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Related Concept Videos

Surveys02:16

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Convenience Sampling Method00:55

Convenience Sampling Method

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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Data Collection by Survey01:07

Data Collection by Survey

The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Systematic Sampling Method01:17

Systematic Sampling Method

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...
Stratified Sampling Method01:16

Stratified Sampling Method

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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...

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Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
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Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

Published on: March 14, 2018

Evaluation of respondent-driven sampling.

Nicky McCreesh1, Simon D W Frost, Janet Seeley

  • 1Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, UK.

Epidemiology (Cambridge, Mass.)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) can yield representative samples, but current inference methods struggle to correct for biases. Caution is advised when interpreting RDS findings due to potential inaccuracies in estimating population characteristics.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research Methods

Background:

  • Respondent-driven sampling (RDS) is a link-tracing technique for surveying hard-to-reach populations.
  • Its effectiveness in real-world scenarios, particularly for estimating health indicators like HIV prevalence, remains under-evaluated.
  • This study assesses RDS performance against comprehensive population data.

Purpose of the Study:

  • To evaluate the accuracy of Respondent-Driven Sampling (RDS) estimates.
  • To compare RDS survey results with total population data in rural Uganda.
  • To assess the efficacy of current RDS statistical inference methods in mitigating sampling bias.

Main Methods:

  • A Respondent-Driven Sampling (RDS) survey was conducted within a known population of 2402 male household heads in rural Uganda.
  • RDS sample characteristics were compared against complete population data on demographics, sexual activity, and HIV status.
  • Analyses included the full RDS sample and a subset of the first 250 recruits.

Main Results:

  • The RDS samples (full and small) generally represented the population but underrepresented younger, higher socioeconomic status, and those with unknown HIV/sexual activity status.
  • Current RDS statistical inference methods did not effectively reduce identified biases.
  • RDS estimates were only marginally more accurate than raw sample proportions, and confidence intervals frequently failed to capture true population values.

Conclusions:

  • While RDS can generate representative samples in well-connected populations, its inference methods are insufficient for bias correction.
  • The ability of RDS to collect data for bias removal and precision measurement remains uncertain.
  • RDS should be viewed as a form of convenience sampling, necessitating careful interpretation of results.