JSM 2005 Online Program

JSM Activity #CE_32T

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

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CE_32T	Wed, 8/10/05, 10:00 AM - 11:45 AM	MCC-L100 E
Complex Samples in SPSS - Continuing Education - CTW
ASA
Instructor(s): Damir Spisic, SPSS, Inc.
Complex samples are frequently utilized for conducting surveys in various disciplines. In order to make proper inferences from survey data, the sample design information must be used for all analyses. The SPSS Complex Samples add-on module provides a comprehensive framework for the use of complex designs in both the sampling and estimation stages of the process. Central to the module is a plan file produced by the CSPLAN procedure and aided by the wizards. It contains a sample design for use by the sampling procedure CSSAMPLE, and an analysis design for the statistical analysis procedures CSDESCRIPTIVES, CSTABULATE, CSGLM and CSLOGISTIC. This workshop will introduce complex sampling concepts such as strata, clusters and sampling weights, and show how they are used to specify single and multi-stage designs. Its goal is to help attendees gain confidence in specifying and utilizing complex designs available within the SPSS framework. We will also compare designs used for sampling and the ones used for analyses. Examples will illustrate use of designs and their effects on inferences for all population parameter estimates. Attendees are expected to have basic knowledge of statistical inference. Familiarity with complex sample designs is desirable but not required.