The strategic role of Human Resource Development in managing core competencies

The Resource-Based View (R-BV) of strategy emphasizes the importance of core competencies as the critical basis for sustainable competitive advantage. Yet, discussions of strategy typically ignore the role of the Human Resource Development (HRD) function in core competency development and management. The literature on strategic HRD is reviewed to locate the R-BV as a basis for redefining HRD's role in organizational strategy. Three strategic roles for the HRD function in core competency management are proposed and discussed: participating in strategic planning, developing core competencies, and protecting them. Specific tasks for each role are proposed. The final section identifies needs for further research as well as the practical implications from this discussion.     

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.     

Preserving data utility via BART

When preparing data for public release, information organizations face the challenge of preserving the quality of data while protecting the confidentiality of both data subjects and sensitive data attributes. Without knowing what type of analyses will be conducted by data users, it is often hard to alter data without sacrificing data utility. In this paper, we propose a new approach to mitigate this difficulty, which entails using Bayesian additive regression trees (BART), in connection with existing methods for statistical disclosure limitation, to help preserve data utility while meeting confidentiality requirements. We illustrate the performance of our method through both simulation and a data example. The method works well when the targeted relationship underlying the original data is not weak, and the performance appears to be robust to the intensity of alteration.     

Changing Signs: The Political Pragmatism of Poststructuralism

In a recent issue of The New Republic, Martha Nussbaum singled out Judith Butler for a particularly venomous attack. Nussbaum's criticism was very simple: good theory leads to practical political action, and Butler's theories do not. Nussbaum's criticism of Butler echoes a general criticism leveled against the emerging field of queer theory. Many advocates of sexual rights have suggested that poststructuralist approaches do not have practical political application. In this essay, the authors argue that Butler's work points to a new political pragmatism. In the past, many advocates of sexual rights have focused on essentialized notions of sexual identity. A politically pragmatic approach, informed by Butler's theories, can shift the debate so that the issue of homophobic discrimination, rather than sexual identity, becomes a primary focus of the sexual rights movement.     

Random-Effects Modeling of Categorical Response Data

In many applications observations have some type of clustering, with observations within clusters tending to be correlated. A common instance of this occurs when each subject in the sample undergoes repeated measurement, in which case a cluster consists of the set of observations for the subject. One approach to modeling clustered data introduces cluster-level random effects into the model. The use of random effects in linear models for normal responses is well established. By contrast, random effects have only recently seen much use in models for categorical data. This chapter surveys a variety of potential social science applications of random effects modeling of categorical data. Applications discussed include repeated measurement for binary or ordinal responses, shrinkage to improve multiparameter estimation of a set of proportions or rates, multivariate latent variable modeling, hierarchically structured modeling, and cluster sampling. The models discussed belong to the class of generalized linear mixed models (GLMMs), an extension of ordinary linear models that permits nonnormal response variables and both fixed and random effects in the predictor term. The models are GLMMs for either binomial or Poisson response variables, although we also present extensions to multicategory (nominal or ordinal) responses. We also summarize some of the technical issues of model-fitting that complicate the fitting of GLMMs even with existing software.     

Older family-carers' views on the future accommodation needs of relatives who have an intellectual disability

Over the past 20 years, research across the developed world has identified trends towards ageing of the population with learning/intellectual disability. Alongside is the recognition that family-carers are also ageing. Recently, the UK government has sought to identify the future housing needs of dependents with intellectual disability residing with older carers. Consequentially, a city council commissioned this qualitative study involving 28 older carers. The findings indicate a need for information about housing options, a lack of practical support and feelings of marginalisation. The conclusions suggest key roles for social services in providing proactive support and advice to family-carers, and a greater degree of joint working between social services departments and housing agencies. These resonate with similar international studies and provide the opportunity to focus on the perspectives of family-carers. Their experiences are real, and if welfare professionals are to work with people with intellectual disability and their family-carers, acknowledging their views is essential.     

On the Breslow–Holubkov estimator

Breslow and Holubkov (J Roy Stat Soc B 59:447–461 1997a) developed semiparametric maximum likelihood estimation for two-phase studies with a case–control first phase under a logistic regression model and noted that, apart for the overall intercept term, it was the same as the semiparametric estimator for two-phase studies with a prospective first phase developed in Scott and Wild (Biometrica 84:57–71 1997). In this paper we extend the Breslow–Holubkov result to general binary regression models and show that it has a very simple relationship with its prospective first-phase counterpart. We also explore why the design of the first phase only affects the intercept of a logistic model, simplify the calculation of standard errors, establish the semiparametric efficiency of the Breslow–Holubkov estimator and derive its asymptotic distribution in the general case.