Simple Random Sampling

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Statistical Methods 13 Sampling Techniques

www.statstutor.ac.uk

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 divided into different groups from which we sample randomly !

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Chapter 8: Quantitative Sampling

www.csun.edu

iv. Types of Probability Sampling Techniques 1. Simple Random a. In simple random sampling, a researcher develops an accurate sampling frame, selects elements from the sampling frame according to a mathematically random procedure, and then locates the exact element that was selected for inclusion in the sample. 2. Systematic Sampling a.

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Chapter 4 - Stratified Random Sampling

cals.arizona.edu

each stratum. With only one stratum, stratified random sampling reduces to simple random sampling. The population mean (μ) is estimated with: ()∑ = = + + + = L i N N NL L N Ni i N 1 1 1 2 2 1 1 μˆ μˆ μˆ L μˆ μˆ where N i is the total number of sample units in strata i, L is the number of strata, and N is the total

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SAMPLING TECHNIQUES & DETERMINATION OF SAMPLE …

ijecm.co.uk

Simple Random Sampling In the Simple random sampling method, each unit included in the sample has equal chance of inclusion in the sample. This technique provides the unbiased and better estimate of the parameters if the population is homogeneous. Stratified Random Sampling

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Sampling and Sample Size Determination

sbselearning.strathmore.edu

Probability Sampling Methods Simple Random Sampling the purest form of probability sampling. Assures each element in the population has an equal chance of being included in the sample Random number generators Probability of Selection = Sample Size Population Size

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“Sampling Strategies” - NATCO

www.natco1.org

selected. Four main methods include: 1) simple random, 2) stratified random, 3) cluster, and 4) systematic. Non-probability sampling – the elements that make up the sample, are selected by nonrandom methods. This type of sampling is less likely than probability sampling to produce representative samples.

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Introduction to Simulations in R

www.columbia.edu

sampling in R sample() simple random sample sample(c("H","T"), size = 8, replace = TRUE) # fair coin sample(1:6, size = 2, replace = TRUE, prob=c(3,3,3,4,4,4)) #loaded dice replace=TRUE to over ride the default sample without replacement prob= to sample elements with di erent probabilities, e.g. over sample based on some factor

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