Learn to Perform Confirmatory Factor Analysis in Stata with Data from the General Social Survey (2016) PDF Download

Author: Catherine Zimmer
Publisher:
ISBN: 9781529700091
Category : Factor analysis
Languages : en
Pages :

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Book Description
This example introduces readers to confirmatory factor analysis (CFA). CFA is used to model how well latent variables are related to multiple observed variables that serve as measurements of the latent variables. In contrast to exploratory factor analysis (EFA), the links of particular latent variables to particular observed variables are specified in advance and tested statistically, not derived from the data. CFA is a type of structural equation model (SEM) used for measurement of concepts. These measurement models can be components of larger SEM models with latent variables being predictors of other variables or outcomes. This example introduces readers to the basic theory and assumptions associated with CFA, estimators and the interpretation of estimates, the associated hypothesis tests, results production, and reporting. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

Author: Catherine Zimmer
Publisher:
ISBN: 9781529700114
Category : Path analysis (Statistics)
Languages : en
Pages :

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Book Description
This dataset introduces readers to path analysis. Path analysis is a special case of structural equation modeling (SEM) with only observed variables that allows for models with multiple dependent variables. Many path analyses include causally linked dependent variables modeled as mediators. These models allow effects to be decomposed into direct effects and indirect effects. This example introduces readers to the basic theory and assumptions associated with path analysis, estimators and the interpretation of estimates, the associated hypothesis tests, results production, and reporting. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

Author: Alan C. Acock
Publisher: Stata Press
ISBN: 1597180432
Category : Computers
Languages : en
Pages : 357

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Book Description
"A Gentle Introduction to Stata, Second Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will not only be able to use Stata well but also learn new aspects of Stata easily. 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 portion 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 should 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). 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 chose to arrange the topics as they would be 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 and 2006, are used throughout the book. The focus of the book is especially helpful for those in psychology and the 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. The second edition of the book has been updated to reflect new features in Stata 10 and includes a new chapter on the use of factor analysis to develop valid, reliable scale measures."--Publisher's website.

Author: Catherine Zimmer
Publisher:
ISBN: 9781526499486
Category : Graphic methods
Languages : en
Pages :

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Book Description
This dataset is designed for teaching how to create and interpret a stem and leaf plot. The dataset is a subset of the 2016 General Social Survey, and the example shows a stem and leaf plot for respondent's number of children by the social class of the respondent for African Americans. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

Author: Marley W. Watkins
Publisher: Routledge
ISBN: 1000426858
Category : Psychology
Languages : en
Pages : 210

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Book Description
This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using Stata. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of Stata code and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face when applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.

Author: Mehmet Mehmetoglu
Publisher: SAGE
ISBN: 1529788463
Category : Social Science
Languages : en
Pages : 421

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Book Description
Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: · Original case studies and data sets · Practical exercises and lists of commands for each chapter · Downloadable Stata programmes created to work alongside chapters · A wide range of detailed applications using Stata · Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.

Author: Abigail-Kate Reid
Publisher:
ISBN: 9781526489395
Category : Social sciences
Languages : en
Pages :

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Book Description
It is sometimes the case that the variables we want to use in our statistical analyses do not fulfil the assumptions of the method we wish to use. In other cases, we might want to transform a variable so that its distribution can meaningfully be compared with the distribution of another variable. This process of transformation is also known as standardisation. In this guide, we introduce some of the transformations and methods for standardisation that are commonly in use and when to use them. We demonstrate these using data from the General Social Survey 2004-2016. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

Author: J. Micah Roos
Publisher: SAGE Publications
ISBN: 154437514X
Category : Social Science
Languages : en
Pages : 107

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Book Description
Measurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit. Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code for R, Stata, and Mplus.

Author: Julie Scott Jones
Publisher:
ISBN: 9781526498649
Category : Interpersonal attraction
Languages : en
Pages :

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Book Description
This dataset is for learning to use Bartlett's test of Homogeneity of Variances. The dataset is a subset of data derived from the 2016--17 General Social Survey (GSS), and the example tests whether how individuals rate their own physical attractiveness varies by sex amongst a group of married respondents. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

Author: Alan C. Acock
Publisher:
ISBN: 9781597181778
Category : Stata
Languages : en
Pages :

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Book Description
Discovering Structural Equation Modeling Using Stata is devoted to Stata's sem command and all it can do. Learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. Each model covered is presented along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. The datasets used are downloadable, and you are encouraged to run the programs in a hands-on approach to learning. A particularly exciting feature of Stata is the SEM builder. This graphic interface for structural equation modeling allows you to draw publication-quality path diagrams and to fit the models without writing any programming code. When you fit a model with the SIM builder, Stata automatically generates the complete code that you can save for future use. Use of this unique tool is extensively covered in an appendix, and brief examples appear throughout the text. A miminal background in multiple regression is sufficient to benefit from this text. While it would be helpful to have some experience with Stata, it is not essential. Though the primary audience is those who are new to structural equation modeling, those who are already familiar with it will find this text useful for the Stata code it covers. Overall, the text is intended to be practical and will serve as a useful reference --