Applications of Simulations in Social Research


There are a number of ways in which the simulation exercises can be useful in program evaluation contexts. First, they provide a powerful teaching tool (Eamon, 1980; Lehman, 1980). Students of program evaluation can explore the relative advantages of these designs under a wide variety of conditions. In addition, the simulations show the student exactly how an analysis of these designs could be accomplished using real data. Second, the simulations provide a way to examine the possible effects of evaluation implementation problems on estimates of program effect (Mandeville, 1978; Raffeld et al., 1979; Trochim, 1984). Just as NASA explores difficulties in a space shuttle flight using an on-ground simulator, the data analyst can examine the possible effects of attrition rates, floor or ceiling measurement patterns, and other implementation factors. Finally, simulations make it possible to examine the potential of new data analysis techniques. When bias is detected in traditional analysis and analytic solutions are forthcoming, simulations can be a useful adjunct to statistical theory.

Applications For Teaching

Simulations offer several advantages for teaching program evaluation. First, students can construct as well as observe the simulation program in progress and get an idea of how a real data analysis might unfold. In addition, the simulation presents the same information in a number of ways. The student can come to a better understanding of the relationships between within-group pretest and posttest means and standard deviations, bivariate plots of pre-and postmeasures that also depict group membership, and the results of the ANCOVA regression analyses. Second, the simulations illustrate clearly some of the key assumptions that are made in these designs and allow the student to examine what would happen if these assumptions are violated. For instance, the simulations are based on the assumption that within-group- pre-post slopes are linear and that the slopes are equal between groups. The effects of allowing the true models to have treatment interaction terms or nonlinear relationships can be examined directly with small modifications to the simulation program as Trochim (1984) illustrated for the RD design. Third, the simulations demonstrate the importance of reliable measurement. By varying the ratio of true score and error term variances, the student can directly manipulate reliability and show that estimates of effect become less efficient as measures become less reliable. Finally, simulations are an excellent way to illustrate that apparently sensible analytic procedures can yield biased estimates under certain conditions. This is shown most clearly in the simulations on the NEGD. Although the apparent similarity between the design structures of the RE and NEGD might suggest that traditional ANCOVA regression models are appropriate, the simulations clearly show this to be false and thereby confirm the statistical literature in the area (Reichardt, 1979).

Applications for the Study of Design Implementation

The validity of estimates from the simulation exercises contained in this manual depend on how well they are executed or implemented in the field. There are many implementation problems occurring in typical program evaluations--attrition problems, data coding errors, floor and ceiling effects on measures, poor program implementation, and so on--that degrade the theoretical quality of these designs (Trochim, 1984). Clearly, there is a need for improved evaluation quality control (Trochim and Visco, 1985), but when implementation problems cannot be contained, it is important for the analyst to examine the potential effects of such problems on estimates of program gains. This application of simulation is analogous to simulation studies that NASA conducts to try to determine the effects of problems in the functioning of the space shuttle or a communications satellite. There, an exact duplicate of the shuttle or satellite is used to try to recreate the problem and explore potential solutions. In a similar way, the program evaluator can attempt to recreate attrition patterns or measurement difficulties to examine their effects of the analysis and discover analytic corrections that may be appropriate. The analyst can directly manipulate the models of the problems in order to approximate their reality more accurately and to examine the performance of a design under more varied situations. Such simulations are useful in that they can alert the analyst to potential bias and even indicate the direction of bias under various assumptions.

Applications For the Investigation of New Analyses

One of the most exciting uses of simulation involves the examination of the accuracy and viability of "new" statistical techniques that are designed to address the deficiencies of previous models. There are two reasons why simulations are particularly valuable here. First, the conditions that the analysis will yield unbiased estimates. Second, simulations allow the analyst to examine the performance of the analysis under degraded conditions or conditions that do not perfectly match the mathematical ideal. Thus, simulations can act as a proving ground for new analyses that supplement and extend what is possible through mathematical argument alone.

This application of simulations can be illustrated well by reflecting on the NEGD simulations, where the estimates of program effect were clearly biased. This bias is well know in the methodological literature (Reichardt, 1979) and results from unreliability (measurement error) in the preprogram measure under conditions of nonspecific able group nonequivalence. One suggestion for addressing this problem analytically is to conduct what is usually called a reliability-corrected analysis of Covariance to adjust for pretest unreliability in the NEGD. The analysis involves correcting the pretest scores separately for each group using the following formula:

Xadj = X + rxx(xi - xmean)

where:

Xadj = the adjusted or reliability corrected pretest

xmean = the within-group pretest mean

xi = pretest score for case i

rxx = an estimate of pretest reliability

The analyst must use an estimate of reliability and there is considerable discussion in the literature (Reichardt, 1979; Campbell and Boruch, 1975) about the assumptions underlying various estimates (for example, test-retest or internal consistency). The reader is referred to this literature for more detailed consideration of this issues. The choice of reliability estimate is simplified in simulations because the analyst knows the true reliability (as discussed earlier). This adjusted pretest is then used in place of the unadjusted pretest for the NEGD simulations.

To illustrate the correction, simulations were conducted under the same conditions in the NEGD exercises using the reliability-corrected ANCOVA. It is clear that the reliability corrected NEGD analysis yields unbiased estimates, thus lending support to the idea that this correction procedure is appropriate, at least for the conditions of these simulations.

Simulations have been used to explore and examine the accuracy of a wide range of statistical analyses for program evaluation including models for adjusting for selection biases in NEGD (Trochim and Spiegelman, 1980; Muthen and Joreskog, 1984); for correcting for misassignment with respect to the cutoff in RD designs (Campbell et al., 1979; Trochim, 1984), and for assessing the effects of attrition in evaluations (Trochim, 1982).


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Copyright 1996, William M.K. Trochim