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B>KEY BENEFIT: " After reading this book, a user with limited statistical experience should have a stronger ability to understand the description they provide of the variable or relationship being described. This guide to the statistical procedures in SPSS Base 8.0 contains several examples for each procedure, starting with default operations and moving on through the most commonly used optional specifications. It discusses the assumptions required by each procedure, checking the data for these assumptions, and finding strategies to deal with data that do not meet required assumptions. The bulk of this guide focuses on output, on understanding what the statistics mean and how the different statistics relate to each other. The book is designed to be used with the SPSS Base 8.0 User's Guide.
This is a guide to the statistical procedures in SPSS Base 7.5, containing several examples for each procedure, starting with default operations and proceeding to the most commonly-used optional specifications. This text also discusses the assumptions required by each procedure, checking the data for those assumptions, and finding strategies to deal with data which does not meet required assumptions.
This book is designed to be used with the SPSS Base 9.0 for Windows User's Guide. This guide to the statistical procedures in SPSS Base 9.0 contains several examples for each procedure, starting with default operations and moving on through the most commonly used optional specifications. It discusses the assumptions required by each procedure, checking the data for those assumptions, and finding strategies to deal with data that do not meet the required assumptions. The bulk of this guide focuses on the output, on understanding what the statistics mean and how the different statistics relate to each other. After reading this book, a user with limited statistical experience should have a much stronger ability to evaluate the different numbers that appear in the output and to understand the description they provide of the variable or relationship being analyzed.
Applied Social Research focuses on the real world context of applied research. It discusses the often overlooked, yet essential process of planning: poor planning inevitably produces inadequate research. The text helps researchers decide how to approach their applied research problems and to think through the major issues in the design and analysis of their project. 'Applied Social Research' also discussed the idea that in applied social research the use of a single method type is unlikely to provide answers to the often complex set of research questions being addressed and highlights the benefits of using two or more research methods in the one study. The author argues that such mixed method designs are becoming widely used in applied social research, particularly where the methods combine qualitative and quantitative data, thereby enabling a richer set of data to provide various perspectives on the research topic, removing limitations imposed by using single methods. Examples of such designs are provided throughout, useful mixed method designs are outlined and their advantages discussed.
Making statistics—and statistical software—accessible and rewarding This book provides readers with step-by-step guidance on running a wide variety of statistical analyses in IBM® SPSS® Statistics, Stata, and other programs. Author David Kremelberg begins his user-friendly text by covering charts and graphs through regression, time-series analysis, and factor analysis. He provides a background of the method, then explains how to run these tests in IBM SPSS and Stata. He then progresses to more advanced kinds of statistics such as HLM and SEM, where he describes the tests and explains how to run these tests in their appropriate software including HLM and AMOS. This is an invaluable guide for upper-level undergraduate and graduate students across the social and behavioral sciences who need assistance in understanding the various statistical packages.
Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.
How to Use SPSS® is designed with the novice computer user in mind and for people who have no previous experience of using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report. The book begins with the basics, such as starting SPSS, defining variables, and entering and saving data. It covers all major statistical techniques typically taught in beginning statistics classes, such as descriptive statistics, graphing data, prediction and association, parametric inferential statistics, nonparametric inferential statistics and statistics for test construction. More than 250 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. The book includes a glossary of statistical terms and practice exercises. A complete set of online resources including video tutorials and output files for students, and PowerPoint slides and test bank questions for instructors, make How to Use SPSS® the definitive, field-tested resource for learning SPSS. New to this edition: Fully updated to SPSS 24 and IBM SPSS Statistics Cloud New chapter on ANOVA New material on inter-rater reliability New material on syntax Additional coverage of data entry and management
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-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.
Prompted by an increasing number of mergers and acquisitions (M&As), Denise Dahlhoff investigates the role of marketing-related motives in M&As in the U.S. food industry.
This book helps students develop a conceptual understanding of a variety of statistical tests by linking the statistics with the computational steps and output from SPSS. Learning how statistical ideas map onto computation in SPSS will help students build a better understanding of both. For example, seeing exactly how the concept of variance is used in SPSS-how it is converted into a number based on real data, which other concepts it is associated with, and where it appears in various statistical tests-will not only help students understand how to use statistical tests in SPSS and how to interpret their output, but will also teach them about the concept of variance itself. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, pointing out wherever possible how the SPSS procedure and output connects back to the conceptual underpinnings of the test. Each of the steps is accompanied by annotated screen shots from SPSS, and relevant components of output are highlighted in both the text and in the figures. Sections explain the conceptual machinery underlying the statistical tests. In contrast to merely presenting the equations for computing the statistic, these sections describe the idea behind each test in plain language and help students make the connection between the ideas and SPSS procedures. These include extensive treatment of custom hypothesis testing in ANOVA, MANOVA, ANCOVA, and regression, and an entire chapter on the advanced matrix algebra functions available only through syntax in SPSS. The book will be appropriate for both advanced undergraduate and graduate level courses in statistics.