Analyzing Change- continued
– OCT. 30, 2002
Overview of Lecture:
1. Review of Oct. 28’s #4: does the computer have a built-in
bias that favours a certain kind of analysis (of “technological
change” in our case?)
(maybe, since the computer is a number cruncher,
it favours quantifiable analysis of data)
(yet, we’ve been looking at theories that are not that
kind of research done by social scientists)
2. MANY social scientists measure change through
quantifiable analyses of data:
experimental and correlational research
3. The relationship between science
and technology:
Homer-Dixon’s views and echoes of Postman’s
views re” “techno-hubris”
*************
2. Overview of “number-crunching”
social science research:
- desire to measure quantifiable changes to users’
abilities, attitudes, etc., in light of using
the computer
examples of some kinds of questions:
* Do people’s abilities (in doing X) improve
using computers?
* Have people’s attitudes to computers changed over time?
(are they happier? more frustrated, etc.)
* Are people’s other activities influenced by time spent
using computers?
But how exactly prove that changes have taken
place?
2.1. Research Method #1: Experimental
Study
Experimental Hypothesis: “Students will write better essays
if they use a word processor”
What would you need to do to prove this conclusively?
--> would require independent variable that
is subject to manipulation;
--> control that other potential variables are kept constant;
--> require random selection of subjects into control and
experimental groups;
--> determine what makes a better essay, then do pre and
post testing to see if use of independent variable had predicted
effect.
Impossible to prove at this point because:
* you can’t control all other variables
which would influence how well someone writes, and
* how would you find subjects who could be in the control group?
2.2. Research Method #2: Correlational Study
Hypothesis: Males will do better in computer science
courses than Females
Independent variable: male or female
Dependent variable: marks
(Can’t say that success is DIRECTLY CAUSED
BY ONE’S SEX, only that there might be a CORRELATION between
one’s sex and success in computer science courses.)
--> BTW: at least one study disproved this
correlation. And even if proven, it doesn’t answer the
more important and interesting question, “why?”
What hypothesis might we want to test?:
“With increased computer usage, comes decreased
television viewing.”
What questions would one need on a survey to show
this correlation?
Over the past year, has the time you spend
on the computer (circle your response): |
1 |
2 |
3 |
4 |
5 |
increased alot |
increased somewhat |
stayed the same |
decreased somewhat |
decreased alot |
Over the past year, has the time you spend
watching t.v.: |
1 |
2 |
3 |
4 |
5 |
increased alot |
increased somewhat |
stayed the same |
decreased somewhat |
decreased alot |
More interesting question: “If there is
a change to people’s leisure habits, what do you attribute
this change to?”
Observations: Only empirical researchers might
indeed be called social SCIENTISTS
3. Homer-Dixon on science and technology
:
“We have placed a lot of faith in that ingenuity-producing
powerhouse modern science…..and “Basic science must
be the foundation of any new practical technology”
H-D’s comparison of technological change
across 4 domains of technology from early 19c. to 20c:
Military explosives – billionfold increase
in explosive power
Long-distance communication – trillionfold
improvement in performance
Personal transport – 50 – 300fold
improvement in transport speed
Agriculture – 4fold improvement
What does this tell us?
1. “astonishing changes technology has brought us recently”
2. improvements vary dramatically across domains: “technology
solves some problems faster than others.”
3. there doesn’t seem to be any relationship between the
problems most needing solving and the problems that get solved.
(MS vs. advances in information technologies)
4. another factor explaining the delays between scientific discoveries
and technical inventions, have to do with 4 factors that affect
the pace of scientific discoveries:
- human cognitive limits: beyond our mental capabilities
to comprehend the complexities around us
- the “intrinsic complexities of the research field’s
subject matter
- nature of its scientific institutions
- science’s “social context”
- another factor affecting movement from science to technological
invention is “right economic markets” (e.g., fax
machine)
Where does he predict significant technological advances?:
Genetics and medicine – DNA, etc.
Information processing and computation
Microprocessors, genetic programming, AI
(see prediction re: information-gathering)
Materials engineering
Machine miniaturization
But his parting words echo Postman’s view
of technology: if “miraculous machines become our gods…
we could face “techno-hubris.”
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