Statistical Methods 13 Sampling Techniques
community projectencouraging academics to share statistics support resourcesAll stcp resources are released under a Creative Commons licenceStatistical Methods13 Sampling EllenMarshallUniversityofSheffield
Workshop outlineWe will consider:q Sampling techniques:Ø Non-randomØ ��eld
Sample surveysSubjects included in a study can be selected usingeither:q A non-random sampling approach, orq A random sampling effield
Non-random samplingq Types:Ø Ø Ø Ø Self-selecting samplesConvenience samplesJudgemental samplesQuota sampling: The interviewer has been givenquotas to fill from specified subgroups of thepopulation, e.g. 20 women 20-30 years oldq Can all be very biasedq Not representative of Sheffield
Random samplingRequires:q Random sampling methodq Random number generationq Sampling �eld
Random sampling methodsq Simple Random Sampling: Every member of thepopulation is equally likely to be selected)q Systematic Sampling: Simple Random Samplingin an ordered systematic way, e.g. every 100thname in the yellow pagesq Stratified Sampling: Population divided intodifferent groups from which we sample randomlyq Cluster Sampling: Population is divided into(geographical) clusters - some clusters are chosenat random - within cluster units are chosen withSimple Random effield
Generating random numbersq Best way is to select numbered balls out of abagq Or use random number generatorsØ Many available online, e.g.www.random.org/integersq Or use Excel:Ø E.g. “ randbetween(1,200)” generates arandom number between 1 and ld
Sampling frameq A list of subjects from which a sample ofsubjects is selectedq Examples:Ø Ø Ø Ø MapCensus databaseEmployee databaseTelephone directoryq Need to select subjects at randomq Without a sampling frame, random selectionis iversityofSheffield
Example: simple randomsamplingq Survey ofinsectpopulationliving inwoodlandq Treesnumbered1 to 200q 10 treeschosen effield
Example: Stratified samplingq Foot measurement study of the population of Taiwanq Total sample size of 1,000q Sample for each category selected randomly from thepopulationAgeGroupPopulation (000s)Male0-48305-9100510-14 101615-1992920-29 199330-49 274450 1882Total 10399www.statstutor.ac.ukSampleFemaleTotalMale Female ld
Example: cluster samplingq Survey ofinsectpopulationliving inwoodlandq Squareschosen arandom on thegridq Trees lyingwithin thesquareschosen until10 293949596979899 lenMarshallUniversityofSheffield
Cluster sampling v. stratifiedsamplingq Cluster sampling:Ø CheaperØ Usually not representative of whole populationq Stratified sampling:Ø Sample more representativeØ Good information on heffield
Recapq Random sampling reduces biasq Random sampling requires:Ø A random sampling methodØ Random number generationØ A sampling �eld
Random sampling methods ! Simple Random Sampling: Every member of the population is equally likely to be selected) ! Systematic Sampling: Simple Random Sampling in an ordered systematic way, e.g. every 100th name in the yellow pages ! Stratified Sampling: Population divi
Sampling, Sampling Methods, Sampling Plans, Sampling Error, Sampling Distributions: Theory and Design of Sample Survey, Census Vs Sample Enumerations, Objectives and Principles of Sampling, Types of Sampling, Sampling and Non-Sampling Errors. UNIT-IV (Total Topics- 7 and Hrs- 10)
Lecture 8: Sampling Methods Donglei Du (ddu@unb.edu) . The sampling process (since sample is only part of the population) The choice of statistics (since a statistics is computed based on the sample). . 1 Sampling Methods Why Sampling Probability vs non-probability sampling methods
1. Principle of Sampling Concept of sampling Sampling: The procedure used to draw and constitute a sample. The objective of these sampling procedures is to enable a representative sample to be obtained from a lot for analysis 3 Main factors affecting accuracy of results Sampling transport preparation determination QC Sampling is an
Various Statistical Sampling methods. 4.1 The simplest form of random/statistical sampling consists in selecting the sample unit-by-unit (or, item-by-item), ensuring equal probability of selection to every unit at each draw. This technique of selection is termed as Simple Random Sampling (SRS). SRS are of two types:
2.4. Data quality objectives 7 3. Environmental sampling considerations 9 3.1. Types of samples 9 4. Objectives of sampling programs 10 4.1. The process of assessing site contamination 10 4.2. Characterisation and validation 11 4.3. Sampling objectives 11 5. Sampling design 12 5.1. Probabilistic and judgmental sampling design 12 5.2. Sampling .
2.1 A Review of Credit Scoring Techniques . Credit Scoring techniques are divided into two parts . i. Statistical Based Techniques ii. Artificial Intelligence Based Techniques . 2.1.1 Statistical Based Techniques . Various credit scoring statistical based techniques have been researched on and implemented. These statistical based tech-
3/14/2019 CCO Round Table Presentation 7 HCCA Compliance Institute 2019 13 Fundamentals of Statistical Sampling Non-Probability Sampling Techniques The core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgment of the researcher, rather than random selection
Zoo Animal Nutrition IV Zoo Animal Nutrition IV (2009) was edited by M. Clauss, A. Fidgett, G. Janssens, J.-M. Hatt, T. Huisman, J. Hummel, J. Nijboer, A. Plowman. Filander Verlag, Fürth ISBN-13: 978-3-930831-72-2 To obtain a copy of the book, contact Filander Verlag at info@filander.de Dierenfeld, E. S. Conservation collaborations: nutrition .
