New Name for this Blog

The Data & Statistical Reference Interview blog was renamed to the Data Interest Group for Reference Services blog, or DIGRS for short. At the May 2004 Data Liberation Initiative (DLI) External Advisory Committee (EAC) meeting, a contest was held to find a new name for this blog that had a more memorable acronym. Mark Leggott proposed DIGGER as the acronym. This was shortened to DIGR when Mary MacLeod, also of the EAC, suggested "Interest Group" for the IG in DIGR, which was expanded to: Data Interest Group Resources. Subsequently, the name was changed to Data Interest Group for Reference Services or "diggers" for short.

The Trade Analyser and Units of Measurement

The metadata are not always conveniently located with the data, which can lead to problems. Here is an example of trying to understand the units of measurement associated with statistics from the Trade Analyser.

A patron was referred to the Data Library from the Business Reference Desk. She was using the Trade Analyser database, which is supported online through CHASS at the University of Toronto. She had retrieved some statistics for the quantity and value of fruit puree imported to Ontario from California. The statistics, however, were listed without reference to their unit of measurement. She wanted to know the unit used for the quantity of fruit puree imported. Is it pounds, tonnes, kilograms, or some other weight or volume measurement?

Read the comments for different approaches to answering this question. You are invited to contribute your own solution, too.

Statistics on Vegetarians in Canada

Part of the Data Reference Mystery Series:

A patron would like statistics on vegetarians in Canada.

What constraints have they provided?

Geography: Canada
Population: Individual
Characteristics: Vegetarians

What further information might you like from this user?

Time frame: How recent must the statistics be?
Characteristics: Is the type of vegetarian important (e.g. vegan)?
Purpose: How are the statistics to be used? For example, to define or describe a population, to assess market impact, to study consumer spending patterns?

See the comments for strategies, tools, and results.

Gender Distribution of University Faculty

Part of the Data Reference Mystery Series:

A patron wants statistics on the gender distribution of university faculty in Canada.

This request seems to be for statistics and has the following constraints:

Time frame: ideally 2000 to 2004
Population: Canadian university faculty
Characteristics: gender

What further information would you like from this patron?

Geography: Does she or he require provincial breakdowns as well as the figures for all of Canada?
Characteristics: Will she or he need this broken down by discipline, too?

See the comments for strategies, tools, and results.

Data Reference Mystery Series

At the Atlantic DLI Workshop on April 22-23, 2004, Elizabeth Hamilton taught a session called Reference Mysteries. She discussed a model for conducting a data or statistical reference interview, including the types of follow-up queries to be asked of patrons, and then presented the group with a series of real reference questions.

Organized in small groups of three or four people, questions were disseminated to these teams to apply the data reference interview model and to propose materials or sources that would help the patron with her or his question.

The model consists of structuring the reference interview around four basic steps. First, determine if the patron is asking for data, statistics, or something else. Statistics are defined as quantitative summaries, facts, or figures that someone or some agency has produced from data. Statistics are often found in published tables in print or electronic formats. Data, on the other hand, is the raw material that is used to produce statistics. Data require processing to be of practical use.

The second step involves identifying any constraints that the patron has in mind. What is the unit of analysis or the object described by the data or statistics? Has the geography been constrained? Has a specific time period been requested? What are the characteristics that make up the data or statistics in the request?

The third step is to identify further information that you would like from the patron to help clarify her or his request. What is the intended use of the statistics or data? What resources have they already checked?

Fourth, compose a list of the resources or tools that might be useful to the patron in finding an answer. This will include the strategies employed in searching for material and resources as well as the tools used in the course of conducting the search.

This model treats the reference interview as a process and not as an end-state where an answer pops out of the information-gumball machine. The incorrect assumption with many statistical or data questions is that the outcome will consist of a single number or file at the conclusion of the reference interview. While this may be the case in some instances, there are many times when the patron will need to examine the resources that were located to see if an answer can be found.

With Elizabeth's permission, the series of questions that she circulated in her workshop session will be posted on this Web Log.

Recent Training in Ontario on Data & Statistical Reference

Three presentations at the Orientation for new DLI Contacts, which was held in conjunction with the Ontario DLI Training Workshop at Queen's University on April 5, 2004, addressed different aspects of reference for data and statistics. First, Chuck Humphrey outlined two conceptual frameworks that offer a vocabulary useful in describing the resources in this area. The first framework divides statistics and data into separate categories of statistical information. This model can be helpful in identifying statistical information products. The second model presents a way of thinking about the different dissemination channels used by Statistics Canada in providing access to its statistical information resources.

Secondly, Laine Ruus, in her discussion about the DLI Tool Kit, spoke about methods for finding the right data. She reviewed the elements important in the data reference interview, including the:

• Geographic area to be covered


• Smallest geographic area to be described


• Time period


• Population (unit of observation) to be described


• Subject matter (variables), in terms that can be expressed in numbers


• Objective: what the user intends/needs to do with the numbers


• What software the user intends to use?


• How would the user like the data delivered?

She also described tools useful in finding aggregate data and microdata from Statistics Canada and summarized the information required to locate specific data products:

• Title of the data set/data file


• The appropriate subfile (physical file)


• Are aggregate data/anonymized microdata disseminated?


• Who disseminates the data; who has a copy?


• Under what conditions are they disseminated?


• How much do the data cost?


• What format are they in? What metadata comes with them?

Third, Vince Gray spoke about handling reference questions. He circulated an updated copy of a handout produced by Elizabeth Hamilton entitled, The DLI Reference Shelf. This annotated bibliography provides a helpful list of reference tools for locating and using Statistics Canada products. In his presentation, Vince reminded everyone that the degree of reference service one provides is dependent on the overall level of service that your institution offers. He then discussed questions helpful in the reference interview:

• Why does the patron need data?


• What type of data does the patron need?


• What is the patron looking for?


• What geographic area(s) does the patron need?


• What time period does the patron want?

After discerning the patron's needs, Vince presented several approaches for locating a source that the patron might find helpful.

Copies of Powerpoint files for these presentations and a Word document of Elizabeth's pathfinder are available on the Ontario's DLI workshop website.