This certificate program offers training in sampling design and estimation for individuals who have graduate-level coursework in statistics but desire specific knowledge and training in survey statistics. The program provides graduate-level certification of knowledge of the theories and application of survey sampling and estimation. The program assumes that entering students have graduate-level preparation in probability theory, mathematical statistics, and statistical methods. The certificate program supplements this knowledge with exposure to probability sampling theory, applications in complex survey designs, inferential issues in complex sample survey estimation, and advanced topics in complex survey design.

Prerequisites for the Certificate Program

In addition to capacity limitations of JPSM courses, the admission to this certificate program will be limited to those who have at least a graduate level degree in statistics or biostatistics from an accredited institution. Also eligible to apply are those who have equivalent training in graduate level statistics or those who have successfully completed courses equivalent to SURV 410 (graduate-level probability theory course), 420 (graduate-level mathematical statistics course), 615 and 616 (a two-term sequence in statistical methods), supplemented by a minimum of one year's work experience in survey design or analysis.

The Curriculum

The certificate program consists of 18 credit hours, four required courses and two electives in survey methodology. The required courses are:

Sampling Theory
(SURV 440) Fall

This is an introductory course in sampling theory, presenting simple random sampling, sampling for proportions, estimation of sample size, sampling with varying probabilities of selection, stratification, systematic selection, cluster sampling, double sampling, and sequential sampling.

Applied Sampling
(SURV 625) Spring and Summer

The emphasis of this introductory course is on practical aspects of sample design rather than on theoretical derivations. Topics covered include probability sampling (including simple random, systematic, stratified, clustered, multistage and two-phase sampling methods), sampling with probabilities proportional to size, area sampling, ratio estimation, sampling error estimation, frame problems, nonresponse, and cost factors. Practical sample designs for a variety of household and some establishment surveys will be discussed.

Inference from Complex Surveys
(SURV 742) Spring

This introductory course on the analysis of data from complex sample designs covers: the development and handling of selection and other compensatory weights; methods for handling missing data; the effect of stratification and clustering on estimation and inference; alternative variance estimation procedures; methods for incorporating weights, stratification, clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions. The course will utilize exercises on real survey data to illustrate the methods addressed in class. Students will learn the use of computer software that takes account of complex sample design in estimation.

Topics in Sampling
(SURV 744) Spring

This course is an advanced course in selected topics in survey sampling. A selection of the following topics will be covered: weighting and imputation approaches for handling missing data; small area estimation; sampling methods for rare populations; sample designs for time and space; panel and rotating panel survey designs; maximizing overlap between samples; controlled selection and lattice sampling; sampling with probabilities proportionate to size without replacement; multiple frame sampling; adaptive cluster sampling; capture - recapture sampling; sampling for telephone surveys; sampling for establishment surveys; and measurement error models. Both applied and theoretical aspects of the topics will be examined.