Covariate classification by post–stratification

The plug–in Anderson's covariate classification statistic is constructed on the basis of an initially unclassified training sample by means of posty–stratification. The asymptotic efficiency relative to the discriminant based on an initially classified training sample is evaluated for the case where a covariate is present. Effect of post–stratification is examined.     

A note on information loss in analyzing a mixture model of count data

In this note we consider estimation of a mixture model of count data which is composed of two discrete random variables. Conditional and unconditional estimation procedures are given for estimating the unknown parameter(s) of interest using the likelihood function. Asymptotic relative efficiencies are given to examine the amount of information loss in using the two estimation procedures. Specifically, we study the change in asymptotic relative efficiency, if any, in different parameter settings.     

Bootstrapping in least absolute value regression: an application to hypothesis testing

A Monte Carlo simulation is used to study the performance of hypothesis tests for regression coefficients when least absolute value regression methods are used. In small samples, the results of the simulation suggest that using the bootstrap method to compute standard errors will provide improved test performance     

Learning in and from projects: the learning modes and a learning capability model

Abstract

The notion of ‘project delivery’ is well embedded in and across the management and organizational sciences literature – generating a narrative that reflects and recognizes the instrumental nature of projects and programmes in strategy execution. Project management, as a distinct and well-established body of research enquiry, has increasingly sought to focus our attention on the impacts of complexity, risk and uncertainty in projects; the corollary being a desideratum to strengthen our theoretical understanding of how insight and learning from projects may influence improvements to organizational efficiency. The wider literature suggests that organizational learning remains a challenging proposition, particularly in the context of organizations operating in environments of high complexity. In this paper, we enhance the conversation on organizational learning through a series of case studies, generating evidence of thirteen ‘learning modes’. The paper proposes that mature organizations tend to exhibit a greater number of learning modes and that there is a tendency to capture and socialize knowledge with a greater emphasis on the context of the learning situation rather than the learning artefact in isolation. The empirical evidence gathered in this paper forms the basis of a capability model, characterized by the thirteen modes of learning. The model intimates that learning occurs, and is more effective, when knowledge and information are enacted in practice through the learning modes which form a nucleus of the organizational learning capability. The research concludes with a 'call to action' that emphasizes the strategic importance of learning practices and routines in project oriented-organizations.     

The Architecture of Rural Life: The Dangers of Dense Collective Efficacy for At-Risk Girls

Dense ties and acquaintanceship result in small communities that deal with issues on an individual, informal basis. When issues such as sexual assault arise, if a victim is willing to seek services, studies have found that rurality negatively impedes on this process. The current study takes place in a primarily rural state, representing social control mechanisms somewhat different from cityscapes. The project relies heavily on in-depth interviews from incarcerated girls and women, as well as contributions from community actors, to assess collective efficacy in rural communities, influence of gender dynamics, and the impact on at-risk girls. Tracing participants' community roots, the study gathers structural and cultural characteristics of the locale, assessing social control practices as reported by local professionals. Results challenge literature proclaiming solely positive results from high levels of collective efficacy, finding that strong collective efficacy in nonurban areas gathers close insider ties, but “outsiders,” which includes girls identified in this research, are defined quickly and negatively, placing them in significant peril. Policy recommendations include broader cultural shifts coupled with local involvement with school personnel and church-involved patrons serving the needs of families and at-risk girls.     

Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi‐parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.     

Bias Reduction using Stochastic Approximation

The paper studies stochastic approximation as a technique for bias reduction. The proposed method does not require approximating the bias explicitly, nor does it rely on having independent identically distributed (i.i.d.) data. The method always removes the leading bias term, under very mild conditions, as long as auxiliary samples from distributions with given parameters are available. Expectation and variance of the bias-corrected estimate are given. Examples in sequential clinical trials (non-i.i.d. case), curved exponential models (i.i.d. case) and length-biased sampling (where the estimates are inconsistent) are used to illustrate the applications of the proposed method and its small sample properties.     

An Exploratory Analysis of the Value of the Skills of IT Personnel: Their Relationship to IS Infrastructure and Competitive Advantage

Determining and assessing the requisite skills of information technology (IT) personnel have become critical as the value of IT has risen in modern organizations. In addition to technical skills traditionally expected of IT personnel, softer skills like managerial, business, and interpersonal skills have been increasingly cited in previous studies as mandatory for these employees. This paper uses a typology of IT personnel skills—technology management skills, business functional skills, interpersonal skills, and technical skills—and investigates their relationships to two information systems (IS) success variables, IS infrastructure flexibility and the competitive advantage provided by IS. The study investigates these relationships using the perceptions of chief information officers (CIOs) from mostly Fortune 2000 companies. The contributions of this study are: IT personnel skills do affect IS success, technical skills are viewed as the most important skill set in affecting IS infrastructure flexibility and competitive advantage, and modularity is viewed as more valuable to competitive advantage than integration. Several explanations are offered for the lack of positive relationships between the softer IT personnel skills and the dimensions of IS success used in this study.