The effect of adverse impact in selection practices on organizational diversity: a field study

Based on a sample of 154 organizations across Canada, we examined the influence of the use of different employee selection methods on workplace minority representation rates. Results indicated that usage of cognitive ability and personality testing significantly influences minority representation after controlling for other diversity management practices. In particular, cognitive ability testing appears to be associated with lower levels of minority group representation in organizations as a whole and in their management ranks; personality testing is associated with higher levels of minority representation in organizations. To advance our understanding of the organizational factors that influence minority group representation and the use of different selection practices, we also examined HR manager perceptions of test bias and the effects of employment equity (EEA) legislation on selection test usage. Results indicated that firms covered under employment equity legislation were less likely to use cognitive ability tests. Interestingly, HR managers reported that personality tests may be more biased against minorities than cognitive ability tests. Implications for research and practice are discussed.     

Comparison of three‐dimensional ROC surfaces for clustered and correlated markers,with a proteomics application

We propose a non‐parametric test to compare two correlated diagnostic tests for a three‐category classification problem. Our development was motivated by a proteomic study where the objectives are to detect glycan biomarkers for liver cancer and to compare the discrimination ability of various markers. Three distinct disease categories need to be identified from this analysis. We therefore chose to use three‐dimensional receiver operating characteristic (ROC) surfaces and volumes under the ROC surfaces to describe the overall accuracy for different biomarkers. Each marker in this study might include a cluster of similar individual markers and thus was considered as a hierarchically structured sample. Our proposed statistical test incorporated the within‐marker correlation as well as the between‐marker correlation. We derived asymptotic distributions for three‐dimensional ROC surfaces and subsequently implemented bootstrap methods to facilitate the inferences. Simulation and real‐data analysis were included to illustrate our methods. Our distribution‐free test may be simplified for paired and independent two‐sample comparisons as well. Previously, only parametric tests were known for clustered and correlated three‐category ROC analyses.     

Multivariate ‘analysis of dispersion’ in the presence of intra-class correlation

Summary Subsequent to a review of the effects of familial or intra-class correlation (=ϱ) on the univariateF, or analysis of variance tests, and of methods for obtaining confidence limits for ϱ, results are presented on the effects of familial correlations in tests in multivariate ‘analysis of dispersion’. Methods for obtaining confidence limits are given in the case where a common variance-covariance matrix may be assumed for the successive multivariate samples.     

Difference in difference meets generalized least squares: Higher order properties of hypotheses tests

We investigate the estimation and inference in difference in difference econometric models used in the analysis of treatment effects. When the innovations in such models display serial correlation, commonly used ordinary least squares (OLS) procedures are inefficient and may lead to tests with incorrect size. Implementation of feasible generalized least squares (FGLS) procedures is often hindered by too few observations in the cross-section to allow for unrestricted estimation of the weight matrix without leading to tests with similar size distortions as conventional OLS based procedures. We analyze the small sample properties of FGLS based tests with a formal higher order Edgeworth expansion that allows us to construct a size corrected version of the test. We also address the question of optimal temporal aggregation as a method to reduce the dimension of the weight matrix. We apply our procedure to data on regulation of mobile telephone service prices. We find that a size corrected FGLS based test outperforms tests based on OLS.     

The volunteer matrix: positioning of volunteer organizations

• In the fierce competition that volunteer involving organizations face nowadays over people's willingness to donate their time, marketing strategies should be used. In order to enhance the organization's recruitability (ability to recruit suitable volunteers), it is important for volunteer organizations to understand what the positioning of the organization is and the type of volunteering that is being offered. In the current article we suggest using a marketing tool to increase the ability to recruit volunteers: positioning and perceptual mapping. Our perceptual mapping – the volunteer matrix is based on two dimensions: price (different costs attached to the volunteer experience) and quality (the overall quality of the volunteering experience). Thus, the matrix includes four different positions, and we demonstrate the manners in which different volunteer organizations or roles are positioned in each. Discussed are also the possible implementations of the matrix in order to recruit suitable volunteers and retain them.

Copyright © 2010 John Wiley & Sons, Ltd.     

