IFAD Vietnam RIMS Training Workshop 2011 (3 of 7)

8/3/2019 IFAD Vietnam RIMS Training Workshop 2011 (3 of 7)

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SAMPLING METHODS

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CONTENT

Sampling DesignTypes of Sampling Simple random sampling

Systematic sampling

Multi-stage sampling

Cluster sampling

Define sample size

Types of survey error

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Sampling Methods

Non-probability sampling (no randomization) Exploratory research

Convenience sampling (accidental sampling)

Sampling without knowing the probability of thesample

Probability sampling Generalization for the total population

Undeviating results Sampling knowing the probability is in the sample

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Non-probability Sampling

Convenience sampling (easy sampling)Choose sample based on relationship

network (e.g.. based on friends)

Purposive Sampling (judgmental

sampling)Select sample based on the research objects

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Probability of being chosen is unknown

Cheaper - but unable to generalise, potential for bias


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Probability Sampling

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Simple random sampling Systematic sampling

Stratified sampling

Multi-stage sampling

Cluster sampling


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Simple Random Sampling

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Definition: Each element of the frame has anequal probability of selection and anygiven pair of elements has the same chanceof selection as any other such pair

Selected probability: n/Nwhere n= sample size; N= population size

Principles:

The selected probabilities of all elements

are equal Sampling steps:

Defining the tot population

Listing all elements to be sampled

Numbering all the elements


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Advantages: Simple

Easy to measure sampling errors

Disadvantages: All elements to be listed

Not always choose the best representative sample

Elements may be separate and difficult to gather


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Ex.: estimate the ratio of dental caries among 1200students

List out all students

Number these students from 1-1200

Choose sample size = 100

Choose randomly 100 numbers from 1 - 1200

How to conduct random sampling?


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Excel: data analysis Stata: sample


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Systematic Sampling

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Principle:

Sampling within regular interval depending on sampling

intervals

Advantages: Simple

Easy to measure errors in sampling

Disadvantages:

All elements need to be listed

Must be circulated


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Systematic Sampling

Steps

N = 1200, and n = 60

sampling interval = 1200/60 = 20

List out the students from 1 - 1200 Choose randomly a number within 1 - 20 (e.g..

8)

Choose the 1st student = the 8th student in thelist

Choose the 2nd student = 8 + 20 = the 28thstudent in the list

and so on12


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Systematic Sampling

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Stratified Sampling

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Principles:Categorize the sampling frame into

separate strata. (E.g. age strata,

occupation strata)Choose sample randomly in each stratum


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Stratified Sampling

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Advantages:

Can collect data of the total and of each stratum.

High correctness if the differences within each subgroup are

more homogeneous than those among subgroups. Disadvantages:

Difficult to define strata

Accuracy of sampling no longer exist if there is not enough

elements in each stratum. Can be fixed by selecting the sampling rate based on the total

number of element in each stratum


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Multi-stage Sampling

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Principles:- Consecutive sampling

Ex.:

Sampling element: Household Stage 1: select areas

Stage 2: select buildings

Stage 3: select households


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Cluster Sampling

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Principles:Sampled elements not defined

independently but within a group (cluster)Must indicate logical advantages


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Cluster Sampling

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Principles: The total population is divided into groups (e.g.. living

areas)

Choose randomly a group among these

Within the selected group, choose all households (e.g..households in the group)


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Example of cluster sampling

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Area 4

Area: 5

Area 3

Area 2Area 1


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Cluster Sampling

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Advantages: Simple, because not required to list all sampling

elements in the population

Cheap price

Disadvantages: It possible that elements in the same group are more

similar than those in other groups (homogeneity) This dependence needs to be considered in the

sample size and during the analysis (design effect)


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Choosing Sampling Methods

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Studied population Scale and allocation in terms of geography

Heterogeneity among variables

Availability of the list of sampling elements

Required correctness

Available resources


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Define sample size: SRS

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Consider the following: Accuracy

Differences against each research objectives

Formula Sample size no = I

2 * (pq)accuracy level

Ex.: no = 1.962 * (.5 * .5)

.052

Sample size not dependent on total scale


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Other things about sample size

Defined population Finite Population Correction

n = no/(1 + no/N)

Design Effect

Analysis of subgroups

Increase sample size to supplement unansweredcases

Costs

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Survey Errors

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IFAD Vietnam RIMS Training Workshop 2011 (3 of 7)

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