The factorial analysis of human ability.

Errata slip inserted.

Measuring community strength and social capital

Five case study communities in both metropolitan and regional urban locations in Australia are used as test sites to develop measures of 'community strength' on four domains: Natural Capital; Produced Economic Capital; Human Capital; and Social and Institutional Capital. The paper focuses on the fourth domain. Sample surveys of households in the five case study communities used a survey instrument with scaled items to measure four aspects of social capital - formal norms, informal norms, formal structures and informal structures - that embrace the concepts of trust, reciprocity, bonds, bridges, links and networks in the interaction of individuals with their community inherent in the notion social capital. Exploratory principal components analysis is used to identify factors that measure those aspects of social and institutional capital, while a confirmatory analysis based on Cronbach's alpha explores the robustness of the measures. Four primary scales and 15 subscales are identified when defining the domain of social and institutional capital. Further analysis reveals that two measures - anomie, and perceived quality of life and wellbeing - relate to certain primary scales of social capital.

Transcultural properties of the Composite Scale of Morningness: The relevance of the "morning affect" factor

Morningness scales have been translated into several languages, but it lack of normative data and methodological differences make cross-cultural comparisons difficult. This study examines the psychometric properties and factor structure of the Composite Scale of Morningness (CSM) in samples from five countries: France (n = 627), Italy (n, = 702), Spain (n = 391), Thailand (n. = 503), and Australia (17 = 654). Strong national differences are identified. A quadratic relationship between age and CSM total score was apparent in the Australian data with a downward trend after age 35 yrs. There was no age effect in air), sample in the range from 18 to 29 yrs. Factor analysis identified a three-factor solution in all groups for both men and women. Tucker's congruence coefficients indicate that: (1) this solution is highly congruent between sexes in each culture, and (2) a morning affect factor is highly congruent between cultures. These results indicate there are national differences in factorial structure and that cut-off scores used to categorize participants as morning- and evening-types should be established for different cultural and age groups.

CONSTRUÇÃO E VALIDAÇÃO DA ESCALA DE ANSIEDADE NO TRABALHO (EAT-35)

Ansiedade é um conceito estudado desde a antiguidade sendo amplamente pesquisado em diversos ramos da ciência. A ansiedade pode ser compreendida como um sinal de alerta, um estado emocional que, por vezes, se torna desagradável, sendo vivenciada por todos os seres humanos. Uma forma de mensurar a ansiedade é por meio de escalas válidas e precisas. Por isso, o objetivo desse estudo foi construir e validar uma escala para avaliação de ansiedade no ambiente de trabalho. Com base em três dimensões da ansiedade contidas na literatura, foi construída a Escala de Ansiedade no Trabalho (EAT-35). Os dados foram coletados a partir das respostas dadas por 220 trabalhadores do Estado de São Paulo, com idade média de 34,27 (DP=9,83) sendo a maioria do sexo feminino (84,5%) e com ensino superior (64,5%). Foram calculadas estatísticas descritivas, análise fatorial e alfa de Cronbach. Os resultados revelaram um modelo de três dimensões da ansiedade e cujas dimensões obtiveram adequadas cargas fatoriais e índices de precisão. Com os resultados produzidos pelas análises deste estudo é possível concluir que a Escala de Ansiedade no Trabalho (EAT-35) pode ser utilizada como uma ferramenta para avaliar a ansiedade no trabalho. Novas pesquisas que realizem a aplicação de análises fatoriais confirmatórias são indicadas com o objetivo de se confirmar os resultados obtidos pelas análises fatoriais exploratórias durante a validação da EAT-35.

Probabilistic principal component analysis

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.

Probabilistic principal component analysis

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.

Exchange fairness and employee performance:An examination of the relationship between organizational politics and procedural justice

Data obtained from a manufacturing firm and a newspaper firm in India were used to examine the relationship between organizational politics and procedural justice in three separate studies. Study 1 constructively replicated research on the distinctiveness of the two constructs. Confirmatory factor analyses in which data from the manufacturing firm served as the development sample and data from the newspaper firm served as the validation sample demonstrated the distinctiveness of organizational politics and procedural justice. Study 2 examined the antecedents of the two constructs using data from the manufacturing firm. Structural equation modeling (SEM) results revealed formalization and participation in decision making to be positively related to procedural justice but negatively related to organizational politics. Further, authority hierarchy and spatial distance were positively related to organizational politics but unrelated to procedural justice. Study 3 examined the consequences of the two constructs in terms of task and contextual performance using data from the newspaper firm. Results of SEM analysis revealed procedural justice but not organizational politics to be related to task performance and the contextual performance dimensions of interpersonal facilitation and job dedication. © 2004 Elsevier Inc. All rights reserved.

