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Dissertation Information for Lei Zeng

NAME:
- Lei Zeng
- (Alias) Marcia Lei Zeng

DEGREE:
- Ph.D.

DISCIPLINE:
- Library and Information Science

SCHOOL:
- University of Pittsburgh (USA) (1992)

ADVISORS:
- Edie Rasmussen

COMMITTEE MEMBERS:
- Harry Elvin Broadbent III
- Allen Kent
- Anne Woodsworth
- Thomas C. Kuo

MPACT Status: Fully Complete

Title: An evaluation of the quality of Chinese-language records in the OCLC OLUC database and a study of a rule-based data validation system for online Chinese cataloging

Abstract: The purpose of the study was to evaluate the quality of Chinese-language records in a large multilingual bibliographic database, and to develop a viable approach for improving its quality. Research questions included current quality of and commonly occurring errors in the member-contributed Chinese records in the OCLC database, and the development of a set of production rules for a data validation system for Chinese online cataloging. 1,306 member-contributed Chinese records were randomly selected from the OCLC database and classified into a OCLC Sample and an RLIN Sample based on source of the record. Errors were categorized into 3 classes and 19 subgroups: Format errors covered incorrect use of tags, indicators, subfield codes, and incorrect punctuation and spaces as required by ISBD; Content errors included missing fields, inconsistency between fields and/or attributes, etc.; and Editing and inputting errors included inadequate spacing, misspelling, etc.

Under the OCLC standards, 72.5% of the records in the OCLC sample and 89.4% in the RLIN sample contained errors, with error means of 2.23 and 4.76. But the above number for the RLIN Sample will be reduced by nearly half if the RLIN standards are applied. Error occurrence rankings were based on types and positions of error distribution. Content errors formed the largest percentage of the total errors, format errors the smallest, and editing and inputting errors in between. Field 245, 260, 4/8xx, and 7xx had the highest rates of error occurrence. Error frequency distribution in both samples obeyed the generalized Poisson distribution model rather than a normal distribution. Regression results gave a weak and insignificant relationship between times a record was enhanced and the errors occurring in the record.

Production rules for eliminating common errors were developed, focusing on improving completeness, consistency, and correctness of a record. A post-test indicated that completeness checking was the most successful, followed by consistency checking. The rules covered 11 of the total 19 kinds of errors. At least 64% of the errors which occurred in the OCLC Sample and 70% of the errors in the RLIN Sample could be detected automatically by applying the production rules.

MPACT Scores for Lei Zeng

A = 0
C = 2
A+C = 2
T = 0
G = 0
W = 0
TD = 0
TA = 0
calculated 2008-01-31 06:24:38

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