Sampling Procedure
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By: Jalen Rebolledo and Manilou Allanic Factors affecting sample selection. sampling methods and its advantages and disadvantages Steps on random sampling
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Sampling involves selecting a subset of a population to study rather than examining the entire population. There are two main types of sampling: probability sampling, where units are selected randomly, and non-probability sampling, where units are selected purposefully. Some common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling techniques include quota sampling, purposive sampling, availability sampling, and snowball sampling.Types of Sampling
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Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population
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The document discusses sampling design and methods. It begins by defining key terms like population, sampling frame, and sample size. It then describes different sampling techniques including probability methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability methods such as convenience sampling, judgment sampling, quota sampling, and snowball sampling. The document provides examples and explanations of how each sampling method works. It concludes by noting some factors to consider when determining sample size.Chapter 4 sampling procedure
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This document discusses different sampling methods used in research. It begins with defining sampling as selecting a representative part of the population to determine characteristics of the whole. The sampling process involves defining the population, selecting a sampling method, and determining sample size. Probability sampling methods like random, stratified, cluster and systematic sampling aim to give all units an equal chance of being selected. Non-probability methods like convenience, judgmental, snowball and quota sampling do not use chance and focus on easily available units. The document provides details on each sampling method and their advantages and disadvantages.Sampling techniques 2
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This document discusses different sampling techniques used in statistics. It describes the reasons for using samples, which are to obtain information more quickly, at lower cost, and with greater accuracy compared to surveying entire populations. It distinguishes between probability sampling, where units have a known chance of selection, and non-probability sampling, where selection probabilities are unknown. Specific non-probability methods discussed include quota, judgement, snowball, and chunk sampling. Probability methods covered are simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling.Sampling research method
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Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in eachSampling....
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This document discusses developing a sample plan, which involves six steps: 1) defining the relevant population, 2) obtaining a population list, 3) designing the sample method and size, 4) drawing the sample, 5) assessing the sample, and 6) resampling if necessary. It also covers basic sampling concepts and different probability and non-probability sampling methods.Stratified Random Sampling
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The document discusses stratified random sampling, which involves dividing a population into homogeneous subgroups called strata and randomly sampling from each stratum. It describes how to form strata based on common characteristics, how to select items from each stratum such as through systematic sampling, and how to allocate the sample size to each stratum proportionally according to the stratum's size within the overall population. An example is given of allocating a sample of 30 across 3 strata based on their relative population sizes.SAMPLING MEANING AND TYPES 
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Probability and non-probability sampling were discussed. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling aim to ensure each unit has an equal chance of selection. Non-probability methods like convenience sampling, judgmental sampling, and snowball sampling rely on accessibility and do not ensure equal chance of selection. While statistical agencies prefer probability sampling, businesses often use non-probability sampling for market research due to increased respondent cooperation.I. lecture sampling
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Title: Sampling in research
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At the end of session participants will be
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Venue:
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Date: January 27, 2016
Time: 11:00 am to 12:00 pm
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Zulfiqar Behan
zulfiqarbehan@yahoo.com
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2) There are two main types of sampling - probability sampling which gives all units an equal chance of being selected, and non-probability sampling which does not give all units an equal chance.
3) Common probability methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Common non-probability methods include convenience sampling, judgment sampling, and quota sampling.Sampling
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Quota sampling involves dividing a population into subgroups and selecting samples to represent each subgroup in proportion to its share of the overall population. It allows investigation of traits in subgroups and relationships between subgroups. While quick and cost-effective, it cannot calculate sampling error and may not represent the entire population. Snowball sampling relies on referrals from initial subjects to identify new subjects. It is useful for hidden populations but represents less control and potential sampling bias. Both methods limit statistical inference and introduce subjectivity.Sampling
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This document discusses different sampling methods used in research. It describes probability sampling techniques like simple random sampling, stratified sampling, cluster sampling, systematic random sampling, and multistage sampling. It also describes non-probability sampling methods such as convenience sampling, quota sampling, judgmental sampling, snowball sampling. The document notes that probability sampling allows inferences about the population, while non-probability sampling does not. It also discusses sources of sampling error and ways to reduce them, such as using appropriate sampling techniques and sample size.More Related Content
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A workshop on Sampling & Types of Sampling delivered by me Zulfiqar Behan.
