On Samples And Sampling
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Learning Objectives: 1. Understand strategies for selecting a sample 2.Understand how to determine the required sample size
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Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
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This document discusses sampling methods used in research. It lists the group members conducting the research and covers topics including probability and non-probability sampling methods. For probability sampling, it describes random sampling, stratified sampling, systematic sampling, and cluster sampling. It provides examples and discusses the advantages and disadvantages of systematic sampling. For non-probability sampling it discusses convenience sampling, judgmental sampling, quota sampling, and snowball sampling, giving examples of when each method would be used. It concludes with a brief definition of sampling size.
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This document defines key population and sampling concepts for research. It discusses target and accessible populations, and how samples are selected using probability and non-probability sampling methods. Probability methods like simple random, stratified random, cluster random, and systematic sampling aim to select a representative sample where every member has an equal chance of being selected. Non-probability methods like convenience, purposive, and snowball sampling do not aim for representativeness. Sample size is important to reduce error and increase power to detect relationships.
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1) Sampling is the process of selecting a subset of individuals from within a population to gather information about the whole population. It allows researchers to learn about a larger population without studying every member.
2) There are two main types of sampling: probability sampling, where every member of the population has a known chance of being selected, and non-probability sampling, where members are selected in a non-random way.
3) Some common probability sampling methods include simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. Common non-probability methods include convenience sampling and purposive sampling.
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1. The document discusses various sampling terms and methods used in scientific research. It defines key concepts like population, sample, sampling, parameter, and statistic.
2. It explains different types of sampling methods including probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling as well as non-probability sampling methods like purposive sampling and convenience sampling.
3. The stages of sample selection are outlined as selecting a sampling frame, determining the sampling method, planning sample selection, defining the target population, and estimating sample size. Potential sources of error in sampling and important considerations for determining adequate sample size are also covered.
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Sampling techniques
The document discusses various sampling methods and concepts in research methodology. It defines key terms like population, sample, sampling frame, probability sampling, and non-probability sampling. It then explains different probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling methods and compares the advantages and disadvantages of different approaches. The document emphasizes the importance of representative sampling.
Sampling1[1]
Sampling is the process of selecting a subset of a population to make inferences about the whole population. It allows researchers to study populations that would be too large or impractical to study in their entirety. There are two main types of sampling - probability sampling, which allows results to be generalized to the larger population, and non-probability sampling, which is typically used for qualitative research where generalizability is not the goal. Sample size and how representative the sample is of the overall population impacts how well results can be generalized.
sampling methods
This document outlines different sampling methods used in quantitative and qualitative research. It discusses the purpose of sampling, stages in selecting a sample, and types of probability and non-probability sampling. For quantitative research, it describes random sampling, stratified random sampling, cluster sampling, and systematic sampling. For qualitative research, it discusses purposive sampling techniques like maximal variation sampling, typical case sampling, and theory or concept sampling. The document stresses the importance of representation and generalization in samples and notes some ethical considerations in data collection like maintaining trust and informing participants.
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Sampling
What is sampling and Why sample?Selecting part of a larger groupPurpose is to make generalizations about the population from the smaller group.Why not study everyone?
--Cost: US Decennial Census, 1990, cost $1.5 billion, 2010, $14 billion
--Quality: When data are collected on a large scale, quality declines
--Timeliness
Advantages of samplingSelecting less than the entire population has advantages:
Less expensive
Less time
Better quality control
Key Sampling Concepts
Copyright ©2002, William M.K. Trochim, All Rights Reserved
Key sampling conceptsDistinction between the theoretical population and the actual study populationGeneralizations can be made only to the actual population
Theoretical Population
The Study or Accessible Population
Theoretical
Study
Sampling Frame
The list or procedure defining the POPULATION (From which the sample will be drawn.)
Examples:
Telephone book
Voters list
Mailing list
Distinguish sampling frame from sample.
Theoretical
Study
Sampling Frame
Selected Sample
Those selected to participate in the study
Not all will necessarily participate
--Non deliverable surveys
--Incomplete surveys
Theoretical
Study
Sampling Frame
Selected Sample
Actual
sample
Subjects who complete the study and data is used in data analysis
Ratio of actual to selected is called response rate
Key Concepts in samplingRandom sampling error
--the difference between the sample results and the results of a census conducted using identical procedure.Systematic sampling error
--due to sampling methods or imperfection in execution. Sampling bias
--biased results due to improper sampling (e.g., a sample of a population in which some members of the population are less likely to be included than others)
Sampling bias: an example1936 Presidential election. Literary Digest poll through telephone incorrectly predicted Alf Landon the winnerProblems?
--a biased sample
--response bias: respondents were more likely to express their preference; non-respondents had a different opinion
Rule:If you wish to make inferences to the population from which the sample was drawn, a Probability sample must be used.
--each element of the population has a known non-zero probability of selection.
