Assignment sampling techniques
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This document is a 9-page assignment on sampling techniques submitted by Danish Alam to Dr. Fahd Amjad. It defines key sampling terms and describes two main types of sampling: probability sampling and non-probability sampling. Probability sampling techniques discussed include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling techniques examined include convenience sampling, judgment sampling, snowball sampling, ad hoc quota sampling, and case study sampling. Diagrams are provided showing the types of probability and non-probability sampling.
Assignment sampling techniques
- 1. Subject: Statistical Inference by Dr. Fahd Amjad Page 1 of 9 Assignment: Sampling Techniques Subject: Inferential Statistics Submitted to: Dr. Fahd Amjad Submitted by: Danish Alam Roll No. FA-15 RMS-002 Program: MS (MS) Department: Management Sciences
- 2. Subject: Statistical Inference by Dr. Fahd Amjad Page 2 of 9 Sampling Techniques Sample: Definition: “It is the subset of population.” Or “It is the collection of data or elements from population by a defined procedure.” Each element of sample is known as sample points. For Example: A sample of heights of 10 students collected from a population of 30 students in a class. Sampling: Definition: “the act of taking a portion or sample from population is called sampling.” Or “It is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of whole population.” Sampling Techniques: There are two types of sampling; Probability Sampling Non-Probability Sampling 1) Probability Sampling (Definition): Probability or random sampling gives all the members of population a known chance of being selected for inclusion in the sample and this does not depend upon previous events in the selection process. In other words, the selection of individuals does not affect the chance of anyone else in the population being selected. Or
- 3. Subject: Statistical Inference by Dr. Fahd Amjad Page 3 of 9 Probability sampling is a sampling technique wherein the samples are gathered in a process that gives all the individuals in the population equal chances of being selected. There are four types of Probability Sampling; i) Simple Random Sampling ii) Systematic Sampling iii) Stratified Sampling iv) Cluster or Multi-Stage Sampling Diagram: Diagram Showing types of Probability Sampling. i) Simple Random Sampling: This is the ideal choice as it is a perfect random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection. Or In statistics, a simple random sample is a subset of individuals (a sample) chosen from a larger set (a population). Each individual is chosen randomly and entirely by chance, such that Simple Random Sampling Systematic Sampling Stratified Sampling Cluster or Multi- Stage Sampling
- 4. Subject: Statistical Inference by Dr. Fahd Amjad Page 4 of 9 each individual has the same probability of being chosen at any stage during the sampling process ii) Systematic Random Sampling: It is defined as “the statistical method involve in the selection of elements from an ordered sampling frame.” Or Systematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every Kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. Or A random sampling with a system is called Systematic Random Sampling. From the sampling frame, a starting point is chosen at random, and choices thereafter are at regular intervals. Advantage: The sample usually will be easier to identify than it would be if simple random sampling were used. For Example: Selecting every 100th listing in a telephone book after the first randomly selected listing. For Example, if we have a listed population of 6000 members and wish to draw a sample of 2000, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point. The one potential problem with this method of sampling concerns the arrangement of elements in the list? If the list is arranged in any kind of order e.g. if every 30th house is smaller than the others from which the sample is being recruited, there is a possibility that the sample produced could be seriously biased. iii) Stratified Sampling: Stratified sampling is a variant on simple random and systematic methods and is used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub- populations (or strata), base on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each stratum, usually by random or systematic sampling methods. Or
- 5. Subject: Statistical Inference by Dr. Fahd Amjad Page 5 of 9 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. Advantage: If strata are homogeneous, this method is as “precise” as simple random sampling but with a smaller total sample size. For Example: The basis for forming the strata might be department, location, age, industry type, etc. Stratified sampling methods is further divided into two types; In Proportionate Sampling, the strata sample sizes are made proportional to the strata population sizes. For Example, if the first strata are made up of males, then as there are around 50% of males in the UK population, the male strata will need to represent around 50% of the total sample. In Disproportionate Methods, the strata are not sampled according to the population sizes, but higher proportions are selected from some groups and not others. This technique is typically used in a number of distinct situations: The costs of collecting data may differ from subgroup to subgroup. We might require more cases in some groups if estimations of populations’ values are likely to be harder to make i.e. the Market Research World. Larger the sample size (up to certain limits), the more accurate any estimations are likely to be. iv) Cluster or Multi-Stage Sampling: Benefits and Usage: Cluster sampling is a frequently-used, and usually more practical, random sampling method. It is particularly useful in situations for which no list of the elements within a population is available and therefore cannot be selected directly. As this form of sampling is conducted by randomly selecting subgroups of the population, possibly in several stages, it should produce results equivalent to a simple random sample. It is often used in marketing research. The sample is generally done by first sampling at the higher level(s) e.g. randomly sampled countries, then sampling from subsequent levels in turn e.g. within the selected countries sample counties, then within these postcodes, then within these households, until the final stage is reached, at which point the sampling is done in a simple random manner e.g. sampling people within the selected households. Or
