The strength of relationships among research variables for calculating sample size for quantitative study is… - RRB Nsg. Superintendent - 29 April 2025 (Shift-3rd)

RRB Nsg. Superintendent - 29 April 2025 (Shift-3rd)

Nursing Research & Statistics

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Appeared in: RRB Nsg. Superintendent - 29 April 2025 (Shift-3rd)

Explanation

Power analysis is the statistical method used to estimate the required sample size for a quantitative study.
A key component of power analysis is the 'effect size', which is defined as the strength of the relationship among research variables.
Researchers use an estimated effect size (based on prior studies or clinical significance) within a power analysis to calculate how many participants are needed to detect that effect.
Therefore, the method that uses the 'strength of relationships' to calculate sample size is power analysis.

Option A: Attrition refers to the loss of participants during a study. While it's crucial to account for attrition by increasing the initial sample size, it is not the method used to calculate the base sample size from the strength of variable relationships.
Option B: Population homogeneity (less variability) can reduce the required sample size. However, it is a characteristic of the population that influences the power calculation, not the method itself.
Option D: Subgroup analysis is the examination of specific segments of the sample. It necessitates a larger initial sample size to ensure adequate power for each subgroup, but it is an analytical goal, not the foundational method for calculating sample size based on relationship strength.

Visual 1: Flowchart: Illustrating the process of conducting a power analysis, showing inputs (Alpha, Power, Effect Size) and the output (Sample Size N).
Visual 2: Diagram: A seesaw diagram balancing the four components of power analysis (Power, Sample Size, Effect Size, Alpha) to show how changing one affects the others.

Clinical Relevance

Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Sample size determination in quantitative research as background academic context rather than a clinical decision trigger.
Nurses must be able to critically appraise research to engage in evidence-based practice. Understanding sample size helps determine if a study's findings are trustworthy or if the study was 'underpowered' and may have missed a true effect.
When reading a study, if the results are not statistically significant (p > 0.05), a nurse should check if the sample size was adequate. A small sample might be the reason for non-significant findings, not necessarily the absence of a true treatment effect.
What if a study reports a new intervention is 'not effective' but used a very small sample? A clinically savvy nurse would question this conclusion, recognizing the study might have had low power (a high chance of a Type II error), and would look for larger, more definitive studies before dismissing the intervention.

How to Approach the Question

First, identify the core question: It asks about the basis for calculating sample size in a quantitative study, specifically related to the 'strength of relationships'.
Recognize that 'strength of relationships' is the definition of 'effect size' in statistics.
Scan the options to see which one is a statistical procedure that uses 'effect size' to determine sample size.
Evaluate 'Power analysis': This is the primary statistical method for this purpose. It directly incorporates effect size.
Evaluate the other options: 'Attrition', 'homogeneity', and 'subgroup analysis' are all factors that influence the required sample size, but they are not the statistical method based on relationship strength. They are practical or population-related considerations.
Conclude that Power Analysis is the correct method that formally uses the strength of relationships (effect size) as a key input.

Concept Tested & Keywords

Concept Tested: Sample size determination in quantitative research
Stem keywords: sample size, quantitative study, strength of relationships, research variables
Lead-in keywords: based on

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