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Typology: Essays (university)

2019/2020

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Download acbDGISFICGIEGIUASHDFUDGUIFSGIG and more Essays (university) Physics in PDF only on Docsity! Instructor: Dr. Kapil Jain, IIPS-DAVV 1 Dr. Kapil Jain, IIPS-DAVV, Indore SAMPLING METHODS LEARNING OBJECTIVES 2 Learn the reasons for sampling Develop an understanding about different sampling methods Distinguish between probability & non probability sampling Discuss the relative advantages & disadvantages of each sampling methods Instructor: Dr Kapil Jain Population definition 3 A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population. Instructor: Dr Kapil Jain SAMPLE 4 A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005) Why sample? Resources (time, money) and workload Gives results with known accuracy that can be calculated mathematically The sampling frame is the list from which the potential respondents are drawn Registrar’s office Class rosters Must assess sampling frame errors Instructor: Dr Kapil Jain SAMPLING…… 5 What is your population of interest? To whom do you want to generalize your results? All doctors School children Indians Women aged 15-45 years Other Can you sample the entire population? Instructor: Dr Kapil Jain SAMPLING……. 6 3 factors that influence sample representative-ness Sampling procedure Sample size Participation (response) When might you sample the entire population? When your population is very small When you have extensive resources When you don’t expect a very high response Instructor: Dr Kapil Jain Instructor: Dr. Kapil Jain, IIPS-DAVV Instructor: Dr. Kapil Jain, IIPS-DAVV 2 7 SAMPLING BREAKDOWN Instructor: Dr Kapil Jain SAMPLING……. 8 TARGET POPULATION STUDY POPULATION SAMPLE Instructor: Dr Kapil Jain What is Sampling? Population Sample Using data to say something (make an conclusion) with confidence, about a whole (population) based on the study of a only a few (sample). Sampling Frame Sampling Process What you want to talk about What you actually observe in the data conclusion 9 Instructor: Dr Kapil Jain What is Sampling? Sampling is the process of selecting observations (a sample) to provide an adequate description and robust inferences of the population The sample is representative of the population. There are 2 types of sampling: Non-Probability sampling (Thurday’s lecture) Probability sampling 10 Instructor: Dr Kapil Jain Types of Samples 11 Probability (Random) Samples Simple random sample Systematic random sample Stratified random sample Multistage sample Multiphase sample Cluster sample Non-Probability Samples Convenience sample Purposive sample Quota Instructor: Dr Kapil Jain Classification of Sampling Methods Sampling Methods Probability Samples Simple Random Cluster Systematic Stratified Non- probability QuotaJudgment Convenience Snowball 12 Instructor: Dr Kapil Jain Instructor: Dr. Kapil Jain, IIPS-DAVV Instructor: Dr. Kapil Jain, IIPS-DAVV 5 STRATIFIED SAMPLING……. 25 Draw a sample from each stratum Instructor: Dr Kapil Jain CLUSTER SAMPLING 26 Cluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those areas is selected. Population divided into clusters of homogeneous units, usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. All units from the selected clusters are studied. Instructor: Dr Kapil Jain CLUSTER SAMPLING……. 27 Advantages : Cuts down on the cost of preparing a sampling frame. This can reduce travel and other administrative costs. Disadvantages: sampling error is higher for a simple random sample of same size. Instructor: Dr Kapil Jain CLUSTER SAMPLING……. 28 • Identification of clusters – List all cities, towns, villages & wards of cities with their population falling in target area under study. – Calculate cumulative population & divide by 30, this gives sampling interval. – Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster. – Random no.+ sampling interval = population of 2nd cluster. – Second cluster + sampling interval = 4th cluster. – Last or 30th cluster = 29th cluster + sampling interval Instructor: Dr Kapil Jain CLUSTER SAMPLING……. 29 Two types of cluster sampling methods. One-stage sampling. All of the elements within selected clusters are included in the sample. Two-stage sampling. A subset of elements within selected clusters are randomly selected for inclusion in the sample. Instructor: Dr Kapil Jain CLUSTER SAMPLING……. 