sampling techniques & samples types

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