organizational patterns in argument

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Hello, please respond to the following post, if you agree or not and why.

One paragraph of 5 full sentences is enough; APA citation as needed.

“My favorite article of the three is “How Many Zombies Do You Know?” because it was amusing to read. Although some of the data the author used was not real, he created graphs to illustrate his ideas and that it seem as though his sources were credible. All authors, including Gelman and Romeroy, started off with strong introductions in an effort to appeal to their audience. I noticed that every article was well organized, and each author inserted a topic sentence into the first or second paragraph. According to Paine (2013), the purpose of a topic sentence is, “announces the paragraph’s subject and makes a statement or claim that the rest of the paragraph will support or prove” (Paine, p. 417). All authors achieved this purpose”

See article below:

READINGS: “How Many Zombies Do You Know?” Using Indirect Survey Methods to Measure Alien Attacks and Outbreaks of the Undead

  • ANDREW GELMAN AND GEORGE A. ROMEROY
  • Andrew Gelman, a respected and award winning professor of statistics and political science at Columbia University, wrote on his blog that he created this unpublished paper to do some “humorous fun poking” but also to illustrate how a very real cutting edge survey method could be used for solving difficult research problems. As you read and enjoy this, notice he uses the conventions of the scientific article genre.

1 Introduction

Zombification is a serious public health and public safety concern (Romero, 1968, 1978) but is difficult to study using traditional survey methods. Zombies are believed to have very low rates of telephone usage and in any case may be reluctant to identify themselves as such to a researcher. Face to face surveying involves too much risk to the interviewers, and internet surveys, although they originally were believed to have much promise, have recently had to be abandoned in this area because of the potential for zombie infection via computer virus.

In the absence of hard data, zombie researchers1 have studied outbreaks and their dynamics using differential equation models (Munz et al., 2009, Lakeland, 2010) and, more recently, agent based models (Messer, 2010). Figure 1 shows an example of such work.

But mathematical models are not enough. We need data.

1 By “zombie researchers,” we are talking about people who research zombies. We are not for a moment suggesting that these researchers are themselves zombies. Just to be on the safe side, however, we have conducted all our interactions with these scientists via mail.

2 Measuring zombification using network survey data

Zheng, Salganik, and Gelman (2006) discuss how to learn about groups that are not directly sampled in a survey. The basic idea is to ask respondents questions such as, “How many people do you know named Stephen/Margaret/etc.” to learn the sizes of their social networks, questions such as “How many lawyers/teachers/police officers/etc. do you know,” to learn about the properties of these networks, and questions such as “How many prisoners do you know” to learn about groups that are hard to reach in a sample survey. Zheng et al. report that, on average, each respondent knows 750 people; thus, a survey of 1500 Americans can give us indirect information on about a million people.

Figure 1: From Lakeland (2010) and Messer (2010). There were other zombie graphs at these sites, but these were the coolest.

5 This methodology should be directly applicable to zombies or, for that matter, ghosts, aliens, angels, and other hard to reach entities. In addition to giving us estimates of the populations of these groups, we can also learn, through national surveys, where they are more prevalent (as measured by the residences of the people who know them), and who is more likely to know them.

A natural concern in this research is potential underreporting; for example, what if your wife2 is actually a zombie or an alien and you are not aware of the fact. This bias can be corrected via extrapolation using the estimates of different populations with varying levels of reporting error; Zheng et al. (2006) discuss in the context of questions ranging from names (essentially no reporting error) to medical conditions such as diabetes and HIV that are often hidden.

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