Coding Practices for LibQUAL+® Open-Ended Comments

Neurohr, Karen; Ackermann, Eric G.; O'Mahony, Daniel P.; White, Lynda S.

doi:10.18438/b80g6v

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The third category discusses practical approaches of using LibQUAL surveys for qualitative and quantitative analysis (such as Voorbij, 2012;Neurohr, Ackermann, O'mahony, & White, 2013;Killick, Weerden, & Weerden, 2014;Deltor & Ball, 2015). Thompson, Cook, & Health, 2001;Hossain, 2016;Boyce, 2017) during the process of the LibQUAL assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring an Academic Library’s Performance and Service: A Case Study in Georgia Southern University Library

Li¹

2017

IJoL

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Abstract:This paper shares the Georgia Southern University Library's experience of LibQUAL in user-centered and service-oriented academic learning environment. On the basis of reviewing the library literature on LibQUAL, this study presents the process of data analysis and data visualization for academic library assessments. Using the 2016 LibQUAL Survey at the Georgia Southern University Library as a sample, this study illustrates basic methods of analyzing and interpreting the LibQUAL Survey Raw Data and User Response Raw Data saved in Excel files. Also mentioned in this study are other common statistical tools and a set of general procedures, including data analysis, data validation, data migration, data mining, and data visualization, for academic library assessments to identify patron needs and satisfactory factors. ABSTRACTThis paper shares the Georgia Southern University Library's experience of LibQUAL in usercentered and service-oriented academic learning environment. On the basis of reviewing the library literature on LibQUAL, this study presents the process of data analysis and data visualization for academic library assessments. Using the 2016 LibQUAL Survey at the Georgia Southern University Library as a sample, this study illustrates basic methods of analyzing and interpreting the LibQUAL Survey Raw Data and User Response Raw Data saved in Excel files. Also mentioned in this study are other common statistical tools and a set of general procedures, including data analysis, data validation, data migration, data mining, and data visualization, for academic library assessments to identify patron needs and satisfactory factors.

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Section: Literature Reviewmentioning

confidence: 99%

Measuring an Academic Library’s Performance and Service: A Case Study in Georgia Southern University Library

Li¹

2017

IJoL

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“…Of all, 55.8 per cent of respondents reported only one coder and an additional 26.9 per cent reported two coders. Time was the single largest challenge experienced by participants, followed by difficulties with the process of coding, insufficient technology and insufficient staff (Neurohr et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, respondents reported that they were analyzing LibQUALϩ comments to make improvements within the library and to communicate with administrative and non-administrative university stakeholders. Only 25.5 per cent of respondents listed publication and conference presentation as among their uses of the data (Neurohr et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Comment sifting: pragmatic qualitative analysis of LibQUAL + Comments

Luther

2015

Library Hi Tech News

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Purpose – The purpose of this paper is to present a practical, low-tech and adaptable methodology for converting large amounts of comment data into actionable, qualitative analysis. Design/methodology/approach – In 2013, the Horace W. Sturgis Library of Kennesaw State University conducted the LibQUAL+ survey, receiving 1,653 open-ended comments. In a home-grown approach to comment analysis, the Sturgis Library targeted specific areas of operational significance (chat, ILL, etc.) to analyze in depth. Keywords and synonyms were identified, comments containing these terms were evaluated and a document known as a mini-report summarized the analysis for each area. Findings – Over the summer of 2013, two librarians created 12 mini-reports. The mini-report format allowed investigators to build up their understanding of the organization incrementally and proved a valuable means of summarizing qualitative analysis and sharing it with various stakeholders. Research limitations/implications – Not all comments are coded, and therefore, potentially valuable information is lost. By limiting focus to specific operational areas, the methodology does not capture themes that are diffused throughout the organization. Some areas of operational significance are difficult to isolate by keywords. Originality/value – This paper contributes to the literature on pragmatic qualitative analysis. Many existing models of qualitative analysis are resource intensive, in terms of time, technology and staff training. This paper presents a variation on a methodology that is more adaptable, allowing the researchers to explore within large pools of comment data without having to code and analyze every comment.

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“…Although the teams considered a variety of software packages for coding, such as Atlas.ti or NVivo, due to cost restrictions (project funding did not cover the purchase of software for all team members tasked with coding), the time necessary to train team members in these software packages, and computer hardware considerations (eight individuals on the Columbia team were using five different computer operating systems), the team chose a coding approach using Microsoft Word, developed at the Brown University Library (Neurohr, Ackermann, O'Mahony, & White, 2011).…”

Section: Codingmentioning

confidence: 99%