Dr. J. ANAIAPPAN M.D., D.C.HSenior Assistant Professor

Department of Community medicine Kilpauk Medical College

Introduction Types of sampling methods Probability sampling methods Non-probability sampling methods Choice of sampling methods

Definition of samplingWhat is the need for sampling?What defines a proper sample?

Definition: Sampling is a process by which some persons

/objects / elements /events are selected from the predetermined population for carrying out studies and drawing inferences about the population as a whole.

Sampling is a process of selecting a required number of individuals from the study population so as to make observations on the sample instead of whole population

Principle of sampling : To get maximum information about the population with

minimum effort and with limited resourcesObjectives of sampling : Estimation of population parameters (proportion or mean) from the

sample statistics To test the hypothesis about the population from which the samples are

drawn

Studying the entire population is difficult It will be costly, time consuming and not feasible Studying the whole population is impossible and

unnecessary

If sampling is done properly : Accurate and reliable estimates can be made More characteristics or details can be collected Project management is easy Can get best possible results in least possible time

Sampling is inevitable when : Population is infinite Results are required in a short time Area is wide Resources are limited

What determines a proper sample? Representativeness Unbiased selection Adequacy of the sample

Representativeness: Sample has all the important characteristics and similar

distribution Requires knowledge of variables and their distribution

in the population Statistical sampling methods – gives reasonable

guarantee of representativeness

Bias occurs when : Wide difference between the estimate of the sample &

the true population value Some members are underrepresented or

overrepresented than others in the population Own bias or prejudice Laziness and sloppiness

Reasons for a biased sample : Faulty selection of sample Substitution Faulty demarcation of sampling units Non-response

Good sampling results in : Reduction of cost Saving of time Reduction in manpower requirement

Gives more accurate results than attempts to study the entire population

Population : ( universe ) The group of individuals or units possessing certain

predetermined characteristic intended for the study Population is an aggregate of elements (ie) persons,

objects, households or specified events

Representative sample : It has all the characteristics with similar distribution as

that of the population from which it is drawnSampling frame : It is the list of all elements – persons, households,

objects, specified events or units – in the population eg. Voter’s list

Sampling unit : It is the constituent elements of a population which are

to be sampled from the population and cannot be further subdivided for the purpose of sampling at a time

It is the unit of selection in the sampling process (eg) a person, a patient, a household, a village, a town, a hospital or a district

Sampling Fraction : The proportion of population that is included in the

sample (eg) 20%Sample : A finite subset of a population, a portion chosen from a

defined populationSample size : The number of units in a sample

Sampling error is any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size

Basics of Sampling TheoryBasics of Sampling Theory

Population

Element

Defined target population

Sampling unit

Sampling frame

Types of sampling :

Probability sampling or Random sampling

Non-Probability sampling or Non-Random sampling

It uses some form of random selection All units in the study population have an equal chance

for being chosen for the study Best among all the methods Most powerful statistical analysis on the results can be

done subsequently

Random sampling methods are : Simple random sampling (unrestricted) Systematic random sampling (quasi-random) Stratified random sampling Cluster sampling (area sampling) Multistage sampling Multiphase sampling

Difference between random and non-random sampling is selection of sample unit does not ensure a known chance to the units being selected

May lead to unrepresentative samples It lacks accuracy in view of selection bias

Does not involve random selection Subject to prejudice and bias of researcher May not represent the population well Used when there is no sample frame for the population Mostly used in qualitative research like exploratory

research, opinion surveys and marketing studies

Methods : Purposive sampling (judgemental sampling) Convenience sampling (oppurtunity sampling) Quota sampling Expert opinion sampling Snowball sampling (chain sampling, chain referral

sampling or referral sampling)

Important and frequently used methods : Simple random sampling Systematic random sampling Stratified random sampling Cluster sampling Multi-stage sampling

Define the study population ( N ) Prepare a proper sampling frame (n) Determine the sample size Select the required number of samples

Selection of required number of samples by : Lottery method – small population Random number method – by using standard tables

( Tippet’s table, Fisher and Yate’s table and Kendall and Smith’s table )

Computer generated random numbers

Advantages : Personal bias is eleminated Representative of a homogenous population No need for thorough knowledge of the units of

population Accuracy of the sample can be tested Used in other methods of sampling

Disadvantages : Cannot be used for large population When there is large difference between units Units of sample lie apart geographically Cost and time of collection of data are more Logistically more difficult in field conditions

Simple & convenient way of selecting a sample Requires less time and cost Sample is spread evenly over entire reference

population Can be used in infinite population

This method requires sampling frame Units are selected at an uniform interval Useful when information is collected from units which

are in serial order (ie) enteries in register, house in blocks etc

Method : Identify the sample size (n) Put the population in sequential order & number them

serially – sampling frame Identify total no.of units in the population (N)

