How Stratified Random Sampling Works. 17-1-2015 · Stratified Sampling: Stratified sampling explained through an example Sampling is important in statistics, and in this video I look at the method of stratified sampling as an approach to sampling., 9-2-2020 · Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally..

### Simple Random Sampling ph.ucla.edu

Sample Size Stratified Random Samples stattrek.com. Sample Size: Stratified Random Samples. (In a subsequent lesson, we re-visit this problem and see how stratified sampling compares to other sampling methods.) Problem 1. At the end of every school year, the state administers a reading test to a sample of 36 third graders. The school, A sampling method in which the size of the sample drawn from a particular stratum is not proportional to the relative size of that stratum. For example, a stratum could be large supermarkets, which may only account for 20% of all grocery stores – although they account for 80% of grocery sales. In this case, a disproportionate sample would be used to represent the large supermarkets to.

Examples of sampling methods Sampling approach Food labelling research examples Strategy for selecting sample Food labelling studies examples Simple random sampling Every member of the population being studied has an equal chance of being selected In a study examining longitudinal trends in use of 2 Stratified sampling 3-2-2020 · Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or …

This means that each stratum has the same sampling fraction (n/N), Stratified random sampling is a better method than simple random sampling. Stratified random sampling divides a population into subgroups or strata, and random samples are taken, in proportion to the … Stratified random sampling methods often are used when there is interest in the differences between homogeneous subgroups and the larger sample population as a whole. Let’s say that a population of business clients can be divided into three groups: Generation X, millennials, and baby boomers.

PDF In order to answer Stratified random sampling . Systematic random sampling is a system in which every ninth case after the start is randomly selected and has a simple advantage in Stratified random sampling is a type of probability sampling using which researchers can divide the entire population into numerous non-overlapping, homogeneous strata. Final members for research are randomly chosen from the various strata which leads to cost reduction and improved response efficiency. This sampling method is also called “random quota sampling".

sampling . i.e. random: Every element in the population has a none non-zero probability of being selected; sampling involves random selection (equal chance) Non-probability sampling . i.e. non-random: Do not know in advance how likely that any element of the population will be selected for the sample; non-random selection (not equal chance) Stratified random sampling methods often are used when there is interest in the differences between homogeneous subgroups and the larger sample population as a whole. Let’s say that a population of business clients can be divided into three groups: Generation X, millennials, and baby boomers.

This cannot be considered random since the males had better chances of being selected as part of the sample. When to Use Disproportional Sampling. Disproportional sampling allows the researcher to give a larger representation to one or more subgroups to avoid underrepresentation of the said strata. Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade‐offs inherent in selecting a sampling design: to select

Stratified Random Sampling: Definition. Stratified random sampling is used when your population is divided into strata (characteristics like male and female or education level), and you want to include the stratum when taking your sample.The stratum may be already defined (like census data) or you might make the stratum yourself to fit the purposes of your research. sampling . i.e. random: Every element in the population has a none non-zero probability of being selected; sampling involves random selection (equal chance) Non-probability sampling . i.e. non-random: Do not know in advance how likely that any element of the population will be selected for the sample; non-random selection (not equal chance)

Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade‐offs inherent in selecting a sampling design: to select This cannot be considered random since the males had better chances of being selected as part of the sample. When to Use Disproportional Sampling. Disproportional sampling allows the researcher to give a larger representation to one or more subgroups to avoid underrepresentation of the said strata.

Examples of sampling methods Sampling approach Food labelling research examples Strategy for selecting sample Food labelling studies examples Simple random sampling Every member of the population being studied has an equal chance of being selected In a study examining longitudinal trends in use of 2 Stratified sampling In order to fully understand stratified sampling, it’s important to be confident in your understanding of probability sampling, which leverages random sampling techniques to create a sample. Stratified sampling is also commonly referred to as proportional sampling or quota sampling. When is Stratified Sampling Used? Stratified sampling is

Stratified Sampling Definition: The Stratified Sampling is a sampling technique wherein the population is sub-divided into homogeneous groups, called as ‘strata’, from which the samples are selected on a … 9-2-2020 · Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally.

