Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods
暫譯: 多變量社會科學數據分析：統計機器學習方法

Name: Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods
Price: 2812 TWD
Availability: OnlineOnly
Author: Moustaki, Irini, Steele, Fiona, Chen, Yunxiao
ISBN: 1032763728

Moustaki, Irini, Steele, Fiona, Chen, Yunxiao

出版商: CRC
出版日期: 2026-02-10
售價: $2,960
貴賓價: 9.5 折 $2,812
語言: 英文
頁數: 481
裝訂: Quality Paper - also called trade paper
ISBN: 1032763728
ISBN-13: 9781032763729
相關分類: Machine Learning

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商品描述

Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.

After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.

Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.

Features

Contains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.
Connects topics with terminology and principles of machine learning.
Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.
Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.
Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.
Covers models for hierarchical and longitudinal data and their connections to latent variable models.
Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.

The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

商品描述(中文翻譯)

根據作者在該領域的多樣化研究和教學經驗，多變量社會科學數據分析：統計機器學習方法（第三版）使讀者能夠基本理解如何在社會科學中使用關鍵的多變量方法。這一版對數學和統計的要求最低，擴展了主題，包括圖形建模、縱向數據模型、類別變數的結構方程模型，以及有序、名義和連續變數的潛在類別分析。它還將這些主題與機器學習的術語和原則相連接，旨在幫助讀者理解多變量分析方法與數據科學領域進展之間的聯繫。

在書的第一部分描述數據總結方法後，作者考慮了回歸分析。這一章節提供了書的兩個部分之間的聯繫，標誌著從描述性方法轉向推論性方法。其餘的文本處理基於模型的方法，主要對生成數據的過程進行推論。

作者大量依賴來自各個學科的數值範例，提供了對這些方法的目的和運作的見解，以及對分析結果的解釋。許多相同的範例在整本書中被用來說明方法之間的聯繫。在大多數章節中，作者提出了超越傳統實踐的進一步工作建議，鼓勵讀者在社會科學研究中探索新領域。

特色

包含有關無向圖形建模和縱向數據模型的新章節，以及新的材料，如K-means、交叉驗證、類別變數的結構方程模型、有序、名義和連續變數的潛在類別分析，以及缺失數據的處理。
將主題與機器學習的術語和原則相連接。
提供多個現實應用的範例，包括投票偏好、社會態度、教育評估、再犯率和健康。
涵蓋總結、描述和探索多變量數據集的方法，包括縱向數據。
通過詳細介紹項目反應理論、連續和類別數據的因素分析以及類別潛在變數模型等方法，建立潛在變數建模的統一方法。
涵蓋層次和縱向數據模型及其與潛在變數模型的聯繫。
提供文本或書籍網站中的數據集完整版本，並在網站上提供實施分析的軟體代碼。

本書為數學和統計訓練有限的學生和研究人員提供了一個平衡且易於接觸的資源。它作為多變量分析課程的實用資源，以及在應用研究中應用這些技術的指南。

作者簡介

Irini Moustaki is a professor of Statistics in the Department of Statistics at the London School of Economics and Political Science. She received her bachelor's degree in Statistics and Computer Science from the Athens University of Economics and Business and her MSc and PhD in Statistics from the LSE. Her research interests are in latent variable models and structural equation models. Her methodological work includes treating missing data, longitudinal data, outlier detection, goodness-of-fit tests, and advanced estimation methods. Furthermore, she has made methodological and applied contributions to comparative cross-national studies and epidemiological studies on rare diseases. Irini received an honorary doctorate from the Faculty of Social Sciences, Uppsala University, in 2014. She is a Fellow of the British Academy. She was the Executive Editor of the journal Psychometrika from November 2014 to December 2018 and the President of the Psychometric Society from July 2021 to July 2022.

Fiona Steele is a Professor of Statistics in the Department of Statistics at the London School of Economics and Political Science (LSE). She holds a Ph.D. in Social Statistics from the University of Southampton. Her research interests are in developments of statistical methods that are motivated by social science problems. Her areas of expertise include longitudinal data analysis, multilevel and latent variable modelling, and dyadic data analysis. She has worked on a range of applications in demography, education, family psychology and health. Fiona has directed several research grants on methods for multilevel and longitudinal data analysis. She also led the development of, and contributed modules to, the popular online 'LEMMA' course on multilevel modelling. Fiona is a Fellow of the British Academy and was awarded a CBE and the Royal Statistical Society Howard Medal for her contributions to social statistics.

