Class-specific tests of spatial segregation based on nearest neighbor contingency tables

The spatial interaction between two or more classes might cause multivariate clustering patterns such as segregation or association, which can be tested using a nearest neighbor contingency table (NNCT). The null hypothesis is randomness in the nearest neighbor structure, which may result from random labeling (RL) or complete spatial randomness of points from two or more classes (which is henceforth called CSR independence ). We consider Dixon's class-specific segregation test and introduce a new class-specific test, which is a new decomposition of Dixon's overall chi-squared segregation statistic. We analyze the distributional properties and compare the empirical significant levels and power estimates of the tests using extensive Monte Carlo simulations. We demonstrate that the new class-specific tests have comparable performance with the currently available tests based on NNCTs. For illustrative purposes, we use three example data sets and provide guidelines for using these tests.     

A contingency matrix approach to IHRM

The global economy is a reality. The challenge facing international human resource management (IHRM) is not whether to use widely recognized HR concepts and techniques but rather how to effectively adapt and fit them across cultures. The purpose of this article is to suggest and demonstrate a contingency matrix approach to IHRM. After first explaining what is meant by the contingency matrix approach, the remainder of the article uses a sampling of the HR research literature from Japan, Germany, Mexico, and China to show how a contingency matrix could be used for effective IHRM. © 1997 John Wiley & Sons, Inc.     

A class of statistical models to weaken independence in two-way contingency tables

In this paper we study a new class of statistical models for contingency tables. We define this class of models through a subset of the binomial equations of the classical independence model. We prove that they are log-linear and we use some notions from Algebraic Statistics to compute their sufficient statistic and their parametric representation. Moreover, we show how to compute maximum likelihood estimates and to perform exact inference through the Diaconis-Sturmfels algorithm. Examples show that these models can be useful in a wide range of applications.     

The causal analysis of contingency tables: A return to first principles

Given the undoubtedly major advances in the analysis of contingency tables which have been achieved over the past ten years or so (see for example, Fienberg, 1980; Upton, 1978; Everitt, 1977; Haberman, 1978, 1979), it might seem rather unnecessary to want to return to first princples again. However, the need arises precisely because of these advances; for progress in the specifically causal analysis of contingency tables has not matched the other advances at all. Whilst Fienberg devoted a chapter to causal analysis, he made it clear that he views “the assignment of numerical values [to the arrows in a path diagram] as problematic, and [he] would limit [the analysis] to an indication of sign for causal relationships, in a fashion similar to that described by Blalock (1964)” (Fienberg, 1980, pp. 91–92). Considering how far quantitative-variable causal analysis has developed since Blalock (1964), it becomes clear that the causal analysis of qualitative data is still at a rather primitive stage. Indeed, Haberman (1978, 1979), in his two-volume survey of the analysis of qualitative data, does not mention it at all. The problem, I believe, is that log-linear and logit methodology are not particularly suited to the logic of causality in contingency tables. In order to derive a suitable method, it is necessary to uncover the logic underlying causality when applied to qualitative variables. A few others have taken seriously the idea that a direct analysis of the form of a contingency table can lead to fruitful results (see, especially, for example, Boudon, 1967), but their work has been overshadowed by the statistically more profound advances made in log-linear methods. This article is an attempt to provide a statistically rigorous analysis based on the direct interpretation of causality embodied in a contingency table.     

A strategic contingency approach to expatriate assignment management

This exploratory study examines how expatriate management practices differ with respect to three international management strategies (local, centralised or global) in 27 mature multinational corporations. In particular, this study examines the categories of expatriate assignments, the strategic integration of expatriate assignments into leadership development and the types of human resource practices that firms utilise to manage expatriates. Results indicate that organisations differentially assign expatriates based on the firm's respective international management strategy. Firms with a global management strategy made greater use of developmental expatriates, had a greater number of senior managers with expatriate experience and had a stronger focus on leadership development through expatriation. Firms did not differ with respect to their use of expatriate management practices based on their international management strategy.     

On X2- tests for contingency tables of negative multinomial types

Om te toets of die twee kenmerke waarvolgens die frekwensies in 'n gebeurlikseidstabel opgestel is onafhanklik is, word gewoonlik 'n X² toets gebruik. In die opstelling van so 'n toets word die totale frekwensie as vas beskou. In hierdie artikel word aangetoon hoe 'n X²-toets nog gebruik kan word wanneer die steekproefneming voortgesit word totdat 'n vooraf vasgestelde frekwensie in een selbereik word. Die totale frekwensie is dan 'n stogastiese veranderlike. Die eienskappe van die randverdelings van 'n negatiewe multinomiaalverdeling word gebruik om die parameters wat in die X²-verdeling optree te skat.     

Unconditional tests for association in 2 × 2 contingency tables in the total sum fixed design

The asymptotic approach and Fisher's exact approach have often been used for testing the association between two dichotomous variables. The asymptotic approach may be appropriate to use in large samples but is often criticized for being associated with unacceptable high actual type I error rates for small to medium sample sizes. Fisher's exact approach suffers from conservative type I error rates and low power. For these reasons, a number of exact unconditional approaches have been proposed, which have been seen to be generally more powerful than exact conditional counterparts. We consider the traditional unconditional approach based on maximization and compare it to our presented approach, which is based on estimation and maximization. We extend the unconditional approach based on estimation and maximization to designs with the total sum fixed. The procedures based on the Pearson chi‐square, Yates's corrected, and likelihood ratio test statistics are evaluated with regard to actual type I error rates and powers. A real example is used to illustrate the various testing procedures. The unconditional approach based on estimation and maximization performs well, having an actual level much closer to the nominal level. The Pearson chi‐square and likelihood ratio test statistics work well with this efficient unconditional approach. This approach is generally more powerful than the other p‐value calculation methods in the scenarios considered.     

