3.sampling

7/28/2019 3.Sampling

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SAMPLING

• Sampling: is a process used to study a

response to an intervention in a small

population that can be applied to a larger population



What is the purpose of

sampling?

• To draw conclusions about populations

from parameters such as

mean,total,variance standard deviationetc



The sampling process

• Defining the target population .

• Specifying a sampling frame a set of items or

events possible to measure• Specifying a sampling method for selecting items

or events from the frame

• Determining the sample size

• Implementing the sampling plan• Sampling and data collecting

• Reviewing the sampling process



Types of Sampling

• Non probability Sampling /Non random

Sampling

• Probability/Random Sampling’



Non probability Sampling /Non

random SamplingCONVENIENCE SAMPLING

Uses the most readily available subjects

and is the easy method to obtain subjects

Problem: risk of bias is very great

and sample tends to be self selecting:



Purposive Sampling

Quota sampling

• The population is first segmented into

mutually exclusive sub-groups

• Each stratum of the population is

represented proportionally

• selection of the sample is non-random



Judgement Sampling

• When researcher select sample member to

conform to some criteria

• It involves the assembling of a sample of

persons with known or demonstrable

experience and expertise in some area.



Snowball Sampling

• Snowball sampling is especially useful

when you are trying to reach populations

that are inaccessible or hard to find

• Identify someone who meets the criteria

for inclusion in your study. You then ask

them to recommend others who they mayknow who also meet the criteria



Probability Sampling

• Simple Random Sampling

• Systematic Random Sampling

• Cluster Random Sampling

• Stratified Random Sampling

• Multi phase Sampling



Simple Random Sampling

• Objective: To select n units out of N such that each NCn has an equal chance of being selected.

• N = the number of cases in the sampling frame

• n = the number of cases in the sample• NCn = the number of combinations (subsets) of n from N

• f = n/N = the sampling

• Procedure: Use a table of random numbers, a computer random number generator, or a mechanical device to select

the sample.• Probability of selection=

Sample size/population size



Systematic Random Sampling

• Number the units in the population from 1

to N

• Decide on the n (sample size) that you want

or need

• k = N/n = the interval size

• Randomly select an integer between 1 to k

• Take every kth unit



Stratified Random Sampling

Determine the variables to use for stratification

and there proportion in the population.

Select proportionate or disproportinatestratification

Divide the population into non-overlapping

groups (i.e., strata) N1, N2, N3, ... Ni, such that

N1 + N2 + N3 + ... + Ni = N. Then do a simple

random sample of f = n/N in each strata .



Cluster (Area) Random Sampling

• Divide population into clusters (usually

along geographic boundaries)

• Randomly sample clusters

• Measure all units within sampled clusters



Multi-Stage Sampling

• When we combine sampling methods, we

call this Multi-stage sampling.

• Also known as sequential sampling and

Double Sampling

• Mostly used with stratified or cluster

design.

3.sampling

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