How to Learn Statistics and SPSS Software with Discovering Statistics Using IBM SPSS Statistics 4th Edition
Discovering Statistics Using IBM SPSS Statistics 4th Edition Pdf
If you are looking for a comprehensive and accessible textbook on statistics that covers both theory and practice, then you might want to check out Discovering Statistics Using IBM SPSS Statistics by Andy Field. This book is designed to help students from various disciplines learn how to use statistics and SPSS software in their research projects. In this article, we will give you an overview of what this book is about, who is the author, what are the main features of the 4th edition, and what are the topics covered in each chapter.
Discovering Statistics Using Ibm Spss Statistics 4th Edition Pdf
Introduction
Statistics can be a daunting subject for many students, especially if they have no prior background or interest in it. However, statistics is an essential tool for conducting and evaluating research in many fields, such as psychology, education, business, health sciences, and social sciences. Therefore, learning statistics can help students develop critical thinking skills, understand scientific methods, and communicate their findings effectively.
What is Discovering Statistics Using IBM SPSS Statistics?
Discovering Statistics Using IBM SPSS Statistics is a textbook that aims to make statistics fun and engaging for students. It combines humour, cartoons, anecdotes, and examples from real-life research to explain statistical concepts and techniques in a clear and intuitive way. It also guides students through the process of using SPSS software to perform data analysis and interpretation. The book covers a wide range of topics, from basic descriptive statistics to advanced multivariate models. It also includes exercises, quizzes, videos, datasets, case studies, and online resources to help students practice and apply what they learn.
Who is the author of the book?
The author of the book is Andy Field, who is a professor of quantitative methods at the University of Sussex, UK. He has over 20 years of experience in teaching and researching statistics and has won several awards for his teaching excellence and innovation. He is also the author of several other books on statistics, such as An Adventure in Statistics: The Reality Enigma and How to Design and Report Experiments. He is known for his quirky and humorous style of writing and his use of pop culture references and fictional characters to illustrate statistical ideas.
What are the main features of the 4th edition?
The 4th edition of the book was published in 2013 and has several new and improved features, such as:
A new web-based facility that allows students to practice questions online and get instant feedback and solutions.
A mobile study facility that enables students to access revision material and study tips on their smartphones and tablets.
More instructor support materials for different disciplines, such as education, sport sciences, business, management, and health sciences.
More compatibility with recent versions of SPSS software, up to and including version 20.0.
New sections on topics such as replication, open science, Bayesian thinking, multilevel linear models, and more.
Content overview
The book is divided into five parts, each containing several chapters. Here is a brief summary of what each part and chapter covers:
Part One: The Basics of Research
This part introduces the fundamentals of research design, data collection, measurement, sampling, ethics, and hypothesis testing. It also explains how to use SPSS software to enter, manipulate, and transform data.
Chapter 1: Why is my evil lecturer forcing me to learn statistics?
This chapter explains why statistics is important for research and how it can help students answer questions, test theories, and make decisions. It also discusses the types of data, variables, scales of measurement, and levels of measurement.
Chapter 2: Everything you ever wanted to know about statistics (well, sort of)
This chapter introduces the concepts of descriptive and inferential statistics, parameters and statistics, sampling error and sampling distribution, standard error and confidence intervals, null hypothesis significance testing (NHST), p-values and effect sizes.
Chapter 3: The IBM SPSS Statistics environment
This chapter shows how to use SPSS software to create data files, enter data, define variables, label values, compute new variables, recode values, select cases, split files, weight cases, sort cases, merge files, aggregate data, transpose data, and save data.
Part Two: Describing Data
This part covers the methods of summarizing and presenting data using graphs, tables, measures of central tendency, measures of dispersion, measures of skewness and kurtosis. It also explains how to deal with outliers, missing values, normality assumptions, and non-parametric tests.
Chapter 4: Exploring data with graphs
This chapter demonstrates how to use SPSS software to create different types of graphs for different types of data. It also explains how to interpret graphs and avoid common pitfalls.
Chapter 5: The beast of bias
```html Chapter 6: Non-parametric models
This chapter introduces the concept of non-parametric models, which are alternative methods of data analysis that do not rely on normality assumptions or parametric tests. It also explains how to use SPSS software to perform non-parametric tests such as the sign test, the Wilcoxon signed-rank test, the Mann-Whitney U test, the Kruskal-Wallis H test, and the Friedman test.
Part Three: Bivariate Statistics
This part covers the methods of exploring and testing the relationships between two variables using correlation, regression, and t-tests. It also explains how to use SPSS software to perform these analyses and interpret the results.
Chapter 7: Correlation
This chapter introduces the concept of correlation, which is a measure of the strength and direction of the linear relationship between two variables. It also explains how to use SPSS software to calculate and graph different types of correlation coefficients, such as Pearson's r, Spearman's rho, Kendall's tau-b, and point-biserial correlation.
Chapter 8: Regression
This chapter introduces the concept of regression, which is a method of predicting one variable from another variable using a mathematical equation. It also explains how to use SPSS software to perform different types of regression analyses, such as simple linear regression, multiple linear regression, hierarchical multiple regression, and logistic regression.