Date: 27th Jan 2016
workshop titled introduction to research methodology facilitators 1.Kiran Hashmi 2. Zulfiqar Behan
Title: Sampling in research
SLOs
At the end of session participants will be
able to Know types of sampling
Application of sampling
Venue:
JamiaMillia College of Education
Date: January 27, 2016
Time: 11:00 am to 12:00 pm
Facilitator:
Zulfiqar Behan
zulfiqarbehan@yahoo.com
It was a wonderful workshop for M.Ed class and teaching faculty of Jamia Milia College of Education Malir Karachi.
workshop were hand and mind oriented participants took active interest.
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2) There are two main types of sampling - probability sampling which gives all units an equal chance of being selected, and non-probability sampling which does not give all units an equal chance.
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This document defines probability sampling and describes four main types: simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling. Probability sampling involves selecting samples in a way that gives every member of the population an equal and known chance of being chosen. It aims to result in a sample that accurately represents the larger population. The document provides examples of how to select samples for each of the four probability sampling techniques.Ch16 sampling design & sampling procedures
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This document discusses different types of sampling methods used in research. It defines key terms like population, sample, and sampling techniques. There are two main types of sampling: probability sampling, where every unit has an equal chance of being selected; and non-probability sampling, which does not use random selection. Some probability sampling methods described are simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling techniques discussed include quota sampling, convenience sampling, purposive sampling, snowball sampling, and self-selection sampling.Similar to Sampling Procedure (20)
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This document discusses various topics related to sampling and data collection, including:
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2. It discusses different levels of measurement for data like nominal, ordinal, interval, and ratio scales.
3. It covers sampling techniques including probability methods like simple random sampling, systematic sampling, stratified random sampling, and cluster sampling as well as non-probability methods like purposive sampling, quota sampling, snowball sampling, and convenience sampling.
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Sampling.pptx
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This document discusses different types of sampling methods used in research. It describes probability sampling methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling which give each unit an equal chance of being selected. It also describes non-probability sampling methods like convenience sampling, quota sampling, judgmental sampling, and snowball sampling where units are chosen non-randomly based on accessibility or the researcher's judgment. The document explains the advantages and disadvantages of each sampling method for research purposes.Sampling
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This document discusses sampling methods used in statistics. It describes sampling as selecting individual observations from a target population to make statistical inferences. There are two main types of sampling: probability sampling, where every unit has an equal chance of selection, and non-probability sampling, where some population elements have no chance of selection. Some common probability sampling methods described are simple random sampling, stratified random sampling, and cluster random sampling. Non-probability sampling methods discussed include convenience sampling and snowball sampling. The purposes, advantages, and disadvantages of sampling are also outlined.Types of Sampling .pptx
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This document discusses different types of sampling methods used in research. It describes probability sampling methods such as simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling techniques including convenience sampling, purposive or judgement sampling, snowball sampling, and quota sampling. The key aspects of each sampling method are defined along with their advantages and disadvantages.
sampling techniques used in research 
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The document discusses different sampling methods used in statistics. It defines key terms like population and sample. It describes probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling which give each unit an equal chance of selection. It also covers non-probability sampling techniques like convenience sampling which do not guarantee equal selection probability. The advantages and disadvantages of different approaches are provided.handouts-in-Stat-unit-7.docx
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1) There are several common sampling techniques used in research including simple random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, purposive sampling, and snowball sampling.
2) Sampling provides benefits such as reduced time, cost, and resource deployment while still allowing for accurate data collection. However, sampling also poses risks such as bias, difficulties in selecting a truly representative sample, and inadequate knowledge of sampling techniques by the researcher.
3) Both the advantages and disadvantages of sampling depend on selecting an appropriate technique and implementing it properly. Sampling aims to make research more efficient but requires statistical knowledge to draw meaningful conclusions.Sampling techniques market research
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This document discusses key concepts related to sampling design and procedures. It defines important terms like population, census, and sample. It then outlines the 5 main steps in the sample design process: 1) defining the target population, 2) determining the sampling frame, 3) selecting a sampling technique, 4) determining the sample size, and 5) executing the sampling process. It also discusses probability and non-probability sampling techniques and when each is most appropriate to use.
