The Interpretation of Coefficients in Models with Qualitative Dependent Variables*

A substantial body of empirical accounting, finance, management, and marketing research utilizes single equation models with discrete dependent variables. Generally, the interpretation of the coefficients of the exogenous variables is limited to the sign and relative magnitude. This paper presents three methods of interpreting the coefficients in these models. The first method interprets the coefficients as marginal probabilities and the second method interprets the coefficients as elasticities of probability. The third method utilizes sensitivity analysis and examines the effect of hypothetical changes in exogenous variables on the probability of choice. This paper applies these methods to a published research study.     

A Study of the Impact of an Integrated Information Technology on the Time Utilization of Information Workers

This paper examines the impact of the implementation of an integrated information technology (IIT) on the time utilization of information workers as mediated by the content of the information work performed. The IIT in this study is a system of personal computers linked together in a network for sharing computing, data, and communication resources. The subjects of the study were two groups of information workers in an academic field setting. One group consisted of knowledge workers whose chief activity was to advise and counsel students. The other group represented lower level information workers who provided support service to an academic department. A two-dimensional framework was used to classify each of the activities an information worker performs relative to its importance and programmability within the context of the information work. Using work sampling methodology, it was determined that the IIT was associated with the knowledge workers' reallocation of work time to important yet relatively unstructured tasks. The lower level information workers became more efficient in performing highly structured skill-based activities. However, there was no statistically significant improvement in the effectiveness of the work completed as perceived by the clients of both groups of information workers. The results suggest that when an IIT is implemented, the manner in which newly available time is utilized depends on the nature of the information work performed by the users.     

A Note on Consistency in Utility Assessment*

A methodology for determining a von Neumann-Morgenstern utility function is outlined based on the axioms crucial to such a function. Reconciliation of inconsistent judgments is facilitated using the theory of reciprocal matrices. Numerical measures of the collective divergence of a set of judgments from perfect consistency or coherency are provided.     

INTEGRATING EMPIRICAL RESEARCH INTO THE PUBLIC POLICY PROCESS: AN ILLUSTRATION

A number of critical questions have arisen surrounding the value of using empirical data and analytical procedures in the formulation of social and/or public policy. This exploratory study illustrates how multivariate analyses can contribute to an objective formulation of compromise positions on a complex public policy issue. Both multidimensional scaling and analysis of variance were used to study how the perceptions of three divergent key influential groups could be integrated into a final legislative package. This researchbased methodology provides a more logical approach to public policy formulation than that typically offered by political compromise.     

关于随机误差标准差的几点思考

从多种角度加深理解标准差的含义,有助于应用.用不同的代号表示贝塞尔公式和标准差的定义式不够妥当.     

TIME-VARYING PARAMETER ESTIMATION AS EXPLORATION*

A sequential time-variation method is utilized in an attempt to detect changes over time in the effectiveness of market communication for an ethical drug. Sudden changes are indicated at three different points in time, and two of these are confirmed by subsequent analysis and information. Gradual changes are also indicated, but these are not confirmed by market information or statistical analysis.     

Rule-Based Expert Systems and Linear Models: An Empirical Comparison of Learning-By-Examples Methods*

Building models of expert decision-making behavior from examples of experts’ decisions continues to receive considerable research attention. In the 1960's and 70's, linear models derived by statistical methods were studied extensively. More recently, rule-based expert systems derived by induction algorithms have been the focus of attention. Few studies compare the two approaches. This paper reports on a study that compared linear models derived by logistic regression with rule-based systems produced by two induction algorithms—ID3 and the genetic algorithm. The techniques performed comparably in modeling the experts at one task, graduate admissions, but differed significantly at a second task, bidder selection.     

Non-Normality and the Design of Control Charts for Averages*

Non-normality has a significant effect on the performance of control charts for averages. The design considerations for a control chart for averages must include recognition of the degree of non-normality of the underlying data. The performance of a control chart may be judged on its ability to correctly identify the probabilities of assignable causes of variation and chance causes of variation in a process. This paper examines the effects of non-normality, as measured by skewness and kurtosis, on the performance, and hence the design, of control charts for averages and provides an alternative method of designing charts for averages of data with non-normal distributions.     

A Computational Study of Replicated Clustering with an Application to Market Segmentation*

In most commercial applications of k-means clustering, researchers choose one set of kseed points to start the partitioning process; often, the initial set of seeds is chosen randomly. Using Monte Carlo simulation, we show that significant benefits are associated with replicated starting configurations that incorporate seed selection procedures based on a hierarchical clustering of sample points drawn from the original data matrix. A real-world application of the approach is then presented.     

Necessary and Sufficient Conditions of Unacceptable Solutions in LP Discriminant Analysis*

Characterization of unacceptable solutions for linear programming (LP) discriminant models have been discussed in the literature and the results presented so far are not satisfactory. This paper establishes necessary and sufficient conditions of unacceptable solutions for a number of LP models, addresses the practical implications of these conditions, and discusses the relationship between unacceptable solutions and multiple solutions.