Hello,
I plan to conduct a cross-sectional survey among mothers (caregivers) in one of the rural regions of my country. The survey will assess mothers'/caregivers' knowledge and practices regarding breastfeeding, child nutrition, and health. The study does not have a control group. Is this information enough to understand the formula required for the sample size calculation?
I really appreciate any help you can provide.
I will suggest you check peer-reviewed articles similar to your study to see how the sample sizes were calculated. If the formula cannot be found in the articles, you can make use of the sample size from one of the articles, state the reasons given in the article and cite it in your study write-up. Hope this helps.
Hello Arno,
If your goal is parameter estimation (rather than hypothesis testing), then for probability-based sampling methods, the features to consider in deriving an estimated target sample size are:
1. The variance of the trait/feature/attribute you're trying to estimate (higher variance = larger required sample size, or N);
2. The precision with which you'd like to estimate the parameter (higher precision = larger N);
3. The risk you're willing to take on of the estimate differing from the true value by more than the target precision (lower risk = larger N);
4. The specific sampling method you use;
5. Whether the population is finite or infinite. If finite, the approximate size.
There are on-line calculators for these types of computations, if you are using simple random sampling, but most are designed for estimating population proportions (e.g., percent of voters endorsing candidate "X"). Here's an example: https://goodcalculators.com/sample-size-calculator/
An excellent resource is the classic text: Cochran, W. G. (1991). Sampling techniques (3rd ed.). New York: Wiley. ISBN: 978-0-471-16240-7
Good luck with your work.
An important consideration is your intentions regarding statistical analysis. My best guess is you'd be using correlational statistics like Pearson's or Spearman's because you have a one-group design. Some statistics texts have tables which provide minimum Ns for different levels of power at different alphas (typically .05 and .01) for different statistics , e.g. Jaccard & Becker, Statistics for the Behavioral Sciences, 4th Edition. Likewise, online power calculators like GPower offer a similar service.
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Hello, for an observational cross-sectional study, I suggest you use the formula for sample size for finite populations.
The formula used to calculate the sample size in a cross-sectional survey study is (Za)^2 [p*q] where N= d^2, and d is the meaningful difference between those exposed and those unexposed.
Calculating the sample size for a cross-sectional survey study involves several factors, including the desired level of precision, the confidence level, the estimated variability in the population, and the margin of error you find acceptable. The formula commonly used for estimating the sample size in a cross-sectional survey is based on the formula for calculating the sample size in a population proportion, and it's given by:
What is the formula for the sample size of a population proportion?
n = N*X / (X + N – 1), where, X = Zα/22 *p*(1-p) / MOE2, and Zα/2 is the critical value of the Normal distribution at α/2 (e.g. for a confidence level of 95%, α is 0.05 and the critical value is 1.96), MOE is the margin of error, p is the sample proportion, and N is the population size.
Here's a step-by-step guide on how to use this formula:
Keep in mind that this formula assumes a simple random sample and that the population size is large enough relative to the sample size (typically when the population is at least 10 times larger than the sample size). If your population is small, you may want to consider using the finite population correction.
Also, if your study involves multiple subgroups or strata, you may need to calculate sample sizes for each stratum and then combine them.
Remember, calculating the sample size is just one step in the planning process. Other considerations, such as budget constraints, feasibility, and the potential for non-response, should also be taken into account in the design of your survey.
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