### Stratified Sampling Research Methodology

Methods in Sample Surveys JHSPH OCW. 9-2-2020 · Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally., This cannot be considered random since the males had better chances of being selected as part of the sample. When to Use Disproportional Sampling. Disproportional sampling allows the researcher to give a larger representation to one or more subgroups to avoid underrepresentation of the said strata..

### What is Stratified Sampling? Definition of Stratified

Stratified Random Samples Definition Characteristics. element sampling techniques (such as simple random sampling, systematic sampling or by PPS sampling). Simple one-stage cluster sample: List all the clusters in the population, and from the list, select the clusters – usually with simple random sampling (SRS) strategy. All units (elements) in the sampled clusters are selected for the survey. https://simple.wikipedia.org/wiki/Sampling_(statistics) collection of these samples constitute a stratified sample Stratified random sampling examples pdf. If a simple random sample selection scheme is used in each stratum then the corresponding sample is called a stratified random sample. Reasons for stratification. • To obtain estimates of known precision for certain subdivisions of the population by treating each subdivision as a stratum..

Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it. This means that each stratum has the same sampling fraction (n/N), Stratified random sampling is a better method than simple random sampling. Stratified random sampling divides a population into subgroups or strata, and random samples are taken, in proportion to the …

element sampling techniques (such as simple random sampling, systematic sampling or by PPS sampling). Simple one-stage cluster sample: List all the clusters in the population, and from the list, select the clusters – usually with simple random sampling (SRS) strategy. All units (elements) in the sampled clusters are selected for the survey. Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling, but few use this method for population-based surveys. Rapid surveys are no exception, since they too use a more complex sampling scheme. So why should we be concerned with simple random sampling? The main reason is to learn the theory of sampling.

Stratified Sampling Definition: The Stratified Sampling is a sampling technique wherein the population is sub-divided into homogeneous groups, called as ‘strata’, from which the samples are selected on a … There is a big difference between stratified and cluster sampling, that in the first sampling technique, the sample is created out of random selection of elements from all the strata while in the second method, the all the units of the randomly selected clusters forms a sample.

Stratified random sampling is a type of probability sampling using which researchers can divide the entire population into numerous non-overlapping, homogeneous strata. Final members for research are randomly chosen from the various strata which leads to cost reduction and improved response efficiency. This sampling method is also called “random quota sampling". Chapter 11 . Systematic Sampling . The systematic sampling technique is operationally more convenient than the simple random sampling. It also ensures at the same time that each unit has equal probability of inclusion in the sample. In this method of sampling, the first unit is selected with the help of random numbers and the remaining units

collection of these samples constitute a stratified sample Stratified random sampling examples pdf. If a simple random sample selection scheme is used in each stratum then the corresponding sample is called a stratified random sample. Reasons for stratification. • To obtain estimates of known precision for certain subdivisions of the population by treating each subdivision as a stratum. collection of these samples constitute a stratified sample Stratified random sampling examples pdf. If a simple random sample selection scheme is used in each stratum then the corresponding sample is called a stratified random sample. Reasons for stratification. • To obtain estimates of known precision for certain subdivisions of the population by treating each subdivision as a stratum.

STRATIFIED RANDOM SAMPLING - A representative number of subjects from various subgroups is randomly selected. Suppose we wish to study computer use of educators in the Hartford system. Assume we want the teaching level (elementary, middle school, and … There is a big difference between stratified and cluster sampling, that in the first sampling technique, the sample is created out of random selection of elements from all the strata while in the second method, the all the units of the randomly selected clusters forms a sample.

PDF In order to answer Stratified random sampling . Systematic random sampling is a system in which every ninth case after the start is randomly selected and has a simple advantage in In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum.

Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling or stratification, the strata This means that each stratum has the same sampling fraction (n/N), Stratified random sampling is a better method than simple random sampling. Stratified random sampling divides a population into subgroups or strata, and random samples are taken, in proportion to the …

17-1-2015 · Stratified Sampling: Stratified sampling explained through an example Sampling is important in statistics, and in this video I look at the method of stratified sampling as an approach to sampling. Stratified sampling strategies. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. For instance, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x 1 = m/X males, x 2 = f/X females) should reflect this proportion.

## Stratified sampling Wikipedia

Chapter 4 Stratified Sampling IIT Kanpur. Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it., This cannot be considered random since the males had better chances of being selected as part of the sample. When to Use Disproportional Sampling. Disproportional sampling allows the researcher to give a larger representation to one or more subgroups to avoid underrepresentation of the said strata..

### RANDOM SAMPLING IN SAS Using PROC SQL and PROC

Stratified Sampling Definition. based on the following; Systematic random sampling, Stratified types of sampling, Cluster sampling, Multi-stage sampling, Area sampling, Types of probability random sampling Systematic sampling Thus, in systematic sampling only the first unit is selected randomly and …, Methods of sampling. To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic ….

SIMPLE RANDOM SAMPLING—a sampling method where n units are randomly selected from a population of N units and every possible sample has an equal chance of being selected STRATIFIED RANDOM SAMPLING—a sampling method where the population is first divided into mutually exclusive groups called strata, and simple random sampling is based on the following; Systematic random sampling, Stratified types of sampling, Cluster sampling, Multi-stage sampling, Area sampling, Types of probability random sampling Systematic sampling Thus, in systematic sampling only the first unit is selected randomly and …

Stratified sampling strategies. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. For instance, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x 1 = m/X males, x 2 = f/X females) should reflect this proportion. Therefore, stratified sampling and cluster sampling are used to overcome the bias and efficiency issues of the simple random sampling. Stratified Sampling. Stratified random sampling is a sampling method in which the population is first divided into strata (A stratum is a homogeneous subset of the population).

Therefore, stratified sampling and cluster sampling are used to overcome the bias and efficiency issues of the simple random sampling. Stratified Sampling. Stratified random sampling is a sampling method in which the population is first divided into strata (A stratum is a homogeneous subset of the population). Chapter 4: Stratified Random Sampling The way in which was have selected sample units thus far has required us to know little about the population of interest in advance of selecting the sample.

This cannot be considered random since the males had better chances of being selected as part of the sample. When to Use Disproportional Sampling. Disproportional sampling allows the researcher to give a larger representation to one or more subgroups to avoid underrepresentation of the said strata. Stratified random sampling is a sampling technique in which the population is divided into groups called strata. The idea behind stratified sampling is that the groupings are made so that the population units within a group are similar.

Stratified sampling divides your population into groups and then samples randomly within groups. Simple random sampling samples randomly within the whole population, that is, there is only one group. There are several reasons why people stratify. Stratified sampling strategies. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. For instance, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x 1 = m/X males, x 2 = f/X females) should reflect this proportion.

9-2-2020 · Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally. 3-2-2020 · Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or …

Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics. Stratified random sampling can be of two types (1) proportionate stratified sampling and (2) disproportionate stratified random sampling. When the size of the sample is

(3) Selects the sample, [Salant, p58] and decide on a sampling technique, and; (4) Makes an inference about the population. [Raj, p4] All these four steps are interwoven and cannot be considered isolated from one another. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques. In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum.

Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade‐offs inherent in selecting a sampling design: to select 9-2-2020 · Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally.

A sampling method in which the size of the sample drawn from a particular stratum is not proportional to the relative size of that stratum. For example, a stratum could be large supermarkets, which may only account for 20% of all grocery stores – although they account for 80% of grocery sales. In this case, a disproportionate sample would be used to represent the large supermarkets to Stratified random sampling can be of two types (1) proportionate stratified sampling and (2) disproportionate stratified random sampling. When the size of the sample is

