Masters Theses

Date of Award

5-1998

Degree Type

Thesis

Degree Name

Master of Science

Major

Information Sciences

Major Professor

William C. Robinson

Committee Members

J. Michael Pemberton, Gretchen Whitney

Abstract

Autism is an internationally-occurring, life-long developmental disability for which there is no known cure. There are various treatments for the symptoms of autism, but progress on a cure has been hampered by a lack of consensus on what causes the disability. This thesis uses bibliometric tools of information science to investigate the various etiologies being researched by country, by author, and by publication source from when the term "autism" was first coined in 1943 through sources published in 1996.

There are at least six categories of etiology into which autism causation literature can be arranged: allergy/auto-immunologically induced, biologically based, environmentally caused, genetically determined, neurologically based, or psychological in origin. These six causations were tracked over the 53 years of autism etiology literature published. Abstracts in the following online databases (as well as their print counterparts) were the primary source of data collection: BIOSIS Previews, EMBASE, MEDLINE, and PsycINFO.

Although the main research material used for the study is secondary source bibliographic abstracts, the bulk of the literature review broadly investigates primary source material about autism causes. Since readers of this thesis are likely to be more familiar with information sciences than they would be with autism etiology research, the literature review concentrates on autism literature with bibliometrics sources introduced as they explain and support the methods used, results, and discussion.

This work provides general results from all of the data collected and combines results for certain areas of interest, such as autism causation by decade or the types of causations being investigated from one country to the next. The quantitative results are broken out by type, time period, and location. Other pertinent findings-such as the emphasis on childhood, direct conflicts in the literature, and specific peculiarities of the databases used-are also incorporated.

The final chapter applies bibliometric analyses to the results. Bradford's Law of Scattering (concerning the number of journals that publish articles on autism causation), Goffinan's Epidemic Theory (concerning how autism etiology research ideas are generated), and Lotka's Law (concerning author productivity on the specific topic of autism causation) are explored. Some non-bibliometric discussion is also incorporated into the implications, such as autism's evolution and the appearance of "countries" that no longer exist. This chapter finishes this research by suggesting what avenues of further research to pursue and how to continue autism causation research investigations. Suggested methods of data collection and the use of citation analysis were also introduced as possible considerations for a follow-on study of autism causation literature.

Although the initial literature review revealed how multifaceted and distinct are the autism causes under investigation, the final results presented in this research do not bode well for finding a cure for autism, or even reaching consensus on what causes it. Autism's etiology is unclear. A mixture of preconditions could trigger it. It could be biologically based. It could be congenital, or environmental, or none of these. While this chaotic uncertainty is not promising for the future of autism research, it is an ideal framework from which to map a bibliometric research project. The limits placed on the information mapped, however, accounts for some of the non-conformance with expected outcomes. Even so, the results conformed to expected outcomes in more ways than they did not and show that autism etiology research has taken many roads and continues in many divergent directions. As the body of information concerning autism causation continues to grow, the application of bibliometric measures should grow more accurate and more predictive of the future of autism causation research, perhaps showing where all of these paths converge.

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