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Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.

This study aims to statistically investigate the place of the eurozone countries in the framework of the international economy and particularly within the most advanced non Euro‐currency countries; second it attempts to explain the eventual discrepancies in the performing of the eurozone from the most advanced non‐eurozone countries by the weaknesses of some eurozone members. The discriminant analysis as an investigation tool has been chosen as an as unbiased as possible investigation technique. Of course, every discriminant analysis requires classification criteria. The criteria adopted in this study result to more or less the same conclusions.

Most econometric studies prefer the popular econometric techniques employing classical regression techniques, while methods of multivariate statistics and non‐linear regressions occupy a minor place as statistical tools. Numerous and multivariate data call for multivariate techniques, which at the cost of losing information and details allow for better a perception of the data structure. Therefore, a great part of the statistical analysis focuses on multivariate techniques and non‐linear regression.

The study concludes that despite the present budget and debt crisis hitting some major and minor eurozone members the “real” economy of the eurozone posseses a first class place in the World economy – both in relative and absolute terms. During the course of the study effort was paid to balancing the tools of investigation and the fertility of the results, in particular to approach questions such as: what is present condition of the eurozone? How solid are the predictions for a hanging collapse of the euro currency and the eurozone? At the end of the study is given an apercu on the transition of the member countries to the eurozone and their economic status by the end of 2011 tries to soften the fears for the eurozone future.

This study tries to analyze the position of the eurozone countries from an arithmetic/objective perspective, ignoring as much as possible the (geo) political and national interests of the principal countries involved as an effort to check the solidity of the fears. Not all parameters of the economy can enter the study. The author has chosen a few variables, which to their opinion reflect the overall performance of an economy. Parameters relating to financial aspects have nowadays in great degree become autonomous and call for special inquiry. The study seeks to add to econometric studies carried out by national and international institutions and Universities. It mainly concerns the statistical techniques and to treat eurozone as a whole entity vs the rest of the developed non‐eurozone world. Indeed, the study tries to defend the eurozone using objective data against a multitude of gloomy predictions, raised by several world partners, for the performance and the future of the eurozone.

Today’s growing numbers of contractor selection methodologies reflect the increasing awareness of the construction industry for improving its procurement process and performance. This paper investigates contractor classification methods that link clients’ selection aspirations and contractor performance. Multivariate techniques were used to study the intrinsic link between clients’ selection preferences, i.e. project‐specific criteria (PSC) and their respective levels of importance assigned (LIA), during tender evaluation for modelling contractor classification models in a data set of 68 case studies of UK construction projects. The logistic regression (LR) and multivariate discriminant analysis (MDA) were used. Results revealed that both techniques produced a good prediction on contractor performance and indicated that suitability of the equipment, past performance in cost and time on similar projects, contractor relationship with local authority, and contractor reputation/image are the most predominant PSC in the LR and MDA models among the 34 PSC. Suggests contractor classification models using multivariate techniques could be developed further.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

The purpose of this paper is to examine the evolution of the literature on technology transfer and culture, identifying the main contents of the current body of knowledge encompassing culture and technology transfer (TT), thus contributing to a better understanding of the relationship between TT and culture based on bibliometric and multivariate statistical analyses of the relevant body of literature.

Data for this study were collected from the Web of Science (WoS) Core Collection database. Based on a bibliometric analysis and in-depth empirical review of major TT subjects, supported by multivariate statistical analyses, over 200 articles were systematically reviewed. The use of these methods decreases biases since it adds rigor to the subjective evaluation of the relevant literature base.

The exploratory analysis of the articles shows that first, culture is an important topic for TT in the literature; second, the publication data demonstrate a great dynamism regarding the different contexts in which culture is covered in the TT literature and third, in the last couple of years the interest of stimulating a TT culture in the context of universities has continuously grown.

This study focuses on culture in the context of TT and identifies the main contents of the body of knowledge in the area. Based on this first insight, obtained through more detailed bibliometric and multivariate analyses, it is now important to develop and validate a theory on TT culture, emphasizing the dimensions of organizational culture, entrepreneurial culture and a culture of openness that fosters economic and societal spillovers, and to link those dimensions to the performance of TT activities.

From the practical point of view, managers in companies and universities should be aware of the importance of identifying those dimensions of culture that contribute most to the success of their TT activities.

Despite several literature reviews on the TT topic, no studies focusing specifically on culture in the context of TT have been developed. Therefore, given the multifaceted nature of the research field, this study aims to expand and to deepen the analysis of the TT literature by focusing on culture as an important and commonly cited element influencing TT performance.

Most quality management researchers make inadequate use of statistical techniques, especially multivariate statistical methods. Applies two multivariate analysis techniques, principal component analysis (PCA) and correspondence analysis (CA), to analyse potential barriers to total quality management (TQM) implementation in Hong Kong’s service and manufacturing industries. Describes and demonstrates the applicability of these techniques as analysis tools for quality researchers and practitioners. Conducts PCA on a set of survey data and produces four orthogonal dimensions to potential barriers to TQM implementation, then applies CA in order to corroborate the findings of PCA. In addition, CA provides a simultaneous graphical representation of the data organized under different categories which shows how the potential barriers relate to one another and to the different types of industry.

This paper aims to present real time user searches in a Greek academic library OPAC (University of Macedonia Library) in relation to user profile.

Using as a test bed a Greek academic library and its OPAC's transaction logs along with a system implanted questionnaire, data were gathered, processed and analyzed using multivariate statistical analysis techniques.

In making a synthesis of the analyzed data, a series of questions related to everyday library work were answered, giving libraries a tool to apply the gained knowledge in order to make decisions regarding their OPAC, their user education programs and their reference services.

The present paper focuses on the analysis of those variables that were considered to be the most representative for constructing a user profile.

This paper builds upon the techniques of data collection and presents a new tool for analyzing them statistically. Data derived from libraries were processed and analyzed statistically using the classical descriptive statistics. The suggested multivariate statistical method is designed to become a tool for analyzing qualitative data and to be used in a variety of library applications. It is also particularly helpful in analyzing cross‐tabular data in the form of numerical frequencies and allows all associations amongst pairs of variables to be analyzed as well as each association between a variable and itself.