Changing the Conversation Messages for Improving Public Understanding of Engineering (2008) / Chapter Skim
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1 Introduction
1 Introduction
Pages 17-38
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. Polling data comparing scientists and engineers show that the public sees engineers as being more responsible for creating economic growth and preserving national security than scientists, as well as more likely to make strong leaders.
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Some knowledge about how engineering work is done, for example, is fundamental to technological literacy. To be fully capable and confident in a technology-dependent society, every citizen should understand something of the process of engineering and how engineering and science, among TABLE 1-1 Comparative Characteristics Associated with Engineers and Scientists, 2003 and 1998 Don't Decline to Engineers Scientists Neither Know Answer Creates economic growth 2003 69% 25% 2% 3% *
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Decision making on these and other topics will involve trade-offs, as we attempt to simultaneously manage limited resources while sustaining quality of life. Public discourse and the democratic process could be enhanced if citizens understood more about how engineers are trained and what the practice of engineering entails.
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. However, because of differences in methods of data collection and in defining engineering, it is difficult to compare the absolute numbers of four-year engineering degrees awarded in China and India to those awarded in the United States.
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Although researchers and policy makers disagree on the nature and extent of the engineering "shortage" in the United States, few dispute the need to attract capable students, especially girls and certain minorities, into technical careers. Women, African Americans, Hispanics, Native Americans, and some Asian American groups are significantly underrepresented in engineering, based on their proportions in the population at large (Box 1-1)
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: 50.7 percent Proportion enrolled in degree-granting institutions, 2004: 57.4 percent Proportion of bachelor's degrees in engineering, 2004: 20.5 percent Proportion of tenured/tenure-track appointments on U.S. engineering faculties, 2005: 10.6 percent Proportion employed as engineers, 2003: 11.0 percent African Americans Proportion of U.S.
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First, no apparent effort has been made in the engineering community to develop consistent messages. Second, few organizations involved in promoting public understanding of engineering have developed their messages in a systematic, scientific way or tested the effectiveness of their messages.
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For example, entire industries have attempted to remake their public image using branding techniques. The dairy industry's "Got Milk" campaign (www.bodybymilk.com)
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A typical positioning statement answers seven core questions about a brand: 1.
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A measure of the campaign's success is that today people discuss the speed of their processors, and even mention their name. We can imagine Intel using something like the following positioning statement, which we crafted based on the history of the Intel Inside® Program (Intel Corporation, 2008)
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From new farming equipment and safer drinking water to electric cars and faster microchips, engineers use their knowledge to improve people's lives in meaningful ways. One of several taglines we tested reads: Because dreams need doing To develop and test messages and taglines, the marketing company conducted research in the form of focus groups and surveys.
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The second way marketing firms use research is to test messages and taglines. Testing can reveal the most popular or appealing brand elements, but more important, it can reveal unanticipated problems.
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(The committee discusses other technical issues, including factors that affect generalizability of data, in an annex to this chapter.) The NAE Messaging Project This project is based on the hypothesis that concise, effective messaging can help correct misconceptions about, and improve the image of, engineers and engineering.
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Less direct evidence of impact might be obtained by tracking changes in responses to periodic national surveys, such as those on professional prestige conducted by Harris Interactive; commissioning new surveys, for example, of high school students views about engineering; or analyzing factors leading to changes in enrollments in engineering schools.
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The committee notified a number of groups about the posting, including NAE members; the National Academies Teacher Advisory Council; a number of engineering societies (e.g., American Society of Mechanical Engineers, Institute for Electrical and Electronics Engineers, American Society of Civil Engineers, National Society of Professional Engineers, National Society of Black Engineers, Society of Women Engineers, American Society of Engineering Education) ; the International Technology Education Association, which represents K–12 technology education teachers; the Association of Science-Technology Centers, which represents many science and technology museums; and the National Association for College Admission Counseling.
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A separate CD contains complete data tables for the online survey and a PDF version of the full report. REFERENCES AAAS (American Association for the Advancement of Science)
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Sample from the 2006 Profiles of Engineering and Engineering Technology Colleges, American Association of Engi neering Education. Available online at http://www.asee.org/publications/profiles/ upload/2006ProfileEng.pdf.
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Table 47: Engineering degrees awarded, by degree level and sex of recipient, 1966–2004. Available online at http://www.nsf.gov/statistics/nsf07307/pdf/tab47.pdf.
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The most commonly used inferential statistic is sampling tolerance, often called the margin of error. We prefer the former term, because the m argin of error suggests, incorrectly, that there is something wrong with the data, whereas sampling tolerance refers to the difference between results from the sample and results anticipated in the target population as a whole.
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This means that we can be 95 percent certain that the value for the true population falls somewhere within the margin of error around what we observed in our sample. For example, as Table 1-3 shows, for a sample of 600 people, if 20 percent chose a particular answer choice, the sampling tolerance would be +/– 3.2 percent, and the answer range would be between 17.8 percent and 23.2 percent. This means that we can predict with 95 percent certainty that the percentage of individuals in the population we drew our sample from fall within the calculated range.
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There are several aspects of our survey method that might affect generalizability. First, because our survey required respondents to have Internet access, we could not include people who did not have access.
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The reasons for non-responses vary but can include disinterest in or aversion to the survey topic or discomfort with the survey methodology (e.g., keyboarding in an Internet-based survey)
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