Methodology Draft: The Goal Of The Study Was To Look ✓ Solved

Methodology Draft Purpose: The goal of the study was to look

Methodology Draft Purpose: The goal of the study was to look at the data and get a sense of what people think about marijuana legalization in the United States. Sampling: To study the relationship between legalization of marijuana and race, age, religious beliefs, gender, and education. Narrow to the people of Bridgeport. Use a sample of 1,000 participants. Use social media, marijuana-related websites, and local distribution to recruit participants. Survey contains ten questions about marijuana. Offer incentives by randomly selecting 10 participants to receive small monetary or gift rewards (total budget ~$1,000). Data: Classify respondents by age groups (18-30, 30-50, 50+), religion, race (Asian, Black, White), education, and gender. Analyze data in Excel and present results using percentage pie charts for each category and report counts of support vs opposition to legalization.

Paper For Above Instructions

Study background and objective

This methodology outlines a cross-sectional public-opinion study in Bridgeport to measure attitudes toward marijuana legalization and to describe how opinions vary by race, age, religion, gender, and education. The aim is to recruit and analyze responses from N = 1,000 adults using a 10-question survey, classify respondents into demographic categories (age: 18–30, 30–50, 50+; race: Asian, Black, White; religion; education; gender), and report results using percentage distributions and pie charts. The approach balances feasibility with representativeness and follows established survey best practices (Dillman, Smyth, & Christian, 2014).

Study design

The study will use a descriptive, cross-sectional survey design to capture opinions at a single point in time. A mixed-mode recruitment strategy (online social media, marijuana-related websites, and local distribution channels such as community centers and libraries) will improve coverage and reach subgroups that vary in internet use (Bethlehem, 2010). The fixed sample size (1,000) provides adequate precision for subgroup percentage estimates: with n≈1,000, a proportion estimate has a margin of error of approximately ±3 percentage points at 95% confidence for overall estimates, and larger but acceptable error for major subgroups.

Sampling and recruitment

Target population: adults (≥18 years) residing in Bridgeport. A non-probability quota-style approach will be used to ensure minimal representation across key strata (age, race, gender, education, religion). Recruitment channels include targeted social media advertising by zip code, postings on local and marijuana-related websites, and in-person flyers with QR codes at community sites. To reduce volunteer bias, quota monitoring will be employed so enrollment is paused for oversampled strata and prioritized for undersampled groups (Bethlehem, 2010; Dillman et al., 2014).

Survey instrument

The instrument will be a brief online/print questionnaire of ten items focused on (a) support or opposition to legalization (binary and Likert follow-up), (b) perceived harms and benefits, (c) personal use history, and (d) demographic items (age bracket, race, religion, gender, education). Questions will be piloted with ~30 respondents to test clarity and timing. Question wording will follow standard survey principles to minimize social desirability and measurement error (Tourangeau, Rips, & Rasinski, 2000).

Incentives and response rate strategy

To boost participation, 10 respondents will be randomly selected to receive modest monetary or gift rewards, with a total incentive budget near $1,000. Evidence shows that modest unconditional incentives or prize draws increase response without substantially biasing estimates when applied uniformly (Singer & Ye, 2013). Recruitment materials will clearly explain eligibility, voluntary participation, and the random drawing procedure to maintain transparency.

Data management and quality control

All survey responses will be collected through a secure platform and exported to Excel for cleaning and analysis, following the user's preference. Data cleaning steps: remove duplicates, verify zip codes for Bridgeport residency, check for straight-lining or implausible completion times, and document all exclusions. Demographic variables will be coded into the prescribed categories (age brackets 18–30, 30–50, 50+; race coded as Asian, Black, White; education levels collapsed into meaningful groups). Missing-data patterns will be examined; simple imputation is not planned for primary descriptive estimates.

Data analysis plan

Primary analyses are descriptive: calculate frequencies and percentages for support vs opposition to legalization overall and within demographic strata. For visual presentation, create pie charts for each key variable (age, race, religion, education, gender) to display percentage distributions as requested. Cross-tabulations will show support/opposition percentages by stratum (for example, percent supporting legalization among 18–30 vs 30–50 vs 50+). Where appropriate, 95% confidence intervals for proportions will be computed to quantify precision. All computations and charts will be prepared in Excel with documented formulas and source data tabs to enable reproducibility.

Ethical considerations

Participation will be voluntary and anonymous. The consent statement will explain the study purpose, data use, privacy protections, and incentive process. No sensitive personal identifiers will be collected; only demographic categories and opinion items. The project will seek local institutional review or a determination of exempt status if affiliated with an institution, following human-subjects guidelines, and will comply with local data-protection norms.

Limitations

This design uses a mixed non-probability recruitment strategy and therefore cannot produce strict probability-based inference for the whole Bridgeport population. Quota balancing reduces but does not eliminate selection bias; results should be interpreted as indicative of patterns rather than exact population parameters (Bethlehem, 2010). Self-report bias and social desirability may affect responses, particularly on substance-use items. The 1,000-person ceiling improves feasibility but limits precision for small subgroup analyses.

Outputs and reporting

Final deliverables will include: (1) a cleaned dataset, (2) Excel tables of frequencies and crosstabs, (3) pie charts for each demographic variable showing percentage distributions, and (4) a short report summarizing key findings (overall support/opposition counts, subgroup contrasts, and limitations). Visuals will be captioned and optimized for clarity to support dissemination to stakeholders and community partners.

Conclusion

This methodology provides a practical, transparent framework to gather and display community opinions on marijuana legalization in Bridgeport using a 1,000-person sample, targeted recruitment, a 10-item survey, modest incentives, and Excel-based analysis. While the design trades off full probability representativeness for feasibility, it produces timely, well-documented descriptive evidence about how attitudes vary across age, race, religion, gender, and education.

References

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  • Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188.
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  • Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. Wiley.
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  • Singer, E., & Ye, C. (2013). The use and effects of incentives in surveys. The ANNALS of the American Academy of Political and Social Science, 645(1), 112–141.
  • Volkow, N. D., Baler, R. D., Compton, W. M., & Weiss, S. R. (2014). Adverse health effects of marijuana use. New England Journal of Medicine, 370(23), 2219–2227.

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