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8/8/2019 36847049 Sampling Distribution 1
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Sampling
Group I
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Sampling
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Sampling Distribution
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Sampling Distribution If we had a sampling
distribution, we
would be able topredict the 68, 95
and 99% confidence
intervals for where
the populationparameter should
be!
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Sampling Distribution
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Sampling Distribution Sampling Error
In sampling contexts, the standard error is called sampling error . Sampling
error gives us some idea of the
precision of our statistical estimate. A
low sampling error means that we hadrelatively less variability or range in the
sampling distribution
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Random Sampling Methods Some Definitions
Before understanding methods some basic
terms have to be understood. These are: 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 fraction
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Random Sampling Methods Simple Random Sampling
The simplest form of random sampling
Objective: To select n units out of N such
that each N C n has an equal chance of being
selected.
Procedure: Use a table of random numbers,a computer random number generator, or a
mechanical device to select the sample.
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Random Sampling Methods Stratified Random Sampling
It is also sometimes called proportional or quota random sampling, involves
dividing your population into
homogeneous subgroups and then
taking a simple random sample in eachsubgroup.
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Random Sampling Methods
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Systematic Random Sampling
Here are the steps you need to follow in order
to achieve a systematic random sample: number the units in the population from 1 to N
decide on the n (sample size) that you wantor need
k = N/n = the interval size
randomly select an integer between 1 to k
then take every kth unit
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Random Sampling Methods Cluster sampling
This sampling implies dividing the population
into clusters and drawing random sampleseither from all clusters or selected ones
In cluster sampling, we follow these steps:
divide population into clusters (usually along
geographic boundaries) randomly sample clusters
measure all units within sampled clusters
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Multi-stage sampling
Here the sampling is selected invarious stages but only the last stagesare studied
It could be a combination of
1. Simple + simple sampling2. Simple + systematic sampling
3. Systematic + systematic sampling
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Non-probability Sampling
Non-probability sampling does not employ the rules of probability theory.
They do not claim representativenessand are usually used for qualitativeexploratory analysis.
The different types are:
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Non probability sampling Purposive Sampling
In purposive sampling, we sample witha purpose in mind. We usually wouldhave one or more specific predefinedgroups we are seeking.
Here some variables are givenimportance and it represents theuniverse
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Non probability sampling Convenience Sampling
Here all those persons who are most conveniently available or whoaccidentally come in contact during acertain period of time in the research.
Also called Haphazard or accidentalsampling
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Non-Probability Sampling Quota Sampling
In quota sampling, you select people non-
randomly according to some fixed quota.There are two types of quota sampling:
proportional and non proportional . In
proportional quota sampling you want to
represent the major characteristics of thepopulation by sampling a proportional amount
of each.
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Non probability sampling Non-proportional quota sampling is a
bit less restrictive. In this method, you
specify the minimum number of
sampled units you want in each
category
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Non-Probability Sampling Snowball Sampling
In snowball sampling, you begin by identifying
someone who meets the criteria for inclusionin your study. You then ask them to
recommend others who they may know who
also meet the criteria.
Snowball sampling is especially useful when
you are trying to reach populations that are
inaccessible or hard to find.
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