Method : Divide N/n = sampling interval (k) Identify a random no.which is less than or equal to ‘k’ Select every n’th item starting with a random one

Dividing the population into subgroups or strata - stratification

Units within the stratum are homogenous and between the strata are heterogeneous

From each stratum a simple random sample is selected and combined together to form the required sample from the population

Two types : Unequal size - Proportional stratified random sampling Equal size – Disproportionate stratified random

sampling

Sample size in each stratum is Unequal size - proportionate to the no. of units in each

stratum Equal size - disproportionate to the no. of units in

each stratum

Advantages : Every unit in the stratum has the same chance of being

selected More representative Ensures proportionate representation Greater accuracy Greater geographical concentration

Limitations: Division of population into strata needs more money,

time and statistical experience Improper stratification leads to bias – if there is

overlapping of strata

The whole population is divided into groups called clusters.

Each cluster is representative of the population Clusters are selected randomly A random sample is then is taken from within each

cluster

Lot of clusters are sampled so that the results can be generalized for whole population

Clusters should be as small a possible consistent with the time & cost limitations

No. of units in each cluster must be more or less equal Is a simple random sample of cluster of elements

Examples : WHO 30 clusters for coverage evaluation survey Pulse polio immunization coverage evaluation survey

Eg: In a PHC estimate the proportion of infants with age 6 months to 1 yr who are fully immunized .

1) Identification of total population and the geographical area

2) Identification of age group to be included3) Listing of all villages4) Tabulation

village Population Cumulative population

clusters

1.Adgaon 947 947 12.Asgaon 1208 2155 23.Borphal 712 2867 34.Bilaspur 3012 5879 4,5,65.Chitegaon 631 6510 76.Dhoregaon 1709 8219 87.Esapur 413 8632 98.Girnar 1203 9835 109.Goregaon 5153 14988 11,12,13,14,1510.Himmatpur 3128 18116 16,17,1811.Lalwadi 3689 21805 19,20,2112.Puri 1529 23334 2213.Solegaon 2604 25938 23,24,2514.Tisgaon 3210 29148 26,27,2815.Yeoti 2057 31205 29,30TOTAL 31205

5) Sampling interval (S.I.): Total cumulative Population 31205 S.I. = ------------------- = 1040. Number of clusters 306) Selection of a starting point7) Selecting subsequent clusters

C2 = random number + S.I.= 0196+1040= 1236 C30 = c29 + S.I.

8) Selecting first household in a cluster9) Collection of information

Advantages Disadvantages Cuts down the cost of

preparing sampling frame and cost of travelling between selected units

Eliminates the problem of “packing”

Sampling errors is usually higher than for a simple

Random sample of the same size

Used for large and diverse populations (eg) nation, region or state

Usually carried out in phases Involves more than one sampling methods Example : Estimating the problem of Iodine

deficiency disorders in India

First stage : few states are randomly selected Second stage : few districts from above states Third stage : few blocks from above districts Fourth stage : few villages from above districts Fifth stage : few households from each village

ADVANTAGES : Sample frame for individual units not required Cuts down the cost of preparing sample frame DISADVANTAGES : final sample may not be representative of the total

population Sampling error is increased, when compared with

simple random sampling

Non-probability Sampling

Does not involve random selection Subject to prejudice and bias of investigator May or may not represent the population well Used when there is no sampling frame Used in qualitative research If the investigator is experienced may yield valuable

results

Convenience sampling Judgemental /purposive sampling Quota sampling Snow ball sampling

Accidental, opportunity, accessibility or haphazard sampling

Use of readily available persons for the study-sample of convenience

Stopping people in a street corner, people select themselves in response to public notices-risk of bias is greater.

Lack of representativeness Used for making pilot studies

Judgmental sampling Researchers knowledge about the population can be

used to hand-pick sample members, knowledgeable about the study

Used in newly developed instruments can be pretested and evaluated

Researcher utilizes knowledge about the population –representativeness into the sampling plan

Population is divided into quotas – age, socioeconomic status, religion etc.

Number of units within each quota –personal judgment of the investigator.

Used by quantitative researchers Used in public opinion studies

Network or chain/referral sampling Research population of specific traits-difficult to

identify Early sample members asked to refer other people

who meet eligibility criteria Sampling hidden populations-homeless or IV drug

users-respondent driven sampling (rds),variant of snow ball sampling.

METHOD BEST WHEN Simple random whole population sampling is available Stratified random when specific sampling subgroups are to be investigated

METHOD BEST WHEN Systematic random when a stream of sampling representative people are available Cluster sampling when population groups are separated & access to all is difficult

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