The following types of data were analyzed:

1. Types of Data The following types of data were analyzed: • Origin: Users can initiate an IM reference transaction from a variety of locations on the Auraria Library website.  Transcript analysis began by determining where the user was on the website when s/he initiated the IM reference transaction. • Gender: While IM reference service users are not required to provide any specific information about themselves before beginning a transaction, some do choose to state their name.  Often, a name will suggest the gender of the user.  In cases where the gender was reasonably assumed, it was recorded in the analysis.  In cases where a name did not clearly indicate gender, the field was marked as unknown. • Age:  Because of the diversity of the campus population, researchers thought it might be useful to determine if there is a difference in IM reference service use based on age.  However, age is challenging to determine even in face-to-face reference interviews; attempting to glean information from IM transcripts proved futile and this category was abandoned. • School:Auraria Library users may be affiliated with one of three schools: CCD, MSCD, or UCD.  Additionally, Colorado residents may use certain materials at the Auraria Library. The Auraria Library web page and specifically the Auraria IM reference service may be accessed from literally any place in the world and used by any person regardless of affiliation; therefore, the researchers proceeded with caution when recording affiliation information.   • Status:  This category was used to indicate whether the user was a student, an instructor, or an alum.  The term “Other” was included to reflect users who specifically identified as something other than student, instructor, or alum.  Due to the proportionally large number of “Other” users who identified as “Parent,” that category was added.  Students, instructors, and alum from institutions other than CCD, MSCD, and UCD were identified as “Other.” • Year: This category was used to identify graduate students, undergraduate students, instructors, or “not applicable.”  “Not applicable” refers to instances in which the user self-identified specifically as someone other than a student and therefore it was clear that the category of “year” was not applicable. Graduate, undergraduate, or instructor status was determined only if the user specifically identified as one of the three, either by stating so or by asking a question about a class that could easily be identified as a graduate or undergraduate class.  In all other cases, this field was marked unknown. • English as a Second Language (ESL): This category was designed to capture the number of ESL IM reference service users through language nuances.  This category was approached with caution as well; it is difficult to determine whether poor grammar on IM results from lack of English language proficiency due to ESL status, to lack of proficiency in a native speaker, or even due to the user employing chat-style conversation techniques which are less grammatically correct.  • Type of question & Subject area: Roper examined the first question each user asked in order to determine the classification into which that reference transaction fell.  These transactions were coded as one of the following: University information, Library information, Reserves, Databases, Catalog, and Reference. In addition to identifying specific areas of study, other subject areas were included, such as Textbooks, Policy, and Interlibrary Loan.    Applications These categories were selected because they closely reflected the curiosity of Auraria librarians interested in learning what patron groups use IM reference service, and because they match the categories used in previous research of this type. These categories also reflect the unique nature of the Auraria Campus, where three institutions share the same library resources. • What types of data might your institution code for in a transcript analysis of this nature? • Has your institution undertaken an IM reference service satisfaction survey? If so, how would your transcript analysis markers compare to those used to compile your survey? • What elements would a transcript analysis capture that might be missed in satisfaction survey results?