30 • Freq c f cluster • I 2000 2000 1 • II 3000 5000 2 • III 1500 6500 • IV 4000 10500 3 • V 5000 15500 4, 5 • VI 2500 18000 6 • VII 2000 20000 7 • VIII 3000 23000 8 • IX 3500 26500 9 • X 4500 31000 10 • XI 4000 35000 11, 12 • XII 4000 39000 13 • XIII 3500 44000 14,15 • XIV 2000 46000 • XV 3000 49000 16 • XVI 3500 52500 17 • XVII 4000 56500 18,19 • XVIII 4500 61000 20 • XIX 4000 65000 21,22 • XX 4000 69000 23 • XXI 2000 71000 24 • XXII 2000 73000 • XXIII 3000 76000 25 • XXIV 3000 79000 26 • XXV 5000 84000 27,28 • XXVI 2000 86000 29 • XXVII 1000 87000 • XXVIII 1000 88000 • XXIX 1000 89000 30 • XXX 1000 90000 • 90000/30 = 3000 sampling interval Instructor: Dr Kapil Jain Instructor: Dr. Kapil Jain, IIPS-DAVV Instructor: Dr. Kapil Jain, IIPS-DAVV 6 Difference Between Strata and Clusters 31 Although strata and clusters are both non- overlapping subsets of the population, they differ in several ways. All strata are represented in the sample; but only a subset of clusters are in the sample. With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous Instructor: Dr Kapil Jain MULTISTAGE SAMPLING 32 Complex form of cluster sampling in which two or more levels of units are embedded one in the other. First stage, random number of districts chosen in all states. Followed by random number of talukas, villages. Then third stage units will be houses. All ultimate units (houses, for instance) selected at last step are surveyed. Instructor: Dr Kapil Jain MULTISTAGE SAMPLING…….. 33 This technique, is essentially the process of taking random samples of preceding random samples. Not as effective as true random sampling, but probably solves more of the problems inherent to random sampling. An effective strategy because it banks on multiple randomizations. As such, extremely useful. Multistage sampling used frequently when a complete list of all members of the population not exists and is inappropriate. Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated with traditional cluster sampling. Instructor: Dr Kapil Jain MULTI PHASE SAMPLING 34 Part of the information collected from whole sample & part from subsample. In Tb survey MT in all cases – Phase I X –Ray chest in MT +ve cases – Phase II Sputum examination in X – Ray +ve cases - Phase III Survey by such procedure is less costly, less laborious & more purposeful Instructor: Dr Kapil Jain MATCHED RANDOM SAMPLING 35 A method of assigning participants to groups in which pairs of participants are first matched on some characteristic and then individually assigned randomly to groups. The Procedure for Matched random sampling can be briefed with the following contexts, Two samples in which the members are clearly paired, or are matched explicitly by the researcher. For example, IQ measurements or pairs of identical twins. Those samples in which the same attribute, or variable, is measured twice on each subject, under different circumstances. Commonly called repeated measures. Examples include the times of a group of athletes for 1500m before and after a week of special training; the milk yields of cows before and after being fed a particular diet. Instructor: Dr Kapil Jain QUOTA SAMPLING 36 The population is first segmented into mutually exclusive sub- groups, just as in stratified sampling. Then judgment used to select subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. It is this second step which makes the technique one of non- probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years Instructor: Dr Kapil Jain Instructor: Dr. Kapil Jain, IIPS-DAVV Instructor: Dr. Kapil Jain, IIPS-DAVV 7 CONVENIENCE SAMPLING 37 Sometimes known as grab or opportunity sampling or accidental or haphazard sampling. A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Instructor: Dr Kapil Jain 38 CONVENIENCE SAMPLING……. Use results that are easy to get 38Instructor: Dr Kapil Jain Judgmental sampling or Purposive sampling 39 - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched Instructor: Dr Kapil Jain PANEL SAMPLING 40 Method of first selecting a group of participants through a random sampling method and then asking that group for the same information again several times over a period of time. Therefore, each participant is given same survey or interview at two or more time points; each period of data collection called a "wave". This sampling methodology often chosen for large scale or nation-wide studies in order to gauge changes in the population with regard to any number of variables from chronic illness to job stress to weekly food expenditures. Panel sampling can also be used to inform researchers about within- person health changes due to age or help explain changes in continuous dependent variables such as spousal interaction. There have been several proposed methods of analyzing panel sample data, including growth curves. Instructor: Dr Kapil Jain Questions??? 41 Instructor: Dr Kapil Jain Instructor: Dr. Kapil Jain, IIPS-DAVV