alan and others ...
perhaps what my overall concern is ... and others have expressed this from
time to time in varying ways ... is that
1. we tend to teach stat in a vacuum ...
2. and this is not good
the problem this creates is a disconnect from the question development
phase, the measure development phase, the data collection phase, and THEN
the analysis phase, but finally the "what do we make of it" phase.
this disconnect therefore means that ... in the context of our basic stat
course(s) ... we more or less have to ASSUME that the data ARE good ...
because if we did not, like you say .... we would go dig ditches ...at this
point, we are not in much of a position to question the data too much
since, whether it be in a book we are using or, some of our own data being
used for illustrative examples ... there is NOTHING we can do about it at
this stage.
it is not quite the same as when a student comes in with his/her data to
YOU and asks for advice ... in this case, we can clearly say ... your data
stink and, there is not a method to "cleanse" it
but in a class about statistical methods, we plod on with examples ...
always as far as i can tell making sufficient assumptions about the
goodness of the data to allow us to move forward
bottom line: i guess the frustration i am expressing is a more general one
about the typical way we teach stat ... and that is in isolation from other
parts of the question development, instrument construction, and data
collection phases ...
what i would like to see .. which is probably impossible in general (and
has been discussed before) ... it a more integrated approach to data
collection ... WITHIN THE SAME COURSE OR A SEQUENCE OF COURSES ... so that
when you get to the analysis part ... that we CAN make some realistic
assumptions about the quality of the data, quality of the data collection
process, and make sense of the question or questions being investigated
At 02:01 PM 4/20/01 +1000, Alan McLean wrote:
>All of your observations about the deficiencies of data are perfectly
>valid. But what do you do? Just give up because your data are messy, and
>your assumptions are doubtful and all that? Go and dig ditches instead?
>You can only analyse data by making assumptions - by working with models
>of the world. The models may be shonky, but they are presumably the best
>you can do. And within those models you have to assume the data is what
>you think it is.
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