Technical Writing, October 24th, 2013
Upcoming SlideShare
Loading in...5
×
 

Technical Writing, October 24th, 2013

on

  • 254 views

 

Statistics

Views

Total Views
254
Views on SlideShare
216
Embed Views
38

Actions

Likes
0
Downloads
3
Comments
0

1 Embed 38

http://www.phillalexander.com 38

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Technical Writing, October 24th, 2013 Technical Writing, October 24th, 2013 Presentation Transcript

  • TODAY 1) Checking in with Dr. Anderson 2) Dr. Phill’s advice for making your research readable 3) Activity: making information user friendly 4) Homework
  • Anderson Since we last discussed readings, you’ve read chapter 7 and chapter 13 from the Anderson text. As I’ve said before, Anderson does a great job with the book; I sort of feel like he nailed it. But let’s review the key points before we move into some more report-targeted activities.
  • In Chapter 7, there are 7 guidelines for research. See how that works? 7 in 7?
  • Guideline 1: Review your research objectives.
  • What? This one’s easy: know what you’re trying to find out. It’s easy to get lost while researching. Keep reminding yourself what you want to know.
  • Guideline 2: Arrange your information in analyzable form.
  • What? Research is useless if the data ends up so dense or so poorly formatted (or overwhelming) that it cannot be used for the purposes it is meant to be used for. This is a biggie. We’re coming back here in a bit for an activity.
  • Guideline 3: Look for meaningful relationships in the information.
  • What? It used to be just look for “relationships.” But rhetorically, they need to be meaningful. Say you have car accident data. There are two Samoans, and both of them just had fender benders. NOT really meaningful. But if there are 45 people under the age of 18, and all of them wrecked because they were texting, THAT means something.
  • Guideline 4: interpret each relationship for your readers.
  • What? Why did the two Samoans not mean much? Because it’s not a statistically relevant sample. Why do the 45 youths mean something? 45 participants with the same outcome indicates high possibility for a one-to-one relationship. You need to tell the reader that.
  • Guideline 5: Explain why each relationship is important to your readers.
  • What? Do you like young people? Do you like them… alive? Do you think they have phones? Then let’s look at this texting problem!
  • Guideline 6: Recommend actions based on your analysis.
  • What? We won’t do this until our proposals. BUT… To keep our example going, wouldn’t we suggest finding a way to keep kids from texting while driving?
  • Guideline 7: Think critically about your analysis.
  • What? In a report, you get facts, you give them to us. If your analysis is bad, you end up muddying the facts. So… don’t do that! Be thoughtful, and think through what you’re doing with the information you find. With great fact comes great responsibility.
  • So that’s Anderson on reports. Next up, in chapter 13, he talks to us about using graphics.
  • Guideline 1: Look for places where graphics can increase your communication’s usefulness and persuasion.
  • What? This was my biggest comment on your instructions. Sometimes you NEED a photo or a screenshot. Look for those places.
  • Guideline 2: Select the graphic that will be most effective.
  • What? Know when to use what sort of chart or graph. This is not the best use below.
  • Guideline 3: Make each graphic easy to understand and use.
  • What? Remember the graphic is there to help. It should… help. If it’s super-complex, misleading, shoddy, or shifty it won’t do the job.
  • Guideline 4: Use color to support your message.
  • What? Remember the use of color can make it so that people notice certain words or can make I clear that numbers like 2, 3, and 4 all go together.
  • Guideline 5: Use graphic software and existing graphics effectively.
  • What? Basically: 1) don’t reinvent the wheel and 2) no one likes a crappy Photoshop job. If you design an image, do it right. 
  • Guideline 6: Integrate your graphics with your text.
  • What? You want the text to flow into and around the graphic. A graphic sitting all by itself can be very confusing.
  • Guideline 7: Get permission and cite the sources in your graphics.
  • What? It’s still data. You treat it just like everything else. Except screenshots from Ted, because I’m not citing the last slide. 
  • Guideline 8: Avoid graphics that mislead.
  • What? You want to be careful with your representation of statistics. Real life example: I saw a report from one of the programs where I was a student that boasted a 100% Native American graduation rate: Me. The huge 100% bar on their graph was JUST ME.
  • So after some Anderson… The really important, like super key points, from what we read for the last few days are that you have data that you will use in your report, but you can’t just go get it and stick it in the report. Sort of like how you wouldn’t get ground beef, cans of tomatoes and beans, an onion, some peppers and various spices and chuck them on the table (you’d make chili!), you don’t just throw raw data at people. That’s why we call it raw data. Cook it!
  • Dr. Phill’s Four Ways to Cook Your Data ‘till It’s Done
  • Way 1: Cut that fat!
  • You’re going to have a plethora of data. Of datai? Of datasususus? Anyway… The goal is to only relate to the reader what she needs. If you’re researching the safest cars for families, the data sheet might include the colors it comes in. Not important. Cut the fat!
  • Way 2: Flavor it Right
  • To continue the metaphor… If you see raw chicken, Ragu sauce, a block of cheese, bread crumbs, eggs, and a box of spaghetti sitting on my counter… You don’t expect I’m going to serve you meatloaf, right?
  • A secret: humans are pattern recognizing machines. So use that when organizing your data. Sequence things in ways that make arguments already. The Mercedes C is best in class, five star safety rating. It has 20 airbags. It has dynamic anti-lock brakes. It’s made of win. (see how a case is being made with the data?)
  • Way 3: Chop it up Fine
  • Sometimes data is just overwhelming. Information is everywhere, and thanks to technology, we generate even more of it each and every second. While I was talking just now, more data flew into existence. Sometimes you need to carve out just the right pieces.
  • An example: if you’ve been following the “debates” about the Affordable Care Act (AKA Obamacare), there are claims– data points, as individuals talk and write– that it is KILLING small businesses.
  • But if you take an actual example, there’s a man who was on FOX news last night. He employs 4 people. He claims that Obamacare “means I can’t hire more employees.”
  • The ACA states that at over 50 employees, an employer must kick in insurance. So that guy would have to expand by 47 employees to be hurt.
  • Ergo, taken as a chopped out piece, this data doesn’t say what it said. But we had to cut around it to find that.
  • Way 4: Make it easy to swallow.
  • Look at this:
  • Imagine it in writing. Reports show that Washington state had seven percent unemployment, while Montana had five (repeat with new number 48X). All the data is on that map. But we can chew on that. Write it in paragraph form and I’ll break into hives.
  • So… Sadly, there’s no set recipe for “report.” That’s why we’ve been looking at audiences and expectations and such. There is, however, for our class activity! On the course website there are links to two documents from Apple: The data sheet for the original iPad and the data sheet for the brand new iPad Air. Please pair up with someone, get to a computer, and open those links.
  • The activity Pretend for this activity Dr. Phill runs a writing center that owns 10 original iPads for student use. Your job is to explain to me in the report– and we just need a data treatment for this activity, not the whole report– the upgrades from the original model to the new model. Think about what we talked about today as you treat the data.
  • When you finish treating the data Email it to me. For Tuesday, read Anderson, Chapter 14. Remember your case study is due via email on Tuesday. Have a good weekend! Stay warm!