Longitudinal Meta-analysis

The goal of meta-analysis is to integrate the research results of a number of studies on a specific topic. Characteristic for meta-analysis is that in general only the summary statistics of the studies are used and not the original data. When the published research results to be integrated are longitudinal, multilevel analysis can be used for the meta-analysis. We will demonstrate this with an example of longitudinal data on the mental development of infants. We distinguish four levels in the data. The highest level (4) is the publication, in which the results of one or more studies are published. The third level consists of the separate studies. At this level we have knowledge about the degree of prematurity of the group of infants in the specific study. The second level are the repeated measures. We have data about the test age, the mental development, the corresponding standard deviations, and the sample sizes. The lowest level is needed for the specification of the meta-analysis model. Both the way in which the multilevel model has to be specified (the Mln-program is used) as the results will be presented and interpreted.     

Performance of empirical Bayes estimators of random coefficients in multilevel analysis: Some results for the random intercept-only model

For a multilevel model with two levels and only a random intercept, the quality of different estimators of the random intercept is examined. Analytical results are given for the marginal model interpretation where negative estimates of the variance components are allowed for. Except for four or five level-2 units, the Empirical Bayes Estimator (EBE) has a lower average Bayes risk than the Ordinary Least Squares Estimator (OLSE). The EBEs based on restricted maximum likelihood (REML) estimators of the variance components have a lower Bayes risk than the EBEs based on maximum likelihood (ML) estimators. For the hierarchical model interpretation, where estimates of the variance components are restricted being positive, Monte Carlo simulations were done. In this case the EBE has a lower average Bayes risk than the OLSE, also for four or five level-2 units. For large numbers of level-1 (30) or level-2 units (100), the performances of REML-based and ML-based EBEs are comparable. For small numbers of level-1 (10) and level-2 units (25), the REML-based EBEs have a lower Bayes risk than ML-based EBEs only for high intraclass correlations (0.5).     

Bayesian time series analysis of periodic behaviour and spectral structure

Analysis, model selection and forecasting in univariate time series models can be routinely carried out for models in which the model order is relatively small. Under an ARMA assumption, classical estimation, model selection and forecasting can be routinely implemented with the Box–Jenkins time domain representation. However, this approach becomes at best prohibitive and at worst impossible when the model order is high. In particular, the standard assumption of stationarity imposes constraints on the parameter space that are increasingly complex. One solution within the pure AR domain is the latent root factorization in which the characteristic polynomial of the AR model is factorized in the complex domain, and where inference questions of interest and their solution are expressed in terms of the implied (reciprocal) complex roots; by allowing for unit roots, this factorization can identify any sustained periodic components. In this paper, as an alternative to identifying periodic behaviour, we concentrate on frequency domain inference and parameterize the spectrum in terms of the reciprocal roots, and, in addition, incorporate Gegenbauer components. We discuss a Bayesian solution to the various inference problems associated with model selection involving a Markov chain Monte Carlo (MCMC) analysis. One key development presented is a new approach to forecasting that utilizes a Metropolis step to obtain predictions in the time domain even though inference is being carried out in the frequency domain. This approach provides a more complete Bayesian solution to forecasting for ARMA models than the traditional approach that truncates the infinite AR representation, and extends naturally to Gegenbauer ARMA and fractionally differenced models.     

Promise and peril in implementing pay‐for‐performance

Why would managers abandon pay‐for‐performance plans they initiated with great hopes? Why would employees celebrate this decision? This article explores why managers made their decisions in 12 of 13 pay‐for‐performance “experiments” at Hewlett‐Packard in the mid‐1990s. We find that managers thought the costs of these programs to be higher than the benefits. Alternative managerial practices such as effective leadership, clear objectives, coaching, or training were thought a better investment. Despite the undisputed instrumentality of pay‐for‐performance to motivate, little attention has been given to whether the benefits outweigh the costs or the “fit” of these programs with high‐commitment cultures like Hewlett‐Packard was at the time. © 2004 Wiley Periodicals, Inc.     

Target marketing in a social marketing context: gender differences in importance ratings of promoted intrinsic and extrinsic restricted exchange benefits of military enlistment

Social marketing involves influencing voluntary behaviour of people towards a social end. Examples of social marketing range from campaigns to encourage healthy physical behaviour to efforts to encourage tax support for public education, overcome racism, or stimulate ‘patriotic’ behaviour. Using data from a summer 2001 telephone survey of 600 young adults, this paper investigates how recruitment campaigns may benefit from considering gender differences in importance ratings of a variety of benefits promoted by the military in the USA. Importance ratings are assessed using differences in means and variances tests. Findings identify gender specific differences that may be used to enhance recruitment campaigns. Copyright © 2004 Henry Stewart Publications     

Strategy as Simulacra? A Radical Reflexive Look at the Discipline and Practice of Strategy*

ABSTRACT Over the past three decades strategic management has become a crucial aspect of business education and practice. At the core of strategic management – linking technique to worldview – is modelling (e.g. value chain, SWOT analysis) whereby the complex elements of strategic thinking are simplified. This accounts in large part for the apparent popularity of strategic management as complex interrelationships are pursued through relatively simple models. Yet has the field of strategic management realized the third order of simulacra? Is strategic management a model of simulation whereby reality has been replaced by hyperreality? A review of the extant literature on strategy explores the study and practice of strategy as a discourse, engulfed by its own truth effects. An examination of the concepts of reflexivity demonstrates the value of a postmodern radical reflexive account through the application of Baudrillard's (1983 , 1988 , 1991 , 1994 ) simulation and simulacra. It is through the development of a radical reflexive discourse of strategy as simulacra, this paper critically examines the study and practice of strategy and the lessons we can take from this perspective.     

Public Investment, Congestion, and Private Capital Accumulation

This paper analyses the impact of public investment on the dynamics of private capital formation in an intertemporal optimising market-clearing framework. The key feature characterising the analysis is that the public good is treated as a durable capital good, subject to congestion. We show how in the presence of congestion the effect of government investment on private capital formation involves a tradeoff between the degree of substitution between private and public capital in production and the degree of congestion. Both lump-sum and distortionary tax financing are considered, with this tradeoff being tightened in the latter case