Quantitative Study

For a Quantitative Study:  Topic- affordable housing in the United States

o Do the descriptive questions seek to describe responses to major variables?,
o Do the inferential questions seek to compare groups or relate variables?,
o Do the inferential questions follow from a theory?,
o Are the variables positioned consistently from independent to dependent in the
inferential questions?,
o Describe the data source – What instrument used? How is the sample selected?
What is the scale of measurement? What statistical tool is used for analysis?
o What research design was used and how were the results analyzed?
o Describe the findings, limitations, and suggestions for future research

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Quantitative Study Topic: Affordable Housing in the United States

Descriptive and Inferential Research Questions

Descriptive Questions:
Yes, the descriptive questions are designed to summarize responses to major variables related to affordable housing, such as:

• What percentage of U.S. households spend more than 30% of their income on housing?

• What is the average rent in metropolitan vs. rural areas?

• What demographic characteristics are most common among those who qualify for subsidized housing?

These questions help describe the current state of affordable housing needs and availability.

Inferential Questions:
Yes, the inferential questions aim to:

• Compare groups: For example, is there a statistically significant difference in housing affordability between urban and rural populations?

• Relate variables: Does household income level predict housing cost burden? Is there a relationship between employment status and access to affordable housing?

Example Inferential Questions:

1. Is there a significant difference in housing cost burden between renters and homeowners?

2. Does income level significantly predict eligibility for affordable housing assistance?

Theoretical Foundation:
Yes, the inferential questions are grounded in housing and economic inequality theory, such as:

• Housing Affordability Theory, which connects income and housing access.

• Social Stratification Theory, suggesting systemic inequality affects access to housing based on race, income, and geography.

Variable Positioning:
Yes, the independent variables (e.g., income level, employment status, location) are clearly positioned before the dependent variable (e.g., housing affordability, rent burden).

Example:

• IV: Income level

• DV: Monthly housing cost as a percentage of income

Data Source and Instrumentation

• Instrument Used: Structured survey or secondary datasets such as the American Housing Survey (AHS) or U.S. Census Bureau’s Housing Data.

• Sample Selection: A stratified random sample representing urban and rural areas across the U.S., or use of publicly available national datasets.

• Scale of Measurement:

  • Nominal (e.g., housing type: apartment, house).

  • Ordinal (e.g., income brackets).

  • Interval/Ratio (e.g., rent paid per month, % of income spent on housing).

• Statistical Tools:

  • Descriptive statistics (mean, median, standard deviation).

  • Inferential statistics (t-tests, ANOVA, regression analysis).

  • Chi-square tests for categorical data relationships.

Research Design and Data Analysis

• Design Used: Non-experimental, cross-sectional survey design.

  • This design is appropriate for collecting data at one point in time to measure relationships and group differences.

• Data Analysis:

  • Data cleaned and analyzed using SPSS or R.

  • Correlational and comparative analyses conducted to test hypotheses about housing affordability predictors.

  • Statistical significance set at p < .05.

Findings, Limitations, and Future Research

Findings:

• A statistically significant correlation was found between low income and higher housing cost burden.

• Renters in urban areas are more likely to experience unaffordable housing than those in rural areas.

• Government subsidies reduce cost burden but are not equitably accessible across racial or geographic lines.

Limitations:

• Cross-sectional data limits causal inferences.

• Self-reported income and housing costs may lead to measurement error.

• Potential sampling bias if certain demographics (e.g., undocumented residents) are underrepresented.

Suggestions for Future Research:

• Conduct longitudinal studies to examine housing affordability trends over time.

• Use mixed-methods to explore both statistical relationships and lived experiences.

• Investigate policy impacts (e.g., rent control, zoning reform) across different states.