Chapter 9: Comparing two means
This chapter introduces the concept of comparing two means, which is a method of testing whether there is a significant difference between the average scores of two groups on a variable. It also explains how to use SPSS software to perform different types of t-tests, such as independent-samples t-test, paired-samples t-test, and one-sample t-test.
Part Four: Multivariate Statistics
This part covers the methods of exploring and testing the relationships between more than two variables using advanced regression techniques, analysis of variance (ANOVA), and multivariate analysis of variance (MANOVA). It also explains how to use SPSS software to perform these analyses and interpret the results.
Chapter 10: Moderation, mediation and more regression
This chapter introduces the concepts of moderation and mediation, which are ways of examining how the relationship between two variables is influenced by a third variable. It also explains how to use SPSS software to test for moderation and mediation effects using multiple regression and path analysis.
Chapter 11: Comparing several means: ANOVA (GLM 1)
This chapter introduces the concept of comparing several means, which is a method of testing whether there is a significant difference between the average scores of more than two groups on a variable. It also explains how to use SPSS software to perform different types of ANOVA, such as one-way ANOVA, factorial ANOVA, repeated-measures ANOVA, mixed-design ANOVA, and ANCOVA.
Chapter 12: Factorial ANOVA (GLM 2)
This chapter expands on the concept of factorial ANOVA, which is a type of ANOVA that involves two or more independent variables. It also explains how to use SPSS software to perform factorial ANOVA and interpret the main effects and interaction effects.
Chapter 13: Repeated-measures designs (GLM 3)
This chapter expands on the concept of repeated-measures designs, which are types of designs that involve measuring the same participants on more than one occasion. It also explains how to use SPSS software to perform repeated-measures ANOVA and interpret the within-subjects effects and between-subjects effects.
Chapter 14: Mixed design ANOVA (GLM 4)
This chapter expands on the concept of mixed design ANOVA, which is a type of ANOVA that involves both independent groups and repeated measures. It also explains how to use SPSS software to perform mixed design ANOVA and interpret the mixed effects.
Part Five: Advanced Topics
This part covers some advanced topics in statistics that are relevant for more complex research questions and data structures. These topics include multivariate analysis of variance (MANOVA), exploratory factor analysis (EFA), categorical data analysis, and multilevel linear models (MLM).
Chapter 15: Multivariate analysis of variance (MANOVA) and discriminant analysis
This chapter introduces the concept of multivariate analysis of variance (MANOVA), which is a method of testing whether there is a significant difference between groups on more than one dependent variable. It also explains how to use SPSS software to perform MANOVA and discriminant analysis, which is a method of classifying cases into groups based on their scores on multiple variables.
Chapter 16: Exploratory factor analysis
This chapter introduces the concept of exploratory factor analysis (EFA), which is a method of identifying the underlying dimensions or factors that explain the patterns of correlations among a set of variables. It also explains how to use SPSS software to perform EFA and interpret the factor loadings, factor scores, and factor rotation.
Chapter 17: Categorical outcomes: chi-square and loglinear analysis
This chapter introduces the concept of categorical outcomes, which are outcomes that have two or more discrete categories or levels. It also explains how to use SPSS software to perform chi-square tests and loglinear analysis, which are methods of testing the associations and interactions among categorical variables.
Chapter 18: Categorical outcomes: logistic regression
This chapter introduces the concept of logistic regression, which is a type of regression that predicts a binary outcome (such as yes/no, success/failure, etc.) from one or more predictor variables. It also explains how to use SPSS software to perform logistic regression and interpret the odds ratios, confidence intervals, and model fit.
Chapter 19: Multilevel linear models (MLM)
This chapter introduces the concept of multilevel linear models (MLM), which are types of models that account for the hierarchical or nested structure of data. For example, students nested within classes, patients nested within hospitals, etc. It also explains how to use SPSS software to perform MLM and interpret the fixed effects, random effects, and variance components.
Conclusion
In conclusion, Discovering Statistics Using IBM SPSS Statistics is a comprehensive and engaging textbook that covers both the theory and practice of statistics using SPSS software. It is suitable for students from various disciplines who want to learn how to use statistics and SPSS software in their research projects. The book is written in a humorous and conversational style that makes statistics fun and accessible. The book also provides many exercises, quizzes, videos, datasets, case studies, and online resources to help students practice and apply what they learn.
FAQs
What is the difference between the 4th edition and the 5th edition of the book?
The 5th edition of the book was published in 2018 and has some updated features, such as:
A new chapter on structural equation modelling (SEM).
A new section on multilevel logistic regression.
A new section on latent growth curve models.
A new section on Bayesian statistics.
A new section on reproducible research.
More compatibility with recent versions of SPSS software.
Where can I find the solutions to the exercises in the book?
You can find the solutions to the exercises in the book on the companion website: https://study.sagepub.com/field5e. You will need to register with your email address and create a password to access the solutions.
Where can I find the datasets used in the book?
You can find the datasets used in the book on the companion website: https://study.sagepub.com/field5e. You can download them as SPSS data files (.sav) or Excel files (.xlsx).
Where can I find the videos that accompany the book?
You can find the videos that accompany the book on the companion website: https://study.sagepub.com/field5e. You can watch them online or download them as MP4 files.
How can I contact the author of the book?
You can contact the author of the book by email: a.field@sussex.ac I have already finished writing the article. Here is the custom message you requested: 71b2f0854b