--Randomness is the bases for sample selection
--Tend to give approximate representation of the population
--Allow for prediction of size of error in estimation
Key concepts: sample sizeThe larger the better?The main key is not size, but representativenessRule of thumb for Quantitative research
--A minimum of 30 per group
--A minimum of 10 per variable for multiple regressionIn Qualitative research, it doesn't matter as much
-- Not trying to infer generalizability
Types of Samples
Probability
Non-Probability
Convenience
Purposive
Simple Random
Systematic Random
Multi-stage Random
Stratified Random
Cluster Random
Quota
Simple Random SamplingEach element in the po
Sampling
This document discusses sampling techniques used in research. It defines a population as the entire group being studied, while a sample is a subset of the population. There are two main types of sampling: probability sampling, where every member has an equal chance of being selected, and non-probability sampling, where members do not have an equal chance. Some common probability techniques include simple random sampling, stratified random sampling, and cluster sampling. Common non-probability techniques include convenience sampling, quota sampling, purposive sampling, and snowball sampling. The document outlines the advantages and disadvantages of sampling, and differences between probability and non-probability sampling.
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This document discusses key concepts in quantitative research methods including research, samples, populations, random and non-random sampling techniques. It defines research as a careful investigation to discover new facts or interpret existing facts. A sample is a subset of a population used to gain insights about the whole. Random sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select representative samples, while non-random methods like convenience and purposive sampling are not generalizable. The document also discusses qualitative research and purposive sampling techniques.
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This document defines key concepts in probability and non-probability sampling. It explains that probability sampling uses random selection to select samples from a population, with four main types listed: simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Non-probability sampling relies on the researcher's judgment rather than random selection, with common types being convenience sampling, consecutive sampling, quota sampling, judgmental sampling, and snowball sampling. Examples are provided to illustrate each sampling technique.
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The document discusses different sampling techniques used in research. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling which allow statistical inferences about a population. Non-probability sampling techniques include convenience sampling, snowball sampling, and purposive sampling which rely on the researcher's judgment. The key differences between probability and non-probability sampling are that probability sampling reduces bias by randomly selecting participants while non-probability sampling does not assign equal chance of selection.
Sampling method
This document discusses different sampling methods used in research studies. It describes probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It explains that probability sampling allows for statistical measurement of random error. The document also covers non-probability sampling methods including convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling. These do not allow for statistical measurement of variability and bias. The key sampling methods and their advantages and disadvantages are summarized.
Sampling methods
This document discusses different sampling methods used in research. It defines population and sample, and explains that sampling is used to select a subset of a population when the entire population is too large. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection and allows results to be generalized to the population, while non-probability sampling relies on the researcher's judgment and results cannot be generalized. Specific probability sampling methods described include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Non-probability sampling methods mentioned are convenience sampling, snowball sampling, quota sampling, and judgmental sampling.
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This document discusses various sampling methods used in research. It defines key terms like population, element, sampling unit, and sample. It describes probability sampling methods like random sampling, systematic sampling, and stratified sampling. It also covers non-probability methods like convenience sampling, judgment sampling, quota sampling, and snowball sampling. The document provides details on how to implement these various sampling techniques and discusses factors to consider for determining sample size.
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This document discusses sampling methods and techniques. It defines key terms like population, sample, random sampling, and non-random sampling. Random sampling techniques include table of random numbers, lottery sampling, and systematic sampling. Non-random techniques include accidental, quota, and convenience sampling. The optimal sample size depends on factors like cost, time, and desired accuracy. Random sampling is preferred when possible as it avoids selection bias and better represents the population. Proper planning is important to define the population, sampling unit, and method.
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On Samples And Sampling
- 1. “ On Samples And Sampling” Title drawn from Elisabeth Kubler-Ross’ anagramatic phraseologies: “On death and dying” ; “On grief and grieving” ; “Real taste of life (On life and living) ” Ehi Igumbor School of Public Health University of the Western Cape
- 4. Populations and Samples Population (Group about whom you wish to gather data defined by person, place and time) Sample (Sub-group of total study population)
- 15. Types of Samples
- 24. Sampling Frame
- 28. Probability Sa mples- Simple Random
- 30. Probability Sa mples- Stratified Random
- 32. Probability Sa mples- Systematic Samples
- 34. Probability Sa mples- Cluster or Area Sample
- 71. EXAMPLE: Sample Size Calculation Where n = Sample size N = Population size e = Level of precision or Sampling of Error which is ± 5% Yamane’s formula: * Reference: Yamane, Taro. 1967. Statistics, An Introductory Analysis,2 nd Ed. New York: Harper and Row.
- 72. # of Health Facilities per Province Source: Digital Healthcare Solutions (PTY) LTD . Comprehensive Health Services Information for Southern Africa: Hospital & Nursing YearBook, 2007.
- 73. Sample Size Calculation: Total number of health facilities in the study: 350 *Reference: Yamane, Taro. 1967. Statistics, An Introductory Analysis,2 nd Ed. New York: Harper and Row.
- 75. Sampling Techniques Total # of health facilities Weighted Sample Eastern Cape 783 71 Free State 293 27 Gauteng 383 35 Northern Cape 124 11 KwaZulu-Natal 610 55 North West 398 36 Mpumalanga 280 25 Limpopo 499 45 Western cape 485 44 Total 3855 350
- 76. # of Facilities Selected for the study