Pub Date : 2020-12-23DOI: 10.1080/08963568.2020.1847547
B. Silver
Abstract Reading licenses can be a challenge with the dry complicated language, but licensing business data is an even bigger challenge. Data providers tend to be less familiar with the academic arena than other business vendors, and they can be much more protective of their content. This article covers the basics of reviewing business data licenses including areas of potential trouble and examples of some language found in licenses. It covers topics like accessing the data, who the users are, vendor requirements to be in compliance, and the difference in how faculty may expect to access the data and how that may conflict with the vendor’s expectations. It also provides basic tips on how to modify and negotiate the license.
{"title":"Licensing business data in Academia: Negotiating and balancing needs","authors":"B. Silver","doi":"10.1080/08963568.2020.1847547","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847547","url":null,"abstract":"Abstract Reading licenses can be a challenge with the dry complicated language, but licensing business data is an even bigger challenge. Data providers tend to be less familiar with the academic arena than other business vendors, and they can be much more protective of their content. This article covers the basics of reviewing business data licenses including areas of potential trouble and examples of some language found in licenses. It covers topics like accessing the data, who the users are, vendor requirements to be in compliance, and the difference in how faculty may expect to access the data and how that may conflict with the vendor’s expectations. It also provides basic tips on how to modify and negotiate the license.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"175 - 183"},"PeriodicalIF":1.3,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43184972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.1080/08963568.2020.1867399
Douglas Southard
The Data Librarian’s Handbook aims to introduce the reader to the relatively new field of data librarianship. The authors are both professional data librarians, working in the United Kingdom. Robin Rice is Data Librarian at EDINA at the University of Edinburgh, and John Southall is Data Librarian for the Bodleian Libraries at the University of Oxford. Southall and Rice had two intended audiences in writing the Handbook: library science students and professors on the one hand, and professional data librarians on the other. The authors acknowledge that there have been other books written on the topic of data librarianship, but they believe their book fits an unmet need by providing practical advice for the new data librarian. Because research data management has developed as a profession so recently, most people working in the field have not received formal training in this area, and have had to learn on the job. The authors believe their handbook helps to fill this gap in the literature.
{"title":"The Data Librarians Handbook, edited by Robin Rice and John Southall, London, Facet Publishing, 2016, 177 pp., $95.00, ISBN 978-1-78330-1","authors":"Douglas Southard","doi":"10.1080/08963568.2020.1867399","DOIUrl":"https://doi.org/10.1080/08963568.2020.1867399","url":null,"abstract":"The Data Librarian’s Handbook aims to introduce the reader to the relatively new field of data librarianship. The authors are both professional data librarians, working in the United Kingdom. Robin Rice is Data Librarian at EDINA at the University of Edinburgh, and John Southall is Data Librarian for the Bodleian Libraries at the University of Oxford. Southall and Rice had two intended audiences in writing the Handbook: library science students and professors on the one hand, and professional data librarians on the other. The authors acknowledge that there have been other books written on the topic of data librarianship, but they believe their book fits an unmet need by providing practical advice for the new data librarian. Because research data management has developed as a profession so recently, most people working in the field have not received formal training in this area, and have had to learn on the job. The authors believe their handbook helps to fill this gap in the literature.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"372 - 374"},"PeriodicalIF":1.3,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1867399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43713013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1080/08963568.2020.1847548
Alice Kalinowski, Todd Hines
Abstract Business researchers are using increasingly large and unique datasets on a wide range of topics. For librarians without prior experience with large-scale business and finance data it can be overwhelming to figure out where to even start. This article surveys eight things someone interested in providing more data-related business research support should know as they get started, such as the types of data used, data acquisition, and merging datasets. Because providing data support to business researchers is one way for librarians to provide value-added services, this article attempts to help others start working more with data.
