__Methods of sampling design__
__INTRODUCTION__
When conducting research, it is almost always impossible to study the entire population that you are interested in. For example, if you were studying political views among college students in the United States, it would be nearly impossible to survey every single college student across the country. If you were to survey the entire population, it would be extremely timely and costly. As a result, researchers use samples as a way to gather data A sample is a subset of the population being studied. It represents the larger population and is used to draw inferences about that population. It is a research technique widely used in the social sciences as a way to gather information about a population without having to measure the entire population There are several different types and ways of choosing a sample from a population, from simple to complex. __MEANING OF SAMPLE__
· A portion, piece, or segment that is representative of a whole. · An entity that is representative of a class; a specimen. · A usually digitized audio segment taken from an original recording and inserted, often repetitively, in a new recording · a set of individuals or items selected from a population for analysis to yield estimates of, or to test hypotheses about, parameters of the whole population __MEANING OF SAMPLING__
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 Non probability sampling methods and work through the major types in each. Sampling is a method of selecting experimental units from a population so that we can make decision about the population.
__MEANING OF SAMPLING DESIGN__ Sampling design is a design, or a working plan, that specifies the population frame, sample size, sample selection, and estimation method in detail. Objective of the sampling design is to know the characteristic of the population. The "best" sample design depends on survey objectives and on survey resources. For example, a researcher might select the most economical design that provides a desired level of precision. Or, if the budget is limited, a researcher might choose the design that provides the greatest precision without going over budget. __DEFINITION OF SAMPLING DESIGN__
Sharon L. Lohr's SAMPLING DESIGN provides a modern introduction to the field of sampling. With a multitude of applications from a variety of disciplines, the book concentrates on the statistical aspects of taking and analyzing a sample. Overall, the book gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys *Elements of Sample Design*.
**Sampling method**. Sampling method refers to the rules and procedures by which some elements of the population are included in the sample. Some common sampling methods are simple random sampling, stratified sampling, and cluster sampling . **Estimator**. The estimation process for calculating sample statistics is called the estimator. Different sampling methods may use different estimators. For example, the formula for computing a mean score with a simple random sample is different from the formula for computing a mean score with a stratified sample. Similarly, the formula for the standard error may vary from one sampling method to the next. __METHODS OF SAPMLING__
We may then consider different types of probability samples. Although there are a number of different methods that might be used to create a sample, they generally can be grouped into one of **two** categories: *probability samples* or *non-probability* samples. ** **
**I. PROBABILITY SAMPLING**
A **probability sampling** method is any method of sampling that utilizes some form of *random selection*. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. ** **
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**I.1Simple Random Sampling**
The simplest form of random sampling is called **simple random sampling**. Pretty tricky, huh? Here's the quick description of simple random sampling: **Objective**: To select *n* units out of *N* such that each _{N}C_{n} has an equal chance of being selected. **Procedure**: Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample. Simple random sampling is simple to accomplish and is easy to explain to others. Because simple random sampling is a fair way to select a sample, it is reasonable to generalize the results from the sample back to the population. Simple random sampling is not the most statistically efficient method of sampling and you may, just because of the luck of the draw, not get good representation of subgroups in a population. To deal with these issues, we have to turn to other sampling methods. ** **
**I.2 Stratified Random Sampling**
**Stratified Random Sampling**, also sometimes called *proportional* or *quota *random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup. In more formal terms:
**Objective**
: Divide the population into non-overlapping groups (i.e., *strata*) N_{1}, N_{2}, N_{3}, ... N_{i}, such that N_{1} + N_{2} + N_{3} + ... + N_{i} = N. Then do a simple random sample of f = n/N in each strata. **I.3 Systematic Random Sampling**
This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements - every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000. Creating such a sample includes three steps: 1. Divide number of cases in the population by the desired sample size. In this example, dividing 10,000 by 1,000 gives a value of 10. 2. Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10 - say we pick 7. 3. Starting with case number chosen in Step 2, take every tenth record (7, 17, 27, etc.). More generally, suppose that the N units in the population are ranked 1 to N in some order (e.g., alphabetic). To select a sample of n units, we take a unit at random, from the 1st k units and take every k-th unit thereafter. The advantages of systematic sampling method over simple random sampling include: 1. It is easier to draw a sample and often easier to execute without mistakes. This is a particular advantage when the drawing is done in the field. 2. Intuitively, you might think that systematic sampling might be more precise than SRS. In effect it stratifies the population into n strata, consisting of the 1st k units, the 2nd k units, and so on. Thus, we might expect the systematic sample to be as precise as a stratified random sample with one unit per stratum. The difference is that with the systematic one the units occur at the same relative position in the stratum whereas with the stratified, the position in the stratum is determined separately by randomization within each stratum. Here are the steps you need to follow in order to achieve a **systematic random sample**: - number the units in the population from 1 to N
- decide on the n (sample size) that you want or need
- k = N/n = the interval size
- randomly select an integer between 1 to k
- then take every kth unit
**I.4** **Cluster (Area) Random Sampling**
The problem with random sampling methods when we have to sample a population that's disbursed across a wide geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled. Imagine taking a simple random sample of all the residents of New York State in order to conduct personal interviews. By the luck of the draw you will wind up with respondents who come from all over the state. Your interviewers are going to have a lot of traveling to do. It is for precisely this problem that **cluster or area random sampling** was invented. In cluster sampling, we follow these steps: - divide population into clusters (usually along geographic boundaries)
- randomly sample clusters
- measure all units within sampled clusters
__Methods of sampling design__ __INTRODUCTION__ When conducting research, it is almost always impossible to study the entire population that you are interested in. For example, if you were studying political views among college students in the United States, it would be nearly impossible to survey every single college student across the country. If you were to survey the entire population, it would be extremely timely and costly. As a result, researchers use samples as a way to gather data A sample is a subset of the population being studied. It represents the larger population and is used to draw inferences about that population. It is a research technique widely used in the social sciences as a way to gather information about a population without having to measure the entire population There are several different types and ways of choosing a sample from a population, from simple to complex. __MEANING OF SAMPLE__ · A portion, piece, or segment that is representative of a whole. · An entity that is representative of a class; a specimen. · A usually digitized audio segment taken from an original recording and inserted, often repetitively, in a new recording · a set of individuals or items selected from a population for analysis to yield estimates of, or to test hypotheses about, parameters of the whole population __MEANING OF SAMPLING__ 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 Non probability sampling methods and work through the major types in each. Sampling is a method of selecting experimental units from a population so that we can make decision about the population. __MEANING OF SAMPLING DESIGN__ Sampling design is a design, or a working plan, that specifies the population frame, sample size, sample selection, and estimation method in detail. Objective of the sampling design is to know the characteristic of the population. The "best" sample design depends on survey objectives and on survey resources. For example, a researcher might select the most economical design that provides a desired level of precision. Or, if the budget is limited, a researcher might choose the design that provides the greatest precision without going over budget. __DEFINITION OF SAMPLING DESIGN__ Sharon L. Lohr's SAMPLING DESIGN provides a modern introduction to the field of sampling. With a multitude of applications from a variety of disciplines, the book concentrates on the statistical aspects of taking and analyzing a sample. Overall, the book gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys *Elements of Sample Design*. |