Question at position 7 Dr. Sean is studying the effectiveness of a mobile mental health app across entire China. They randomly select 10 states, then within each state randomly select 5 hospitals, and finally sample 20 patients from each hospital who used the app. What sampling method is being used?c) Multistage samplingd) Convenience samplinga) Simple random samplingb) Stratified random sampling

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Question at position 7 Dr. Sean is studying the effectiveness of a mobile mental health app across entire China. They randomly select 10 states, then within each state randomly select 5 hospitals, and finally sample 20 patients from each hospital who used the app. What sampling method is being used?c) Multistage samplingd) Convenience samplinga) Simple random samplingb) Stratified random sampling

BlackTom AI · Accepted Answer

Question restatement: Dr. Sean is evaluating a mobile mental health app by sampling across China. The design described is: randomly selecting 10 states, then within each state randomly selecting 5 hospitals, and finally sampling 20 patients from each hospital who used the app. What sampling method is being used?

Option a) Simple random sampling

Explanation: Simple random sampling means every individual in the entire population has an equal chance of being chosen, typically via randomization of individuals. Here, individuals are selected through a hierarchical process across states and hospitals, not by selecting individuals directly from the whole population at random. This makes simple random sampling inaccurate for describing the method used.

Option b) Stratified random sampling

Explanation: Stratified sampling divides the population into strata (for example by region or stage) and then samples from each stratum, often proportionally. In this scenario, although there is a multi-level structure, the selection is not first by creating strata with specified proportions and then sampling within them in a way that ensures representation across strata; instead, it proceeds in stages through clusters (states, hospitals) with random selections at each stage. Therefore, it does not fit the classic stratified sampling framework.

Option c) Multistage sampling

Explanation: Multistage sampling involves selecting samples in successive stages, typically starting with larger clusters and progressively sampling within those clusters. Here, the process starts with selecting 10 states (clusters), then within each state selecting 5 hospitals (sub-clusters), and then sampling 20 patients from each hospital. This nested, sequential sampling at multiple levels is characteristic of multistage sampling, which is often used for large, geographically dispersed populations.

Option d) Convenience sampling

Explanation: Convenience sampling draws participants who are readily accessible, without randomization or systematic structure to ensure representativeness. The description explicitly uses random selection at each stage (states, then hospitals), which rules out convenience sampling as the method used. Convenience sampling would not involve random selection across hierarchical levels.

Summary: Across the given design, the sampling proceeds through multiple hierarchical levels with randomization at each stage, matching the definition of multistage sampling. The other options fail to capture the staged, cluster-based random selection process described.

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