What do you think?
Rate this book
Foundations of Behavioral Statistics: An Insight-Based Approach
With humor, extraordinary clarity, and carefully paced explanations and examples, Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research. Utilizing the general linear model to demonstrate how different statistical methods are related to each other, Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression. Special features include SPSS and Excel demonstrations that offer opportunities, in the book’s datasets and on Thompson’s website, for further exploration of statistical dynamics.
457 pages, Paperback
First published March 21, 2006
1 person is currently reading
7 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
January 20, 2018
My review is biased; I had Dr. Thompson as a professor for my stats courses at Texas A&M while in graduate school. Like any other graduate student, you don't necessarily understand the value of what's being taught to you at the time when you have to worry about midterms, finals, papers, and (in Dr. Thompson's classes) oral exams.
The point of this text is not to teach you the mechanical steps of statistical calculation (software can do that for you) but rather understanding the logic underlying them. The ultimate goal, in my opinion, is to arrive at the idea that many statistical procedures are best understood as part of a single, unifying hierarchical framework (i.e., the General Linear Model). This revelation allows you to understand how (most) statistical methods are related and different from one another. There is quite a lot of emphasis on reporting effect sizes and confidence intervals in order to report meaningful statistics, as well as what these actually mean in context.
As you get further into the text, chapters will require more than one reading. This may make it seem undesirable in a textbook, but it's actually due to his "insight-based approach." You'll have lots of "aha!" moments, and you'll arrive at a point of truly understand the nuances. To put it into perspective, his tests were essentially written as "riddles" (that's how it felt, at least) where, if you had a true grasp of the content, you could deduce the answer.
Dr. Thompson recommended reading "Reading Research and Statistics" by Schuyler W. Huck in order to further grasp how to report statistics procedures and results accurately and what should (and should not) be included. I found this text really helpful (and still use it) as it also cites direct quotes from research to explain good and bad examples of reporting results. Just thought I'd mention it.
The point of this text is not to teach you the mechanical steps of statistical calculation (software can do that for you) but rather understanding the logic underlying them. The ultimate goal, in my opinion, is to arrive at the idea that many statistical procedures are best understood as part of a single, unifying hierarchical framework (i.e., the General Linear Model). This revelation allows you to understand how (most) statistical methods are related and different from one another. There is quite a lot of emphasis on reporting effect sizes and confidence intervals in order to report meaningful statistics, as well as what these actually mean in context.
As you get further into the text, chapters will require more than one reading. This may make it seem undesirable in a textbook, but it's actually due to his "insight-based approach." You'll have lots of "aha!" moments, and you'll arrive at a point of truly understand the nuances. To put it into perspective, his tests were essentially written as "riddles" (that's how it felt, at least) where, if you had a true grasp of the content, you could deduce the answer.
Dr. Thompson recommended reading "Reading Research and Statistics" by Schuyler W. Huck in order to further grasp how to report statistics procedures and results accurately and what should (and should not) be included. I found this text really helpful (and still use it) as it also cites direct quotes from research to explain good and bad examples of reporting results. Just thought I'd mention it.
Displaying 1 of 1 review