Murphy, S. A. (2015). How data visualization supports academic library assessment: Three examples from The Ohio State University Libraries using Tableau. College & Research Libraries News, 76(9), 482-486.

This article describes the Ohio State University (OSU) Libraries used Tableau, a data visualization program, to display data for 3 different projects. The first project features an interactive word cloud that library users can use to explore materials on human trafficking (http://go.osu.edu/OSUL_HumanTraffickingAwareness). The second project helps inform library faculty and staff on collection use, such as the number of times a rare item has been requested. The third project helps library faculty and staff filter, query, and visualize the results of LibQual surveys.

Although other programs, such as Microsoft Excel, have similar features (e.g. text lookup, data filtering, data graphing), Tableau simplifies the process of making useful data visualizations. It can create these visualizations from a variety of data sources. OSU library users, staff, and faculty at OSU have reported that they make understanding the data much easier, which can aid the Libraries in demonstrating their impact and in decision making.

 
Stemmer, J. K., & Mahan, D. M. (2015). Investigating the Relationship of Library Usage to Student Outcomes. College & Research Libraries, crl15-704.

This study uses data from library user surveys on resources, services, and space, and institutional data on student learning and outcomes. It was conducted at Bellarmine University, a private university in Louisville, KY. Survey data was collected in 2007, 2008, 2010, and 2012. Unlike many library user surveys, which are anonymized, this survey linked students to their responses. The study’s research questions were:
1. Does library usage have a significant positive correlation with whether a freshman student returns in his/her next year of undergraduate study?
2. Does library usage have a significant positive correlation with whether a freshman, sophomore, junior or senior student graduates in undergraduate study?
3.Does library usage have a significant positive correlation with  grade point average (GPA) for freshmen, sophomore, junior or senior students?

Using a series of regressions, the study found that library usage tends to be positively associated with positive student outcomes. However, usage and related outcomes tends to vary between students of differnt years. For instance, students in their junior year and beyond use the library as an information resource instead of just a study space. In fact, upper level students who took advantage of the library’s resources, services, and space were more likely to have significantly better academic outcomes. The study findings and its authors make a compelling argument for the use of non-anonymous surveys.

 
Cherry, E., Rollins, S. H., & Evans, T. (2013). Proving our worth: The impact of electronic resource usage on academic achievement. College & Undergraduate Libraries, 20(3-4), 386-398.
This study investigated the relationship between undergraduates’ uses of electronic resources and GPAs at Samford University. The electronic resources included “e-books, e-journals, full-text databases, citation-only databases, and reference works” (p. 390), and they were accessed in the fall 2010 semester. Login information collected from the library’s proxy server was correlated with GPA.

The major finding was that students with higher GPAs were more likely to use e-resources and to use them at a higher frequency than students with lower GPAs. The correlation between use and GPA was also greater for upperclassmen compared to freshmen.

 
Crawford, G. A. (2015). The Academic Library and Student Retention and Graduation: An Exploratory Study. portal: Libraries and the Academy, 15(1), 41-57.

This study examined the relationship between institutional and library expenses, library use, and graduation and retention rates. The data came from the National Center for Education Statistics IPEDS (Integrated Postsecondary Educational Data System) and Academic Library Surveys from 1,328 institutions. Many of the variables were normalized via full-time equivalent (FTE), including library service, library expenses, instruction expenses, research expenses, public service expenses, academic support expenses, and student service expenses. The researcher ran a series of ANOVAs to see which variables had the highest correlation with graduation and retention.

The main finding was that library expenses per FTE and instruction expenses per FTE had the highest correlations with graduation and retention. Library expenses per FTE had the highest correlation with graduation rates (bachelor’s degree within 4, 5, or 6 years) and the second highest with retention rate. The researcher was also surprised to find that the total library service index per FTE, which was a variable indicating how often a student used any library service, had the lowest correlation to graduation and retention rates.

 
Kot, F. C., & Jones, J. L. (2014). The Impact of Library Resource Utilization on Undergraduate Students’ Academic Performance: A Propensity Score Matching Design. College & Research Libraries, crl14-616.
This study examines the impact of utilizing three library resources on first-term GPA. The three resources were workstations, study rooms, and research clinics. The participants consisted of 8,652 undergraduates at Georgia State University from three consecutive entering years. This study used Astin’s inputs, environment, output (I-E-O) model as its conceptual framework. The researchers also used propensity score matching to mitigate the effects of self-selection bias. Basically this matching allowed researchers to identify students with similar background characteristics and then place them in treatment and control groups in a quasi-experimental research design.By using propensity score matching the researchers were able to “substantially decrease the relationship between library resource utilization and student characteristics” (p. 578). Utilization of each of the resources was associated with higher GPAs, with the largest gains coming from using study rooms and attending research clinics.

