Graduation Year


Document Type




Degree Granting Department

Measurement and Evaluation

Major Professor

Kromrey, Jeffrey D.


verisimilitude, path analysis, model fit, theory testing, precision


The empirical testing of theories is an important component of research in any field. Yet despite the long history of science, the extent to which theories are supported or contradicted by the results of empirical research remains ill defined. Quite commonly, support or contradiction is based solely on the "reject" or "fail to reject" decisions that result from tests of null hypotheses that are derived from aspects of theory. Decisions and recommendations based on this forced and often artificial dichotomy have been scrutinized in the past. Such an overly simplified approach to theory testing has been vigorously challenged in the past. Theories differ in the extent to which they provide precise predictions about observations. The precision of predictions derived from theories is proportional to the strength of support that may be provided by empirical evidence congruent with the prediction.

However, the notion of precision linked to strength of support is surprisingly absent from many discussions regarding the appraisal of theories. In the early 1990s, Meehl presented an index of corroboration to summarize the extent to which empirical tests of theories provide support or contradiction of theories. This index is comprised of a closeness component and an estimate of precision. The purpose of this study was to evaluate the utility of this index of corroboration and its behavior when employing path analytic methods in the context of social science research. The performance of a multivariate extension of Meehl's Corroboration Index (Ci) was evaluated using Monte Carlo methods by simulating traditional path analysis. Five factors were included in the study: model complexity, level of intolerance, verisimilitude, sample size and level of collinearity. Results were evaluated in terms of the mean and standard error of the resulting multivariate Ci values.

Of the five central design factors investigated, the level of intolerance was observed to be the strongest influence on mean Ci. Verisimilitude and model complexity were not observed to be strong determinants of the mean Ci. The lack of sensitivity of the index to the other design factors led to a proposed alternative conceptualization of the multivariate corroboration index to guide future research efforts.