> Robert and others,
> I have been away from the bench a few days but hope it is not too late to
> address some of your grievances. Pls see embedded comments below.
>
> ----- Original Message -----
>
> > There is NO WAY (repeat sotto voce -no way) that you can justify
> > or support applying a normal distribution to anything but randomly
> > selected populations. The only way this makes any sense is if you
> > hire randomly, completely randomly.
>
> [JR]First, it seems to me that you confuse a) the method of sampling a
> population with b) the characteristic distribution of the population.
There
> are many ways a population can evolve to a normal distribution regardless
of
> how the
> population is observed.
>
> > If you actually do have a normal distribution underlying employee
> > performance, your hiring practices would be absolutely broken.
>
> [JR]Do you have a rationale for this seemingly nonsensical statement?
> And notice that the distribution does not "underlie" employee performance,
> it is simply a characterization of the spectrum of employees performances.
> It is not a
> foundational or driving factor but only a view of the spectrum of employee
> performance appraisals.
>
> But more importantly, notice that the discussion was not about
performance.
> It was about appraisals of performance. We hold open the possibility
that
> the appraisal is far from a true estimate of actual performance.
>
> > Since the assumption about normal distributions is wrong, so is
> > everything that follows.
>
> [JR]If you get beyond the statistics of management and HR of management
and
> get on to the cybernetics of management then you can comprehend that the
> issue is not about the initial assumption but about a process for
converging
> to true appraisals of performance by appraisal writers. So the initial
> assumption can be dead wrong and everything that follows can be quite
useful
> . If you understand self-correcting systems an assumption can be wrong
and
> the outcome resulting from that assumption can be quite illuminating. It
is
> called testing the hypothesis. Didn't they teach that in your school?
>
> > Lest you don't understand the concept here: If you take all the
> > heights of players in the NBA, do you think you will get a normal
> > distribution? Or weights of people who go to weight watchers?
>
> [JR]I have no reason to believe that the spectrum of the height of NBA
> players
> should approximate a normal distribution. In fact, I would expect a
Poisson
> distribution. More importantly we should note that this is the old Red
> Herring trick. However, I do not mind a hypothesis that a normal
> distribution describes the heights of NBA players as long as you test the
> hypothesis.
>
> Enough of 7th grade statistics. Let's focus on performance appraisals.
>
> Although I am not in favor of individual performance appraisals I will say
> that if such are to be done then the appraisal should address the fit
> between a) the challenges of the job and b) the contribution of the
> individual. A
> performance appraisal is not a recitation of activity. It is a measure of
> variance from expectation. The variance can be either positive or
> negative. And we keep in mind that this is the actual performance
variance
> compounded with additional errors on the part of the appraiser (which may
be
> either positive or negative, as well).
>
> Also, I will say that the Quality of Performance Appraisals should be a
> major factor in the appraisal of any person authorized to write appraisals
> of others.
>
> So for each individual we have a job demand vector and an perceived
> achievement vector. If each employee in the group achieved exactly 100%
of
> the job demands there would
> be no normal distribution of group appraisal results. Likewise, if each
> employee in the group achieved exactly 50% of their job demands there
would
> be no normal distribution. But it is likely that some employees
> overachieved and some underachieved. So we get a distribution of
variances.
> Whether this distribution is normal or not is irrelevant. Likewise,
whether
> I assume (make a hypothesis) it is normal is irrelevant -- as long as the
> hypothesis is tested. After all, we are concerned here not with improved
> employee performance but with improved accuracy of appraisals.
>
> We should note that in the case where 100% achievement is demonstrated
very
> little learning will likely transpire in the future because there is
> apparently little error
> being made so no basis for learning.
>
> So now we can ask what distribution of variances a good manager would like
> to see.
> + All employees performing at 100%? No future in that.
> + All performing at 130%? Nice job by the employees but the manager has
to
> be
> appraised negatively for spending money on competencies that obviously are
> not needed.
> + All performing at 50%? Not quite prudent from any viewpoint.
>
> So let's assume that in the ideal case we would want performance to
average
> about 90%. Can we decide what spectrum of variance would be ideal? Sure
we
> can. No one comes to work with identical learning style, enthusiasm,
> proficiency, etc. every day. Some days you eat the bear and some days the
> bear eats you. Aha! The makings of a normal distribution.
>
> If each person exhibits a normal distribution over time regarding
individual
> performance what would the group distribution look like?
>
> Now do you want to howl that the distribution will not be exactly normal?
> Goodness, then howl if you must --- while the rest of us manage.
>
> The central fact remains that very few people are qualified to write
> appraisals on others but if the HR system makes it happen we should focus
on
> the performance of the writers, first, and the performance of the worker
> bees, second.
>
> And to those who are tired of this topic I apologize for the long posting.
>
>
>
>