e-O-Level Physics Learning Through Diagrams

Singapore Asia Publishers Pte Ltd
3
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 You will find this book interesting: Physics concepts presented in a diagrammatic form. Specially written to ease learning and to stimulate interest in Physics, this book will help students in acquiring and reinforcing Physics concepts, and especially the difficult ones, more easily and effectively.

This book makes learning easier through the following features:

  1. Learning Outcomes - Learning outcomes on the header point out the concepts that you should focus on in the process of learning.
  2. Important Concepts and Key Terms - The important concepts and key terms are presented clearly in simple language. Further explanations linked to the diagrams help you better understand the concepts.
  3. Interesting Visuals - Visual aids such as concept maps, flow charts and annotated diagrams are integrated to make the concepts easier to understand and remember.
  4. Real-life Examples - These examples show real-life application of concepts and explain the inquiries on the phenomena that happen in our everyday lives.
  5. Worked Examples - Step-by-step worked examples help to reinforce your skills in solving problems.
  6. Instant Facts - These are extra information that can help you acquire a more in-depth understanding of the topic under discussion.

This book complements the school curriculum and will certainly help in your preparation for the examinations.

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Additional Information

Publisher
Singapore Asia Publishers Pte Ltd
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Published on
Apr 26, 2012
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Pages
249
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ISBN
9789814387408
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Best For
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Language
English
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Genres
Education / General
Education / Secondary
Science / General
Science / Physics / General
Study Aids / Study Guides
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Content Protection
This content is DRM protected.
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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.

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