Sample Size: Stratified Random Samples. (In a subsequent lesson, we re-visit this problem and see how stratified sampling compares to other sampling methods.) Problem 1. At the end of every school year, the state administers a reading test to a sample of 36 third graders. The school 18-11-2013 · Sampling 03: Stratified Random Sampling Rahul REST API concepts and examples - Duration: 8:53. WebConcepts Recommended for you. 8:53. How to Create A …

Stratified sampling strategies. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. For instance, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x 1 = m/X males, x 2 = f/X females) should reflect this proportion. Quota Vs Stratified Sampling • In Stratified Sampling, selection of subject is random. Call-backs are used to get that particular subject. • Stratified sampling without call-backs may not, in practice, be much different from quota sampling. • In Quota Sampling, interviewer …

Stratified sampling strategies. Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. For instance, if the population consists of X total individuals, m of which are male and f female (and where m + f = X), then the relative size of the two samples (x 1 = m/X males, x 2 = f/X females) should reflect this proportion. In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum.

Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics. (3) Selects the sample, [Salant, p58] and decide on a sampling technique, and; (4) Makes an inference about the population. [Raj, p4] All these four steps are interwoven and cannot be considered isolated from one another. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques.

Stratified sampling refers to a type of sampling method . With stratified sampling, the researcher divides the population into separate groups, called strata. Then, a probability sample (often a simple random sample ) is drawn from each group. Stratified sampling has … Sample Size: Stratified Random Samples. (In a subsequent lesson, we re-visit this problem and see how stratified sampling compares to other sampling methods.) Problem 1. At the end of every school year, the state administers a reading test to a sample of 36 third graders. The school

Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it. STRATIFIED RANDOM SAMPLING. Stratified random sampling is a technique which at tempts to restrict the possible samples to those which are ``less extreme'' by ensuring that all parts of the population are represented in the sample in

### 058-2009 Selecting a Stratified Sample with PROC SURVEYSELECT

Stratified Random Sampling Definition. Quota Vs Stratified Sampling • In Stratified Sampling, selection of subject is random. Call-backs are used to get that particular subject. • Stratified sampling without call-backs may not, in practice, be much different from quota sampling. • In Quota Sampling, interviewer …, Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics..

Chapter 4 Stratified Random Sampling - University of Arizona. Quota Vs Stratified Sampling • In Stratified Sampling, selection of subject is random. Call-backs are used to get that particular subject. • Stratified sampling without call-backs may not, in practice, be much different from quota sampling. • In Quota Sampling, interviewer …, Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling or stratification, the strata.

### How Stratified Random Sampling Works

058-2009 Selecting a Stratified Sample with PROC SURVEYSELECT. Stratified random sampling is a type of probability sampling using which researchers can divide the entire population into numerous non-overlapping, homogeneous strata. Final members for research are randomly chosen from the various strata which leads to cost reduction and improved response efficiency. This sampling method is also called “random quota sampling". https://en.wikipedia.org/wiki/Simple_random_sample Therefore, stratified sampling and cluster sampling are used to overcome the bias and efficiency issues of the simple random sampling. Stratified Sampling. Stratified random sampling is a sampling method in which the population is first divided into strata (A stratum is a homogeneous subset of the population)..

sampling . i.e. random: Every element in the population has a none non-zero probability of being selected; sampling involves random selection (equal chance) Non-probability sampling . i.e. non-random: Do not know in advance how likely that any element of the population will be selected for the sample; non-random selection (not equal chance) STRATIFIED RANDOM SAMPLING. Stratified random sampling is a technique which at tempts to restrict the possible samples to those which are ``less extreme'' by ensuring that all parts of the population are represented in the sample in

Dec 11, 2013 · How to Get a Stratified Random Sample: Steps Stratified random sampling examples. Sample question: You work for a small company of 1,000 people and want to find out how they are saving for retirement. Use stratified random sampling to obtain your sample. Step 1: Decide how you want to stratify (divide up) your population. There is a big difference between stratified and cluster sampling, that in the first sampling technique, the sample is created out of random selection of elements from all the strata while in the second method, the all the units of the randomly selected clusters forms a sample.

