Abstract submitted to the ESRA Conference, Prague, June 2007

<dd><h3>Studying human populations.  A new curriculum for social statistics</h3>

<dd><h4>Presenter:  Nicholas T. Longford, SNTL, Reading, England</h4>

<dd><p>Email: ntl@sntl.co.uk</p>

<p>
The presentation, based on a monograph recently submitted for publication, 
describes a new curriculum for social statistics centred around the basic 
concepts of sampling and measurement.  Statistics is defined as making 
decisions in the presence of uncertainty which arises as a consequence of 
limited resources at our disposal.  By resources, we mean not only finance, 
but also time, expertise, instruments, goodwill and availability of the 
respondents, and the like.  If these were not limited, the entire 
population that we study could be enumerated -- the values of the key 
variable(s) established for every one of its members.  With limited 
resources, we have to resort to short-cuts: sampling (observing only a 
sample from the population) and imperfect measurement (recording the values
of a manifest instead of the latent variable).  This motivates two central
topics: sampling methods and the study of measurement processes.
</p><p>
Statistical models can be introduced as descriptions of infinite populations,
which are usually considered as a matter of convenience.  To promote 
integrity, an admission is made upfront that linearity of the models is 
adopted as a matter of analytical convenience.  Sampling methods and 
model-based estimation represent two distinct perspectives:  in sampling, 
the values of the variables on the population are regarded as fixed, and 
the act of inclusion into the sample is the sole source of uncertainty;
in model-based methods, the values of the outcome variable(s) are regarded
as random.  The Bayesian paradigm is introduced to complement these two 
perspectives; in Bayesian analysis, all observed quantities are regarded 
as fixed and all quantities with unknown values as random.  
</p><p>
Non-response or, more generally, failure to collect the data as per plan, 
is a ubiquitous problem in all large-scale studies of human populations, 
and methods for dealing with it are indispensable for a statistical analyst. 
The curriculum follows the innovative research on the EM algorithm and 
multiple imputation by Don Rubin and his ex-students, and introduces some
less standard applications of their missing-data principle.  One of them
is a general method for dealing with imperfect measurement:  the values 
of the latent variable are treated as the missing information. 
</p><p>
With limited resources in mind, the necessity for study design, in all its 
aspects (sampling, questionnaire, measurement, coding, collation of auxiliary 
information, and the like) is emphasised.  Experimental design is introduced
as the setting in which the assignment of the values of some or all of the 
variables is under the designer's control, and can be randomised.  Without 
it, making causal inferences is very difficult and requires some unverifiable
assumptions.  In observational studies, to which we resort when experimental 
design cannot be exercised, the focus rests on the assignment process.  The 
definition of potential outcomes is introduced, connected to sampling methods, 
and treated as another example of incompleteness. 
</p><p>
Advanced or specialised topics include clinical trials, multilevel analysis, 
generalized linear models and meta-analysis.  In general, hypothesis testing
is de-emphasised because it is not conducive to making good decisions.  

<br><br><br>
May 2007