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Instructor Class Description

Time Schedule:

Leonard Clark Johnson
NMETH 522
Seattle Campus

Data Management for Research Professionals

Surveys industrial strength data management, using techniques applicable to multi-center, longitudinal research trials with survey instruments. Involves challenges research professionals face as they graduate from a student project to a study with hundreds of cases, variables, multiple survey instruments and a staggered, repeated sampling protocol. Credit/no-credit only.

Class description

NMETH 522: "Data Management for Research Professionals" is a comprehensive survey of 'industrial strength' data management techniques applicable to any multi-center, longitudinal research trial using survey instruments. It speaks specifically to the challenges research professionals face when they graduate from a "student project" with 40 cases and 50 variables to one with hundreds of cases/variables, multiple survey instruments, and staggered/repeated sampling.

Student learning goals

General method of instruction

Topics to be covered include: Tracking, data entry, error identification/correction, data dictionary generation and save file preparation. A complex case study has been prepared that will provide the context within which we will learn to confidently manipulate data structures when unusual analyses are required. The course uses SPSS and emphasizes data management using syntax and a variety of SPSS procedures and programming constructs.

Recommended preparation

In this course advanced literacy skills are assumed. Consequently, all students should be comfortable with the Windows operating system as well as the use word processing and spreadsheet applications. These skills can be self-taught or acquired through formal course work. All students should discuss their background and expectations with the instructor before registration - entry codes are required.

Class assignments and grading

This will be a "lab" course with most class time dedicated to hands-on experiences with real data and real world data management problems.

This course is graded on the Credit/ No credit basis. To receive a passing grade a students must complete the assigned homework.


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by Leonard Clark Johnson
Date: 05/20/2004