logging in or signing up stat sampling hoser Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Copy Does not support media & animations WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 21 Category: Education License: Some Rights Reserved Like it (0) Dislike it (0) Added: May 24, 2012 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Statistics: Statistics Sampling TechniquesRandom Sampling: Random Sampling It is the method of selecting a sample size (n) from a universe (N) such that each member of the population has an equal chance of being included in the sample and all possible combinations of size (n) have an equal chance of being selected as the sample. Copyright 2008 PresentationFx.com | Redistribution Prohibited | Image © 2008 Thomas Brian | This text section may be deleted for presentation .Random Sampling: Copyright 2008 PresentationFx.com | Redistribution Prohibited | Image © 2008 Thomas Brian | This text section may be deleted for presentation . Random Sampling A prerequisite fo r the randomness of the selection is a complete listing of the population. There are several ways of drawing sample units at random. Lottery sampling – usually carried out by assigning numbers to each member of the population.Random Sampling: Random Sampling We may write down the names of each member of the population on pieces of paper, then placed on a box or lottery drum. Make sure to shake them thoroughly. From the box or lottery drum, the required number of sample units are picked.Random Sampling: Table of Random Numbers – Under this technique, the selection of each member of he population is left adequately to chance, and every member of the population has an equal chance of being chosen. Random SamplingRandom Sampling: Random Sampling -613238 -990065 067217 -655118 -253755 -715080 -482228 011007 -946267 -028290 -252131 -613844 019182 -381570 615413 -596369 -983341 -796267 -492824 -329285 -240271 -434045 -827812 691115 -556579 543481 039218 008326 066624 -132954 -475358 -759112 Table of Random NumbersSystematic Sampling: Systematic Sampling This method use prior knowledge of the individuals comprising a universe with the end in view to increasing precision and representation of samples. When sample units are obtained by drawing every , say, 4 th or 7 th or 10 th item on a list, the process of selecting the sample is called systematic sampling .Systematic Sampling: Systematic Sampling This method involves selecting every nth element of a series representing the population. A complete listing is required in this method. Under this system, the sample units may be picked in the following manner, where, for example, N=100 n=10Systematic Sampling: Systematic Sampling The value of n may be obtained by dividing the total number of elements in the population by the desired sample size. Thus, N 100 --- = ------ = 10th n 10Types of Systematic Sampling: Types of Systematic Sampling Stratified Sampling – the population is first divided into groups – based on homogeneity – in order to avoid the possibility of drawing samples whose members come only from one stratum. The distribution of sampling units is proportionate to the total number of units in each stratum. The bigger the stratum, the more sample units are drawn, the less population, the less sample units.Stratified Sampling: Stratified Sampling In surveys or market research, we often have to stratify or assign into groups the items in the universe to be samples. After dividing into two or more groups based on homogeneity, the final sample will still be a probability so long as a random method of selection is used in drawing the sample units from each of the strata or groups.Cluster Sampling: Cluster Sampling It is sometimes referred to as an area sample because it is frequently applied on a geographical basis. Districts or blocks of a municipality or city constitute the clusters. Cluster sampling is useful in selecting the sample when blocks in a community or city are occupied by heterogeneous groups.Non-random Sampling: Non-random Sampling Under this methodology, not all members of the population are given equal chances to be chosen. Certain elements in the population are deliberately left out in the choice of the sample for varied reasons. It is also called non-probability sampling or judgment sampling.Non-random Sampling: Non-random Sampling It makes use of judgment in the selection of items to be put into the sample or in making decisions as to responses needed. He validity of the sample is based on the soundness of the judgment of whoever makes the choice. Non-random or judgment sampling finds its greatest use in fields like market research, or in employment departments of companies.Types of Non-random or Judgment Sampling: Types of Non-random or Judgment Sampling Purposive Sampling – based on certain criteria laid down by the researcher. People who satisfy the criteria are interviewed. Quota sampling – relatively quick and inexpensive method to operate. Each interviewer is given definite instructions about the section of the public he is to question, but the final choice of the actual persons is left to his own convenience or preference.Types of Non-random or Judgment Sampling: Types of Non-random or Judgment Sampling Convenience Sampling – A researcher might want to find out the popularity of a radio program. Since the researcher has a telephone, he might simply use it and “randomly” pick his samples from the telephone directory. This method, of course, biased against non-telephone users. You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.