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/


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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.

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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
http://cpdoer.net/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://cpdoer.net/

3 Sampling probability non probability.ppt

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