Yunxiao Chen is an Associate Professor of Statistics in the Department of Statistics at the London School of Economics and Political Science (LSE). He holds a Ph.D. in Statistics from Columbia University in the City of New York. His research focuses on the intersection of multivariate statistics and machine learning, where he develops models, computational algorithms, and statistical theories for learning from complex data and applies them to education, psychology, and other social science disciplines. Dr. Chen has received numerous awards, including the 2018 Brenda H. Lloyd Dissertation Award from the National Council on Measurement in Education and the 2022 Early Career Award from the Psychometric Society. He was also a Spencer Foundation/NAEd Postdoctoral Fellow at the United States National Academy of Education from 2018 to 2020. His work has appeared in leading journals in statistics and machine learning, such as the Journal of the American Statistical Association, Biometrika, Journal of the Royal Statistical Society, Series A (Statistics in Society), and the Journal of Machine Learning Research. Additionally, Dr. Chen serves as an associate editor for several prominent publications, including Psychometrika, the British Journal of Mathematical and Statistical Psychology, the Journal of Educational and Behavioural Statistics, and Psychological Methods. This book, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, draws on his years of experience teaching and researching in the field.

David John Bartholomew was a professor of Statistics in the Department of Statistics at the London School of Economics and Political Science from 1973 to 1996, when he became an emeritus professor. His research interests were in the areas of stochastic modelling, social measurement, factor analysis and latent variable modelling. Bartholomew published some 25 books and more than 140 research papers. He served as pro-director of the LSE from 1988 to 1991. He served as co-editor of the Journal of the Royal Statistical Society, Series B, from 1966 to 1969 and president from 1993 to 1995. He was awarded the Guy medal in bronze in 1971. He was elected a fellow of the British Academy in 1987. Professor Bartholomew passed away in 2017.

作者簡介(中文翻譯)

伊莉尼·穆斯塔基（Irini Moustaki）是倫敦政治經濟學院統計系的統計學教授。她在雅典經濟與商業大學獲得統計學和計算機科學的學士學位，並在倫敦政治經濟學院獲得統計學的碩士和博士學位。她的研究興趣包括潛變量模型和結構方程模型。她的方法論工作涵蓋缺失數據處理、縱向數據、異常值檢測、擬合優度檢驗和先進的估計方法。此外，她在比較跨國研究和罕見疾病的流行病學研究中做出了方法論和應用貢獻。伊莉尼於2014年獲得烏普薩拉大學社會科學院的榮譽博士學位。她是英國學院的院士。她曾於2014年11月至2018年12月擔任期刊Psychometrika的執行編輯，並於2021年7月至2022年7月擔任心理測量學會的會長。

菲奧娜·史蒂爾（Fiona Steele）是倫敦政治經濟學院統計系的統計學教授。她擁有南安普敦大學社會統計學的博士學位。她的研究興趣在於受社會科學問題啟發的統計方法發展。她的專業領域包括縱向數據分析、多層次和潛變量建模，以及雙向數據分析。她在人口學、教育、家庭心理學和健康等多個應用領域工作過。菲奧娜曾指導多個關於多層次和縱向數據分析方法的研究計劃。她還主導了流行的在線多層次建模課程「LEMMA」的開發，並貢獻了多個模組。菲奧娜是英國學院的院士，並因其對社會統計的貢獻而獲得CBE和皇家統計學會霍華德獎章。

陳雲霄（Yunxiao Chen）是倫敦政治經濟學院統計系的副教授。他在紐約哥倫比亞大學獲得統計學博士學位。他的研究專注於多變量統計學和機器學習的交集，開發模型、計算算法和統計理論，以從複雜數據中學習，並將其應用於教育、心理學和其他社會科學學科。陳博士獲得了多項獎項，包括2018年全國教育測量委員會的布倫達·H·勞埃德論文獎和2022年心理測量學會的早期職業獎。他還曾於2018年至2020年擔任美國國家教育學院的斯賓塞基金會/NAEd博士後研究員。他的研究成果發表在統計學和機器學習的領先期刊上，如美國統計協會期刊、Biometrika、皇家統計學會期刊B系列（社會中的統計）和機器學習研究期刊。此外，陳博士還擔任多個知名出版物的副編輯，包括Psychometrika、英國數學與統計心理學期刊、教育與行為統計期刊和心理方法。本書多變量社會科學數據分析：統計機器學習方法基於他多年在該領域的教學和研究經驗。

大衛·約翰·巴索洛繆（David John Bartholomew）於1973年至1996年擔任倫敦政治經濟學院統計系的統計學教授，並於1996年成為名譽教授。他的研究興趣包括隨機建模、社會測量、因子分析和潛變量建模。巴索洛繆出版了約25本書籍和140多篇研究論文。他於1988年至1991年擔任倫敦政治經濟學院的副院長。他曾於1966年至1969年擔任皇家統計學會期刊B系列的共同編輯，並於1993年至1995年擔任會長。他於1971年獲得青銅獎的蓋伊獎章，並於1987年當選為英國學院院士。巴索洛繆教授於2017年去世。