In 1993, the Dairy Management and Profitability (Dairy-MAP) program began in Pennsylvania to create opportunities for people in the dairy industry to learn new business management skills. The Dairy-MAP program has several components: classroom workshops, on-farm discussion tours, and electronic education materials on a Web site. The face-to-face components of Dairy-MAP have reached over 1,500 dairy producers and agribusiness personnel. In 1995, the Dairy-MAP Web site was developed and has had nearly 10,000 users who logged onto the site. In 1998, a voluntary survey was conducted (a) to determine if the Web site could be used to enhance the face-to-face education in the Dairy-MAP program and (b) to characterize the kinds of users accessing the Web site, and (c) to determine the type and frequency of information being used on the Web site. Results of the survey are summarized in this article.

The survey was completed on a volunteer basis. No rewards were given for completion of the survey. Less than 10% of the total (~10,000) site users completed the survey. The survey was announced to Dairy-MAP alumni in a quarterly newsletter that was mailed to them. The survey was announced to Web users in postings to lists that were related to dairy management and agriculture. A special announcement encouraging Web site visitors to complete the survey was placed at the top of the Dairy-MAP Web site. The survey took about five minutes to complete and users were free to complete all or just part of the survey

Only 26% those that completed the survey were alumni of the Dairy-MAP program. Users who were Dairy-MAP alumni were most frequently using the workshop announcement and calendar features of the Web site and were not using the supplemental management materials very frequently. Because of the low numbers of alumni who accessed the site, using the Web was not useful for providing additional subject information or opportunities for on-line questions and answers about specific topics.

The following characteristics are based on all of the Web site users. Seventy-one percent of the users were from the United States, with the remaining 29% from countries such as New Zealand, Germany, Japan, and South Africa. The majority, 53%, of users were from commercial Internet providers, while educational institutions brought about 45% of the users on-line. The on-line survey indicated that males make up 57% of users, and the average age of a user is 37 years old. Eighty-four percent of users listed an occupation related to either agriculture or to education.

The Web site provided information about the Dairy-MAP program: descriptions of the workshops, current workshop dates, and contact information was available. Newsgroups or bulletin boards were available for users to post questions that could be answered by other users or by Dairy-MAP program staff. There were interactive dairy management worksheets (from the Dairy Initiative at the University of Minnesota), links to other sites with related management information, and an interactive calendar with upcoming agricultural events in Pennsylvania.

Of the users who completed the survey, 20% indicated that they rarely visited the site, 49% indicated that they occasionally visited the site, and 13% indicated that they visited the site monthly. It is obvious that users are after certain types of information. The interactive dairy management worksheets occupy 17% of a users time online. The available publications on the Dairy-MAP Web site occupy 14% of their time. The remaining time spent online at the Dairy-MAP Web site is distributed evenly between calendar, newsgroup, and workshop information.

The Web site has not been effective as an educational supplement for the Dairy-MAP program due to low numbers of alumni accessing the site, but the site has provided increased visibility for the Dairy-MAP program outside of Pennsylvania. The survey provided information about user characteristics and about frequency of use of specific parts of the site. However, the low number of users completing the survey compared to the total number of users accessing the site may bias the information collected. Surveys such as this one may be a very effective way to document the impact of a Web site. The survey information needs to be tied directly to Web site use and tabulated automatically. Users should receive some sort of reward, access to additional information for example, for completing questions about usefulness of Web site elements.

Tables, charts, and graphs are common elements of research and Extension papers. Without them, written expository exercises will be devoid of visual tools that help convey a message to the audience. In particular, tables and graphs facilitate value comparisons, help discern trends, and indicate the magnitude or lack of differences between and/or among test variables when used in conjunction with statistical analysis. One type of table, called a supertable, overcomes the need for many tables and/or graphs while presenting many comparisons in common, easily understood dimensions. The same structure that renders supertables larger than ordinary tables also makes it easy to get the message across to the intended audience without overwhelming them. This paper demonstrates the design and construction of a supertable for a typical Extension survey.

Since 1992, the Texas Agricultural Extension Service has been participating in the 4-H Wildlife and Fisheries School Enrichment Program by developing modules administered to selected schools throughout the state. One module entitled "Wildlife Success Stories and Endangered Species," seeks to teach elementary students about wildlife conservation and management. Module components include a large, freestanding display, videotape, lesson plans for classroom activities, and testing materials (Higginbotham, 1992). The module also features an interactive computer program making use of video, animation, text and sounds to educate the school children on wildlife issues.

