sampling class

8/7/2019 sampling class

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Sampling a Population


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Sampling

The process ofselecting a number of individuals for a

study in such a way that the individuals represent the

larger group from which they wereselected

SampleSample

the representativesselected for a study whosecharacteristicsexemplify the larger group from which

they wereselected

PopulationPopulation

the larger group from which individuals areselected to

participate in a study


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Thepurpo

sefor

sampling

To gather data about the population in

orde

r to make

an infe

renc

ethat can b

egeneralized to the population


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Thesampling proc

ess

POPULATION (N)

SAMPLE (n)

INFERENCE

IS THE SAMPLE

REPRESENTATIVE?


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Sampling Process...

2. Determine sample size (n)

3. Select sample

1. Define population (N) to be sampled


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1. Define population to be sampled...

(Identifying a suitable sampling frame)

Identify the group of interest and itscharacteristics to which the findings of

thestudy will be generalized

called the targettarget population

oftentimes the accessibleaccessible or

availableavailable population must be

used


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2.Deciding on a suitable sample size...

Thesize of thesample influences boththe representativeness of thesample

and thestatistical analysis of the data

larger samples are more likely

to detect a difference betweendifferent groups

smaller samples are more likely

not to be representative


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Rules of thumb for determining the sample size...

2. For smaller samples (N 100), there is little point in

sampling. Survey the entire population.

1. The larger the population size, the smaller the percentage

of the population required to get a representative sample

4. If the population size is around 1500, 20% should be

sampled.

3. If the population size is around 500 (give or take 100),

50% should be sampled.

5. Beyond a certain point (N = 5000), the population size is

almost irrelevant and a sample size of 400 may be

adequate.


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3. Select the sample...

(Estimating response rate and actual sample size)

(Response rate will be higher if sample is good representative )

Response Rate =Total number of responses

Total no.

of sample-(ineligible + unreachable)

Sample size= n 100re%


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Approaches to quantitative sampling...

2.2. NonrandomNonrandom (non-probability): does not have

random sampling at any state of the sample

selection; increases probability of sampling bias

(Research purpose is directed towards evaluating how a small

group is doing for the purpose of explanation or case study,

like: Which attributes attract people to join job)

1.1. RandomRandom (Probability)(Probability): allows a procedure governed

by chance to select the sample; controls for sampling

bias(to arrive at conclusions and to make predictions affecting the

population)


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Random sampling methods...

(Probability Sampling)

2. Stratified sampling

3. Cluster sampling

4. Systematic sampling

1. Simple random sampling


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Simple random samplingSimple random sampling: the process of selecting a

sample that allows individual in the defined population

to have an equal and independent chance of being

selected for the sample

Steps in random sampling...

1. Number each of the cases in your sampling frame

with a unique number. The first case is numbered 0,

the second 1 and so on.

2. Select cases using random numbers until your actual

sample size is reached.


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advantagesadvantages

easy to conductstrategy requires minimum knowledge of the

population to be sampled

disadvantagesdisadvantages

need names of all population members

may over- represent or under- estimate sample

members

there is difficulty in reaching all selected in the sample


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Systematic samplingSystematic sampling: the process of selecting

individuals within the defined population from a

list by taking every KKth name.

Steps in systematic sampling...

1. Obtain a list of the population.

2. Determine what

KKis equal to by dividing the size ofthe population by the desired sample size.

4. Starting at that point, take every KKth name on the

list until the desired sample size is reached.

3. Start at some random place in the population list.

5. If the end of the list is reached before the desired

sample is reached, go back to the top of the list.


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sample selection is simple


all members of the population do not have an equalchance of being selected

the KKth person may be related to a periodical order in

the population list, producing unrepresentativeness

in the sample


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Stratified samplingStratified sampling: It is a modification of random

sampling in which you divide the population into two or

more relevant and significant strata based on one or anumber of attributes.

Steps in stratified sampling...

1. Choose the stratification variable or variables.

2. Divide the sampling frame into the discrete strata.

3. Number each of the cases within each stratum with

a unique number.

4. Select your sample using either simple random or

systematic sampling.


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It can be used.

when population consist of a number of

heterogeneoussub-population and elements within a given subpopulation

are relatively homogenous.


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more precise sample

can be used for both proportions and stratification

sampling

sample represents the desired strata


need names of all population members

there is difficulty in reaching all selected in the sample

researcher must have names of all populations


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Cluster samplingCluster sampling: method in which the population is divided

into groups, any of which can be considered a representative

SampleSteps in cluster sampling...

1. Identify and define a logical cluster.

2. List all clusters (or obtain a list) that make up the

population of clusters.3. Estimate the average number of population members per

cluster.

6. Randomly select the needed number of clusters

5. Determine the number of clusters needed by dividing the

sample size by the estimated size of a cluster.

7. Include in your study all population members in each

selected cluster.


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efficient

researcher doesnt need names of all population

members

reduces travel to site

useful for educational research

disadvantagesdisadvantagesfewer sampling points make it less like that the sample

is representative


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Nonrandom

sampling m

ethod

s...

2. Purposive sampling3. Quota sampling

1. Convenience sampling

4. Snowball sampling


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1. Convenience samplingConvenience sampling: the process

of including whoever happens to beavailable at the time

called accidental or haphazard

sampling


difficulty in determining how much of

the effect (dependent variable) resultsfrom the cause (independent variable)


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2. Purposive samplingPurposive sampling: the process

whereby the researcher selects asample based on experience or

knowledge of the group to be sampled

called judgment sampling


potential for inaccuracy in the

researchers criteria and resultingsample selections


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3. Quota samplingQuota sampling: the process whereby

a researcher gathers data fromindividuals possessing identified

characteristics and quotas


people who are less accessible (more

difficult to contact, more reluctant to

participate) are under-represented


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4. Snowball sampling

It is commonly used when it is difficult to identifymembers of the desired population, for example

people who are working while claiming

unemployment benefit.

Make contact with one or two cases in thepopulation.

Ask these cases to identify further cases.

Ask these new cases to identify further new cases(and so on).

Stop when either no new cases are given or the

sample is as large as is manageable.


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Mistak

esto b

econ

sciou

sof...

2. Sampling bias

which threaten to render a studys

findings invalid

1. Sampling error


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Sampling errorSampling error

the chance and random variation invariables that occurs when any sample

is selected from the population

sampling error is to be expected


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to avoid sampling error, a censuscensus of

the entire population must be taken

to control for sampling error,

researchers use various sampling

methods


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Sampling biasSampling bias

nonrandom differences, generally thefault of the researcher, which cause the

sample is over-represent individuals or

groups within the population andwhich lead to invalid findings

sources of sampling bias include the

use of volunteers and available groups

sampling class

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