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Sampling refers to selecting a subset of a population for study. There are two main types of sampling: probability sampling, where every member of the population has a known, non-zero chance of being selected; and non-probability sampling, where some members are more likely to be selected than others. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Common non-probability sampling methods include convenience sampling, quota sampling, snowball sampling, and purposive sampling. Sample size and sampling method depend on factors like the study objectives, resources available, and characteristics of the target population.Unit 2 MARKETING RESEARCH
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This document discusses sampling and sampling design. It defines key terms like population, sample, census. It notes the features, limitations and types of sampling including probability and non-probability sampling methods. It also covers determining sample size, sampling distribution, attitudes measurement and different types of scales used to measure attitudes like nominal, ordinal, ratio and interval scales. Criteria for a good scale include validity and reliability. Different types of validity like content, construct, predictive and concurrent validity are also discussed.Sampling.ppt
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This document discusses different types of sampling methods. It begins by explaining the purpose of sampling is to obtain information about populations by taking samples and computing estimators. There are two main types of errors: sampling errors due to random selection and non-sampling errors such as non-response or measurement errors. Some common sampling methods described include simple random sampling, stratified sampling which divides a heterogeneous population into homogeneous groups, systematic sampling which selects every k-th item, cluster sampling which groups items into clusters, and multistage sampling which has multiple stages of selection. Other non-probability sampling methods mentioned include convenience sampling and purposive or judgment sampling.Sampling 
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This document discusses different sampling methods and terms. It defines key concepts like population, sample, element, and provides characteristics of a good sample. It outlines advantages and limitations of sampling and categorizes sampling methods into probability and non-probability. Specific probability methods like simple random sampling, stratified sampling, and systematic sampling are described in detail along with their procedures and advantages/disadvantages. Non-probability methods like quota sampling, accidental sampling and judgmental sampling are also briefly introduced.Population and Sampling.pptx
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Meaning & Definition of Population & Sampling, Types of Sampling - Probability & Non-Probability Sampling Techniques, Characteristics of Probability Sampling Techniques, Types of Probability Sampling Techniques, Characteristics of Non-Probability Sampling Techniques, Types of Non-Probability Sampling Techniques, Errors in Sampling, Size of sample, Application of Sampling Technique in ResearchSampling for Business Research 
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A Crux of the sampling chapter in the book: Essentials of Business Research: A Guide to Doing Your Research Project by Jonathan Wilson.
The content of the book is used under Creative Commons Attribution.
Sampling
SamplingAMIT ROY
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Sampling is used to learn about a population by studying a subset of it. It allows researchers to gather information in a time and cost-effective manner. There are two main types of sampling: probability sampling, where every item has an equal chance of being selected, and non-probability sampling, which has no basis for estimating selection probabilities. Some common sampling designs include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and quota sampling. Good sample design ensures representativeness, adequacy, independence, and homogeneity while accounting for resources and study goals.Sampling
SamplingAMIT ROY
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Sampling is used to learn about a population by studying a subset of it. It allows researchers to gather information in a time and cost-effective manner. There are two main types of sampling: probability sampling, where every item has an equal chance of being selected, and non-probability sampling, which has no basis for estimating selection probabilities. Some common sampling designs include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and quota sampling. Good sample design ensures representativeness, adequacy, independence, and homogeneity while accounting for resources and study goals.types of data in research, measurement level, sampling techniques, sampling t...
types of data in research, measurement level, sampling techniques, sampling t...SRM UNIVERSITY, SIKKIM
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- 1. SAMPLING PROCEDURE BY JALEN REBOLLEDO & MANILOU ALLANIC
- 2. Factors Affecting Sample Selection 1. Sample Size 2. Sampling Technique 3. Heterogeneity of Population 4. Statistical Techniques 5. Time and Cost
- 3. Sampling Methods 1. Probability Sampling a. Simple-random sampling b. Systematic Sampling c. Stratified sampling d. Cluster sampling 2. Non- Probability Sampling a. Quota sampling b. Voluntary sampling c. Purposive sampling d. Availability sampling e. Snowball sampling
- 4. Random Sampling Steps: 1. Decide on the size of the sample. 2. Divide the sample into sub-sets or sub-samples. 3. Select the appropriate sub-sample 4. Put together the sub-sample reults
- 5. ADVANTAGES & DISADVANTAGES Sampling Techniques Advantages Disadvantages Random Sampling - Accuracy - Chance - Unavailable list of the entire population Stratified Sampling - Divides - Can be combined with other techniques - Biased Systematic Sampling - Similar to Random Sampling - Sometimes permits bias Cluster Sampling - Collecting data is easier - Prone to bias Quota Sampling - Quick - Bias