A researcher wants to ensure that every group in a population has an equal chance of being selected. Which sampling technique is most appropriate?

1. A

   Simple Random Sampling

2. B

   Stratified Random Sampling

3. C

   Purposive Sampling

4. D

   Convenience Sampling

Appeared in: NORCET 9 Mains - 2025

Explanation

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• Stratified random sampling involves dividing a population into smaller, homogeneous subgroups called strata.
• The primary purpose of this method is to ensure that every subgroup is adequately represented in the final sample.
• After stratification, a random sample is drawn from each stratum, guaranteeing inclusion from all defined groups.
• This technique is ideal for heterogeneous populations where the researcher needs to compare different subgroups.

Why Other Options Were Wrong

• Option A: Simple random sampling gives every individual an equal chance of being selected, but it does not guarantee that every group within the population will be represented. Small groups can be missed by chance.
• Option C: Purposive sampling is a non-probability method where the researcher selects subjects based on their judgment and the study's purpose, not on random chance. It does not give every group an equal opportunity for selection.
• Option D: Convenience sampling is a non-probability method that involves selecting the most easily accessible subjects. It is highly prone to bias and does not ensure that the sample represents the broader population or its subgroups.

Related Visual

• Visual 1: Flowchart - A flowchart illustrating the steps of stratified random sampling: 1. Define Population -> 2. Divide into Strata (e.g., Group A, Group B, Group C) -> 3. Randomly Sample from Each Stratum -> 4. Combine to form Final Sample. This visually separates it from simple random sampling.
• Visual 2: Diagram - A diagram showing a diverse population being separated into three distinct color-coded groups (strata), with arrows indicating that a random selection of individuals is taken from each of the three groups.

Clinical Relevance

• Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Sampling Techniques in Research as background academic context rather than a clinical decision trigger.
• In nursing research, stratified sampling is crucial for ensuring that findings are relevant across different patient populations. For example, when studying the effectiveness of a new patient education program, a researcher might stratify by age (pediatric, adult, geriatric) to ensure the program's impact on each group is assessed.
• This method helps in obtaining a representative sample when studying health outcomes in a hospital with different units (e.g., ICU, medical-surgical, pediatrics), ensuring nurses or patients from each unit are included.
• What if? If a researcher wanted to compare outcomes between male and female nurses in a large hospital system, they would use stratified sampling with gender as the strata to guarantee both groups are equally represented for a valid comparison.

How to Approach the Question

• First, identify the key requirement in the question stem: the researcher wants to 'ensure that every group... has an equal chance of being selected.'
• Interpret this phrase. It means the researcher wants to guarantee that all subgroups are represented in the sample.
• Next, review the definitions of the four sampling techniques provided in the options.
• Eliminate the non-probability sampling methods (Purposive and Convenience) because they do not involve 'chance' in a statistical sense and cannot guarantee representation.
• Compare the two probability methods: Simple Random and Stratified Random. Ask yourself which one is specifically designed to handle 'groups'.
• Recall that 'Stratified' sampling's main purpose is to divide the population into groups (strata) and sample from each one, which directly matches the researcher's goal.

Concept Tested & Keywords

• Concept Tested: Sampling Techniques in Research
• Stem keywords: researcher, sampling technique, population, group, equal chance
• Lead-in keywords: most appropriate

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