A ten question pre-test survey, coupled with inquiries about gender, ethnicity, and school demographics, was administered prior to the module's arrival on each school's campus. Data collected from the pre-test established benchmark knowledge, demographics, and counts of students who responded to the survey.

Two days following exposure to the educational module, the same set of students were tested again using a 10-question post-test. When reckoned against their corresponding scores in pre-test, post-test measures students' gain in knowledge on the subject. County Extension agents helped administer the tests. Survey forms were processed at the Extension Data Center of the Texas Agricultural Extension Service, Texas A&M University at College Station.

The notion that tables or graphs should only convey "few" dominant ideas has been considered to be the golden rule of presentation. Seemingly oblivious to such principle, Tufte (1983) developed the idea of supertables, which he defined as a type of elaborate table with an organized, sequential detail and reference-like quality. A supertable is a concentrated table with numerous row variables (Gravely, 1998). In brief, supertables make use of tabular columns as posting places for categorized topics unified into one or few dominant themes ("dimensions"). Rows are treated as containers for classification variables ("paragraph headings") with the familiar titles such as age, gender, and other variables of interest to the audience. Unlike any other tables, supertables require that row variables be arranged in a logical sequence akin to a book's chronological chapters developing into a story.

PROC TABULATE procedure in SAS was used to produce the supertable printout. Other statistical software such as SPSS (R) and Statgraphics (R) may also have similar procedure that can produce the same output. The pre-test and post-test was designated as columnar topics with percent respondent counts as the unifying dimension across two tests. Gender, ethnicity, schools, and the 5-questionnaire variables served as the row or "paragraph" headings. It is for expediency that only frequencies from five out of the ten questionnaires in the survey were included in the table. In practice, additional dimensions and classification variables can be added to the table to give a good coverage to a subject of interest. Once the rough supertable was generated, a plain text editor was used to refine it by putting a separator line between "paragraphs" and deleting rows flagged by SAS to contain missing data. Separator lines distinctly identify one data "paragraph" from another while increasing the readability of the final table. Note that the deletion of rows with missing data caused some percentage values to fall short of 100% when subtotaled.

The refined supertable is shown in Table 1. Values within the supertable represent percentage counts of respondents broken down into demographic and wildlife questionnaire sections spanning across the two tests. Two characteristics of displayed values are noticeable in this table: (a) values within a class variable were arranged in decreasing order (high to low) of magnitude, and (b) class variables were logically arranged such that the table starts from a specific variable (such as gender) on top and then to a more general one (such as ethnicity) towards the middle part. Effectiveness of these two structural strategies increases with increasing number of variables. Both of them facilitate the perception and identification of data trends and make for easy comparison between extreme values (for example, high vs. low) in adjacent columns or rows of the same variable.

Overall, the objective of the supertable is to provide logically ordered numerical and/or textual values that would support any espoused finding or to answer a research question (Is the wildlife module effective?) using the least number of possible dimension (percent count). Indeed, there are advantages to using supertables. By employing many sequentially arranged class variables that addressed the different aspects of the subject matter, a supertable progressively offers easily comprehensible, bite-sized pieces of information to the intended audience without overwhelming them.

When used alone, a supertable minimizes the introduction of extraneous data and redundant statistics, a feat not easily accomplished when many small tables, charts, and/or graphs are used to prove a point. Despite all its virtues, the effectiveness of supertables depends mostly on when and how one uses them. The power and simplicity of supertables can best be harnessed when use in typical project reports, briefings, summaries, and overhead transparencies commonly used in Extension programming. However, one drawback to its use is that it cannot be adapted as a content material for developing slide presentations due to its large size.

Gravely, A.R. (1998). Your guide to survey research using the SAS system. Cary, NC: SAS Institute, Inc.

Higginbotham, B. (1995). Wildlife success stories and endangered species: A 4-H school enrichment program. Progress Report. College Station: Texas Agricultural Extension Service.

Tufte, E.R. (1983). The visual display of quantitative information. Cheshire, CN.. Graphics Press.

SAS is a registered trademark of SAS Institute Inc. in the USA and other countries. (R) indicates USA registration. SPSS and Statgraphics are registered trademarks of their respective owners.

This article is online at http://joe.org/joe/2000february/ent-tt.html.

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