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Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.

Prior research on the police decision to use deadly force has tended to neglect multivariate relationships, particularly at the situational level. This paper makes use of data describing deadly force incidents in Philadelphia during two time periods (1970‐1978 and 1987‐1992) and employs multivariate analyses to identify situational predictors of police shootings involving gun‐assaultive suspects. Findings from the multivariate analyses are then used in a pilot effort to develop predictive risk classifications of deadly force incidents. Identification of predictors of deadly force is helpful not only in assessing the relative contributions of situational variables but also in shaping our understanding of the behavior of line officers who are forced, by the nature of their work, to make split‐second decisions involving life and liberty with minimal guidance and support from the police department.

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.

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.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.

The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services.

The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well.

Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present.

Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs.

Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques.

Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes.

The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.

The findings of this paper throw light on the focal research areas within RFID in the supply chain, which serves as an effective guideline for future research in this area. This research, therefore, contributes to filling the gap by carrying out an SLR of contemporary research studies in the area of RFID applications in supply chains. To date, SLR augmented with BA has not been used to study the developments in RFID applications in supply chains.

We analyze 556 articles from years 2001 to date using Systematic Literature Review (SLR). Contemporary bibliometric analysis (BA) tools are utilized. First, an exploratory analysis is carried, out revealing influential authors, sources, regions, among other key aspects. Second, a co-citation work analysis is utilized to understand the conceptual structure of the literature, followed by a dynamic co-citation network to reveal the evolution of the field. This is followed by a multivariate analysis is performed on top-100 cited papers, and k-means clustering is carried out to find optimal groups and identify research themes. The influential themes are then pointed out using factor analysis.

An exploratory analysis is carried out using BA tools to provide insights into factors such as influential authors, production countries, top-cited papers and frequent keywords. Visualization of bibliographical data using co-citation network analysis and keyword co-occurrence analysis assisted in understanding the groups (communities) of research themes. We employed k-means clustering and factor analysis methods to further develop these insights. A historiographical direct citation analysis also unveils potential research directions. We observe that RFID applications in the supply chain are likely to benefit from the Internet of Things and blockchain Technology along with the other machine learning and visualization approaches.

Although several researchers have researched RFID literature in relation to supply chains, these reviews are often conducted in the traditional manner where the author(s) select paper based on their area of expertise, interest and experience. Limitation of such reviews includes authors’ selection bias of studies to be included and limited or no use of advanced BA tools for analysis. This study fills this research gap by conducting an SLR of RFID in supply chains to identify important research trends in this field through the use of advanced BA tools.

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.