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Sample Size Calculation01:19

Sample Size Calculation

6.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Related Experiment Video

Updated: Feb 22, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

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Sample Size Calculations for Population Size Estimation Studies Using Multiplier Methods With Respondent-Driven

Elizabeth Fearon1, Sungai T Chabata2,3, Jennifer A Thompson2

  • 1Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom.

JMIR Public Health and Surveillance
|September 16, 2017
PubMed
Summary
This summary is machine-generated.

This study provides a sample size calculation method for population size estimation using multiplier methods and respondent-driven sampling surveys. It aims to reduce random error and improve precision, especially when the proportion of service recipients (P) is low.

Keywords:
HIVdata collectionpopulation surveillanceresearch designsample sizesampling studiessex workerssurveys and questionnaires

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Social Sciences

Background:

  • Existing guidance for population size estimation using multiplier methods with respondent-driven sampling (RDS) surveys lacks specific direction on sample size decisions.
  • Accurate population size estimates are crucial for public health interventions and resource allocation.

Purpose of the Study:

  • To develop and present a method for sample size calculation in population size estimation studies employing multiplier methods and RDS surveys.
  • To reduce the random error and enhance the precision of population size estimates.

Main Methods:

  • The study utilizes a formula where population size is estimated by dividing the number of services/objects (M) by the proportion of recipients in a survey (P).
  • A novel approach for sample size calculation was developed by interpreting variance estimation methods for multiplier methods, considering design effects and RDS.
  • The methodology was applied to estimate the population size of female sex workers in Harare, Zimbabwe.

Main Results:

  • Population size estimates derived from multiplier methods and RDS surveys exhibit significant variance.
  • Random error in estimates is influenced by uncertainty in both M and P, with low estimates of P exacerbating this uncertainty.
  • Higher sample size requirements correlate with greater assumed design effects in the survey.

Conclusions:

  • A method is proposed to assess the impact of sample size on the precision of population size estimates obtained through multiplier methods and RDS.
  • Given the high uncertainty, particularly with small P, researchers are advised to consider longer service attendance reference periods or distributing more unique objects to increase P.
  • Balancing precision with potential biases is essential when determining sample size and study design parameters.