Sampling methods in medical research

Dr Bindu John Pulparampil

What is Sampling ?
Procedure by which some members of a population are selected as representative of the entire population

The sub-group thus selected to represent the whole population is known as SAMPLE

Methods Of  Sampling
Several methods are used to ascertain a particular aspect of the population,through an unbiased sample drawn from the population

Sampling is divided in two categories

 1. Probability Sampling

 2. Non probability Sampling

Probability sampling

  • It is any method of sampling that utilizes some form of random selection
  • The procedure should assure that the different units in the  population have equal probabilities of being chosen.

Non probability sampling

  • It does not involve random selection
  • May or may not represent the population well
  • Used when researcher lacks a sampling frame for the population

 Probability sampling includes

  • Simple Random Method
  • Systematic Sampling
  • Stratified Sampling
  • Cluster Sampling
  • Multistage Sampling

Non-probability Sampling includes

  • Accidental Sampling
  • Voluntary Sampling
  •  Purposive Sampling
  • Quota Sampling

Simple Random Sampling
A sample selected such that each possible sample combination has equal probability of being chosen.

Also called Unrestricted random sampling

Two types of Simple Random Sampling

1 ) Simple random sampling without replacement

  • In this method the population elements can enter the sample only once
  • The units once selected is not returned to the population before the next draw 

2 ) Simple random sampling with replacement

The population units may enter the sample more than once

Methods of selection of a simple random sampling:

  • Lottery Method
  • Table of Random numbers
  • Random number selections using calculators or computers

 Systematic Random Sampling

  • Also called Quasi-random sampling
  • Divide the population size by the sample size, to get sampling fraction
  • Select a random number between 1 and sampling fraction, which is the first sampling unit
  • Systematically select the remaining sample units, by adding sampling fraction

 Stratified Random Sampling

  • Stratification means division into groups.
  • In this method the population is divided into a number of subgroups or strata
  • From each stratum a simple random sample is selected and combined together to form the required sample from the population

 Multi- Stage Sampling

  • Used in large scale investigations
  • First stage- preparation of large sized sampling units
  • Randomly selecting a certain number
  • Second stage- Another list prepared from them
  • Sub-samples drawn by random sampling

 Multi –Phase Sampling

  • Used to obtain supplementary information
  • Certain items of information collected from all units of sample
  • Other items collected from only some of sampling units

 Cluster Sampling

  • Each sampling unit is a collection or cluster of elements
  • Used when units of population are natural groups or clusters like wards, villages etc
  •  The group is taken as a sampling unit


Accidental Sampling

  • The “person on the street” interviews conducted frequently by television news programs
  • Sampling those most convenient
  • Gets a quick reading of public opinion
  • Also called Haphazard or Convenience Sampling

Voluntary sampling

  • The sample is self selected
  • Sample consists of people who chose themselves by responding to a general appeal.
  • They often over represent people with strong opinions, most often negative opinions.

 Purposive Sampling

  • Sampling with a purpose in mind
  • Handpicking supposedly typical or interesting cases
  • Reaches a targeted sample quickly
  • Also known as Judgemental sampling

 Types of Purposive Sampling

  • Sampling for specific types of people – modal instance, expert, or quota sampling.
  • Sampling for diversity – heterogeneity   sampling.
  • Snowball sampling – capitalize on informal social networks to identify specific  respondents who are hard to locate otherwise

 Modal instance sampling

  • Sampling the most frequent case, or the “typical” case
  • Is only sensible for informal sampling contexts.

 Expert sampling
 It involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area

Quota sampling

  • The population is first segmented into mutually exclusive sub-groups.
  • People are selected  nonrandomly according to some fixed quota
  • Judgement is used to select the subjects or units from each segment based on a specified proportion
  • Convenience sampling within population groups

  The quota sampling can be two types – Proportional and non proportional

  • Proportional quota sampling :It is  representing  the major characteristics of the population by sampling a proportional amount of each
  • Non-proportional quota sampling : The minimum number of sampled units in each category is specified

Heterogeneity Sampling
When  all opinions or views are to be included  and  are not concerned about representing these views proportionately

Snowball sampling

  • It begins by identifying someone who meets the criteria for inclusion in the  study.
  • They are asked  to recommend others who they may know who also meet the criteria

 Other Kinds Of  Sampling

  • Event Sampling : Using routine or special events as the basis for sampling
  • Time Sampling : Recognising that different parts of the day, week or year may be significant

Probability Sampling

  • Simple Random – Selection at Random
  • Systematic – Selecting every nth case
  • Stratified – Sampling within groups of Population
  • Cluster – Surveying whole clusters of Population
  • Multistage – Sub samples from large sample

Non- Probability Sampling

  • Accidental – Sampling those most convenient
  • Voluntary – Sample is self selected
  • Purposive – Handpicking typical cases
  • Quota – Sampling w/n groups of Population
  • Snowball – building sample thru informants

 The non- probability sampling is convenient and economical, the problem is that the results are unconvincing, as there are no criteria to measure representativeness or to assess the accuracy of estimators.

The element of randomness, in sampling procedures is essential for describing the representativeness and precicision of the survey and estimators

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