Cross-cultural confirmatory factor analysis of the CES-D in Spanish and Mexican dementia caregivers

The Center for Epidemiologic Studies-Depression Scale (CES-D) is the most frequently used scale for measuring depressive symptomatology in caregiving research. The aim of this study is to test its construct structure and measurement equivalence between caregivers from two Spanish-speaking countries. Face-to-face interviews were carried out with 595 female dementia caregivers from Madrid, Spain, and from Coahuila, Mexico. The structure of the CES-D was analyzed using exploratory and confirmatory factor analysis (EFA and CFA, respectively). Measurement invariance across samples was analyzed comparing a baseline model with a more restrictive model. Significant differences between means were found for 7 items. The results of the EFA clearly supported a four-factor solution. The CFA for the whole sample with the four factors revealed high and statistically significant loading coefficients for all items (except item number 4). When equality constraints were imposed to test for the invariance between countries, the change in chi-square was significant, indicating that complete invariance could not be assumed. Significant between-countries differences were found for three of the four latent factor mean scores. Although the results provide general support for the original four-factor structure, caution should be exercised on reporting comparisons of depression scores between Spanish-speaking countries.

A novel neural network technique for exploratory data analysis

This paper surveys the context of feature extraction by neural network approaches, and compares and contrasts their behaviour as prospective data visualisation tools in a real world problem. We also introduce and discuss a hybrid approach which allows us to control the degree of discriminatory and topographic information in the extracted feature space.

Factor analysis and discriminant validity:a brief review of some practical issues

Growth in availability and ability of modern statistical software has resulted in greater numbers of research techniques being applied across the marketing discipline. However, with such advances come concerns that techniques may be misinterpreted by researchers. This issue is critical since misinterpretation could cause erroneous findings. This paper investigates some assumptions regarding: 1) the assessment of discriminant validity; and 2) what confirmatory factor analysis accomplishes. Examples that address these points are presented, and some procedural remedies are suggested based upon the literature. This paper is, therefore, primarily concerned with the development of measurement theory and practice. If advances in theory development are not based upon sound methodological practice, we as researchers could be basing our work upon shaky foundations.

Statnote 11: the two-factor analysis of variance

Experiments combining different groups or factors are a powerful method of investigation in applied microbiology. ANOVA enables not only the effect of individual factors to be estimated but also their interactions; information which cannot be obtained readily when factors are investigated separately. In addition, combining different treatments or factors in a single experiment is more efficient and often reduces the number of replications required to estimate treatment effects adequately. Because of the treatment combinations used in a factorial experiment, the degrees of freedom (DF) of the error term in the ANOVA is a more important indicator of the ‘power’ of the experiment than simply the number of replicates. A good method is to ensure, where possible, that sufficient replication is present to achieve 15 DF for each error term of the ANOVA. Finally, in a factorial experiment, it is important to define the design of the experiment in detail because this determines the appropriate type of ANOVA. We will discuss some of the common variations of factorial ANOVA in future statnotes. If there is doubt about which ANOVA to use, the researcher should seek advice from a statistician with experience of research in applied microbiology.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

Exploratory data analysis with non-linear and missing data in geochemistry

Exploratory analysis of data seeks to find common patterns to gain insights into the structure and distribution of the data. In geochemistry it is a valuable means to gain insights into the complicated processes making up a petroleum system. Typically linear visualisation methods like principal components analysis, linked plots, or brushing are used. These methods can not directly be employed when dealing with missing data and they struggle to capture global non-linear structures in the data, however they can do so locally. This thesis discusses a complementary approach based on a non-linear probabilistic model. The generative topographic mapping (GTM) enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate more structure than a two dimensional principal components plot. The model can deal with uncertainty, missing data and allows for the exploration of the non-linear structure in the data. In this thesis a novel approach to initialise the GTM with arbitrary projections is developed. This makes it possible to combine GTM with algorithms like Isomap and fit complex non-linear structure like the Swiss-roll. Another novel extension is the incorporation of prior knowledge about the structure of the covariance matrix. This extension greatly enhances the modelling capabilities of the algorithm resulting in better fit to the data and better imputation capabilities for missing data. Additionally an extensive benchmark study of the missing data imputation capabilities of GTM is performed. Further a novel approach, based on missing data, will be introduced to benchmark the fit of probabilistic visualisation algorithms on unlabelled data. Finally the work is complemented by evaluating the algorithms on real-life datasets from geochemical projects.

Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling.

Organisational receptivity for change:combining context and capability to explain competitive advantage

This thesis explores efforts to conjoin organisational contexts and capabilities in explaining sustainable competitive advantage. Oliver (1997) argued organisations need to balance the need to conform to industry’s requirements to attain legitimization (e.g. DiMaggio & Powell, 1983), and the need for resource optimization (e.g. Barney, 1991). The author hypothesized that such balance can be viewed as movements along the homogeneity-heterogeneity continuum. An organisation in a homogenous industry possesses similar characteristics as its competitors, as opposed to a heterogeneous industry in which organisations within are differentiated and competitively positioned (Oliver, 1997). The movement is influenced by the dynamic environmental conditions that an organisation is experiencing. The author extended Oliver’s (1997) propositions of combining RBV’s focus on capabilities with institutional theory’s focus on organisational context, as well as redefining organisational receptivity towards change (ORC) factors from Butler and Allen’s (2008) findings. The authors contributed to the theoretical development of ORC theory to explain the attainment of sustainable competitive advantage. ORC adopts the assumptions from both institutional and RBV theories, where the receptivity factors include both organisational contexts and capabilities. The thesis employed a mixed method approach in which sequential qualitative quantitative studies were deployed to establish a robust, reliable, and valid ORC scale. The adoption of Hinkin’s (1995) three-phase scale development process was updated, thus items generated from interviews and literature reviews went through numerous exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to achieve convergent, discriminant, and nomological validities. Samples in the first phase (semi structured interviews) were hotel owners and managers. In the second phase, samples were MBA students, and employees of private and public sectors. In the third phase, samples were hotel managers. The final ORC scale is a parsimonious second higher-order latent construct. The first-order constructs comprises four latent receptivity factors which are ideological vision (4 items), leading change (4 items), implementation capacity (4 items), and change orientation (7 items). Hypotheses testing revealed that high levels of perceived environmental uncertainty leads to high levels of receptivity factor. Furthermore, the study found a strong positive correlation between receptivity factors and competitive advantage, and between receptivity factors and organisation performance. Mediation analyses revealed that receptivity factors partially mediate the relationship between perceived environmental uncertainty, competitive advantage and organisational performance.

A stage-oriented model (SOM) for e-commerce adoption:a study of Saudi Arabian organisations

Purpose - The purpose of this paper is to construct a new e-commerce innovation and adoption model that takes into account various stages of e-commerce adoption (interactive, non-interactive and stabilised) and covers technological, organisational and environmental factors. This was tested using data collected from manufacturing and service companies in Saudi Arabia (SA) to reveal inhibitors and catalysts for e-commerce adoption. Design/methodology/approach - This study uses new data from surveys from 202 companies and then uses exploratory factor analysis and structural equation modelling for analyses. Findings - This study shows that the new stage-oriented model (SOM) is valid and can reveal specific detailed nuances of e-commerce adoption within a particular setting. Surprising results show that SA is not so very different to developed western countries in respect to e-commerce adoption. However there are some important differences which are discussed in detail. Research limitations/implications - A new SOM for e-commerce adoption is provided which may be used by other IS adoption researchers. Practical implications - Managers responsible for the adoption of e-commerce in SA, the Middle East and beyond can learn from these findings to speed up adoption rates and make e-commerce more effective. Social implications - This work may help spread e-commerce use throughout SA, the Middle East and to other developing nations. Originality/value - The results add to the extremely limited number of empirical studies that has been conducted to investigate e-commerce adoption in the context of Arabic countries.