Organizational citizenship behavior and organizational commitment in Nepal

The present study examines the structure of organizational citizenship behavior (OCB) and its relation to organizational commitment in Nepal. Four-hundred and fifty employees of five Nepalese organizations filled out standardized questionnaires. Exploratory and confirmatory factor analyses revealed two factors of OCB, altruism and compliance, replicating Western models of extra-role behavior. Structural equation analysis showed a positive relation between affective and normative commitment on the one hand and both citizenship factors on the other. Continuance commitment was negatively related to compliance and unrelated to altruism. The findings thus confirmed the structure and usefulness of the concepts in an under-researched geographical area. Findings of the research are discussed within the Nepalese sociocultural context. © Blackwell Publishing Ltd with the Asian Association of Social Psychology and the Japanese Group Dynamics Association 2005.

Statnote 27: Principal components analysis (PCA)

In Statnotes 24 and 25, multiple linear regression, a statistical method that examines the relationship between a single dependent variable (Y) and two or more independent variables (X), was described. The principle objective of such an analysis was to determine which of the X variables had a significant influence on Y and to construct an equation that predicts Y from the X variables. ‘Principal components analysis’ (PCA) and ‘factor analysis’ (FA) are also methods of examining the relationships between different variables but they differ from multiple regression in that no distinction is made between the dependent and independent variables, all variables being essentially treated the same. Originally, PCA and FA were regarded as distinct methods but in recent times they have been combined into a single analysis, PCA often being the first stage of a FA. The basic objective of a PCA/FA is to examine the relationships between the variables or the ‘structure’ of the variables and to determine whether these relationships can be explained by a smaller number of ‘factors’. This statnote describes the use of PCA/FA in the analysis of the differences between the DNA profiles of different MRSA strains introduced in Statnote 26.

Understanding learning strategies in the workplace:a factor analytic investigation

The aim of this study was to validate a scale of learning strategies, as derived from the educational literature, in an organizational context. Participants were 628 call centre employees. Both exploratory and confirmatory factor analyses suggested that a six-factor structure most accurately represented the learning strategies examined. Specifically, three cognitive (extrinsic work reflection, intrinsic work reflection, reproduction) and three behavioural strategies (interpersonal help seeking, help seeking from written material, practical application) were found.

Statnote 28:Canonical Variate Analysis (CVA)

The pattern of correlation between two sets of variables can be tested using canonical variate analysis (CVA). CVA, like principal components analysis (PCA) and factor analysis (FA) (Statnote 27, Hilton & Armstrong, 2011b), is a multivariate analysis Essentially, as in PCA/FA, the objective is to determine whether the correlations between two sets of variables can be explained by a smaller number of ‘axes of correlation’ or ‘canonical roots’.

A survey based analysis of IT adoption and 3PLs' performance

Purpose: In today's competitive scenario, effective supply chain management is increasingly dependent on third-party logistics (3PL) companies' capabilities and performance. The dissemination of information technology (IT) has contributed to change the supply chain role of 3PL companies and IT is considered an important element influencing the performance of modern logistics companies. Therefore, the purpose of this paper is to explore the relationship between IT and 3PLs' performance, assuming that logistics capabilities play a mediating role in this relationship. Design/methodology/approach: Empirical evidence based on a questionnaire survey conducted on a sample of logistics service companies operating in the Italian market was used to test a conceptual resource-based view (RBV) framework linking IT adoption, logistics capabilities and firm performance. Factor analysis and ordinary least square (OLS) regression analysis have been used to test hypotheses. The focus of the paper is multidisciplinary in nature; management of information systems, strategy, logistics and supply chain management approaches have been combined in the analysis. Findings: The results indicate strong relationships among data gathering technologies, transactional capabilities and firm performance, in terms of both efficiency and effectiveness. Moreover, a positive correlation between enterprise information technologies and 3PL financial performance has been found. Originality/value: The paper successfully uses the concept of logistics capabilities as mediating factor between IT adoption and firm performance. Objective measures have been proposed for IT adoption and logistics capabilities. Direct and indirect relationships among variables have been successfully tested. © Emerald Group Publishing Limited.