Practical Multilevel Modeling Using R (Advanced Quantitative Techniques in the S

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subject_code
GPS
gpsr_safety_attestation
true
target_audience
Tertiary education
is_adult_product
false
edition_number
1
binding
paperback
edition
1
MPN
9781071846124
batteries_required
false
manufacturer
SAGE Publications, Inc
Brand
N/A
series_title
Advanced Quantitative Techniques in the Social Sciences
number_of_items
1
pages
256
genre
Research methods: general
part_number
9781071846124
publication_date
2022-12-12T00:00:01Z
unspsc_code
55101500
batteries_included
false
ISBN
9781071846124
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Product Identifiers

Publisher
SAGE Publications, Incorporated
ISBN-10
1071846124
ISBN-13
9781071846124
eBay Product ID (ePID)
28057256540

Product Key Features

Number of Pages
256 Pages
Publication Name
Practical Multilevel Modeling Using R
Language
English
Publication Year
2022
Subject
General
Type
Not Available
Subject Area
Mathematics, Reference
Author
Francis L. Huang
Series
Advanced Quantitative Techniques in the Social Sciences Ser.
Format
Book, Other

Dimensions

Item Height
3.9 in
Item Weight
19 Oz
Item Length
0.2 in
Item Width
2.8 in

Additional Product Features

LCCN
2022-047109
Reviews
Practical examples and simple solutions for complex multilevel analysis tasks make this book a great reference source for students and researchers alike., This book is an excellent book for a graduate level multilevel modeling course that utilizes R; it perfectly combines the depth and breadth on this topic., The book describes multilevel modeling well by minimizing mathematics, utilizing the R software, and moving from a simple case to more complex cases. The author includes the syntax and output so that readers can test it themselves, and visual examples are given. Doctoral students in education and psychology will benefit from this book., A major strength of this book is its accessibility. Huang effortlessly bridges the divide between the sometimes-abstruse literature on advanced statistics and the needs of applied researchers who lack extensive quantitative training. The result is an approachable text that covers all the basics, but also does not shy away from important advanced topics such as diagnostics, detecting and handling heteroscedasticity, and missing data handling methods. This book would make not only a useful guide to the application of multilevel modeling, but could also serve as an excellent companion text for a course on multilevel modeling., A major strength of this book is its accessibility. Huang effortlessly bridges the divide between the sometimes-abstruse literature on advanced statistics and the needs of applied researchers who lack extensive quantitative training. The result is an approachable text that covers all the basics, but also does not shy away from important advanced topics such as diagnostics, detecting and handling heteroscedasticity, and missing data handling methods. This book would make not only a useful guide to the application of multilevel modeling, but could also serve as an excellent companion text for a course on multilevel modeling. -- Kristopher J. Preacher This book is an excellent book for a graduate level multilevel modeling course that utilizes R; it perfectly combines the depth and breadth on this topic. -- Wei Wang
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
1 Introduction 2 The unconditional means model 3 Adding predictors to a random intercepts model 4 Investigating cross-level interactions and random slope models 5 Understanding growth models 6 Centering in multilevel models 7 Multilevel modeling diagnostics 8 Multilevel logistic regression models 9 Modeling data structures with three (or more) levels 10 Missing data in multilevel models 11 Basic power analyses for multilevel models 12 Alternatives to multilevel models
Intended Audience
College Audience
Synopsis
Practical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text., This book provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The accompanying website includes R code and the dataset used in the book.
LC Classification Number
QA278.2.H83 2023

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