Learning From Data

An Introduction to Statistical Reasoning

By Arthur Glenberg, Matthew Andrzejewski

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• ISBN: 978-0-8058-4921-9
• Binding: Hardback
• Published by: Psychology Press
• Publication Date: 9th August 2007
• Pages: 440

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About the Book

Learning from Data focuses on how to interpret psychological data and statistical results. The authors review the basics of statistical reasoning to helpstudents better understand relevant data that affecttheir everyday lives. Numerous examples based on

current research and events are featured throughout.To facilitate learning, authors Glenberg and Andrzejewski:

• Devote extra attention to explaining the more difficult concepts and the logic behind them

• Use repetition to enhance students’ memories with multiple examples, reintroductions of the major concepts, and a focus on these concepts in the problems

• Employ a six-step procedure for describing all statistical tests from the simplest to the most complex

• Provide end-of-chapter tables to summarize the hypothesis testing procedures introduced

• Emphasizes how to choose the best procedure in the examples, problems and endpapers

• Focus on power with a separate chapter and power analyses procedures in each chapter

• Provide detailed explanations of factorial designs, interactions, and ANOVA to help students understand the statistics used in professional journal articles.

The third edition has a user-friendly approach:

• Designed to be used seamlessly with Excel, all of the in-text analyses are conducted in Excel, while the book’s CD contains files for conducting analyses in Excel, as well as text files that can be analyzed in SPSS, SAS, and Systat

• Two large, real data sets integrated throughout illustrate important concepts

• Many new end-of-chapter problems (definitions, computational, and reasoning) and many more on the companion CD

• Online Instructor’s Resources includes answers to all the exercises in the book and multiple-choice test questions with answers

• Boxed media reports illustrate key concepts and their relevance to realworld issues

• The inclusion of effect size in all discussions of power accurately reflects the contemporary issues of power, effect size, and significance.

Learning From Data, Third Edition is intended as a text for undergraduate or

beginning graduate statistics courses in psychology, education, and other

applied social and health sciences.

Reviews

"My teaching assistants and students, as well as other statistics instructors in my department, regard it as the best introductory statistics book available…The connection of the dialogue with the real world … is the book’s greatest strength. It keeps … many of the students engaged in a subject where they often expect to be bored." -Daniel S. Levine, PhD, University of Texas at Arlington

"…it is a rigorous yet clear text with an emphasis on power that …is lacking in many other introductory texts… I love the idea of focusing on Excel…I … have been using Glenberg for the past 4 or 5 years….I will seriously consider its adoption (and almost certainly will adopt it)." -Richard E. Zinbarg, PhD, Northwestern University

Praise for the first edition:
"...an unusually attractive new entry in the introductory statistics sweepstakes....Chapters are well organized....Examples seem to be clear and easy to follow, with a six part scheme used consistently to outline statistical tests."
—Contemporary Psychology

Table of Contents

Contents: Preface. Why Statistics? Part I: Descriptive Statistics. Frequency Distributions and Percentiles. Central Tendency and Variability. z Scores and Normal Distributions. Part II: Introduction to Inferential Statistics. Overview of Inferential Statistics. Probability. Sampling Distributions. Logic of Hypothesis Testing. Power. Logic of Parameter Estimation. Part III: Applications of Inferential Statistics. Inferences About Population Proportions Using the z Statistic. Inferences About µ When o Is Unknown: The Single Sample t Test. Comparing Two Populations: Independent Samples. Random Sampling, Random Assignment, and Causality. Comparing Two Populations: Dependent Samples. Comparing Two Population Variances: The F Statistic. Comparing Multiple Population Means: One-Factor ANOVA. Introduction to Factorial Designs. Computational Methods for the Factorial ANOVA. Describing Linear Relationships: Regression. Measuring the Strength of Linear Relationships: Correlation. Inferences From Nominal Data: The X² Statistic.

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