The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software. Available with Perusall--an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective.
Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the 'why' of statistics and the 'how'. Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs. The book also provides: Original case studies and data sets Practical guidance on how to run and test models in Stata Downloadable Stata programmes created to work alongside chapters A wide range of detailed applications using Stata Step-by-step notes on writing the relevant code. This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.
Alan C. Acock's A Gentle Introduction to Stata, Sixth Editionis aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able not only to use Stata well but also to learn new aspects of Stata. Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book. Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and multilevel modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material. The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material in a natural fashion. Real datasets, such as the General Social Surveys from 2002, 2006, and 2016, are used throughout the book. The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements. The sixth edition incorporates new features of Stata 15. All menus, dialog boxes, and instructions for using the point-and-click interface have been updated. Power-and-sample-size calculations for linear regression are demonstrated using Stata 15's new power rsquared command. This edition also includes new sections that describe how to evaluate convergent and discriminant validity, how to compute effect sizes for t tests and ANOVA models, how to use margins and marginsplot to interpret results of linear and logistic regression models, and how to use full-information maximum-likelihood (FIML) estimation with SEM to address problems with missing data.
Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata (downloadable from the Robert L. Kaufman's website), and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The data sets and the Stata code to reproduce the results of the application examples are available online.
Applied econometric analysis is used across many disciplines and in many branches of economics. Increasingly, data is becoming more readily available and software has become more powerful, enabling the analysis of numerous economic phenomenon. The aim of this ebook is to guide the student through applied econometric examples, using real world data. The focus is on using statistical software, in this case Stata, to perform analysis rather than on econometric theory. The topics explored in this ebook are as follows: initially, the linear regression model is explored and concepts such as coefficients, F-tests and t-tests, and the R2 value are covered. Following from this, some of the most common problems that occur in regression analysis are explored, including the following breaches of the assumptions of the classical linear regression model: multicollinearity, heteroscedasticity and autocorrelation. Topics in time series analysis are also touched upon, including tests for stationarity. Finally, we consider binary dependent variables. This text builds upon the Survey & Questionnaire Design: Collecting Primary Data to Answer Research Questions ebook by Kirby, Bourke & Doran (2016). In that ebook, the methods of primary data collection are discussed, as is how to develop a research question. This ebook builds upon this foundation by showing students how to apply econometric techniques to analyse data that they have collected themselves or sourced from secondary data sources. The text uses the Stata software package. A primer for Stata is presented in Appendix 4 and the companion website (www.justindoran.ie) contains a number of videos that provide a gentle introduction to Stata.
One Hundred Nineteen Stata Tipsprovides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journalsince 2003.
Speaking Stata Graphicsis ideal for researchers who want to produce effective, publication-quality graphs. A compilation of articles from the popular Speaking Statacolumn by Nicholas J. Cox, this book provides valuable insights about Stata's built-in and user-written statistical-graphics commands.
Stata for the Behavioral Sciences, by Michael Mitchell, is theĀ ideal reference for researchers using Stata to fit ANOVA modelsĀ and other models commonly applied to behavioral science data.
Using Stata for Quantitative Analysis, Second Edition offers a brief, but thorough introduction to analyzing data with Stata software. It can be used as a reference for any statistics or methods course across the social, behavioral, and health sciences since these fields share a relatively similar approach to quantitative analysis. In this book, the author teaches the language of Stata from an intuitive perspective, furthering students' overall retention and allowing a student with no experience in statistical software to work with data in a very short amount of time. The self-teaching style of this book enables novice Stata users to complete a basic quantitative research project from start to finish. The Second Edition covers the use of Stata 13 and can be used on its own or as a supplement to a research methods or statistics textbook.