This workbook offers several simulation models that are appropriate
for use of three common research designs for evaluating program
effects. The logic of these simulations can be easily extended
to other relevant research contexts. For instance, many agencies
routinely conduct sample surveys to identify needs and target
populations, assess services that are provided, and compare agency
functioning with the performance of other similar agencies or
with some standard. One would construct simulation models for
survey instruments for the same reasons that they are constructed
for evaluation designs--to improve teaching and general understanding,
to explore problems in implementing the survey (such as non response
patterns), or to examine the probable effect of various analytic
strategies. The key to doing this would again rest on the statistical
model used to generate hypothetical survey responses. A "true
score" measurement model is useful, at least for simple simulations,
but may have to be modified. For instance, assume that one question
on a survey deals with client satisfaction with a particular service
and that the response is a 7-point Likert-type format where 1=very
dissatisfied, 7=very satisfied, and 4=neutral. The analyst could
make the assumption that for some sample or subsample the true
average response is a scale value equal to 5 points (somewhat
satisfied), and that the true distribution of responses is normal
around these values, with some standard deviation. At some point,
the analyst will have to convert this hypothetical underlying
continuous true distribution to the 7-point integer response format
either by rounding or by generating normally distributed random
integers in the first place. Such a variable could then be correlated
or cross-tabulated with other generated responses to explore analytic
strategies for that survey. Similar extensions of the models
discussed here can be made for simulations of routinely collected
management information system (MIS) information, for data for
correlational studies, or for time-series situations, among others.

Simulations are assumptive in nature and vary in quality to the
degree that the reality is correctly modeled. When constructing
a simulation, it is important that the analyst seek out empirical
evidence to support the assumptions that are made whenever this
is feasible. For instance, it should be clear that the simulations
described here could be greatly enhanced if we had more specific
data on how much and what type of attrition typically occurs,
what type of floor or ceiling effects are common, what patterns
of misassignment relative to the cutoff value typically arise
for the RD design, what the typical test-retest reliabilities
(for use in reliability-corrected ANCOVA) might be, and so on.
Although some relevant data will be available in the methodological
literature, all of these issues are context specific and demand
that the analyst know the setting in some detail if the simulations
are to be reasonable.

One way to approach the assumptive nature of the simulation task
is to recognize that reality conditions or constraints in the
models need to be examined systematically across a range of plausible
conditions. This implies that multiple analyses under systematically
varied conditions that are based upon principles of parametric
experimental design are needed in state-of-the art simulation
work. This point is made well by Heiberger et al. (1983:585):

The computer has become a source of experimental data for modern statisticians much as the farm field was to the developers of experimental design. However, many "field" experiments have largely ignored fundamental principles of experimental design by failing to identify factors clearly and to control them independently. When some aspects of test problems were varied, others usually changed as well--often in unpredictable ways. Other computer-based experiments have been ad hoc collection of anecdotal results at sample points selected with little or no design.

Heiberger et al. (1983) go on to describe a general model for
simulation design that allows the analyst to control systematically
a large number of relevant parameters across some multidimensional
reality space, including the sample size, number of endogenous
variables, number of "key points" or condition values,
matrix eigenvalues and eigenvectors, intercorrelations, least
squares regression coefficients, means standard errors, and so
on.

Although rigorous, experimentally based simulations are essential
for definitive analysis of complex problems, they will not always
be feasible or even desirable for many program evaluation contexts.
Instead, it is important to recognize that simulations are generally
a useful tool that can be used to conduct more definitive statistical
studies. However, simulations in program evaluation can provide
the analyst with the means to explore and probe simple relevant
data structures for the purposes of improving the instruction
of research, examining research implementation issues and pilot
testing analytic approaches for problematic data.

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