Latent Variable Modeling Using R

A Step-by-Step Guide

A. Alexander Beaujean

• Publisher: Routledge
• Year: 2014
• ISBN:
  9781317970729
• Edition: 1
• Topic: Psychologie
• Subtopic: Forschung & Methodik in der Psychologie

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs. Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in R.

Each chapter features background information, boldfaced key terms defined in the glossary, detailed interpretations of R output, descriptions of how to write the analysis of results for publication, a summary, R based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own R syntax. Examples using data from published work across a variety of disciplines demonstrate how to use R syntax for analyzing and interpreting results. R functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R functions, packages, and datasets. The book and accompanying website at http://blogs.baylor.edu/rlatentvariable/ provides all of the data for the book's examples and exercises as well as R syntax so readers can replicate the analyses. The book reviews how to enter the data into R, specify the LVMs, and obtain and interpret the estimated parameter values.

The book opens with the fundamentals of using R including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other R latent variable models packages; and solutions for each chapter's exercises.

Intended as a supplementary text for graduate and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. Prerequisites include familiarity with basic statistical concepts, but knowledge of R is not assumed.

1
Introduction to R

Chapter Contents

1.1 Background

1.1.1 Installing R

1.1.2 Starting R

1.1.3 Functions

1.1.4 Packages

1.1.5 Data Input

1.1.6 Access a Variable Within a Dataset

1.1.7 Example: Entering Data and Accessing Variables

1.1.8 Data Manipulation

1.1.9 Missing Data

1.1.10 Categorical Data

1.1.11 Summarize Data

1.1.12 Common Statistics

1.2 Hints for Using R

1.3 Summary

1.4 Exercises

1.5 References & Further Readings

1.1 Background

R is an open-source statistical software programming language and environment for statistical computing. It is currently maintained by the R Development Core Team (an international team of volunteer developers), and the R web page (also known as Comprehensive R Archive Network [CRAN]) is http://www.r-project.org. This is the main site for R information and obtaining the software.

Since R is syntax-based, as opposed to using a point-and-click interface, it may appear too complex for a non-specialist, but this really is not the case. Using syntax allows R a level of ease and flexibility not available with other programs. Take, for example, the process of analyzing a multiple regression model. While point-and-click type software can provide quick results for a single analysis, to analyze different models (e.g., using different predictor sets) or use the information from the regression for another analysis (e.g, make a scatterplot with a line of best fit, check model assumptions), it often takes many point-and-click iterations to produce the desired results. Moreover, if you have to stop your analysis and return to it days or weeks later, it can be hard to remember what you previously accomplished with the analysis or even the point-and-click sequences used to obtain the previous results. With R, though, many of these problems are not an issue. As R can store the results from the regression into objects, you can specify the parts of the regression results that need to be extracted for subsequent analysis. Furthermore, you can analyze multiple models and have R display their coe˚cients in a single window instead of opening many results windows, as many point-and-click programs would produce. Because these multiple models were analyzed using syntax, if you save the syntax in an external file, then you can return to the analysis months later and exactly reproduce the previous results by simply pasting the syntax back into R.

Figure 1.1 Typical on-screen text when starting R.

1.1.1 Installing R

R can be run under Windows, Mac, and Unix-type operating systems. To download R, go to http://www.r-project.org/ and select the CRAN hyperlink. This opens a list of places (mirrors) from which to download the program. Select a hyperlink from a mirror in your country, which loads a page with hyperlinks to download R for your operating system (select the precompiled binary distribution).

There are some graphical user interfaces (GUIs) for R developed by third parties. A partial list can be found at R Wiki (http://rwiki.sciviews.org/doku.php?id=guis:projects) and CRAN (http://www.r-project.org/GUI). There are also many text editors that are either designed to interact with R, or can be modified to do so. Typing R text editor (or something similar) into an Internet search engine will bring up many different options as well as people’s opinions about them.

1.1.2 Starting R

When initially starting R in interactive mode (as opposed to batch mode), the screen looks something like Figure 1.1. The > symbol is called the prompt. It is not typed; instead, it is used to indicate where to type. When writing syntax in R directly, type in all commands at the > prompt. If a command is too long to fit on a single line, a + is used for the continuation prompt.

If you type >, R interprets it as “greater than.”

1.1.3 Functions

R stores variables, data, functions, results, etc, in the computer’...