Testing and imposing Slutsky symmetry in nonparametric demand systems

Maximization of utility implies that consumer demand systems have a Slutsky matrix which is everywhere symmetric. However, previous non- and semi-parametric approaches to the estimation of consumer demand systems do not give estimators that are restricted to satisfy this condition, nor do they offer powerful tests of this restriction. We use nonparametric modeling to test and impose Slutsky symmetry in a system of expenditure share equations over prices and expenditure. In this context, Slutsky symmetry is a set of nonlinear cross-equation restrictions on levels and derivatives of consumer demand equations. The key insight is that due to the differing convergence rates of levels and derivatives and due to the fact that the symmetry restrictions are linear in derivatives, both the test and the symmetry restricted estimator behave asymptotically as if these restrictions were (locally) linear. We establish large and finite sample properties of our methods, and show that our test has advantages over the only other comparable test. All methods we propose are implemented with Canadian micro-data. We find that our nonparametric analysis yields statistically significantly and qualitatively different results from traditional parametric estimators and tests.     

Start-up and ageing in greenfield sites

Greenfield sites are believed to be locations where a separate and distinct approach to the management of people can most easily and successfully be introduced. After discussing different definitions of greenfield sites and examining whether the ageing of greenfield sites should best be characterised as a dichotomy or a continuum, this paper presents a two-dimensional framework of analysis for the employee relations policy of new plants. This characterises greenfield sites and assists in judging the extent to which a strategic HR approach can be sustained. Drawing on eight case studies, the paper examines both the start-up and consolidation phases of new plant development. A number of factors which inhibit sustaining a strategic HR approach are examined, focusing on the tension between ideology and markets. The paper concludes that the four-quadrant matrix approach appears to help assess both the initial approach and management's ability to sustain this over time. Suggestions for further research to test the matrix are made.     

Comparison of misspecified calibrated models: The minimum distance approach

This paper proposes several testing procedures for comparison of misspecified calibrated models. The proposed tests are of the Vuong-type (Vuong, 1989, Rivers and Vuong, 2002). In our framework, the econometrician selects values for model’s parameters in order to match some characteristics of data with those implied by the theoretical model. We assume that all competing models are misspecified, and suggest a test for the null hypothesis that they provide equivalent fit to data characteristics, against the alternative that one of the models is a better approximation. We consider both nested and non-nested cases. We also relax the dependence of models’ ranking on the choice of a weight matrix by suggesting averaged and sup-norm procedures. The methods are illustrated by comparing the cash-in-advance and portfolio adjustment cost models in their ability to match the impulse responses of output and inflation to money growth shocks.     

Simultaneous optimal estimation in linear mixed models

Simultaneous optimal estimation in linear mixed models is considered. A necessary and sufficient condition is presented for the least squares estimator of the fixed effects and the analysis of variance estimator of the variance components to be of uniformly minimum variance simultaneously in a general variance components model. That is, the matrix obtained by orthogonally projecting the covariance matrix onto the orthogonal complement space of the column space of the design matrix is symmetric, each eigenvalue of the matrix is a linear combinations of the variance components and the number of all distinct eigenvalues of the matrix is equal to the the number of the variance components. Under this condition, uniformly optimal unbiased tests and uniformly most accurate unbiased confidence intervals are constructed for the parameters of interest. A necessary and sufficient condition is also given for the equivalence of several common estimators of variance components. Two examples of their application are given.     

Charitable giving and cognitive ability

• This paper presents the first detailed examination of the relationship between cognitive ability and charitable giving using both cross‐sectional and longitudinal analysis. Data from a nationally representative, longitudinal survey of U.S. adults over age 50 indicated that higher cognitive ability—measured through a variety of cognitive tests—was associated with a higher probability of charitable giving, even after controlling for such intervening mechanisms as age, income, wealth, health, and education. This was true in comparisons both across different people at one point in time and within the same persons at different points in time. Understanding this relationship may affect the content and timing of appropriate charitable marketing approaches and help to explain other associations found in previous research on charitable giving.

Copyright © 2010 John Wiley & Sons, Ltd.     

Formulating hypothetical scenarios in correlation stress testing via a Bayesian framework

Correlation stress testing refers to the correlation matrix adjustment to evaluate potential impact of the changes in correlations under financial crises. There are two categories, sensitivity tests and scenario tests. For a scenario test, the correlation matrix is adjusted to mimic the situation under an underlying stress event. It is only natural that when some correlations are altered, the other correlations (peripheral correlations) should vary as well. However, most existing methods ignore this potential change in peripheral correlations. In this paper, we propose a Bayesian correlation adjustment method to give a new correlation matrix for a scenario test based on the original correlation matrix and views on correlations such that peripheral correlations are altered according to the dependence structure of empirical correlations. The algorithm of posterior simulation is also extended so that two correlations can be updated in one Gibbs sampler step. This greatly enhances the rate of convergence. The proposed method is applied to an international stock portfolio dataset.     

