Connections - Winter 2015 - (Page 29)
THE DATA ARE IN?
YOU REMEMBER THE old joke: a kid from the sticks
goes off to college and comes back home to visit. His
proud father puts him in front of a crowd and says,
"son, tell us something you learned at school." The
kid ponders for a moment and says, "Pi R Square."
The poor father winces, "oh, son, everybody knows
pie are round. Cornbread are square." Thus, we begin
our contemplation of data. Are data plural or is data
singular, and-except for the handful of philologists
left on the planet (one of whom I am not) who fight
over such things-can that rather silly argument
actually help us understand what data is all about?
First, a quick attempt to get to the "right" answer
on singular vs plural on data. The answer is yes.
Sometimes data should be a singular noun referring
to collected points of information in the abstract, a
so-called non-count noun (like "hair"), or it can be used
in its plural (like the count noun "hairs") to refer to a
certain bunch of information points, "those particular
data do not mean a thing." An individual data point
could be called a datum according to the great
authority, the OED (Oxford English
Dictionary); however, you rarely hear
such effete speech. Data is a word in
Latin (a past participle, I'm told)
actually meaning "given."
Back to why this matters:
it shows us something.
Data can be viewed as
BY JOHN P. HARRISON,
something we collect in the field or something we
generate in an experiment-both for the purpose
of gaining knowledge. Sometimes, we examine the
data, not to find out something new, but to prove
our hunches or points of view. Thus, our motive is
important: are we trying to learn something looking
at the data, or are we trying to prove something?
This brings us to statistics, which is (or is it
which are?) a system of analyzing data with the
hope of making some determination. Statistics
depends in large part on convention; like accounting,
it makes lots of assumptions about norms and
makes up its own rules. For example, let's imagine
an elderly couple. The wife is in superb health. The
husband is dead. From those data and our good
use of statistics, we can say that on average as a
couple, they're in mediocre health. If you didn't know
the full story on the couple, the data can slip right
Always question the data. Here are but a few quick
checks and a couple of examples. First, check the "face
validity," otherwise known as the "smell test." Secondly,
look for the overarching trend and if this is applicable
to other situations (external validity). Thirdly, check up
on a few of the raw data points, just to see if the math
really works (internal validity). And, here's a qualitative
approach: ask the presenter to poke a hole in his or her
own data, "from where you stand, what is the weakest
aspect of your data?" There's always a weakness, and
the presenter, if honest, knows he's almost hiding
something. Find out what that is.
Once, I was in a meeting where the association's
director of marketing was bragging on and on about
how her self-serving session at a conference scored so
well in the conference's follow up survey.
I noticed that too, and I also noticed
how the poster session at the conference
had done equally as well according to
the survey. "Well, yes, what's your
point?" she said. "There actually
weren't any poster sessions," I
commented. The face validity
of the much-touted
survey then went
down the tubes.
Table of Contents for the Digital Edition of Connections - Winter 2015
Message from the Chair
GSAE News & Events
Improve Outcomes with Data-Driven Decisions
Member Surveys: Getting Back to Basics
Your Data Tells You What Members Want; Use It
Hacking for Your Mission
GSAE Presents Annual Awards
Destination Planner: Meet Charleston
Choices: The Data Are In?
Index of Advertisers
Connections - Winter 2015