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专著推荐 | 教育研究中SPSS的应用(2卷本)

语言学通讯 语言学通讯 2021-05-15

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导语


SPSS是软件英文名称的首字母缩写,全称为Statistical Package for the Social Sciences,即“社会科学统计软件包”,是世界上应用最广泛的专业统计软件之一。

教育统计学是运用数理统计的原理和方法研究教育问题的一门应用科学。其主要任务是研究如何搜集、整理、分析由教育调查和教育实验等途径所获得的数字资料,并以此为依据,进行科学推断,从而揭示蕴含在教育现象中的客观规律。

通讯君推荐SAGE集团的两本SPSS教育研究应用著作,主要从教育统计概述、SPSS应用、描述性统计分析、t检验、方差分析、相关分析、回归分析、卡方检验、项目分析、因子分析、信度分析以及图形统计分析等方面对SPSS在教育统计中的应用进行详细的介绍。力图帮助学习者学习该课程后能掌握各种教育统计的方法与技术;掌握用SPSS进行数据分析处理的基本方法与技术;学习正确理解和解释各种统计分析结果。

为什么要读英文版的著作?可以为广大应用语言学研究工作者量化研究、数据分析的SSCI期刊论文发表提供参考。


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Doing Quantitative Research in Education with SPSS (第二版)

 

Professor Daniel Muijs

 is Chair of Education. Previously he worked at the University of Manchester as Chair of Pedagogy and Teacher Development at the University of Manchester, School of Education, as Chair of School Leadership and Management at the University of Newcastle and as senior at Warwick Institute of Education. He is an acknowledged expert in the field of Educational and Teacher Effectiveness and is co-editor of the journal ‘School Effectiveness and School Improvement’. He has published widely in the areas of educational effectiveness, leadership and research methods, and has conducted research for government agencies (DCSF, NCSL, QCA), Charitable Trusts (Gatsby) and Research Councils (ESRC).

售价:434 元 RMB(含书价、税费、国际报关、国际物流、国内物流、税票等一揽子在内)

简介:Using datasets from real-life educational research and avoiding the use of mathematical formulae, the author guides students through the essential techniques that they will need to know, explaining each procedure using the latest version of SPSS. The datasets can also be downloaded from the book′s website, enabling students to practice the techniques for themselves.

This revised and updated second edition now also includes more advanced methods such as log linear analysis, logistic regression, and canonical correlation.

Written specifically for those with no prior experience of quantitative research, this book is ideal for education students and researchers in this field.

目录

Introduction to Quantitative Research

Experimental and Quasi-Experimental Research

Designing Non-Experimental Studies

Validity, Reliability and Generalizability

Introduction to PASW Statistics (IBM SPSS) and the Data Set

Univariate Statistics

Bivariate Analysis: Comparing Two Groups

Bivariate Analysis: Looking at the Relationship between Two Variables

Multivariate Analysis: Using Regression Models to Look at the Relationship between Several Predictors and One Dependent Variable

Using Analysis of Variance to Compare More Than Two Groups

Developing Scales and Measures: Item and Factor Analysis

One Step beyond: Introduction to Multilevel Modeling and Structural Equation Modeling


Discovering Statistics Using IBM SPSS Statistic第五版

 

Professor Andy Field

is professor of Quantitative Methods at the University of Sussex. Field did his B.Sc. Psychology 1991–1994 at City University London and his D.Phil. at University of Sussex from 1994–1997. Field is the author of several textbooks about statistics, which typically deal with software application of statistical theory in SPSS and R. His books are characterised by an irreverent, sometimes outrageous, writing style that is atypical of academic texts. His student-friendly approach to writing led to The Times Higher Education Supplement dubbing him 'the Harry Potter of the social sciences'.

售价:634 元RMB(含书价、税费、国际报关、国际物流、国内物流、税票等一揽子在内)

优点:本书已经更新到第五版,并且有配合著作的专门网站 https://www.discoveringstatistics.com/

简评:With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughou

样章试读(第一章内容试读PDF):http://www.sagepub.com/sites/default/files/upm-binaries/52063_00_Field_4e_SPSS_Prelims.pdf


第五版更新体现在:

A radical new design with original illustrations and even more colour

A maths diagnostic tool to help students establish what areas they need to revise and improve on.

A revamped digital resource that uses video, case studies, datasets and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills

New sections on replication, open science and Bayesian thinking

Now fully up to date with latest versions of IBM SPSS Statistics©.

目录

Chapter 1: Why is my evil lecturer forcing me to learn statistics?

 

Chapter 2: The SPINE of statistics What is the SPINE of statistics?


Chapter 3: The phoenix of statistics Problems with NHST


Chapter 4: The IBM SPSS Statistics environment

Versions of IBM SPSS Statistics


Chapter 5: Exploring data with graphs The art of presenting data


Chapter 6: The beast of bias What is bias?


Chapter 7: Non-parametric models

When to use non-parametric tests


Chapter 8: Correlation

Modelling relationships


Chapter 9: The Linear Model (Regression)

An Introduction to the linear model (regression)


Chapter 10: Comparing two means

Looking at differences


Chapter 11: Moderation, mediation and multicategory predictors

The PROCESS tool


Chapter 12: GLM 1: Comparing several independent means

Using a linear model to compare several means


Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis of covariance)

What is ANCOVA?

Chapter 14: GLM 3: Factorial designs

Factorial designs


Chapter 15: GLM 4: Repeated-measures designs

Introduction to repeated-measures designs


Chapter 16: GLM 5: Mixed designs

Mixed designs


Chapter 17: Multivariate analysis of variance (MANOVA)


Chapter 18: Exploratory factor analysis

When to use factor analysis


Chapter 19: Categorical outcomes: chi-square and loglinear analysis

Analysing categorical data


Chapter 20: Categorical outcomes: logistic regression

What is logistic regression?


Chapter 21: Multilevel linear models

Hierarchical data

Chapter 22: Epilogue



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