Assessing Model Similarity in Structural Equation Modeling

Two models can be nonequivalent, but fit very similarly across a wide range of data sets. These near-equivalent models, like equivalent models, should be considered rival explanations for results of a study if they represent plausible explanations for the phenomenon of interest. Prior to conducting a study, researchers should evaluate plausible models that are alternatives to those hypothesized to evaluate whether they are near-equivalent or equivalent and, in so doing, address the adequacy of the study’s methodology. To assess the extent to which alternative models for a study are empirically distinguishable, we propose 5 indexes that quantify the degree of similarity in fit between 2 models across a specified universe of data sets. These indexes compare either the maximum likelihood fit function values or the residual covariance matrices of models. Illustrations are provided to support interpretations of these similarity indexes.     

Learning through discussions

Students studying a third‐year e‐commerce subject experienced face‐to‐face and online discussions as an important part of their learning experience. The quality of the students' experiences of learning through those discussions is investigated in this study. This study uses qualitative approaches to investigate the variation in the students' understanding of what they were learning through discussions, and how they went about engaging in them. Quantitative analyses are used to investigate how the students' experience related to their performance. Key outcomes of the study include that the quality of the students' experience of learning through discussions is positively related to their performance and that face‐to‐face and online discussions have qualitatively different benefits for learning.     

Students’ experiences of supervision in academic and industry settings: results of an Australian study

The joint supervision of Research Higher Degree (RHD) students by an industry and university supervisor is likely to increase in forthcoming years with a rise in the number of university–industry collaborations. Research students may become involved in these collaborative arrangements for a variety of reasons and may launch into their RHD without considering how they will serve two masters, and how this complex relationship will affect their RHD experience. Moreover, little research has been conducted to assess the impact of these arrangements on current RHD students’ experiences. The experiences of students with academic and industry supervisors were, therefore, explored in a survey of confirmed RHD students at an Australian research‐intensive university. This paper investigates whether RHD students conducting research in traditional academic settings have a different experience to students working on industry‐related projects in industry settings, specifically in regards to supervision and institutional access and engagement.     

Does (No) Discovery Lead to (ILL) Delivery? Comparing Transaction Logs to ILL Requests to Determine the Persistence of Library Patrons

This study looks at known-item searches within a university catalog that come up with a “no results” response to see whether patrons will then place an ILL request for that material and how persistent a user might be in order to obtain something that they want. Catalog search transaction logs were compared against interlibrary loan (ILL) requests placed during the same time frame. Overall, the rate of persistence, or the frequency with which ILL requests were placed when no results were found in the catalog is very low.     

Discovering User Behavior: Applying Usage Statistics to Shape Frontline Services

This investigation sought to develop a broad view of discovery service user behavior by analyzing vendor-provided and Google Analytics usage data from discovery service implementations at two Indiana University campuses. The results of this analysis demonstrate how usage data can communicate both intermediary and end results of user interactions within discovery services. The findings reveal user behavior trends, which may be used to develop strategies to improve information literacy instruction techniques, as well as discovery service interface enhancements.     

New “Starting Points” for Resources by Subject

The objective of the Starting Points Web page series at The University of Texas Health Science Center at San Antonio (UT HSC) Libraries is to provide specialized information resources in an organized online format. Highlighted resources include databases, journals, UT HSC campus information, funding sources, PubMed® RSS article feeds, and information about professional associations. This paper discusses the development process, planning, challenges, and outcomes of the Starting Points series.     

Sharing MedlinePlus®/MEDLINE® for Information Literacy Education (SMILE): A Dental Public Health Information Project

The SMILE project represented a partnership among the University of Texas Health Science Center at San Antonio Libraries, the Gateway Clinic in Laredo, and the San Antonio Metropolitan Health District. The project focused on improving dental practitioners' access to reliable information resources and integrating the best evidence into public health dental practice. Through its training program, SMILE cultivated a set of “power information users” among the dentists, dental hygienists, and community health workers (promotores) who provided public health preventive care and oral health education. The dental public health practitioners gained information literacy skills and increased their knowledge about reliable sites such as blogs, PubMed®, and MedlinePlus®. This project fostered opportunities for expanded partnerships with public health personnel.