DR DEEPAK CHAWLA DR NEENA SONDHI SAMPLING TECHNIQUES

DR DEEPAK CHAWLA DR NEENA SONDHI SAMPLING TECHNIQUES RESEARCH CONCEPTS AND

SLIDE 9 -1 DR DEEPAK CHAWLA DR NEENA SONDHI Sampling Concepts Population: Population refers to any group of people or objects that form the subject of study in a particular survey and are similar in one or more ways. Element: An element comprises a single member of the population. Sampling frame: Sampling frame comprises all the elements of a population with proper identification that is available to us for selection at any stage of sampling. Sample: It is a subset of the population. It comprises only some elements of the population. Sampling unit: A sampling unit is a single member of the sample. Sampling: It is a process of selecting an adequate number of elements from the population so that the study of the sample will not only help in understanding the characteristics of the population but will also enable us to generalize the results. Census (or complete enumeration): An examination of each and every element of the population is called census or complete enumeration. RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI Advantages of Sample over Census SLIDE 9 -2 Sample saves time and cost. A decision-maker may not have too much of time to wait till all the information is available. There are situations where a sample is the only option. The study of a sample instead of complete enumeration may, at times, produce more reliable results. A census is appropriate when the population size is small. RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -3 Sampling vs Non-Sampling Error Sampling error: This error arises when a sample is not representative of the population. Non-sampling error: This error arises not because a sample is not a representative of the population but because of other reasons. Some of these reasons are listed below: Plain lying by the respondent. The error can arise while transferring the data from the questionnaire to the spreadsheet on the computer. There can be errors at the time of coding, tabulation and computation. Population of the study is not properly defined Respondent may refuse to be part of the study. There may be a sampling frame error. RESEARCH CONCEPTS AND

The Sampling Design Process Define the target Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process

Define the Target Population The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made. The target population should be defined in terms of elements, sampling units, extent, and time. An element is the object about which or from which the information is desired, e. g. , the respondent. A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process. Extent refers to the geographical boundaries. Time is the time period under consideration.

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -4 Sampling Design(technique) Probability Sampling Design - Probability sampling designs are used in conclusive research(descriptive & experimental). In probability sampling design, each and every element of the population has a known chance of being selected in the sample. Types of Probability Sampling Design(technique) Simple random sampling with replacement Simple random sampling without replacement Systematic sampling Stratified random sampling Cluster sampling RESEARCH CONCEPTS AND

Simple Random Sampling Every element has an equal chance of getting selected to be the part of sample. It is used when we don’t have any kind of prior information about the target population. For example: Random selection of 20 students from a class of 50 students. Each student has equal chance of getting selected. Here the probability of selection is 1/50.

Stratified Sampling This technique divides he elements of the population into small subgroups (strata) based on the similarity in such a way that the elements within the group are homogeneous and heterogeneous among the other subgroups formed. And then the elements are randomly selected from each of these strata.

Single Stage Cluster The entire cluster is selected randomly for sampling.

Two-Stage Cluster Here first we randomly select clusters and then from those selected clusters we randomly select elements for sampling

Here the selection of elements is systematic and not random except the first element. Elements of a sample are chosen at regular intervals of population. All the elements are put together in a sequence first where each element has the equal chance of being selected.

Multi-Stage Sampling It is the combination of one or more methods described above. Population is divided into multiple clusters and then These clusters are further divided and grouped into various sub groups (strata) based on similarity. One or more clusters can be randomly selected from each stratum. This process continues until the cluster can’t be divided anymore. For example country can be divided into states, cities, urban and rural and all the areas with similar characteristics can be merged together to form a strata.

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -5 Sampling Design(technique) Non-probability Sampling Designs - In case of non-probability sampling design, the elements of the population do not have any known chance of being selected in the sample. Types of Non-Probability Sampling Design Convenience sampling Judgemental sampling Snowball sampling Quota sampling RESEARCH CONCEPTS AND

Non-Probability Sampling It does not rely on randomization. This technique is more reliant on the researcher’s ability to select elements for a sample. Outcome of sampling might be biased and makes difficult for all the elements of population to be part of the sample equally. This type of sampling is also known as nonrandom sampling.

Convenience Sampling Here the samples are selected based on the availability. This method is used when the availability of sample is rare and also costly. So based on the convenience samples are selected. For example: Researchers prefer this during the initial stages of survey research, as it’s quick and easy to deliver results.

Purposive Sampling This is based on the intention or the purpose of study. Only those elements will be selected from the population which suits the best for the purpose of our study. For Example: If we want to understand the thought process of the people who are interested in pursuing master’s degree then the selection criteria would be “Are you interested for Masters in. . ? ” All the people who respond with a “No” will be excluded from our sample.

Quota Sampling This type of sampling depends of some pre-set standard. It selects the representative sample from the population. Proportion of characteristics/ trait in sample should be same as population. Elements are selected until exact proportions of certain types of data is obtained or sufficient data in different categories is collected. For example: If our population has 45% females and 55% males then our sample should reflect the same percentage of males and females.

Referral /Snowball Sampling This technique is used in the situations where the population is completely unknown and rare. Therefore we will take the help from the first element which we select for the population and ask him to recommend other elements who will fit the description of the sample needed. So this referral technique goes on, increasing the size of population like a snowball.

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -5 Sampling Design(technique) Non-probability Sampling Designs - In case of non-probability sampling design, the elements of the population do not have any known chance of being selected in the sample. Types of Non-Probability Sampling Design Convenience sampling Judgmental sampling Snowball sampling Quota sampling RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -7 Determination of Sample Size Confidence interval approach for determining the size of the sample The following points are taken into account for determining the sample size in this approach. The variability of the population: Higher the variability as measured by the population standard deviation, larger will be the size of the sample. The confidence attached to the estimate: Higher the confidence the researcher wants for the estimate, larger will be sample size. The allowable error or margin of error: Greater the precision the research seeks, larger would be the size of the sample. RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -8 Determination of Sample Size Sample size for estimating population mean The formula for determining sample size is given as: Where n = Sample size σ = Population standard deviation e = Margin of error Z = The value for the given confidence interval RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI SLIDE 9 -9 Determination of Sample Size Sample size for estimating population proportion – 1. When population proportion p is known 2. When population proportion p is not known RESEARCH CONCEPTS AND

DR DEEPAK CHAWLA DR NEENA SONDHI END OF CHAPTER RESEARCH CONCEPTS AND