 

Vance, J. M., Kirk, R., & Gardner, J. G. (2012). Measuring the impact of library instruction on freshman success and persistence. Communications in Information Literacy, 6(1), 49-58.

This study explored the relationship between one shot library instruction classes and student performance and persistence between the first and second year at Middle Tennessee State University. The data came from library, Student Information Unit, and Finance Unit. It included which students had taken a library instruction class in the Fall 2008 and Spring 2009 semesters, demographic variables, academic preparedness variables (e.g. high school GPA, major, standardized entrance exam scores), college courses taken, and grades received for 3,330 students. The data were then tested using various regression models.

Both an ordinary least squares model (OLS) and a Tobit model found library instruction to have a significant positive correlation with student grades. The OLS model found that library instruction could account for 35% of the variation in GPA, and that students who enrolled in a class that received library instruction would have GPA that was an average of 0.09 points higher than a student that was not enrolled in a class that received library instruction.

 

Tenopir, C. (2013). Building evidence of the value and impact of library and information services: Methods, metrics and ROI. Evidence Based Library and Information Practice, 8(2), 270-274.

This paper outlines several factors to consider when determining academic library value. Tenopir explains that determining total library value is difficult because every collection or service may be viewed from multiple perspectives over different periods of time. However, most conceptualizations of value ultimately are concerned with user centered outcomes, namely what library users are able to do because of the academic library and to what level of success. Return on investment calculations and open ended feedback are two of the methods mentioned to build evidence of library value. The LibValue project, which is sponsored by the U.S. Institute of Museum and Library Services, is also mentioned.

 

Stone, G., & Ramsden, B. (2012). Library Impact Data Project: Looking for the link between library usage and student attainment. College & Research Libraries, crl12-406.

This study was part of the Library Impact Data Project, which was a 6 month project funded by Jisc. It investigated the relationship between student library activity and attainment at 8 universities in the UK. Library activities included checkouts, access to e-resources, and access to the library. Attainment referred to the level of degree earned. The data contained information on 33,074 undergraduate students, and were assessed using the Kruskal-Wallis test between groups of students. Focus groups with students were also run at the various institutions.

The study found a positive relationship between checkouts and degree result, and between electronic resource access and degree result. These results were true for the data from all 8 institutions and at the individual institutions that had the data to run the analyses. The results of the focus groups were quite varied, but overall the participants felt that library resources were important, although not necessarily that they were linked to their degree result.

 

Haddow, G. (2013). Academic library use and student retention: A quantitative analysis. Library & Information Science Research, 35(2), 127-136.

This study examined the relationship between academic library use and student retention at Curtin University (Australia). It combined two sets of data over a three semester period, in June 2010, April 2011, and June 2011. The first set of data came from students, and it included their ID, gender, age, level, socioeconomic status (SES) as determined by zip code, and retention, which was defined as ongoing enrollment at a single institution and completing studies in a certain time frame. The second data set came from the library, which included logins to e-resources and borrowing. The library data was split into different use levels, with zero logins/checkouts indicating the lowest level of use, 1-28 logins/checkouts indicating a medium level of use, and 29+ logins/checkouts indicating a high level of use.

Over the three semesters the student population decreased from 6330 to 4883 to 4684. The study did find that retained students did login to e-resources and borrow more physical resources than students who were not retained. In fact, logins tended to increase over time. While older students withdrew at higher rates, no significant associations were found regarding SES, which could be due to the problematic classification of this variable based on zip code.

 

Matthews, J. R. (2012). Assessing library contributions to university outcomes: The need for individual student level data. Library Management, 33(6/7), 389-402.

This conceptual paper examined ways to examine academic library impact from student, instructor, and researcher perspectives. Matthews notes that most LIS studies focus on library instruction and/or information literacy programs. Unfortunately, the small sample size and the stand alone natures of each study make it impossible to clearly correlate library resources and services to user and institutional outcomes. These outcomes include student success, retention, and graduation rates.

Matthews’ suggested approach is to combine library and university data at the individual student level. Once a library user’s library data is combined with institutional and demographic data their identity can be erased from the combined data set.

Additionally, the paper covers student learning frameworks, literature on the library’s contribution to university outcomes, broad-based data analysis, library data collection that supports assessment, collections and services space, virtual space, community space, ways to combine data, and challenges to the process.

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