Hierarchical linear modeling as an example for measuring change over time in a leadership development evaluation context

One of the difficulties in evaluating leadership development is measuring whether and how people change over the time period of the leadership development initiative. Even when change over time is an inherent part of the design and evaluation of leadership development, events may occur outside of the control of evaluators that limit the effectiveness of adequately and accurately assessing change over time. With data from a leadership development initiative designed to account for change over time, this article suggests hierarchical linear modeling (HLM) as a multilevel methodological technique to assess change over time in a leadership development context. This article will use real change over time data from a leadership development initiative and discuss the logic and rationale of HLM. We use HLM as an example of a multilevel methodological tool to investigate typical change over time questions in leadership development evaluation.     

Fertility Decline in Czechoslovakia During the Last Two Centuries

The decline of fertility in Czechoslovakia on the territory of the Czech Socialist Republic began with a rise in the age at marriage; the decline of marital fertility began only after 1860. On the territory of the Slovak Socialist Republic marital fertility began to decline after 1900 without previous significant changes in the age at marriage. The differences between the demographic behaviour in the two parts of Czechoslovakia have persisted, although they are now gradually disappearing. There are other significant regional differences in the fertility decline caused by the overall process of economic and social development. The end of the demographic transition in the Czech Socialist Republic came during the 1930's and in the Slovak Socialist Republic during the 1960's.     

On the robustness of the extreme deviate test for a single multivariate outlier against heavy-tailed distributions

In this paper we assess the sensitivity of the multivariate extreme deviate test for a single multivariate outlier to non-normality in the form of heavy tails. We find that the empirical significance levels can be markedly affected by even modest departures from multivariate normality. The effects are particularly severe when the sample size is large relative to the dimension. Finally, by way of example we demonstrate that certain graphical techniques may prove useful in identifying the source of rejection for the multivariate extreme deviate test.     

Understanding the demographic implications of climate change: estimates of localized population predictions under future scenarios of sea-level rise

Significant advances have been made to understand the interrelationship between humans and the environment in recent years, yet research has not produced useful localized estimates that link population forecasts to environmental change. Coarse, static population estimates that have little information on projected growth or spatial variability mask substantial impacts of environmental change on especially vulnerable populations. We estimate that 20 million people in the United States will be affected by sea-level rise by 2030 in selected regions that represent a range of sociodemographic characteristics and corresponding risks of vulnerability. Our results show that the impact of sea-level rise extends beyond the directly impacted counties due to migration networks that link inland and coastal areas and their populations. Substantial rates of population growth and migration are serious considerations for developing mitigation, adaptation, and planning strategies, and for future research on the social, demographic, and political dimensions of climate change.     

Simple methods for evaluating and comparing binary experiments

We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard parametrization of a binary information source in terms of its specificity and its sensitivity, one of the two confidence parameters is sufficient for a Bayesian belief update conditional on a signal realization. We introduce a confidence-augmented receiver operating characteristic for comparisons of binary experiments for a class of “balanced” decision problems, relative to which the confidence order offers a higher resolution than Blackwell’s informativeness order.

Where observation is concerned, Chance favors only the prepared mind. —Louis Pasteur (1822–1895).

     

A Hybrid Model of Moral Panics: Synthesizing the Theory and Practice of Moral Panic Research

We seek to address criticisms of the concept of moral panics (MPs) by offering a hybrid model of MPs that synthesizes theory and practice of MPs research. A review of the literature on MPs from sociology, media studies and related fields shows a wide variety of usage and lack of conceptual clarity of the term ‘moral panic’. Yet there are few articles explaining how to analyze MPs. We present a theoretical clarification of MPs by addressing elements of scope, intensity and reception, to create distinction from other related theoretical concepts. To develop a working method for researching MPs, one must have an understanding of social conditions that give rise to, sustain and result in the success or failure of MPs, as well as possible lasting effects. We synthesize Cohen’s process-oriented model of MPs and Goode & Ben-Yehuda’s attribution-oriented model of MPs, creating a critical hybrid model of MPs that integrates processes and attributes. We then utilize the hybrid model to offer practical suggestions for researching and analyzing the conditions, processes and effects of MPs, in the hopes of encouraging a more rigorous research agenda for scholars of MPs.     

Models and estimation for systems with recurrent events and usage processes

This paper considers settings where populations of units may experience recurrent events, termed failures for convenience, and where the units are subject to varying levels of usage. We provide joint models for the recurrent events and usage processes, which facilitate analysis of their relationship as well as prediction of failures. Data on usage are often incomplete and we show how to implement maximum likelihood estimation in such cases. Random effects models with linear usage processes and gamma usage processes are considered in some detail. Data on automobile warranty claims are used to illustrate the proposed models and estimation methodology.     

Neuroticism,locus of control,type A behaviour pattern and occupational stress

Abstract

The aim of this study was to investigate the relationship between individual differences in personality and susceptibility to stress in the workplace. Stress in lecturers employed by a computer training organization was assessed by means of self-report and measurement of salivary cortisol output during lecturing and non-lecturing weeks. Neuroticism, Type A behaviour pattern and locus of control were measured. Self-reported stress was found to be much greater during lecturing weeks, but cortisol levels were unaffected by working conditions. There was a significant positive correlation between neuroticism and locus of control and a negative correlation between locus of control and Type A behaviour pattern that approached significance. Multiple regression was employed to explore relations between personality and stress. Subjects with lower neuroticism scores yielded a bigger increase in reported stress, in the lecturing compared with the non-lecturing week, than subjects with high neuroticism scores. Type B subjects showed a progressive decrease in cortisol over the working week while Type A counterparts showed a resurgence of cortisol towards the end of the week. Finally, Type B subjects having an internal locus of control showed a faster decline in cortisol level during the lecturing week than the other subjects. Implications of these results are discussed in terms of the importance simultaneously incorporating a variety of individual differences in personality dimensions and stress indices in research designs.     

Psychometric and Demographic Predictors of the Perceived Risk of Terrorist Threats and the Willingness to Pay for Terrorism Risk Management Programs

A 2009 national telephone survey of 924 U.S. adults assessed perceptions of terrorism and homeland security issues. Respondents rated severity of effects, level of understanding, number affected, and likelihood of four terrorist threats: poisoned water supply; explosion of a small nuclear device in a major U.S. city; an airplane attack similar to 9/11; and explosion of a bomb in a building, train, subway, or highway. Respondents rated perceived risk and willingness to pay (WTP) for dealing with each threat. Demographic, attitudinal, and party affiliation data were collected. Respondents rated bomb as highest in perceived risk but gave the highest WTP ratings to nuclear device. For both perceived risk and WTP, psychometric variables were far stronger predictors than were demographic ones. OLS regression analyses using both types of variables to predict perceived risk found only two significant demographic predictors for any threat—Democrat (a negative predictor for bomb) and white male (a significant positive predictor for airline attack). In contrast, among psychometric variables, severity, number affected, and likelihood were predictors of all four threats and level of understanding was a predictor for one. For WTP, education was a negative predictor for three threats; no other demographic variables were significant predictors for any threat. Among psychometric variables, perceived risk and number affected were positive predictors of WTP for all four threats; severity and likelihood were predictors for three; level of understanding was a significant predictor for two.