Dr. Syed Irfan Ali

SAMPLING DESIGNS IN OPERATIONAL HEALTH RESEARCH

RESEARCHBasic/

applied

Empirical/

theoretical

Health research triangle

Research

Behavioural

Biomedical

Health research

Quantitative

Qualitative

A delegate search/ investigation/

experimentation for discovery/ interpretation of

knowledgeDefined goal/ purpose

Observation/ theory

Changes at celluar levelEnvironment causing cellular

changesInteractions reflecting the KAB of

individual in society

HEALTH RESEARCH

Health research basically is a quantitative type of research & it has three basic components

MeasurementOfvariable

• Organize, order observation

EstimationOfparameters

• Population parameters estimated by statistical methods

Hypothesistesting

• The extent to which chance may account for observation

SAMPLE Definition of sample and

population

Census and sampling

Parameters and statistics

Basis of sampling error

Advantages

• Less resources• More accuracy less non sampling error• Only method in case of infinite population

Disadvantage

s

• Sampling error• Bias

Universe or populationTarget population

Sample frameSample population

Sample designSampling units

TYPES OF ERROR IN SAMPLING

Sampling error

• Faulty selection of the sample

• Substitution

• Faulty demarcation of sampling unit

• Error due to improper choice of the statistics for estimating the population parameters

Non sampling error

• Faulty planning and definitions

• Response errors

• Non-response

• Compiling errors

• Publication errors

TYPES OF SAMPLING DESIGN

• Probability sampling Simple Random Sampling Complex Random Sampling (mixed

sampling) Designs Stratified Sampling Cluster Sampling Area Sampling Systematic Sampling Multistage Sampling Sequential Sampling

• Non probability samplingConvenience or haphazard

samplingPurposive / Deliberate samplingJudgment SamplingQuota Sampling Snowball sampling

PROBABILITY SAMPLING• Subjects of the sample are chosen based on known

probabilities.

• Advantages of probability sampling-

I. The population of interest is clear (because it must be identified before sampling from it.)

II. Possible sources of bias are removed, such as self-selection and interviewer selection effects.

III. The general size of the sampling error can be estimated.

SIMPLE RANDOMSAMPLING

why

• Simplest and easiest, requiring minimum prior knowledge• Free of classification error. Easy collection &

interpretation

basic

• Each element & sample has an equal probability of getting selected

• Element chosen randomly by random tables.

Types

• With replacement- used in infinite population• With out replacement.

Disadvantages

• Useful for a relatively small sample• Sample frame is a pre requisite

From a sample frame

Randomly select

elements

The selected elements form the sample

SYSTEMIC SAMPLING

• Decide on sample size: n

• Divide population of N individuals into groups of k individuals: k = N/n

• Randomly select one individual from the 1st group.• Select every k-th individual thereafter.

•Advantage:

I. The sample usually will be easier to identify than it would be if

simple random sampling were used.

II. Time and labour is relatively small.

III. It yields accurate results if the population is large and

homogenous.

N = 12

n = 4

k = 3

STRATIFIEDSAMPLING population

strata

strata

stratastrata

why

• Subpopulation in an overall population might vary• A weighted mean is obtained

basic

• Based on common characters divided into strata• Mutually exclusive and collectively exhaustive• Less difference with in and greater difference between the strata

Types

• Proportionate- basic stress is on representation of subgroups• Disproportionate- stresses on the validity

Advantages

• Takes into account various sub groups• Increased precision/ validity/ reduced sampling

error• More convenient & lower cost

Disadvantage

s

• Difficult to identify and justify subgroups representing population. Prior information of the characteristic is needed

• Separate sampling frame required for each strata

From a population

Based on an attribute

divide into strata

Select elements

from strata by randomizatio

n

CLUSTER SAMPLING

why

• Large population size• Sampling frame not present• Less resources required

basic

• There are 2 sampling units – PSUs & SSUs. PSUs are selected randomly, from 1 PSUs all SSUs are selected

Types

• Multistage cluster- some not all SSUs are selected randomly

Disadvantages

• Very high error for error to be low Most of the variations should be within the groups not between them or use design having large number of PSUs with small number of SSUs.

naturally occurring groups in a population

Select the PSUs or Clusters Randomly

Select all the SSUs from the selected PSUs

STRATIFICATION VS. CLUSTERING

Clustering1. • Divide population into

comparable groups: schools, cities

2. • Randomly sample some of the groups

3. • More error compared to simple random

4. • Reduces costs to sample

5. only some areas or organizations

Stratification1. • Divide population into groups

different from each other: sexes, races, ages

2. • Sample randomly from each group

3. • Less error compared to simple random

4. • More expensive

5. obtain stratification information before sampling

MULTI STAGE SAMPLING• Complex form of cluster sampling in which two or more levels of units

are embedded one in the other.

• This technique, is essentially the process of taking random samples of preceding random samples.

• It banks on multiple randomizations

• Multistage sampling used frequently when a complete list of all members of the population does not exists or is inappropriate.

• Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large unnecessary, costs associated with traditional cluster sampling.

MULTIPHASE SAMPLING• Part of the information collected from whole sample & part

from subsample.

• In Tb survey cough in all cases – Phase I

• X –Ray chest in MT +ve cases – Phase II

• Sputum examination in X – Ray +ve cases - Phase III

• Survey by such procedure is less costly, less laborious & more purposeful

NON PROBABILITY SAMPLING• Any sampling method where some elements of population have no

chance of selection (these are sometimes referred to as 'out of coverage'/'under covered'), or where the probability of selection can't be accurately determined.

• It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection.

• Hence, because the selection of elements is nonrandom, nonprobability sampling not allows the estimation of sampling errors.

QUOTA SAMPLING

• The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.

• Then judgment used to select subjects or units from each segment based on a specified proportion.

• For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

• In quota sampling the selection of the sample is non-random.

CONVENIENCE SAMPLING• Sometimes known as grab or opportunity sampling or accidental or

haphazard sampling.

• A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.

• This type of sampling is most useful for pilot testing.

PURPOSIVE OR JUDGEMENTAL SAMPLING• The researcher chooses the sample based on who they think would

be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched

SNOW BALL SAMPLING• Find a few people that are relevant to your topic.

• Ask them to refer you to more of them.

SAMPLING DESIGN & ITS ASPECTS-

population Sampling unit

Sampling frame

Parameter at study

resources

Sample size

Type of sample

CHARECTERS OF A GOOD SAMPLE DESIGN• True representative

• Has all characteristics that are present in population

• Sampling error should be small

• Bias is minimal

• Economically viable

• Results can be applied to the universe in general with a reasonable level of confidence or reliability

• Optimum size (adequately large)

IN A NUT SHELL• Probability Sampling

- Simple Random – Selection at Random

- Systematic – Selecting every nth case

- Stratified – Sampling w/n groups of Populn

- Cluster – Surveying whole clusters of P/n

- Multistage – Sub samples from large smpl

• Non- Probability Sampling

- Accidental – Sampling those most convnt

- Voluntary – Sample is self selected

- Purposive – Handpicking typical cases

- Quota – Sampling w/n groups of Ppln

- Snowball – building sample thru informnts

REFERENCES• Parks Text book of preventive and social medicine- K.Park

• Sampling: Design and Analysis Sharon L. Lohr

• Fundamentals of Biostatistics Bernard Rosner

• Principals and practice of biostatistics Dr J B Dixit

• HEALTH RESEARCH METHODOLOGY A Guide for Training in Research Methods-WORLD HEALTH ORGANIZATION

• TextBook of Public Health and Community Medicine Chief Editor RajVir Bhalwar