Slide Number 1Statistical Methods for Sample Surveys (140.640) Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Explore the questions Slide Number 14Slide Number 15Why do we conduct surveys?Type of survey designSlide Number 18Types of Samples Slide Number 20Advantages of probability sample Disadvantages of Non-probability Sampling Slide Number 23Steps in Survey Slide Number 25Slide Number 26Advantages of Sampling Limitations of SamplingModes of survey administrationInterview SurveysSlide Number 31Advantages Disadvantages Self-Administered SurveysSelf-Administered SurveysTelephone InterviewNew Terminology in Computer AgeSurvey concerns Survey concerns Population and Sample Slide Number 41Sampling Frames Slide Number 43Slide Number 44Slide Number 45Problems of Sampling FrameSlide Number 47Slide Number 48Slide Number 49Slide Number 50The objective of sampling theory is to devise sampling scheme which is: Survey Designs Slide Number 53Slide Number 54Slide Number 55Slide Number 56Copyright 2009, The Johns Hopkins University and Saifuddin Ahmed. 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Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.Statistical Methods for Sample Surveys (140.640)Lecture 1Introduction to Sampling MethodSaifuddin AhmedSession Date Description1 01/21/09 Introduction to survey sampling methods2 01/26/0901/28/09Simple random sampling, systematic samplingLab3 02/02/0902/04/09Sample size estimationLab4 02/09/0902/11/09Stratified sampling, sampling with varying probabilities (e.g., PPS) Lab5 02/16/0902/18/09Cluster sampling, multistage samplingLab6 02/23/0902/25/09Weighting and imputationLab7 03/02/0903/04/09Special topics (design issues for pre-post survey sampling for program evaluation and longitudinal study design, WHO/EPI cluster sampling, Lot Quality Assurance Sampling[LQAS], sample size estimation for program evaluation) Project/Lab8 03/09/0903/11/09Case Studies and ReviewProject reviewGrading:Lab 60%Project 40%Text: UN Handbook.pdf(available at the CoursePlus website)Text (Optional): Sampling of Populations: Methods and Applications, 3rd edPaul S. Levy and Stanley LemeshowWiley InterscienceSampling: Design and AnalysisSharon L. LohrDuxbury Press“There is hardly any part of statistics that does not interact in someway with the theory or the practice of sample surveys.The difference between the study of sample surveys and the study of other statistical topics are primarily matters of emphasis”W. Edwards DemingAn example for the scope of surveys:An opinion poll on America’s health concern was conducted by Gallup Organization between October 3- 5, 1997, and the survey reported that 29% adults consider AIDS is the most urgent health problem of the US, with a margin of error of +/- 3%. The result was based on telephone interviews of 872 adults.An opinion poll on America’s health concern was conducted by Gallup Organization between October 3- 5, 1997, and the survey reported that 29% adults consider AIDS is the most urgent health problem of the US, with a margin of error of +/- 3%. The result was based on telephone interviews of 872 adults. Point estimateStudyPopulationMethod of Survey AdministrationSample Size (n)Extent of Sampling ErrorThe outcome variable is Bernoulli random variable. A binary variable have only yes/no category of responses.“Do you consider AIDS is the most urgent health problem of the US?”29% responded “yes”, and 71% responded “no”. Let p = 0.29, and q = 1 – p = 0.71. So, 872 X 0.29 = 253 responded “Yes”, and 872 X 0.71 = 619 responded “No”.In summary, from the raw data, Gallup’s statistician estimated that Here, 1 = “yes”, and 0 = “no” responses. 29.0872253872)0.........1101(1==+++++==∑∑=nxpniiHow much confidence do we have on this “point estimate” (29%) ? From our knowledge of basic statistics, we can construct a 95% confidence interval around p as:That is, =.29 ± 0.03So, 95% CI of p ranges between (.26 to .32).)ˆ(*ˆ05.pseZp±872)71)(.29(.96.129. ±npqp 96.1ˆ±The above mathematical expression could be rephrased as: (29%) (3%)errormargin_of_estimate±)ˆ(*ˆ05.pseZp±222dpqnrrore_of_marginrrore_of_marginrrore_of_margin22296.1)71)(.29(.96.1872872)71)(.29(.96.103.0872)71)(.29(.96.1=====This example also shows that:1. if we know/guess an estimated value (p), we can estimate the required sample size with a specified “margin of error”.2. If sample size even increased significantly, the gain in efficiency would be small.With n=872, margin of error= 0.03With n=1744 (2 times of the original sample size),margin of error = 0.02 . . Improvement in “margin of error” = .03011799/.02129664 = 1.4142136With n=8720 (10 times of the original sample size),margin of error = 0.01Improvement in “margin of error” = .03011799/.00952415= 3.1622777Check: sqrt(2) = 1.4142136 and sqrt(10) = 3.1622777di 1.96*sqrt(.29*.71/872).03011799Stata output:di 1.96*sqrt(.29*.71/1744).02129664di 1.96*sqrt(.29*.71/8720).00952415If sample size even increased significantly, the gain in efficiency would be small.Explore the questions• What was the target population? (US population)• What was the sample population? (Adults whom could be reached by telephone)• How the survey was conducted? (By telephone interview)• How was the sample selected? (Randomly selected from telephone list)• How reliable are the estimates? Precision of the estimates?• How much confidence do we have on the estimates?• Was any bias introduced in the sample selection (process)?• Why 872 adults were selected for the survey?• Can an estimate based on about 1000 respondents represents 187 million adults of the US?All these issues are part of the sample survey methods.Survey is conducted to measure the characteristics of