Taking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives. Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.

Taking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives. Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.

Quantitative marketing has been gaining importance during the last decade. This is indicated by the growing number of model- and method-oriented studies published in leading journals as well as by the many successful applications of quantitative approaches in pricing, advertising, new product planning, and market segmentation decisions. In addition, market research has clearly benefitted from applying advanced quantitative models and methods in practice. Some 60 researchers - among them worldwide leading scholars - offer a broad overview of quantitative approaches in marketing. They not only highlight diverse mathematical and methodological perspectives, but also demonstrate the relevance and practical consequences of applying quantitative approaches to marketing problems.

Emphasizing substantive issues rather than intricate statistical details, this book provides a comprehensive introduction to LISREL for structural equation modeling (SEM) using a non-technical, user-oriented approach that. The emphasis is on: - exposing the reader to the major steps associated with the formulation and testing of a model under the LISREL framework - describing the key decisions associated with each step - highlighting potential problems and limitations associated with LISREL modeling - assisting the interpretation of LISREL input and output files. The overall aim is to provide a critical understanding of what is really involved in LISREL modeling and sensitize the reader against `mechanically' fitting or modifying models. The entire range of decisions associated with the practical application of the LISREL program is covered in a user-friendly fashion. Concrete examples are used throughout to illustrate issues relating to model conceptualization, specification, identification, estimation, evaluation, modification, and cross-validation and illustrated with actual program output. The program is made much more accessible by adopting the more user-friendly SIMPLIS command language for preparing input files. Although primarily aimed at beginning users, readers are directed to further reading together with a comprehensive bibliography for the more advanced user.