Ideologies about work: An exploratory study

This paper focuses upon an as yet under-explored factor believed to underlie most managerial and organizational behavior variables—work ideology. Work ideologies are surveyed for several convenience samples of students and managers to show their ability to be studied and that patterned differences may be discovered. In addition, the historical origins and meanings of four major work ideologies are discussed.     

Lines through the origin—is NO INT the answer?

The fitting of a straight line that must pass through (0, 0) is a frequently encountered application of linear regression. Many computer packages provide a least-squares option sometimes identified as NO INT, or even NOINT, or as ‘suppressing the intercept’. Often these procedures are invalid or do not correspond to the most suitable model. This paper questions the least-squares-line approach and suggests some alternatives. Two measures are proposed for comparing the fit of alternative models.     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

ROBUST METHODS FOR DEPENDENT CELL PEDIGREE DATA

This paper studies a robust approach to the analysis of cell pedigree data, building on the work of Huggins & Marschner (1991) which discussed M-estimation for the so-called bifurcating autoregressive process. The study allows for incomplete observation of the pedigree, and incorporates the possibility of additive effects outliers, as discussed in the time series literature. Some properties of the proposed estimation procedure are studied, including a Monte Carlo investigation of robustness in the presence of contamination.     

Estimation of parameters for rounded data from non-normal distributions

This paper is concerned with how standard estimation procedures perform in terms of eficiency for non-normal rounded data. Previous research has shown that the loss in eficiency due to rounding normal data is small. However, evidence from the non-normal distribution considered in this paper suggests, if rounding is coarse or the distribution is very skewed the loss in efficiency due to rounding can be considerable.