- 6. Subject: Statistical Inference by Dr. Fahd Amjad Page 6 of 9 “It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected.” Advantage: The close proximity of elements can be cost effective (I.e. many sample observations can be obtained in a short time). Disadvantage: This method generally requires a larger total sample size than simple or stratified random sampling. For Example: A primary application is area sampling, where clusters are city blocks or other well-defined areas. Cluster Sampling is generally used if; Cluster sampling is a sampling technique used when "natural" but relatively homogeneous groupings are evident in a statistical population. No list of the population exists. Well-defined clusters, which will often be geographic areas, exist. A reasonable estimate of the number of elements in each level of clustering can be made. Often the total sample size must be fairly large to enable cluster sampling to be used effectively. 2) Non-Probability Sampling (Definition): “Non-Probability sampling is any sampling method where some elements of population have no chance of selection (these are sometimes referred to as ‘out of coverage or under covered’), or where the probability of selection can’t be accurately determined.” Or “A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgment of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques.” There are five main types of Non-Probability Sampling; i. Convenience Sampling ii. Judgment Sampling iii. Snowball Sampling iv. Ad hoc quotas sampling
- 7. Subject: Statistical Inference by Dr. Fahd Amjad Page 7 of 9 v. Case Study Sampling Diagram showing types of Non-Probability Sampling i) Convenience Sampling: A statistical method of drawing representative data by selecting people because of the ease of their volunteering or selecting units because of their availability or easy access. Or It is a non-probability sampling technique. Items are included in the sample without known probabilities of being selected. The sample is identified primarily by convenience. Advantage: Sample selection and data collection are relatively easy. The data in this type of sampling is easily available and can gather data quickly. Disadvantage: It is impossible to determine how representative of the population the sample is. The another disadvantages are the risk that the sample might not represent the population as a whole, and it might be biased by volunteers. For Example: A professor conducting research might use student volunteers to constitute a sample. Convenience Sampling Judgement Sampling Snowball Sampling Ad Hoc Quota Sampling Case Study Sampling
- 8. Subject: Statistical Inference by Dr. Fahd Amjad Page 8 of 9 ii) Judgment Sampling: Judgment sample is a type of non-random sample that is selected based on the opinion of an expert. Results obtained from a judgment sample are subject to some degree of bias, due to the frame and population not being identical. Or The person most knowledgeable on the subject of the study selects elements of the population that he or she feels are most representative of the population. It is a non-probability sampling technique. Advantage: It is a relatively easy way of selecting a sample. Disadvantage: The quality of the sample results depends on the judgment of the person selecting the sample. For Example: A reporter might sample three or four senators, judging them as reflecting the general opinion of the senate. iii) Snowball Sampling: A snowball sample is a non-probability sampling technique that is appropriate to use in research when the members of a population are difficult to locate. A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate, then asks those individuals to provide information needed to locate other members of that population whom they know. Snowball sampling is hardly likely to lead a representative sample, but there are times when it may be the best or only method available. For instance, if you are studying the homeless, you are not likely to find a list of all the homeless people in your city. However, if you identify one or two homeless individuals that are willing to participate in your study, it is likely that they know other homeless individuals in their area and can help you locate them. The same goes for underground subcultures, or any population that might want to keep their identity hidden, such as undocumented immigrants or ex-convicts. Because snowball sampling is hardly representative of the larger study population, it is primarily used for exploratory purposes. iv) Ad Hoc Quota Sampling: Quota sampling is a type of non-probability sampling that involves a two-step process: 1. Specify a list of relevant control categories or quotas such as age, gender, income, or education. A quota is some specific requirement or predefined category. The target population is first segmented into mutually exclusive sub-groups, which means that one individual can be a member of only one category or sub-group. The researcher
- 9. Subject: Statistical Inference by Dr. Fahd Amjad Page 9 of 9 takes special care to obtain a sample that is similar to the target population on some specified control category. 2. Collect a sample that has the same properties as the target population. To do this, the researcher must know the distribution of these properties across that population. For example, let's look at a target population of college students at a local college. Because the researcher can access this data, he knows that in this given population, 43% of the students are male and 57% are female. For a sample size of 1,000, the researcher knows that 430 males and 570 females will need to be interviewed from that population. References: www.google.com www.wikipedia.org.com.