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In the first of a series of "how-to" essays on conducting qualitative data analysis, Ron Chenail points out the challenges of determining units to analyze qualitatively when dealing with text. He acknowledges that although we may read a document word-by-word or line-by-line, we need to adjust our focus when processing the text for purposes of conducting qualitative data analysis so we concentrate on meaningful, undivided entities or wholes as our units of analysis.

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips an

"Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture, environmental sciences, and chemistry. General introductions to multiway data types, methods, and estimation procedures are provided in addition to detailed explanations and advice for readers who would like to learn more about applying multiway methods. Using carefully laid out examples and engaging applications, the book begins with an introductory chapter that serves as a general overview of multiway analysis, including the types of problems it can address. Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly-encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, transformation, as well as robustness and stability issues"--Provided by publisher

This book consists of a practical, example-oriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently.This book is great for those who have experience with Clojure and who need to use it to perform data analysis. This book will also be hugely beneficial for readers with basic experience in data analysis and statistics

This SAGE-only collection offers a systematic, comprehensive overview of the 'best of' secondary data analysis published in our specialist methods journals and empirical subject journals. As such, it offers readers an excellent research and teaching resource from the foremost publisher in the field

The natural world is full of wonder and awe, and the National Parks of the United States are no exception. The first National Park, Yellowstone, was established March 1, 1872. The National Park Service (NPS), founded August 25, 1916, by President Theodore Roosevelt, is an agency of the United States federal government that manages all national parks, many national monuments, and other conservation and historical properties with various title designations. Since its founding, the NPS has preserved natural and cultural resources and values for the enjoyment, education, and inspiration of this and future generations. This analysis attempts to look in depth at National Parks, the species that reside in them, visitor numbers, and visitor reviews on the travel website TripAdvisor. ; https://openriver.winona.edu/urc2018/1004/thumbnail.jpg

Cover -- Half Title Page -- Series Editor -- Title Page -- Copyright -- Series Editor's Note -- Preface -- Contents -- 1. Introduction to Missing Data -- 1.1 Chapter Overview -- 1.2 Missing Data Patterns -- 1.3 Missing Data Mechanisms -- 1.4 Diagnosing Missing Data Mechanisms -- 1.5 Auxiliary Variables -- 1.6 Analysis Example: Preparing for Missing Data Handling -- 1.7 Older Missing Data Methods -- 1.8 Comparing Missing Data Methods via Simulation -- 1.9 Planned Missing Data -- 1.10 Power Analyses for Planned Missingness Designs -- 1.11 Summary and Recommended Readings -- 2. Maximum Likelihood Estimation -- 2.1 Chapter Overview -- 2.2 Probability Distributions versus Likelihood Functions -- 2.3 The Univariate Normal Distribution -- 2.4 Estimating Unknown Parameters -- 2.5 Getting an Analytic Solution -- 2.6 Estimating Standard Errors -- 2.7 Information Matrix and Parameter Covariance Matrix -- 2.8 Alternative Approaches to Estimating Standard Errors -- 2.9 Iterative Optimization Algorithms -- 2.10 Linear Regression -- 2.11 Significance Tests -- 2.12 Multivariate Normal Data -- 2.13 Categorical Outcomes: Logistic and Probit Regression -- 2.14 Summary and Recommended Readings -- 3. Maximum Likelihood Estimation with Missing Data -- 3.1 Chapter Overview -- 3.2 The Multivariate Normal Distribution Revisited -- 3.3 How Do Incomplete Data Records Help? -- 3.4 Standard Errors with Incomplete Data -- 3.5 The Expectation Maximization Algorithm -- 3.6 Linear Regression -- 3.7 Significance Testing -- 3.8 Interaction Effects -- 3.9 Curvilinear Effects -- 3.10 Auxiliary Variables -- 3.11 Categorical Outcomes -- 3.12 Summary and Recommended Readings -- 4. Bayesian Estimation -- 4.1 Chapter Overview -- 4.2 What Makes Bayesian Statistics Different? -- 4.3 Conceptual Overview of Bayesian Estimation -- 4.4 Bayes' Theorem -- 4.5 The Univariate Normal Distribution.

"The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website includes datasets and analysis examples from the book, up-to-date software information, and other resources. Subject areas/Key words: advanced quantitative methods, management, survey, longitudinal, structural equation modeling, handling, how to handle, incomplete, multivariate, social research, behavioral sciences, statistical techniques, textbooks, seminars, doctoral courses, multiple imputation, models, MCAR, MNAR, Bayesian Audience: Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science."--