For studying mental health among the homeless in a large city, which nonprobability sampling technique is most appropriate?

• A. Convenience sampling
• B. Quota sampling
• C. Snowball sampling
• D. Purposive sampling

Answer: (A)

When researching mental health among homeless people in a large city, the practical goal is to gather data quickly from individuals who are accessible, since you likely don’t have a complete list of everyone who is homeless. This makes the easiest-to-reach approach the most sensible option. Convenience sampling recruits participants who are readily available—in this context, people you can approach at shelters, meal programs, day centers, or during street outreach. It’s feasible, fast, and cost-effective, and it lets you collect information from a broad mix of individuals who are currently accessible in real-world settings.

Keep in mind that this method brings bias because those who are easiest to reach may not represent all homeless individuals, and findings won’t be strongly generalizable to the entire homeless population. Other techniques have their uses: quota sampling tries to mirror subgroups but requires knowledge of their proportions and active recruitment to meet quotas, which is tough without a solid frame; snowball sampling relies on social networks and can overemphasize connected individuals; purposive sampling focuses on specific characteristics and limits breadth. In this scenario, the practicality and speed of convenience sampling make it the best fit.