J. Scott Long

Testing structural equation models

Introduction - Kenneth A Bollen and J Scott Long Multifaceted Conceptions of Fit in Structural Equation Models - J S Tanaka Monte Carlo Evaluations of Goodness-of-Fit Indices for Structural Equation Models - David W Gerbing and James C Anderson Some Specification Tests for the Linear Regression Model - J Scott Long and Pravin K Trivedi Bootstrapping Goodness-of-Fit Measures in Structural Equation Models - Kenneth A Bollen and Robert A Stine Alternative Ways of Assessing Model Fit - Michael W Browne and Robert Cudeck Bayesian Model Selection in Structural Equation Models - Adrian E Raftery Power Evaluations in Structural Equation Models - Willem E Saris and Albert Satorra Goodness-of-Fit with Categorical and Other Nonnormal Variables - Bengt O Muthen Some New Covariance Structure Model Improvement Statistics - P M Bentler and Chih-Ping Chou Nonpositive Definite Matrices in Structural Modeling - Werner Wothke Testing Structural Equation Models - Karl G Joreskog

Productivity and Academic Position in the Scientific Career

This paper examines the interrelationship between scientific productivity and academic position, two key dimensions of the scientific career. Contrary to the results of most earlier studies, the effect of departmental location on productivity is found to be strong, whereas the effect of productivity on the allocation of positions is found to be weak. Productivity, as indicated by measures of publications and citations, is shown to have an insignificant effect on both the prestige of a scientists initial academic appointment and on the outcome of institution changes later in the career. Although the relationship between productivity and the prestige of an academic appointment is insignificant at the time a position is obtained, the effect of departmental prestige on productivity increases steadily with time. For those scientists itho change institutions, the prestige of the new department significantly affects changes in a scientists productivity after the move. It is argued that past studies have obtained spurious results due to theirfailure to employ a longitudinal design. Not only do cross-sectional designs provide misleading results regarding the interrelationship between departmental location and productivity, but they also systematically alter the findings regarding the effects of sponsorship and doctoral training on productivity.

RANK ADVANCEMENT IN ACADEMIC CAREERS: SEX DIFFERENCES AND THE EFFECTS OF PRODUCTIVITY*

Advancement in rank is critically important to the career of an academic scientist, and the highly visible nature of the event makes it idealfor studying stratification in science. Concern with universalistic factors in promotion has prompted debates over two issues. First, why do female scientists advance more slowly than male scientists, and why do so few reach the rank offull professor? Second, is promotion driven by the sheer volume of published work as opposed to its quality? Event history analyses clearly indicate that quantity of publications is far more important than various measures of quality of publications in predicting rank advancement; and women are less likely to be promoted than men. About one-half of this sex difference is attributable to differences in levels of variables affecting promotion. Remaining differences are a result of differences in expected timing of promotion to associate professor and to the negative effects of department prestige on promotion to full professor for women.

Cumulative Advantage and Inequality in Science

The hypothesis of cumulative advantage is widely accepted in the sociology of science, but empirical tests have been few and equivocal. One approach, originated by Allison and Stewart (1974), is to see whether inequality of productivity and recognition increases as a cohort of scientists ages. This paper extends their work by examining true cohorts of biochemists and chemists rather than synthetic cohorts. Increasing inequality is observed for counts of publications but not for counts of citations to all previous publications. It is also shown that a mathematical model of cumulative advantage does not imply increasing inequality. When the model is modified to allow for heterogeneity in the rate of cumulative advantage, however, increasing inequality is implied.

Organizational Context and Scientific Productivity

An earlier study found that while a scientists productivity does not affect the prestige of the academic position obtained, the prestige of the position does affect later productivity. In this paper consideration of contextual effects is broadened to include differing organizational contexts of scientific employment. Chances of obtaining employment in a particular context are not strongly affected by productivity. Once employment is obtained in a specific context, individual levels of productivity soon conform to characteristics of that context. These results do not support the idea that scientists are allocated to organizational contexts on the basis of their scientific contributions. Past research indicating that the most productive scientists are recruited to academic locations may have confused the cause of a scientist working in a given context with the effect of working in that context.

Modern methods of data analysis

Introduction - John Fox and J Scott Long Graphical Perception - Ian Spence and Stephan Lewandowsky Describing Univariate Distributions - John Fox A Survey of Smoothing Techniques - Colin Goodall Finding Transformations for Regression Using the ACE Algorithm - Richard D De Veaux Geometry of Multiple Regression and Interactive 3-D Graphics - Georges Monette Regression Diagnostics - Kenneth A Bollen and Robert W Jackman An Expository Treatment of Outliers and Influential Cases A Primer on Robust Regression - Richard A Berk An Introduction to Bootstrap Methods - Robert A Stine Examples and Ideas The Analysis of Social Science Data with Missing Values - Roderick J A Little and Donald B Rubin Selection Bias in Linear Regression, Logit and Probit Models - Jeffrey A Dubin and Douglas Rivers

Testing for IIA in the multinomial logit model

The multinomial logit model is perhaps the most commonly used regression model for nominal outcomes in the social sciences. A concern raised by many researchers, however, is the assumption of the independence of irrelevant alternatives (IIA) that is implicit in the model. In this article, the authors undertake a series of Monte Carlo simulations to evaluate the three most commonly discussed tests of IIA. Results suggest that the size properties of the most common IIA tests depend on the data structure for the independent variables. These findings are consistent with an earlier impression that, even in well-specified models, IIA tests often reject the assumption when the alternatives seem distinct and often fail to reject IIA when the alternatives can reasonably be viewed as close substitutes. The authors conclude that tests of the IIA assumption that are based on the estimation of a restricted choice set are unsatisfactory for applied work.

Estimation and Hypothesis Testing in Linear Models Containing Measurement Error

This paper reviews Joreskogs model for the analysis of covariance structures by first introducing the simpler case of confirmatory factor analysis. The mathematical results necessary for estimation and hypothesis testing are presented in a way which should be more accessible to sociologists than the original sources. The usefulness of Joreskogs techniques is indicated by reformulating a series of models which have been estimated by sociologists using techniques without statistical justification in the format of covariance structures. Identification is considered in this context. The argument is made that these methods can greatly extend our ability to construct structural equation models containing measurement error.

Interuniversity Mobility of Academic Scientists

Previous longitudinal studies of scientists movements in academic jobs found no evidence that research productivity affects prestige attainments. In this paper, however, we find a weak, but significant effect of productivity on the destination prestige of 274 job changes by academic physicists, chemists, mathematicians, and biologists. Major determinants of the prestige of the destination department are prestige of the prior job, prestige of the doctoral department, and the number of articles published in the six years prior to the move. Measures of citation frequency have no detectable effect, however. For promotion in rank at the occurrence of a job change, the major determinants are origin rank, professional age, and citation frequency.

Age, Cohort and Perceived Age Discrimination: Using the Life Course to Assess Self-reported Age Discrimination

Self-reported discrimination is linked to diminished well-being, but the processes generating these reports remain poorly understood. Employing the life course perspective, this paper examines the correspondence between expected age preferences for workers and perceived age discrimination among a nationally representative sample of 7,225 working women, followed between 1972-1989. Analyses find that perceived age discrimination is high in the 20s, drops in the 30s and peaks in the 50s. This curvilinear pattern matches external reports of age preferences and is robust to a variety of controls and model specifications. Additionally, the primary driver of perceived age discrimination is age – not cohort or historical period. These findings suggest that perceived age discrimination is a useful indicator of population-level exposure to work-related age discrimination among women.