{"title":"Eight things to know about business research data","authors":"Alice Kalinowski, Todd Hines","doi":"10.1080/08963568.2020.1847548","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847548","url":null,"abstract":"Abstract Business researchers are using increasingly large and unique datasets on a wide range of topics. For librarians without prior experience with large-scale business and finance data it can be overwhelming to figure out where to even start. This article surveys eight things someone interested in providing more data-related business research support should know as they get started, such as the types of data used, data acquisition, and merging datasets. Because providing data support to business researchers is one way for librarians to provide value-added services, this article attempts to help others start working more with data.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"105 - 122"},"PeriodicalIF":1.3,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43898160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1080/08963568.2020.1847556
C. Jefferson, Katrina Stierholz, Kristin Fontichiaro, Lynette Hoelter
Abstract This article uses Kuhlthau’s Information Search Process (ISP) to illustrate ways that librarians can assist students and collaborate with data providers to improve the data search process. Librarians and data providers share similar data literacy goals, and this paper pools the expertise of both groups to focus on strategies and interventions that support novice researchers. We explore our combined experiences and jointly step through students’ research phases to examine how each group can contribute to improving that experience. We review students’ data literacy needs throughout the research process through the lens of Kuhlthau’s six-stage, iterative ISP. Kuhlthau’s framework, rooted in empathy, maps to existing knowledge about what students do, think, and feel at various stages of the search process. We identify “zones of intervention” that are both consistent with Kuhlthau’s research and are novel to data-rich research projects.
{"title":"Considering data literacy using Kuhlthau’s Information Search Process: Implications for librarians and data providers","authors":"C. Jefferson, Katrina Stierholz, Kristin Fontichiaro, Lynette Hoelter","doi":"10.1080/08963568.2020.1847556","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847556","url":null,"abstract":"Abstract This article uses Kuhlthau’s Information Search Process (ISP) to illustrate ways that librarians can assist students and collaborate with data providers to improve the data search process. Librarians and data providers share similar data literacy goals, and this paper pools the expertise of both groups to focus on strategies and interventions that support novice researchers. We explore our combined experiences and jointly step through students’ research phases to examine how each group can contribute to improving that experience. We review students’ data literacy needs throughout the research process through the lens of Kuhlthau’s six-stage, iterative ISP. Kuhlthau’s framework, rooted in empathy, maps to existing knowledge about what students do, think, and feel at various stages of the search process. We identify “zones of intervention” that are both consistent with Kuhlthau’s research and are novel to data-rich research projects.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"197 - 229"},"PeriodicalIF":1.3,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44457052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.1080/08963568.2020.1847553
A. Wheatley, Martin Chandler, Dawn McKinnon
Abstract McGill University Library is undertaking a collaboration between three librarians and various faculty members from the Desautels Faculty of Management. The project seeks to better understand what data is available for business and finance, where that data is stored across the institution, and how its access can be better promoted and shared among the Faculty. The need to address data coverage and access was originally proposed as a project for the Faculty’s committee on Teaching and Learning Resources. The project team is made up of two Business Librarians, a Data Librarian, an Associate Professor, and a Senior Faculty Lecturer. Progress on this project has been ongoing, though significant delays were caused by the COVID-19 pandemic and the resulting closure of the McGill University campus.