Constructing smooth tests without estimating the eigenpairs of the limiting process

Based on the well known Karhunen–Loève expansion, it can be shown that many omnibus tests lack power against “high frequency” alternatives. The smooth tests of  Neyman (1937) may be employed to circumvent this power deficiency problem. Yet, such tests may be difficult to compute in many applications. In this paper, we propose a more operational approach to constructing smooth tests. This approach hinges on a Fourier representation of the postulated empirical process with known Fourier coefficients, and the proposed test is based on the normalized principal components associated with the covariance matrix of finitely many Fourier coefficients. The proposed test thus needs only standard principal component analysis that can be carried out using most econometric packages. We establish the asymptotic properties of the proposed test and consider two data-driven methods for determining the number of Fourier coefficients in the test statistic. Our simulations show that the proposed tests compare favorably with the conventional smooth tests in finite samples.     

强声波激励下转子叶片的振动分析

声场分析是一类比较复杂的场分析问题。本文基于有限元法,建立了高强声波辐射场中某型转子叶片振动分析的有限元模型,并分别采用球面声波辐射,柱面声波辐射,平面声波辐射和均布声压等作用形式比较了叶片上振动的应力和声压分布。计算了3种叶片,数值计算的结果都与实验结果比较一致,定量揭示了高强声波对转子叶片的影响程度。     

A predictability test for a small number of nested models

We introduce quasi-likelihood ratio tests for one sided multivariate hypotheses to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. The limiting distributions of the test statistics are non-standard. For critical values we consider: (i) bootstrapping and (ii) simulations assuming normality of the mean square prediction error difference. The proposed tests have good size and power properties compared with existing equal and superior predictive ability tests for multiple model comparison. We apply our tests to study the predictive ability of a Phillips curve type for the US core inflation.     

灰色关联度法在特种设备事故统计分析中的应用

本文以提升安全监察部门和监督检验部门综合分析能力为目的,应用灰色关联度分析方法对历年来特种设备事故的统计资料进行综合分析,以承压类特种设备为例,把特种设备总量、不同种类特种设备总量和不同种类特种设备分别造成的经济损失为指标,分析其与事故造成的直接经济损失总量之间的关联性。增强了对于统计数据综合分析的灵活性,克服了不同量纲数据之间无法寻找规律的不足,从不同角度找到指标之间的关联性,为安全监察部门和监督检验部门分析问题开拓了思路,便于其发现关键指标的主要影响因素,提前预防和集中控制力量。     

梁位移的有限单元法

介绍了有限元分析法的基本理论和方法,导出梁的形函数、刚度矩阵。用功能强大的数学软件Mathematica计算梁的挠度和转角。     

Size and Power of the Error Correction Model Cointegration Test. A Bootstrap Approach

The size and power of the ECM cointegration test are investigated by using the 'bootstrap critical values'. The purpose of this paper is to show the ability of the bootstrap technique to produce critical values which are much more accurate than the asymptotic ones. The properties of the test have been studied, using Monte Carlo methods, for three different data generating processes. As regards the size of the test, we find that the ECM cointegration test together with the bootstrap critical values perform better than the ECM cointegration test based on the asymptotic critical values. While as regards the power of the tests, the results prove to be similar for the different versions.     

Tests of Predictive Ability for Vector Autoregressions Used for Conditional Forecasting

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we examine conditional forecasts obtained with a VAR in the variables included in the DSGE model of Smets and Wouters (American Economic Review 2007; 97 : 586–606). Throughout the analysis, we focus on tests of bias, efficiency and equal accuracy applied to conditional forecasts from VAR models. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal weighted average power similar tests for the covariance structure in the linear regression model

This paper suggests a procedure for the construction of optimal weighted average power similar tests for the error covariance matrix of a Gaussian linear regression model when the alternative model belongs to the exponential family. The paper uses a saddlepoint approximation to construct simple test statistics for a large class of problems and overcomes the computational burden of evaluating the complicated integrals arising in the derivation of optimal weighted average power tests. Extensions to panel data models are considered. Applications are given to tests for error autocorrelation in the linear regression model and in a panel data framework.     

Issues in the analysis of co-authorship networks

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.