what is sampling typer of sampling (1).ppt

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This document discusses key concepts in sampling, including population, sample, target population, sampling unit, sampling frame, and different sampling schemes. It defines population as the set of all measurements of interest, and sample as a subset of the population. Probability and non-probability sampling methods are described. Probability sampling, such as simple random sampling, systematic sampling, stratified sampling, and cluster sampling allow results to be generalized to the population, while non-probability convenience and snowball sampling do not.

Population:
a set which includes all
measurements of interest
to the researcher
(The collection of all responses,
measurements, or counts that are of
interest)
Sample:
A subset of the population

Get information about large populations
 Less costs
 Less field time
 More accuracy i.e. Can Do A Better Job of Data
Collection
 When it’s impossible to study the whole
population

Target Population:
The population to be studied/ to which the investigator wants
to generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
List of all the sampling units from which sample is drawn
Sampling scheme
Method of selecting sampling units from sampling frame

 Non-probability/Non-random samples
 Probability/random samples

Convenience samples (ease of access)
sample is selected from elements of a population
that are easily accessible
Snowball sampling (friend of friend….etc.)
Purposive sampling (judgemental)
 You chose who you think should be in the
study

Probability of being chosen is unknown
Cheaper- but unable to generalise
potential for bias

 Random sampling
 Each subject has a known probability of being
selected
 Allows application of statistical sampling theory to
results to:
 Generalise
 Test hypotheses

 Probability samples are the best
 Ensure
 Representativeness
 Precision

 Simple random sampling
 Systematic sampling
 Stratified sampling
 Multi-stage sampling
 Cluster sampling

6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6

Cluster: a group of sampling units close to each
other i.e. crowding together in the same area or
neighborhood

Cluster sampling
Section 4
Section 5
Section 3
Section 2
Section 1

 Stratified sampling. In this type of sampling the
population is divided into smaller groups or strata by
some characteristics and from each of these stratas
members are selected randomly.

Random Sampling Non Random Sampling
You can generalise to the population
defined by the sampling frame
You can not generalise beyond the
sample.
Allows use of statistics test and
hypothesis
Exploratory research generates
hypothesis.
Can estimate research para meters Population parameters are not of
interest
Eliminate bias Adequacy of sample can not be known
Must have random selection of units Cheaper, easier and quicker to carry
out.

Sampling is used to select a subset of individuals from a population to estimate characteristics of the whole population. There are various sampling designs and terminologies discussed. Random sampling aims to give every unit an equal probability of selection and can be done with or without replacement. The document outlines principles of sampling design, advantages like being economical and accurate, and disadvantages like potential for bias. It also discusses planning sample surveys and determining appropriate sample sizes.

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This document discusses sampling methods used in research. It defines key terms like population, sample, sampling units and strategies. The main types of sampling discussed are probability sampling which uses random selection, and non-probability sampling which does not. Specific probability methods covered include simple random sampling, systematic random sampling, stratified random sampling and cluster sampling. Non-probability methods discussed are convenience sampling, purposive sampling, quota sampling, and snowball sampling. The document also addresses sample size determination, sources of error in sampling like sampling error and non-sampling error, and concludes with advantages of sampling.

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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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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 of research including the problem statement, theory, variables, design, instrumentation, sampling, 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 population and sampling methods in research. It defines key terms like population, element, sample, and subject. It also explains different sampling techniques such as random sampling, stratified sampling, cluster sampling, and non-probability sampling methods like convenience sampling, quota sampling, and judgmental sampling. The document outlines advantages and disadvantages of each sampling method and notes that determining an appropriate sample size is important for making valid statistical inferences about a population.

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This document discusses different types of sampling designs used in research. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling which allow samples to be generalized back to the population. It also covers non-probability sampling techniques like convenience sampling, judgmental sampling, quota sampling, and snowball sampling which do not allow generalization. The document provides details on how to implement each sampling method and discusses their relative strengths and weaknesses.

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The document discusses different sampling methods for surveys and research. It describes census as a complete enumeration of all items in a population, while sample survey involves selecting a representative sample. Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select samples in a way that each item has an equal chance of selection and sampling errors can be estimated. Non-probability methods like purposive sampling involve deliberately choosing items without randomization. The key factors in sampling design are representation, sample size, and selection technique to balance accuracy and costs.

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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.

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1. There are two main types of sampling designs - probability sampling and non-probability sampling. Probability sampling allows you to estimate errors and make statistical inferences, while non-probability sampling does not. 2. Some common probability sampling techniques include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. These aim to select a sample that accurately represents the population. 3. When designing a sample, researchers must determine the sampling unit, sampling frame, sample size, and sampling procedure based on factors like the population, budget, and goal of reducing bias and sampling error. Probability sampling is generally preferred but non-probability techniques may also be used for small populations.

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This document defines key terms related to sampling design and methods. It discusses the differences between populations, target populations, and accessible populations. It also defines samples, sampling frames, strata, random selection, and representativeness. The document outlines several probability sampling methods, including simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It also discusses non-probability sampling methods like convenience sampling, purposive sampling, and quota sampling.

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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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This document discusses various sampling techniques used in research. It defines key terms like population and sample. It describes probability sampling methods like simple random sampling, stratified sampling, systematic sampling, and cluster sampling. For each method, it provides the basic approach, advantages, and disadvantages. Non-probability sampling techniques like purposive sampling and quota sampling are also briefly introduced. The document aims to explain different sampling methods and help readers select the appropriate technique for their research needs.

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The document discusses various sampling methods used in research. It defines key terms like population, sampling element, sampling frame, and inference. It then explains probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It discusses their advantages and disadvantages. The document also covers non-probability sampling methods like convenience sampling and purposive sampling. It provides examples of different types of purposive sampling and discusses their advantages over random sampling in certain research objectives.

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