3 Sampling probability non probability.ppt
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Numeracy & Quantitative Methods
Laura Lake
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Probability samplea method of sampling that uses ofrandom selection so that all units/ cases in the populationhave an equal probability of being chosen.
Non-probability sample does not involve randomselection and methods are not based on the rationale ofprobability theory.
Types of Sampling
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Why is probability sampling important in quantitative research?
Research finding not based on samples that are biased /
unrepresentative.Based on a sampling frame it enables research to be replicableor repeatable.
Research results can be projected from the sample to the largerpopulation with known levels of certainty/precision (i.e. standarderrors & confidence intervals for survey estimates can be constructed).
Probability Sampling in
Quantitative Research
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To achieve this the sampling frame used needs to:
ensure that the correct population is being sampled i.e. itaddresses the questions of interest
accurately covers all members of the population being studiedso they have a chance to be sampled.
The quality of the population list (sampling frame) i.e.
whether it is up-to-date and complete is the most importantfeature for accuracy in the sampling.
Probability Sampling in
Quantitative Research
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Four main types of probability sampling:
1.Simple random sample
2.Systematic sample3.Stratified random sample
4.Cluster/ multi-stage random sample
Types of Probability
Sampling
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Randomly selecting units from a sampling frame.
Random means mathematically each unit from thesampling frame has an equal probability of being included in
the sample.
Stages in random sampling:
Simple Random Sampling
Definepopulation
Developsamplingframe
Assign eachunit anumber
Randomlyselect the
requiredamount of
randomnumbers
Systematicallyselect random
numbers until itmeets the
sample sizerequirements
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Similar to simple random sample.
No table of random numbers select directly from samplingframe.
Systematic Sampling
Definepopulation
Developsampling
frame
Decide thesample size
Work outwhat fractionof the framethe sample
sizerepresents
Selectaccording tofraction (100sample from1,000 frame
then 10% soevery 10thunit)
First unitselect byrandom
numbersthen every
nth unitselected(e.g. every
10th)
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Gold standard of sampling.
Why? Designed to be more representative of the populationwhere the sampling frame is stratified according to
population variables .
Variables selected for stratifying are determined by thecharacteristics needed by the research.
Stratificationsplitting the population into the differentstrata (variables e.g. gender, age, ethnic background).
Samples can be stratified across more than one variable.
Stratified Random Sample
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As a random sample:
Stratified Random Sample
Define populationDevelop samplingframe accordingto characteristics
required
Determine the
proportion ofeach population
variable ofinterest
Systematic samplingmethods can then befollowed to select
sample unit
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Cluster sampling: selecting a sample based on specific,naturally occurring groups (clusters) within a population.
- Example: randomly selecting 20 hospitals from a list of allhospitals in England.
Multi-stage sampling: cluster sampling repeated at a number oflevels.
- Example: randomly selecting hospitals by county and then asample of patients from each selected hospital.
Cluster/ multi-stagerandom sample
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Three main types of non-probability sampling:
1.Convenience
2.Quota3.Snowball
Non-Probability Sampling
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A sample selected for ease of access, immediately knownpopulation group.
+ good response rate.cannot generalise findings (do not know what populationgroup the sample is representative of) so cannot movebeyond describing the sample.
Convenience Sampling
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Aim is to sample reflecting proportions of population indifferent categories or quotas (e.g. gender, age, ethnicity).
Used in often in market and opinion poll research.
+ easy to manage, quick
only reflects population in terms of the quota, possibility ofbias in selection, no standard error
Quota Sampling
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Useful when a population is hidden or difficult to gain accessto.
The contact with an initial group is used to make contactwith others.
+ access to difficult to reach populations (other methodsmay not yield any results).
- not representative of the population and will result in abiased sample as it is self-selecting.
Snowball Sampling
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How large should my sample be in order for it to berepresentative?
Larger samples are not necessarily better how
representative a sample it depends on the sampling techniqueused andthe size of the population.
Determining sample size is dependent of how much erroryou are prepared to accept in your sample.
Sample Size?
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The larger the sample size the more likely error in the samplewill decrease.
But, beyond a certain point increasing sample size does notprovide large reductions in sampling error.
Accuracy is a reflection of the sampling error and confidencelevel of the data.
Sampling Error and
Confidence
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If a sample has been selected according to probability wecan assess the level of confidence.
Confidence levels will allow you to state, with a certain levelof confidence, that the sample findings would also be found inthe population.
Sampling Error and
Confidence
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Example:
+/ - 3% at 95% confidence level
A confidence interval of +/- 3% at the 95% confidence levelmeans that, 95% of the time, the true answer will be within3% of the survey findings.
Confidence Intervals
Voting behaviour % of poll
Labour 37%
Conservative 35%
Liberal Democrat 22%
Other 6%
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Bryman, A. (2008) Social Research Methods. 3rdEd. Oxford:
Oxford University Press.
David, M. and Sutton, C. (2004) Social Research :The Basics.
London: Sage.
ESRC Survey Measurement Programme. Online: available from
Survey Resource Network http://www.surveynet.ac.uk/
Oppenheim, A. (2000) Questionnaire Design, Interviewing and
Attitude Measurement. London: Continuum
References
http://www.surveynet.ac.uk/http://www.surveynet.ac.uk/8/14/2019 3 Sampling probability non probability.ppt
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This resource was created by the University of Plymouth, Learning from WOeRkproject. This project is funded by HEFCEas part of the HEA/JISC OER release programme.
This resource is licensed under the terms of the Attribution-Non-Commercial-Share Alike 2.0 UK: England& Wales license (http://creativecommons.org/licenses/by-nc-sa/2.0/uk/).
The resource, where specified below, contains other 3rdparty materials under their own licenses. The licenses
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1. The name of the University of Plymouth and its logos are unregistered trade marks of the University. The University reserves all rights
to these items beyond their inclusion in these CC resources.
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Author Laura Lake
Institute University of Plymouth
TitleNumeracy & Quantitative Methods
Sampling: Probability & non-probability sampling
Description
Overview of probability and non-probability sampling techniques in
quantitative research.
Date Created March 2011.
Educational Level Level 5
Keywords
UKOER LFWOERK UOPCPDRM Learning from Woerk WBL Work Based
Learning CPD Continuous Professional Development Probability sample,
non-probability sample, simple random sample, systematic sample,
stratified random sample, cluster/ multi-stage random sample,
stratification, convenience sampling, quota sampling, snowball
sampling, sampling error, confidence intervals.
Creative Commons License Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales license
Back page originally developed by the OER phase 1 C-Change project
University of Plymouth, 2010, some rights reserved
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