An introduction to computer programming via well-structured BASIC. Assuming no prior knowledge of BASIC, this book presents the fundamentals of programming, then shows, through examples and problems, how algorithmic processes from many fields can be transcribed into computer programs. Emphasis is on use of subroutines, and on collections of external subroutines called libraries, as well as on use of top-down design. Section on programming techniques includes advice on how to design, code, test, and debug large programs. Contains varied applications: text, mathematical, business, games, graphics, and music.

During the last two decades, structural equation modeling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Although its roots can be traced back to the first half of this century, when Spearman (1904) developed factor analysis and Wright (1934) introduced path analysis, it was not until the 1970s that the works by Karl Joreskog and his associates (e. g. , Joreskog, 1977; Joreskog and Van Thillo, 1973) began to make general SEM techniques accessible to the social and behavioral science research communities. Today, with the development and increasing avail ability of SEM computer programs, SEM has become a well-established and respected data analysis method, incorporating many of the traditional analysis techniques as special cases. State-of-the-art SEM software packages such as LISREL (Joreskog and Sorbom, 1993a,b) and EQS (Bentler, 1993; Bentler and Wu, 1993) handle a variety of ordinary least squares regression designs as well as complex structural equation models involving variables with arbitrary distributions. Unfortunately, many students and researchers hesitate to use SEM methods, perhaps due to the somewhat complex underlying statistical repre sentation and theory. In my opinion, social science students and researchers can benefit greatly from acquiring knowledge and skills in SEM since the methods-applied appropriately-can provide a bridge between the theo retical and empirical aspects of behavioral research.

This book provides an introduction to VBA for Excel for new users. It covers basic concepts of VBA and of macro programming, and takes the reader through the process of constructing interactive working applications. Features which make it particularly suitable for new and non-technical users are: * step-by-step approach * avoidance of jargon * clear explanation of all new concepts, symbols and objects * emphasis on correct use of VBA development environment * plentiful examples and the use of complete programs rather than disconnected fragments.

A practical guide to building fully operational financial cash flow models for structured finance transactions Structured finance and securitization deals are becoming more commonplace on Wall Street. Up until now, however, market participants have had to create their own models to analyze these deals, and new entrants have had to learn as they go. Modeling Structured Finance Cash Flows with Microsoft Excel provides readers with the information they need to build a cash flow model for structured finance and securitization deals. Financial professional Keith Allman explains individual functions and formulas, while also explaining the theory behind the spreadsheets. Each chapter begins with a discussion of theory, followed by a section called "Model Builder," in which Allman translates the theory into functions and formulas. In addition, the companion website features all of the modeling exercises, as well as a final version of the model that is created in the text. Note: Companion website and other supplementary materials are not included as part of eBook file.

With the availability of software programs, such as LISREL, EQS, and AMOS, modeling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of hypothesizing for a particular data set. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and