{"title":"Collaborating with faculty on data awareness: A case study","authors":"A. Wheatley, Martin Chandler, Dawn McKinnon","doi":"10.1080/08963568.2020.1847553","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847553","url":null,"abstract":"Abstract McGill University Library is undertaking a collaboration between three librarians and various faculty members from the Desautels Faculty of Management. The project seeks to better understand what data is available for business and finance, where that data is stored across the institution, and how its access can be better promoted and shared among the Faculty. The need to address data coverage and access was originally proposed as a project for the Faculty’s committee on Teaching and Learning Resources. The project team is made up of two Business Librarians, a Data Librarian, an Associate Professor, and a Senior Faculty Lecturer. Progress on this project has been ongoing, though significant delays were caused by the COVID-19 pandemic and the resulting closure of the McGill University campus.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"281 - 290"},"PeriodicalIF":1.3,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46145980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-16DOI: 10.1080/08963568.2020.1847552
Erin Wachowicz
Standard & Poor's Compustat North America is a database of financial, statistical, and marketing information. It provides more than 300 annual and 100 quarterly Income Statement, Balance Sheet, Statement of Cash Flows, and supplemental data items on more than 24,000 publicly held companies. Compustat files are available in both annual and quarterly formats. The industrial annual formats offer both historical and restated data. The industrial quarterly formats offer restated data as reported by the company. The restated data allows analysts to compare current and prior years' results on a comparable basis and determine financial trends and growth rates. For most companies, annual and quarterly data is available for a maximum of 20 years and 48 quarters. Standard & Poor's ExecuComp is a unique database for executive compensation analyses. The ExecuComp report library accesses more than 100 up-to-date compensation and financial items for over 2,500 companies (over 24,000 executives). The Center for Research in Security Prices (CRSP) maintains the most comprehensive collection of standard and derived security data available for the NYSE, AMEX and Nasdaq Stock Market. CRSP is a research center at the University of Chicago Graduate School of Business, and maintains historical data spanning from December 1925 to the present. CRSP's trademark of unique issue identifiers tracks a continuous history of securities, providing a seamless time-series examination of the issue's history. CRSP's unparalleled accuracy and dedication to excellence has made CRSP data a staple of academic and commercial research and data analysis throughout the world. The CRSP US Government Bond File is available in two frequencies: daily and monthly. The daily product contains data for about 3,100 marketable US Government securities traded in the secondary market and during the issued trading period from June 14, 1961 to the present. The monthly product contains data for I/B/E/S International Inc. provides timely analyst information on over 18,000 companies in more than 60 countries – more than any other source. In addition, I/B/E/S provides a collection of firms' forecast measures for accounting information. Data terms, such as earnings estimate per share, revenue, net income, and pre-tax profit, are included in forecast measures. The I/B/E/S database is useful for researchers who are interested in analyst forecast information, such as researchers documenting market effects of missing earnings expectations. Eventus performs event studies using data read directly from CRSP stock databases or pre-extracted from any source. The Eventus system includes utility programs to convert calendar …
{"title":"Database Review: Wharton Research Data Services (WRDS)","authors":"Erin Wachowicz","doi":"10.1080/08963568.2020.1847552","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847552","url":null,"abstract":"Standard & Poor's Compustat North America is a database of financial, statistical, and marketing information. It provides more than 300 annual and 100 quarterly Income Statement, Balance Sheet, Statement of Cash Flows, and supplemental data items on more than 24,000 publicly held companies. Compustat files are available in both annual and quarterly formats. The industrial annual formats offer both historical and restated data. The industrial quarterly formats offer restated data as reported by the company. The restated data allows analysts to compare current and prior years' results on a comparable basis and determine financial trends and growth rates. For most companies, annual and quarterly data is available for a maximum of 20 years and 48 quarters. Standard & Poor's ExecuComp is a unique database for executive compensation analyses. The ExecuComp report library accesses more than 100 up-to-date compensation and financial items for over 2,500 companies (over 24,000 executives). The Center for Research in Security Prices (CRSP) maintains the most comprehensive collection of standard and derived security data available for the NYSE, AMEX and Nasdaq Stock Market. CRSP is a research center at the University of Chicago Graduate School of Business, and maintains historical data spanning from December 1925 to the present. CRSP's trademark of unique issue identifiers tracks a continuous history of securities, providing a seamless time-series examination of the issue's history. CRSP's unparalleled accuracy and dedication to excellence has made CRSP data a staple of academic and commercial research and data analysis throughout the world. The CRSP US Government Bond File is available in two frequencies: daily and monthly. The daily product contains data for about 3,100 marketable US Government securities traded in the secondary market and during the issued trading period from June 14, 1961 to the present. The monthly product contains data for I/B/E/S International Inc. provides timely analyst information on over 18,000 companies in more than 60 countries – more than any other source. In addition, I/B/E/S provides a collection of firms' forecast measures for accounting information. Data terms, such as earnings estimate per share, revenue, net income, and pre-tax profit, are included in forecast measures. The I/B/E/S database is useful for researchers who are interested in analyst forecast information, such as researchers documenting market effects of missing earnings expectations. Eventus performs event studies using data read directly from CRSP stock databases or pre-extracted from any source. The Eventus system includes utility programs to convert calendar …","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"1 1","pages":"1-4"},"PeriodicalIF":1.3,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47789274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-14DOI: 10.1080/08963568.2020.1847550
Carol L. Schuetz, Josh Been, Christina Y. Chan-Park
Abstract This case study describes activities developed by academic librarians to provide data literacy opportunities to graduate and undergraduate business students. These opportunities were provided by a data-related program in the libraries and in-class activities. Topic modules for the data program and steps for course-related data visualizations and are discussed. Conclusions reached as to the impact and acceptance of these programs and future directions are presented.