What's in a broken promissory obligation? Developing and testing a multiple component form measure of psychological contract breach

While the literature has suggested the possibility of breach being composed of multiple facets, no previous study has investigated this possibility empirically. This study examined the factor structure of typical component forms in order to develop a multiple component form measure of breach. Two studies were conducted. In study 1 (N = 420) multi-item measures based on causal indicators representing promissory obligations were developed for the five potential component forms (delay, magnitude, type/form, inequity and reciprocal imbalance). Exploratory factor analysis showed that the five components loaded onto one higher order factor, namely psychological contract breach suggesting that breach is composed of different aspects rather than types of breach. Confirmatory factor analysis provided further evidence for the proposed model. In addition, the model achieved high construct reliability and showed good construct, convergent, discriminant and predictive validity. Study 2 data (N = 189), used to validate study 1 results, compared the multiple-component measure with an established multiple item measure of breach (rather than a single item as in study 1) and also tested for discriminant validity with an established multiple item measure of violation. Findings replicated those in study 1. The findings have important implications for considering alternative, more comprehensive and elaborate ways of assessing breach.

Within-role, extra-role and anti-role behaviours in retail banking

Purpose: This paper aims to explore front line employee performance in retail banking and presents distinct components of employee performance, including extra-role and sabotage behaviours. Design/methodology/approach: Data was collected from Irish bank employees. Usable responses were received from 404 respondents and subjected to exploratory factor analysis. Structural Equation Modeling (SEM) was used to undertake a confirmatory factor analysis of the emergent five-factor model. Findings: Results indicate front line employee performance is multi-faceted and comprised of civility, assurance and reliability, customer orientation, as well as extra-role behaviour and anti-role behaviour, or sabotage. Research limitations/implications: This exploratory study focuses on the Irish banking sector. To explore the generalisabilty of results, replication studies among other samples of branch banking employees in other countries are in order. Moreover, our survey is limited to the views of branch employees. We advocate research among bank managers and customers to triangulate potentially divergent views about performance. Practical implications: Findings have implications for recruitment, training and rewards. To ensure new hires are service minded, managers must consider their potential for extra-role or sabotage behaviour. Employees who demonstrate extra-role behaviours must be rewarded to encourage the adoption of such behaviours. Managers must also seek to minimise job stress in order to curtail anti-role behaviours. Originality/value: This paper offers insights into employees' views about their own performance at the front line. It extends the conceptualisation of service quality, by considering extra-role behaviour and sabotage as components of employee performance. © Emerald Group Publishing Limited.

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.

The development of a short scale measuring aggressiveness and anger in competitive athletes

Objectives: The study of aggression and anger in competitive sport relies on accurate and economical measurement via observation, interview and questionnaire. Unfortunately, extant questionnaires have been criticised for having poor validity, are not sport specific, or reflect mood states rather than trait qualities. Therefore, a measure of trait anger and aggressiveness in competitive athletes was developed. Method: A list of statements representing aggressiveness and anger was generated and distributed to competitive athletes from diverse sports. Exploratory and confirmatory analyses were used to verify the theoretically predicted factor structure. Correlations with an extant measure of aggression and anger were used to ascertain concurrent validity. Discriminant validity was tested by comparing males with females, and aggressive with non-aggressive footballers. Results: A 12-item scale (Competitive Aggressiveness and Anger Scale, CAAS) consisting of two subscales was derived using principal component factor analysis with oblimin rotation. Confirmatory factor analysis using structural equation modelling confirmed the overall structure. Test-retest correlation, construct and discriminant validities were good, supporting the utility of the scale as a measure of athlete trait aggressiveness and anger. Conclusions: The CAAS appears to be a useful measure of athletic anger and aggressiveness. Its brevity and ability to discriminate aggressive from non-aggressive athletes should prove useful for future research concerning aggressive behaviour in competitive athletes. © 2006 Elsevier Ltd. All rights reserved.

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.