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:
The third edition has a user-friendly approach:
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.
"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."
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.