{"title":"Here and hereafter: Preparing business students for a data-driven world","authors":"Carol L. Schuetz, Josh Been, Christina Y. Chan-Park","doi":"10.1080/08963568.2020.1847550","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847550","url":null,"abstract":"Abstract This case study describes activities developed by academic librarians to provide data literacy opportunities to graduate and undergraduate business students. These opportunities were provided by a data-related program in the libraries and in-class activities. Topic modules for the data program and steps for course-related data visualizations and are discussed. Conclusions reached as to the impact and acceptance of these programs and future directions are presented.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"291 - 314"},"PeriodicalIF":1.3,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44224986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-10DOI: 10.1080/08963568.2020.1847558
M. Moeller
In business reference, pricing data can be particularly difficult to find especially for specific geographic regions. The Cost of Living Index (COLI) is a dataset that provides answers to some of these more difficult questions. COLI is compiled by the Council for Community and Economic Research (C2ER) every quarter. It is intended to measure the difference between the cost of living in different geographic areas of the United States. Areas included are those with chambers of commerce that volunteer to participate. The averages are calculated based on the reported expenditure patterns for households with incomes in the top 20%. COLI reports spending across broad categories such as groceries and housing as well as the average pricing of specific goods and services in a particular area such as a newspaper or shampoo.
{"title":"ACCRA cost of living index","authors":"M. Moeller","doi":"10.1080/08963568.2020.1847558","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847558","url":null,"abstract":"In business reference, pricing data can be particularly difficult to find especially for specific geographic regions. The Cost of Living Index (COLI) is a dataset that provides answers to some of these more difficult questions. COLI is compiled by the Council for Community and Economic Research (C2ER) every quarter. It is intended to measure the difference between the cost of living in different geographic areas of the United States. Areas included are those with chambers of commerce that volunteer to participate. The averages are calculated based on the reported expenditure patterns for households with incomes in the top 20%. COLI reports spending across broad categories such as groceries and housing as well as the average pricing of specific goods and services in a particular area such as a newspaper or shampoo.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"375 - 378"},"PeriodicalIF":1.3,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43228423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-10DOI: 10.1080/08963568.2020.1847549
B. Dosch, Tyler Martindale
Abstract Business librarians offer many data services to their researchers. These services are often focused more on discovery, visualization, and analysis than general data management. But, with the replication crisis facing many business disciplines, there is a need for business librarians to offer more data sharing and general data management support to their researchers. To find evidence of this data need, 146 business journal’s data sharing policies were reviewed and analyzed to uncover meaningful trends in business research. Results of the study indicate data sharing is not mandated by business journals. However, data sharing is often encouraged and recommended. This journal policy content analysis provides evidence that business researchers have opportunities to share their research data, and with the right data management support, business librarians can play a significant role in improving the data sharing behaviors of business researchers.
