*Psychology: Programming Handbooks

This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the popular statistical software package SPSS.

Introducing the IBM SPSS Modeler, this book guides readers through data mining processesand presents relevant statistical methods. There is a special focus onstep-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. [From the publisher]

A complete overview of SPSS methodologies for medical and health statistics. Medical and health professionals tend to dislike software syntax. Therefore, virtually no syntax, but, rather, simple menu commands are given. The arithmetic is...of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible.

Shows readers how to use SPSS software in statistical data analysis. This [book] uses the GSS (General Social Survey) as a secondary data set and briefly introduces statistical techniques before moving on to demonstrations. It also offers discussions of regression, correlation, t-test, chi-square, lambda, gamma/somers' d, and ANOVA.

An accessible approach to integrating design and statistics, focusing on the processes of posing, testing, and interpreting research questions in the social sciences. The authors analyze real-world data using SPSS software, guiding readers on the overall process of science, focusing on premises, procedures, and designs of social scientific research. Throughout the book, SPSS software is used to showcase the discussed techniques, and detailed appendices provide guidance on key statistical procedures and tips for data management. Includes exercises and a related website features additional data sets and SPSS code.

This updated and extended edition has been written to serve as a ... complete guide and reference-text to students, physicians, and investigators. The present book not only explains classical statistical analyses of clinical trials, but also addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, meta-analyses, and provides a framework of the best statistical methods currently available for such purpose.

A guide for psychology, counseling, and education students in the use of statistics and research designs, combined with guidance on using SPSS in the course of their research. Each chapter covers a research problem, taking the student through identifying research questions and hypotheses; identifying, classifying, and operationally defining the study variables; choosing appropriate research designs; conducting power analysis; choosing an appropriate statistic for the problem; using a data set; conducting data screening and analyses (SPSS); interpreting the statistics; and writing the results related to the problem.

An easy to grasp and non-intimidating approach to statistics for the non-mathematician. The text provides practical coverage of SPSS in every chapter, including screen shots, procedures, exercises, and direction on how to interpret SPSS output. The use of common data sets throughout the book aid in student comprehension.

Demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA.

A hands-on introduction to market research. Using the market research process as a framework, it explains how to collect and describe data and presents the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis and cluster analysis. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22 and how to interpret the output.

This text is meant for both students and researchers who want to gain a basic understanding of common statistical methods used in this field. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. As the statistical methods discussed become more complex and demanding to compute, there is increasing use and integration of statistical software [including both SPSS and Stata].

An introduction to learning about the software Stata as well as guiding the reader to develop a more sophisticated understanding of Stata.

Demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA.

Shows how to manage, describe, and model panel data, presenting models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All necessary data sets and Stata syntax files on http://eswf.uni-koeln.de/panel

This text is meant for both students and researchers who want to gain a basic understanding of common statistical methods used in this field. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. As the statistical methods discussed become more complex and demanding to compute, there is increasing use and integration of statistical software [including both SPSS and Stata].

A comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. Examples using STATA are provided for most of the statistical methods. The book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods particularly relevant and typically used in management research, and to learn how they can be applied using widely available statistical software.

Guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS®. The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods, Multivariate graphics, Canonical correlation, Hotelling's T-squared, Multivariate analysis of variance (MANOVA), and Multiple regression and the general linear model (GLM).

This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.

Offers the first complete introduction on how to perform Qualitative Comparative Analysis (QCA) in the R software environment for statistical computing and graphics with the QCA package. QCA provides an unprecedented scope of functionality for analyzing crisp, multi-value and fuzzy sets. The reader is not required to have knowledge of R, but the book assumes an understanding of the fundamentals of QCA. Although mainly written for political scientists, this book is also of interest to scholars from other disciplines in the social sciences such as sociology, business, management, organization, anthropology, education and health.

Introduces R using Stata terminology. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.

Introduces the structure of data in R, along with subscripting, character manipulation, reshaping data, and other data manipulation basics. Great for understanding the mechanics of R, which may not be explained in statistics texts.

An elementary-level introduction to R. A supplementary R package can be downloaded and contains the data sets. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples.

Utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. The book begins with coverage of standard linear regression and the importance of parsimony in statistical modeling, and includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees;clustering; principal components and partial least squares; and the analysis of text and network data.

Looks at some of the most common tasks performed by business analysts and helps the user navigate R and its 4000 packages.  Includes chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting.  This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses (i.e. models to predict and strategize and DM matches patterns in big data).

Intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. Practical examples are solved in the R computing environment, illustrating proglems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets.

In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods.

MATLAB ...lends itself to a variety of applications across the field of psychology. For the novice looking to use it in experimental psychology research, though, becoming familiar with MATLAB can be a daunting task. MATLAB for Psychologists expertly guides readers through the component steps, skills, and operations of the software, with plentiful graphics and examples to match the reader s comfort level. Using an extended illustration, this concise volume explains the program's usefulness at any point in an experiment, without the limits imposed by other types of software. And the authors demonstrate the responsiveness of MATLAB to the individuals research needs, whether the task is programming experiments, creating sensory stimuli, running simulations, or calculating statistics for data analysis. MATLAB for Psychologists serves a wide audience of advanced undergraduate and graduate level psychology students, professors, and researchers as well as lab technicians involved in programming psychology experiments. 

A variety of disciplines and professions have embraced geospatial technologies for collecting, storing, manipulating, analyzing and displaying spatial data to investigate crime, prosecute and convict offenders, exonerate suspects and submit evidence in civil lawsuits. The applications, acceptability and relevance and procedural legality of each geospatial technologies vary. The purpose of this book is to explain the nature of geospatial technologies, demonstrate a variety of geospatial applications used to investigate and litigate civil and criminal activities and to provide a reference of current acceptability of geospatial technology in the production of evidence. This book is an introductory overview designed to appeal to researchers and practitioners across disciplinary boundaries. The authors of this book are researchers and practitioners across disciplines and professions, experts in the field.

This book focuses on impending serious problems of socio-economic conditions in the sub-Saharan countries utilizing remote sensing and GIS techniques. In Africa, the use of remote sensing data and analyzing techniques such as GIS is essential for any studies that involve an extensive area because more often than not, topographic maps at a scale of 1:50,000 or larger are not available for detailed mapping on the ground. In addition, basic socio-economic and physical data, such as census data, environmental data, and infrastructure data, are lacking or not kept updated for modeling analyses.

This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan...It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues.

Spatial Econometrics is a rapidly evolving field born from the joint efforts of economists, statisticians, econometricians and regional scientists. The book provides the reader with a broad view of the topic by including both methodological and application papers. Indeed the application papers relate to a number of diverse scientific fields ranging from hedonic models of house pricing to demography, from health care to regional economics, from the analysis of R&D spillovers to the study of retail market spatial characteristics.