36847049 Sampling Distribution 1

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Sampling

Group I



Sampling



Sampling Distribution



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!



Sampling Distribution



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



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



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.



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.



Random Sampling Methods



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



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



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



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:



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



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



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.



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



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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