Upcoming SlideShare
Loading in...5







Total Views
Slideshare-icon Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Sampling Sampling Presentation Transcript

    • Sampling
    • • Sampling • Population • Elements • Subject • Sampling unit • Sampling process • Sampling units
    • Sampling • The process of selecting right individual objects, or events as representative for the entire population is known as sampling. For example • Sensex-30 companies • Nifty- 50 companies
    • Population • The population refers to the entire group of people, events or things of interest that researcher wishes to investigate and wants to make inferences. For example • If researcher wants to know the investment pattern of Mumbai city then all residents of Mumbai will be the population.
    • Element • An element is a single member of the population. e.g. • Total students of GICED is 1800, then each student will be the element.
    • Sample • It is a subject of the population • It comprises some members selected from it • In other words, some, but not all elements of the population form sample. • By studying the sample, the researcher should be able to draw conclusion that are generalized to the population of interest E.g. • Sensex-BSE • Nifty-NSE
    • Sampling unit • It is the element or set of element that is available for set selection in some stage of the sampling process
    • Subject • It is a single member of the sample just as an element of the population.
    • Reasons for sampling • Practically impossible to collect data from, or examine every element • Even it is possible, it is prohibitive in terms of time, cost and human resources • Study of sample rather than population is also sometimes likely to produce more reliable data especially when a large number of elements is involved • E.g. election survey by media
    • Representative Sample - • σ 2 • σ • Population – µ • σ 2 • σ
    • Sampling process • Define population • Determine sample frame • Determine sample design • Determine appropriate sample size • Execute sampling process
    • Define population • It must be defined in terms of elements, geographical boundaries and time
    • Determine sample frame • It is a representation of all the elements in the population from which the sample is drawn E.g. • The payroll of an organization would serve as the sampling frame if its members are to be studied
    • Sampling design Two major types of sampling design • Probability • Non probability
    • Probability • The elements in the population have some known, non-zero chance or probability of being selected as a sample object • This design is used when the representative of the sample is of importance in the interest of wider generalizability
    • • Non probability • Elements in the population do not have a known or predetermined chance of being selected as a subject • When time and other factors, rather than generalizability, become critical, non probability sampling is generally used
    • Determine sample size • Is a large sample better than a small sample? • Decision about how large the sample size should be a very difficult one. • We can summarize the factors affecting factors affecting decision on sample size as • Research objective • Cost and time constraint • Amount of the variability in the population itself
    • Probability sampling 1. Unrestricted or simple random sampling 2. Restricted or complex probability sampling • Systematic sampling • Stratified random sampling • Cluster sampling • Double sampling or Area sampling
    • Unrestricted or simple random sampling • In this sampling every element in the population has known and equal chance of being selected as a subject • This sampling design, has the least bias and offers the most generalizability • However this sampling process could become cumbersome and expenive
    • Restricted or complex probability sampling • These probability sampling procedures offer a viable and sometimes more efficient design • More information can be obtained
    • Systematic sampling It involves drawing every nth element in the population starting with randomly chosen element between 1 & nth element
    • Stratified random sampling • It involves a process of segregation followed by random selection of subject from each stratum
    • Cluster sampling • In this target population is first divided into clusters • A specific cluster sampling is area sampling
    • • Double sampling • When further information is needed from subset of the first group from which some information has already been collected for the same study.
    • Non probability sampling • There are two types 1. Convenience 2. Purposive
    • Convenience • Collection of information from members of the population who are conveniently available to provide it.
    • Purposive 1. Judgment 2. Quota
    • 1. Judgment • Most advantageously placed • In the best position to provide information required
    • Quota • Certain group