A quantitative research study collects numerical data that must be analyzed to help draw the study’s conclusions. Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a study’s contextual situation. Learning data analysis is not learning how to use statistical tests to crunch numbers but is, instead, how to use those statistical tests as a tool to draw valid conclusions from the data. Three major pedagogical goals that must be taught as part of learning quantitative data analysis are the following: (a) determining what questions to ask during all phases of a data analysis, (b) recognizing how to judge the relevance of potential questions, and (c) deciding how to understand the deep-level relationships within the data.

The software for a military command and control (C2) system presents an information space in which the C2 operator must manipulate a complex set of information in order to maintain the common tactical picture. A usability test of C2PC, a C2 system currently used by the U.S. Marine Corps, was performed with 13 Marines as participants. They were given problems designed to require actions similar to what they would encounter during real-world Combat Operations Center (COC) operation. The observations revealed an interesting disconnect between the underlying design assumptions and how beginner C2 operators interacted with the system. They were able to perform simple tasks, but could not combine those simple tasks into realistic tasks. These differences highlight the need for using complex scenarios when testing complex systems.

With the rise of Web pages providing interactive support for problem-solving or providing large amounts of information on which a person is expected to act, designers and writers need to consider how a person interacts with increasingly complex information-rich environments and how they intend to use the information. This article examines some of the theory underlying why people make errors early in the problem-solving process when they form an intention. Since these errors are cognitively-based and occur before any physical action, it is harder to analyze their cause or incorporate changes to reduce them in a design. It examines factors which contribute to user errors and which designers and writers must consider to produce documents which reduce user errors in forming intentions.

As technical communication gains the technology to deliver dynamic custom documents, the importance of audience analysis increases. As a major factor in supporting dynamic adjustment of document content, the audience analysis must clearly capture the range of user goals and information needs in a flexible manner. Replacing a linear audience analysis model with a multidimensional model provides one method of achieving that flexibility. With a minimum of three separate dimensions to capture topic knowledge, detail required, and user cognitive ability, this model provides the writer a means of connecting content with information requirements and ensuring the dynamic document fits varying audience needs.

The commonsense principles of modern document design are direct descendants of the principles used in the Books of Hours, a hybridized religious instruction manual created in the commercial scriptoria of the 13th century. This article analyzes the design of Books of Hours and discusses how these medieval documents fit within the four design criteria (supertextual, extra-textual, intratextual, and intertextual) put forth by Kostelnick and Roberts [1]. The analysis reveals the early user of good document design features as the medieval scriptoria worked to address the audience and task requirements of the Books of Hours.

Abstract Technical editors ensure a document communicates with the reader. With XML, active server pages, and dynamic document creation, Web pages are no longer simple hand‐crafted text objects, but dynamic groupings of text assembled moments before the reader views the page. With dynamic documents, high‐level editing tasks will be, at best, vaguely defined during text creation. To maximize the information content, future technical editors require tighter control over information consistency and content.