{"title":"Reading the fine print: A review and analysis of business journals’ data sharing policies","authors":"B. Dosch, Tyler Martindale","doi":"10.1080/08963568.2020.1847549","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847549","url":null,"abstract":"Abstract Business librarians offer many data services to their researchers. These services are often focused more on discovery, visualization, and analysis than general data management. But, with the replication crisis facing many business disciplines, there is a need for business librarians to offer more data sharing and general data management support to their researchers. To find evidence of this data need, 146 business journal’s data sharing policies were reviewed and analyzed to uncover meaningful trends in business research. Results of the study indicate data sharing is not mandated by business journals. However, data sharing is often encouraged and recommended. This journal policy content analysis provides evidence that business researchers have opportunities to share their research data, and with the right data management support, business librarians can play a significant role in improving the data sharing behaviors of business researchers.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"261 - 280"},"PeriodicalIF":1.3,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47383591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-09DOI: 10.1080/08963568.2020.1847555
G. Liu
Abstract The data quality of commercial business and financial databases greatly affects research quality and reliability. The presence of data quality problems can not only distort research results, destroy a research effort but also seriously damage management decisions based upon such research. Although library literature rarely discusses data quality problems, business literature reports a wide range of data quality issues, many of which have been systematically tested with statistical methods. This article reviews a collection of the business literature that provides a critical analysis on the data quality of the most frequently used business and finance databases including the Center for Research in Security Prices (CRSP), Compustat, S&P Capital IQ, I/B/E/S, Datastream, Worldscope, Securities Data Company (SDC) Platinum, and Bureau van Dijk (BvD) Orbis and identifies 11 categories of common data quality problems, including missing values, data errors, discrepancies, biases, inconsistencies, static header data, standardization, changes in historic data, lack of transparency, reporting time issues and misuse of data. Finally, the article provides some practical advice for librarians to facilitate their scholarly communications with researchers on data quality problems.
摘要商业商业和金融数据库的数据质量在很大程度上影响着研究的质量和可靠性。数据质量问题的存在不仅会扭曲研究结果，破坏研究成果，还会严重损害基于此类研究的管理决策。尽管图书馆文献很少讨论数据质量问题，但商业文献报道了广泛的数据质量问题。其中许多问题已经用统计方法进行了系统测试。本文回顾了一系列商业文献，这些文献对最常用的商业和金融数据库的数据质量进行了批判性分析，包括证券价格研究中心（CRSP）、Compustat、标普资本IQ、I/B/E/S、Datastream、Worldscope、证券数据公司（SDC）Platinum，和Bureau van Dijk（BvD）Orbis，确定了11类常见的数据质量问题，包括缺失值、数据错误、差异、偏差、不一致、静态标题数据、标准化、历史数据变化、缺乏透明度、报告时间问题和数据滥用。最后，本文为图书馆员就数据质量问题与研究人员进行学术交流提供了一些实用的建议。
{"title":"Data quality problems troubling business and financial researchers: A literature review and synthetic analysis","authors":"G. Liu","doi":"10.1080/08963568.2020.1847555","DOIUrl":"https://doi.org/10.1080/08963568.2020.1847555","url":null,"abstract":"Abstract The data quality of commercial business and financial databases greatly affects research quality and reliability. The presence of data quality problems can not only distort research results, destroy a research effort but also seriously damage management decisions based upon such research. Although library literature rarely discusses data quality problems, business literature reports a wide range of data quality issues, many of which have been systematically tested with statistical methods. This article reviews a collection of the business literature that provides a critical analysis on the data quality of the most frequently used business and finance databases including the Center for Research in Security Prices (CRSP), Compustat, S&P Capital IQ, I/B/E/S, Datastream, Worldscope, Securities Data Company (SDC) Platinum, and Bureau van Dijk (BvD) Orbis and identifies 11 categories of common data quality problems, including missing values, data errors, discrepancies, biases, inconsistencies, static header data, standardization, changes in historic data, lack of transparency, reporting time issues and misuse of data. Finally, the article provides some practical advice for librarians to facilitate their scholarly communications with researchers on data quality problems.","PeriodicalId":44062,"journal":{"name":"Journal of Business & Finance Librarianship","volume":"25 1","pages":"315 - 371"},"PeriodicalIF":1.3,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08963568.2020.1847555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42730435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：