Testing conditional symmetry against one-sided alternatives in square contingency tables

For a (k×k) square contingency table with ordered categories, letX(Y) denote the row (column) number. The conditional symmetry model is given byP(X=i, Y=j|X<Y)=P(X=j, Y=i |X>Y), ∀i<j. In this paper, we study the likelihood ratio tests of conditional symmetry in a square contingency table against two particular classes of one-sided alternatives. We obtain the maximum likelihood estimators under each alternative. The asymptotic null distributions of the likelihood ratio statistics are shown to have chi-bar square type distributions. A simulation study is performed by comparing the powers of different tests. The theory developed is illustrated by using the famous eye vision data from Stuart (1953).     

Taking a new contour: A novel approach to panel unit root tests

The paper introduces a novel approach to testing for unit roots in panels, which takes a new contour that is drawn along the line given by the equi-squared-sum instead of the traditional one given by the equi-sample-size. We show in the paper that the distributions of the unit root tests are asymptotically normal along the new contour under both the null and the local-to-unity alternatives. Subsequently, we demonstrate that this startling finding may be exploited constructively to invent tools and methodologies for effective inferences in panel unit root models. Simulations show that our approach works quite well in finite samples.     

The new approach in the analysis of 2×2 tables

Conventional statistical analysis of contingency tables is logically untenable. It rejects the knowledge of the prior model of cause, and it tests the null hypothesis of independence which cannot be proved to be true or false. Our approach is based on the knowledge of the prior model of cause, and we test the hypothesis of significant dependence. This hypothesis can be proved to be true or false if data are representative of the homogeneous invisible population.     

Alternative interaction structures in square contingency tables having ordered classificatory variables

Goodman (1972) proposed several models for the analysis of the general I x I square tables with particular emphasis on social mobility data. We demonstrate in this paper, that most of his models can be reproduced by combinations of both new models proposed here and the various well known models that have received considerable attention in the literature. Our presentation here is both concise and simple to comprehend. The various models considered in this study are fitted to ten data sets that include the much analyzed 5×5 Danish and British Social mobility data sets. Results suggest that in some cases more parsimonious models than those considered earlier by various authors are possible for the explanations of the variations in the data analyzed in this study.     

On fitting the conditional difference asymmetry models to square contingency tables with nominal categories

In this paper, we implement the conditional difference asymmetry model (CDAS) for square tables with nominal categories proposed by Tomizawa et al. (J. Appl. Stat. 31(3): 271–277, 2004) with the use of the non-standard log-linear model formulation approach. The implementation is carried out by refitting the model in the 3 ×  3 table in (Tomizawa et al. J. Appl. Stat. 31(3): 271–277, 2004). We extend this approach to a larger 4 ×  4 table of religious affiliation. We further calculated the measure of asymmetry along with its asymptotic standard error and confidence bounds. The procedure is implemted with SAS PROC GENMOD but can also be implemented in SPSS by following the discussion in (Lawal, J. Appl. Stat. 31(3): 279–303, 2004; Lawal, Qual. Quant. 38(3): 259–289, 2004).     

Double symmetry model and its decompositions in square contingency tables having ordered categories

In this paper, we have employed the non-standard log-linear models to fit the double symmetry models and some of its decompositions to square contingency tables having ordered categories. SAS PROC GENMOD was employed to fit these models although we could similarly have used GENLOG in SPSS or GLM in STATA. A SAS macro generates the factor or scalar variables required to fit these models. Two sets of \(4 \times 4\) unaided distance vision data that have been previously analyzed in (Tahata and Tomizawa, Journal of the Japan Statistical Society 36:91–106, 2006) were employed for verification of results. We also extend the approach to the Danish \(5 \times 5\) Mobility data as well as to the \(3 \times 3\) Danish longitudinal study data of subjective health, firstly reported in (Andersen, The Statistical Analysis of Categorical Data, Springer:Berlin, 1994) and analyzed in (Tahata and Tomizawa, Statistical Methods and Applications 19:307–318, 2010). Results obtained agree with those published in previous literature on the subject. The approaches suggest here eliminate any programming that might be required in order to apply these class of models to square contingency tables.     

A new approach to managerial selection

There are two keys to picking the right man for a job: knowing the results the man has achieved on past jobs (not only the names of those jobs) and knowing the objectives of the job to be filled. If he has in the past achieved the results the present job is aiming for—he's the right man.     

A new approach to public investment

Public investment decisions are usually based on multiple objective criteria. In this paper, Optimal Investment Decision Problem is formulated as a Multicriterion Optimal Control Problem. Apart from accounting for the multiple objectives, the dynamics of the economic system can also be incorporated in this formulation. Some techniques of solving these problems are discussed and trade-off schemes for the various objectives are suggested through the associated shadow prices.     

A generalization of Cochran's procedure for the combining of r×c contingency tables*

Abstract Cochran [3] derives a test of association when k 2 × 2 contingency tables are combined. We show in this paper how to extend Cochran's test to the combining of k r×c contingency tables using a multiple comparison technique similar to the one presented by Dunn [4]. An example is included.