摘要：Although large citation databases such as Web of Science and Scopus are widely used in bibliometric research, they have several disadvantages, including limited availability, poor coverage of books and conference proceedings, and inadequate mechanisms for distinguishing among authors. We discuss these issues, then examine the comparative advantages and disadvantages of other bibliographic databases, with emphasis on (a) discipline-centered article databases such as EconLit, MEDLINE, PsycINFO, and SocINDEX, and (b) book databases such as Amazon.com, Books in Print, Google Books, and OCLC WorldCat. Finally, we document the methods used to compile a freely available data set that includes five-year publication counts from SocINDEX and Amazon along with a range of individual and institutional characteristics for 2,132 faculty in 426 U.S. departments of sociology. Although our methods are time-consuming, they can be readily adopted in other subject areas by investigators without access to Web of Science or Scopus (i.e., by faculty at institutions other than the top research universities). Data sets that combine bibliographic, individual, and institutional information may be especially useful for bibliometric studies grounded in disciplines such as labor economics and the sociology of professions. Policy highlights While nearly all research universities provide access to Web of Science or Scopus, these databases are available at only a small minority of undergraduate colleges. Systematic restrictions on access may result in systematic biases in the literature of scholarly communication and assessment. The limitations of the largest citation databases influence the kinds of research that can be most readily pursued. In particular, research problems that use exclusively bibliometric data may be preferred over those that draw on a wider range of information sources. Because books, conference papers, and other research outputs remain important in many fields of study, journal databases cover just one component of scholarly accomplishment. Likewise, data on publications and citation impact cannot fully account for the influence of scholarly work on teaching, practice, and public knowledge. The automation of data compilation processes removes opportunities for investigators to gain first-hand, in-depth understanding of the patterns and relationships among variables. In contrast, manual processes may stimulate the kind of associative thinking that can lead to new insights and perspectives.
