Online Guide to Writing and Research
Chapter 3: Thinking Strategies and Writing Patterns
Patterns for Presenting Information
The problem and solution pattern reflects a form of critical thinking that tends to be more argumentative and evaluative. You may find this pattern useful in writing case studies, critiques, introductions, reports of scientific investigations, literary reviews, political and social discourse, white papers, proposals, many kinds of reports, and essay examinations.
The most common forms of this pattern sequence the information in one of two ways:
(1) problem-process-solution or (2) problem-cause-solution. Both patterns first describe the problem and then proceed through diagnosis and analysis to propose a solution. The diagnosis and analysis may include an evaluation of processes or procedures or a discussion of causes contributing to the problem.
For example, you might use this pattern to write a paper discussing how diabetes is being treated through diet, drugs, exercise, and surgery. Your process analysis would examine the progression of the disease and the different ways to treat it. The solution you propose might include one or more of the current treatment modalities, and you would evaluate the merits of your solution in light of the life-saving procedures you discussed. In the problem-cause-solution pattern, you might describe why diabetes is a serious health problem and the known causes of diabetes, such as diet, genetics, biochemical processes, and obesity. Your proposed solution for preventing diabetes or mitigating its effects would then be based on what you know about these causes.
Here is an example of the problem-cause-solution pattern:
Example of a Problem-Cause-Solution Pattern
Why do artists draw graphics that lie? Why do the world’s major newspapers and magazines publish them?
Although bias and stereotyping are the origin of more than a few graphical distortions, the primary causes of inept graphical work are to be found in the skills, attitudes, and organizational structure prevailing among those who design and edit statistical graphics.
Lack of Quantitative Skills of Professional Artists
Lurking behind the inept graphic is a lack of judgment about quantitative evidence. Nearly all those who produce graphics for mass publication are trained exclusively in the fine arts and have had little experience with the analysis of data. Such experience is essential for achieving precision and grace in the presence of statistics, but even textbooks of graphical design are silent on how to think about numbers. Illustrators too often see their work as an exclusively artistic enterprise . . . Those who get ahead are those who beautify data, never mind statistical integrity. (Tufte, 1983, p. 79)
[The chapter continues to discuss the causes and effects of graphic distortion and examines the sources of integrity and sophistication in the processes of graphic design.]
The conditions under which many data graphics are produced—the lack of substantive and quantitative skills of the illustrators, dislike of quantitative evidence, and contempt for the intelligence of the audience—guarantee graphic mediocrity. These conditions engender graphics that (1) lie; (2) employ only the simplest designs, often unstandardized time-series based on a small handful of data points; and (3) miss the real news actually in the data . . .
How can graphic mediocrity be remedied? . . .
Graphical competence demands three quite different skills: the substantive, statistical, and artistic. Yet now most graphical work, is under the direction of but a single expertise—the artistic. Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose. Substantive and quantitative expertise must also participate in the design of data graphics, at least if statistical integrity and graphical sophistication are to be achieved. (Tufte, 1983, p. 87)
Professor Tufte describes the problem of incompetence in graphical presentations of quantitative information and discusses the causes. His solution, to include content and statistical experts in designing graphics, is evaluated throughout the chapter. He uses descriptions and examples of graphical distortion, integrity, and sophistication to support his conclusion.