sampling techniques & samples types
TRANSCRIPT
SAMPLING TECHNIQUES
& SAMPLES TYPES
By: Puneet GuptaM.Tech (Future Studies and
Planning)
Sampling…
The process of selecting a number of individuals
for a study in such a way that the individuals
represent the larger group from which they were
selected
Sampling…….
SAMPLE
STUDY POPULATION
TARGET POPULATION
A sample is “a smaller collection of units from a population used to determine truths about that population”
The sampling frame A list of all elements or other units containing the elements in a population.
Population…
…the larger group from which individuals are selected to participate in a study
Why Sample?
Get information about large populationsLower costMore accuracy of resultsHigh speed of data collectionAvailability of Population elements.Less field timeWhen it’s impossible to study the whole population
Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability sampling method will be chosen
Plan procedure for selecting sampling units
Determine sample size
Select actual sampling units
Stages in the Selectionof a Sample
The sample must be:1. representative of the population;2. appropriately sized (the larger the better);3. unbiased;4. random (selections occur by chance);
What is Good Sample?
•Probability sample – a method of sampling that uses of random selection so that all units/ cases in the population have an equal probability of being chosen. • Non-probability sample – does not involve random selection and methods are not based on the rationale of probability theory.
Types of SamplingSampling Techniques
Probability Non-Probability
Simple random sample
Systematic random sample
Stratified random sample
Cluster sample
Probability (Random) Samples
Non-Probability Samples– Convenience samples (ease of access)
sample is selected from elements of a population that are easily accessible– Purposive sample (Judgmental Sampling)
You chose who you think should be in the study– Quota Sampling– Snowball Sampling (friend of friend….etc.)
Difference between Probability sampling and Non Probability
PROBABILITY SAMPLING
SIMPLE RANDOM SAMPLING• Applicable when population is small, homogeneous &
readily available• All subsets of the frame are given an equal probability.
Each element of the frame thus has an equal probability of selection. A table of random number or lottery system is used to determine which units
are to be selected.
SIMPLE RANDOM SAMPLINGAdvantages:
Minimal knowledge of population needed Easy to analyze data
Disadvantages: Low frequency of use Does not use researchers’ expertise Larger risk of random error
Simple random sampling
Every subset of a specified size n from the population has an equal chance of being selectedSunil Kumar
• Similar to simple random sample. No table of random numbers – select directly from sampling frame. Ratio between sample size and population size• Then every nth number on the list is selected• N= Sampling Interval
Systematic Sampling
SYSTEMATIC RANDOM SAMPLING
Advantages: Moderate cost; moderate usage Simple to draw sample Easy to verify
Disadvantages: Periodic ordering required
Systematic sampling
Every member ( for example: every 20th person) is selected from a list of all population members.
Sunil Kumar
Stratified Random Sample The population is divided into two or more groups
called strata, according to some criterion, such as geographic location, grade level, age, or income.
Subsamples are randomly selected from each strata.
STRATIFIED RANDOM SAMPLINGAdvantages:
Assures representation of all groups in sample population
Characteristics of each stratum can be estimated and comparisons made
Disadvantages: Requires accurate information on proportions
of each stratum Stratified lists costly to prepare
CLUSTER SAMPLING Cluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those areas is
selected. Population divided into clusters of homogeneous units,
usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. The population is divided into subgroups (clusters) like
families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed
CLUSTER SAMPLING
Advantages: Can estimate characteristics of both cluster and
populationDisadvantages:
The cost to reach an element to sample is very high Each stage in cluster sampling introduces sampling
error—the more stages there are, the more error there tends to be
Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
NONPROBABILITY SAMPLES
CONVENIENCE SAMPLINGConvenience sampling involves choosing respondents at the convenience of the researcher.Advantages Very low cost Extensively used/understoodDisadvantages Variability and bias cannot be measured or
controlled Projecting data beyond sample not justified Restriction of Generalization.
CONVENIENCE SAMPLING
QUOTA SAMPLINGThe population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.Advantages Used when research budget is limited Very extensively used/understood No need for list of population elementsDisadvantages Variability and bias cannot be measured/controlled Time Consuming Projecting data beyond sample not justified
JUDGEMENTAL SAMPLING Researcher employs his or her own "expert” judgment about.Advantages There is a assurance of Quality response Meet the specific objective.Disadvantages Bias selection of sample may occur Time consuming process.
SNOWBALL SAMPLINGThe research starts with a key person and introduce the next one to become a chainAdvantages Low cost Useful in specific circumstances & for locating
rare populationsDisadvantages Not independent Projecting data beyond sample not justified
SNOWBALL SAMPLING