Description
Sampling in medical research involves selecting a subset of individuals from a larger population to study. The sample size calculation is crucial for determining the number of participants needed to ensure statistical validity and reliable results. Factors such as the desired level of confidence, anticipated effect size, variability, and statistical power are considered to strike a balance between precision and practicality. Adequate sample size enhances the generalizability of findings and supports the robustness of research outcomes, providing a foundation for evidence-based medical practices.
Résumé
- The discussion starts with an analogy of sampling from everyday life, like checking a few grains of wheat before buying a whole lot. It highlights the importance of sampling in medical research where a sample is used to draw conclusions about a target population, such as studying foot ulcers in diabetic patients in Mumbai without examining every diabetic case. Scientific sampling methods are emphasized as crucial for valid research, and common terms like population, sample, and sampling are defined.
- The transcript explains the differences between population, study population, and sample in the context of research. The target population is where findings are generalized, while the study population is accessible for the study. A sample is the specific group of participants studied. It presents examples using research questions about injections, needlestick injuries, and prevention policies in India to illustrate the concept of the study population. The terms sampling unit, sampling frame, and sampling scheme are also defined and explained with examples.
- The different types of sampling are categorized into probability and non-probability sampling. Probability sampling includes simple random, stratified random, systematic, cluster, multi-phase, and multi-stage sampling. Non-probability sampling includes purposive, quota, snowball, sequential, and convenience sampling. It provides an overview of each technique, highlighting the importance of random sampling in ensuring every member of the population has an equal chance of being selected.
- In probability sampling, systematic sampling involves selecting every Kth unit. Stratified sampling classifies the population into homogeneous groups, while cluster sampling involves selecting random groups of units. Multi-stage sampling requires several chained samples, and multi-phase sampling involves different phases of sampling with partial data collection in each phase. Non-probability sampling includes judgment sampling, quota sampling, sequential sampling, snowball sampling, and convenience sampling.
- The advantages and disadvantages of probability and non-probability samplings are discussed. Non-probability sampling is suitable for hospital-based studies, while probability sampling is ideal for community-based studies. It then provides steps for conducting the open API for generating random numbers and calculating sample size.
- Lastly, it delves into the sample size calculation formula for descriptive studies (n = p(1-p) * (z/e)^2). The components of the formula are explained, including p (estimated proportion), q (1-p), e (precision level), and z (statistical confidence level). It provides an example of calculating the sample size for diabetic retinopathy prevalence and suggests a strategy for addressing anticipated attrition rates by adding 10% more samples to the calculated number.
Exemple de certificat
À propos des intervenants
Dr Amrut Swami
Professeur associé, médecine communautaire, Tantia Medical College, Rajasthan
Divulgation financière
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