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Random Sampling Method

Random 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. Among the various sampling methods used by...

Systematic Sampling Method

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...

Surveys

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.

Stratified Sampling Method

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...

Unusual Results

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
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Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Related Experiment Videos

[New outcome rates for random sampling telephone surveys].

P Guilbert¹, N Beltzer, A Gautier

¹Direction des affaires scientifiques, Institut national de prévention et d'éducation pour la santé (Inpes), Saint-Denis, France. philippe.guilbert@inpes.sante.fr

Revue D'Epidemiologie Et De Sante Publique

|March 15, 2011

Summary

Survey non-response rates alone are insufficient for quality assessment. New participation and contact rates offer a clearer comparison of survey quality and potential bias.

Related Experiment Videos

Area of Science:

Public Health
Survey Methodology
Epidemiology

Background:

Survey non-response rates are critical quality indicators, but inconsistencies exist in their calculation and use.
High refusal rates can introduce non-response bias into health survey estimates.
This study addresses the need for improved survey quality indicators.

Purpose of the Study:

To compare non-response rates and other quality indicators across two French telephone surveys.
To propose and evaluate alternative indicators for assessing survey quality and comparability.
To highlight the limitations of traditional non-response rates in estimating survey quality.

Main Methods:

Comparison of outcome rates from two random-digit dialing (RDD) telephone surveys: Nicolle (infectious diseases, 2006) and KABP (HIV, 2004).
Detailed description of sample drawing and non-response rate estimation protocols.
Analysis of participation rates and contact rates beyond standard non-response metrics.

Main Results:

Non-response rates differed significantly (36% Nicolle vs. 18% KABP).
However, participation rates were equivalent, influenced by a higher proportion of unreached calls in the KABP survey.
The number of telephone numbers needed per interview varied (2.1 in Nicolle vs. 2.8 in KABP).

Conclusions:

Traditional survey non-response rates are inadequate for comprehensive quality evaluation.
Proposed indicators, including participation rate and contact rate, offer a more transparent method for comparing survey quality.
These indicators, summing to 100%, facilitate easier manipulation and comparison across different surveys.