why your question matters - does it reveal hidden inequality? Inform policy? Challenge assumptions? This shows you understand the broader context.
The more detail you provide, the stronger your proposal - and the more likely we are to see that you're ready to execute the project successfully.
Research Question: Do men and women express different levels of concern about property crime versus violent crime, and does this gender gap vary by urban versus rural residence?
Data Source: General Social Survey (GSS), 2018-2022 waves
Variable FEAR: "Is there any area right around here—that is, within a mile—where you would be afraid to walk alone at night?"
Variables on perceived neighborhood safety and specific crime concerns
Demographic variables: SEX, REGION, URBANICITY
Can cross-tabulate with AGE, RACE, INCOME to control for confounding factors
Approach: Compare fear of crime responses by gender, then stratify by urban/suburban/rural location. Test whether the gender gap in crime fear is larger in cities. Will create summary statistics and visualizations showing fear levels across demographic groups.
Why this matters: Understanding gendered differences in crime perception can inform public safety policy and urban planning, particularly around public transportation and street lighting.
Research Question: What do people think about crime?
Data Source: I will look at crime data
Approach: I will analyze the data and see what patterns emerge