Questions on Sampling 11-20

1. Define Non probability Sampling
The difference between non probability and probability sampling is that non probability sampling does not involve random selection and probability sampling does. It can be divided into two broad types: accidental or purpose. In purposive sampling, we sample with a purpose in mind

2. Define various non probability sampling methods
Modal Instance Sampling
Expert Sampling
Quota Sampling
Non proportional Quota Sampling
Heterogeneity Sampling
Snowball Sampling

3. Define Modal Instance Sampling
In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the "typical" case.

4. Define Expert Sampling
Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area. Often, such a sample is done under the auspices of a "panel of experts. It would be the best way to elicit the views of persons who have specific expertise

5. Define Quota Sampling
In quota sampling, you select people non randomly according to some fixed quota. There are two types of quota sampling: proportional and non proportional. In proportional quota sampling you want to represent the major characteristics of the population by sampling a proportional amount of each

6. Define Non proportional Quota Sampling
Non proportional quota sampling is a bit less restrictive. In this method, you specify the minimum number of sampled units you want in each category. here, you're not concerned with having numbers that match the proportions in the population.

7. Define Heterogeneity Sampling 
The sample for heterogeneity when we want to include all opinions or views, and we aren't concerned about representing these views proportionately. Another term for this is sampling for diversity.

8. Define Snowball Sampling
In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find.

9. Define Accidental Sampling
Its just a means of convenience that this sample is being selected. Based on availability an no specific purpose or thought process is involved in this sampling.

10. What are the other synomnys of accidental sampling
Its also known as Haphazard or Convenience sampling

Questions on Sampling 1- 10

1. Define 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.

2. Define sampling error?

The standard error is called sampling error. Sampling error gives us some idea of the precision of our statistical estimate. A low sampling error means that we had relatively less variability or range in the sampling distribution. The greater the sample standard deviation, the greater the standard error (and the sampling error).

3. The greater your sample size, the smaller the standard error. Why?

Because the greater the sample size, the closer your sample is to the actual population itself. If you take a sample that consists of the entire population you actually have no sampling error because you don't have a sample, you have the entire population.

4. Define 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, We must ensure some process or procedure that assures that the different units in your population have equal probabilities of being chosen

5. Define Stratified Random Sampling.

It also called proportional or quota random sampling, involves dividing theopulation 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) N₁, N₂, N₃, ... N_i, such that N₁ + N₂ + N₃ + ... + N_i = N. Then do a simple random sample of f = n/N in each strata.

This method assures that we represent not only the overall population, but also key subgroups of the population, especially small minority groups.

6. Define Cluster sampling

Cluster sampling is a sampling technique used when "natural" groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group.

7. Define Multi stage sampling

Multistage sampling is a complex form of cluster sampling. Using all the sample elements in all the selected clusters may be prohibitively expensive or not necessary. Under these circumstances, multistage cluster sampling becomes useful. Instead of using all the elements contained in the selected clusters, the researcher randomly selects elements from each cluster. Constructing the clusters is the first stage. Deciding what elements within the cluster to use is the second stage. The technique is used frequently when a complete list of all members of the population does not exist and is inappropriate.

8. Define Systematic random sampling

Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equal-probability method, in which every k^th element in the frame is selected, where k, the sampling interval

9. Difference between Cluster sampling and Stratified sampling

Although cluster sampling and stratified sampling bear some superficial similarities, they are substantially different. In stratified sampling, a random sample is drawn from all the strata, where in cluster sampling only the selected clusters are studied, either in single stage or multi stage.

10. What is the difference between Random Selection & Assignment

Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study.