Therefore, stratified sampling and cluster sampling are used to overcome the bias and efficiency issues of the simple random sampling. Stratified Sampling. Stratified random sampling is a sampling method in which the population is first divided into strata (A stratum is a homogeneous subset of the population). Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade‐offs inherent in selecting a sampling design: to select

Stratified random sampling can be of two types (1) proportionate stratified sampling and (2) disproportionate stratified random sampling. When the size of the sample is Therefore, stratified sampling and cluster sampling are used to overcome the bias and efficiency issues of the simple random sampling. Stratified Sampling. Stratified random sampling is a sampling method in which the population is first divided into strata (A stratum is a homogeneous subset of the population).

(3) Selects the sample, [Salant, p58] and decide on a sampling technique, and; (4) Makes an inference about the population. [Raj, p4] All these four steps are interwoven and cannot be considered isolated from one another. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques. Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.

Multistage sampling divides large populations into stages to make the sampling process more practical. A combination of stratified sampling or cluster sampling and simple random sampling is usually used.. Let’s say you wanted to find out which subjects U.S. school children preferred. Multistage sampling divides large populations into stages to make the sampling process more practical. A combination of stratified sampling or cluster sampling and simple random sampling is usually used.. Let’s say you wanted to find out which subjects U.S. school children preferred.

This means that each stratum has the same sampling fraction (n/N), Stratified random sampling is a better method than simple random sampling. Stratified random sampling divides a population into subgroups or strata, and random samples are taken, in proportion to the … 17-1-2015 · Stratified Sampling: Stratified sampling explained through an example Sampling is important in statistics, and in this video I look at the method of stratified sampling as an approach to sampling.

3-2-2020 · Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or … Methods of sampling. To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic …

Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics. Quota Vs Stratified Sampling • In Stratified Sampling, selection of subject is random. Call-backs are used to get that particular subject. • Stratified sampling without call-backs may not, in practice, be much different from quota sampling. • In Quota Sampling, interviewer …

Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics. A stratified random sample is a random sample in which members of the population are first divided into strata, then are randomly selected to be a...

3-2-2020 · Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or … Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling, but few use this method for population-based surveys. Rapid surveys are no exception, since they too use a more complex sampling scheme. So why should we be concerned with simple random sampling? The main reason is to learn the theory of sampling.

STRATIFIED RANDOM SAMPLING - A representative number of subjects from various subgroups is randomly selected. Suppose we wish to study computer use of educators in the Hartford system. Assume we want the teaching level (elementary, middle school, and … Stratified random sampling methods often are used when there is interest in the differences between homogeneous subgroups and the larger sample population as a whole. Let’s say that a population of business clients can be divided into three groups: Generation X, millennials, and baby boomers.

PDF In order to answer Stratified random sampling . Systematic random sampling is a system in which every ninth case after the start is randomly selected and has a simple advantage in Stratified random sampling divides a population into subgroups or strata, whereby the members in each of the stratum formed have similar attributes and characteristics.

Stratified random sampling can be of two types (1) proportionate stratified sampling and (2) disproportionate stratified random sampling. When the size of the sample is Sample Size: Stratified Random Samples. (In a subsequent lesson, we re-visit this problem and see how stratified sampling compares to other sampling methods.) Problem 1. At the end of every school year, the state administers a reading test to a sample of 36 third graders. The school

STRATIFIED RANDOM SAMPLING - A representative number of subjects from various subgroups is randomly selected. Suppose we wish to study computer use of educators in the Hartford system. Assume we want the teaching level (elementary, middle school, and … Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.

STRATIFIED RANDOM SAMPLING. Stratified random sampling is a technique which at tempts to restrict the possible samples to those which are ``less extreme'' by ensuring that all parts of the population are represented in the sample in Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.

(3) Selects the sample, [Salant, p58] and decide on a sampling technique, and; (4) Makes an inference about the population. [Raj, p4] All these four steps are interwoven and cannot be considered isolated from one another. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques. Chapter 4: Stratified Random Sampling The way in which was have selected sample units thus far has required us to know little about the population of interest in advance of selecting the sample.