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Sample Size Determination for Population Health Study

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This document is a Population Health Case Study focused on calculating the necessary sample size for a research project aimed at assessing city residents' awareness of physical activity guidelines. It defines key statistical values, justifies the use of proportion calculation, outlines the methodology, and discusses potential sources of bias. Description The study targets a population of 100,000 residents and uses statistical principles to determine an adequate sample size:Objective: To assess the population's awareness of physical activity guidelines.Methodology Rationale (Proportion): Proportion is the appropriate measure because the desired outcome is to determine the frequency, or percentage, of awareness within various categories1. An estimated proportion of 0.5 (50%) is used since no previous awareness data is available2222.Statistical Parameters:Population (N): 100,000 3Margin of Error (E): 0.07 (7%) 4Confidence Interval (CI): 95% 5Z-score: 1.96 (based on 95% CI) 6Sample Size Calculation:$$n = frac{Z^2 times p times (1-p)}{E^2}$$$$n = frac{1.96^2 times 0.5 times (1- 0.5)}{0.07^2} = 196$$Adequacy: A calculated sample size of 196 is deemed adequate because the 0.07 margin of error and 95% confidence level imply the sample is statistically sufficient to obtain significant and replicable outcomes with high precision7.Bias and Challenges: The study acknowledges various sources of bias common in large population studies, including sampling bias, selection bias, attrition bias, under-coverage bias, and non-response bias8888. These issues will be addressed using random sampling techniques and through community outreach efforts.

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Population Health Case Study




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Population Health Case Study

Target Population

The study aims to assess the population's awareness of the physical activity guidelines in

the city. Therefore, the study's target population is the city’s residents, with a population of

100,000. Various sources of bias may be witnessed in such studies, especially with a large

population. They include sampling and selection bias, attrition bias and under-coverage bias.

Other biases may include convenience sampling bias and unresponsive bias. Some of the simple

steps that will be taken include integrating diverse demographics and distributing the research

across a broad geographical location. Sampling bias could also be resolved using random

sampling techniques to minimize bias issues.

Values

Population—(N)—this is the city's total population—100 000.

The margin of error (E)- 0.07- this is the acceptable deviation.

Confidence Interval- (CI)- 95%.

Z score- the Z score is based on the 95% C. I am 1.96

Proportion- estimated proportion is 50%- 0.5 because a previous number is not provided.

Proportion or Mean

Proportion is used in this research because of the nature of the variables. For instance, the

desired outcome for this study is to determine the frequency, including in various categories.

Therefore, the proportion will be more accurate. The proportion of 0.5 indicates sufficiency even

in unknown true proportions.

Sample Size and Adequacy

Calculation method;

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