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Fundamental Statistics For The Behavioral Sciences,9780495811251
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Fundamental Statistics For The Behavioral Sciences



Pub. Date:
Wadsworth Publishing

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This is the 7th edition with a publication date of 1/25/2010.
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Customer Reviews

A clear and comprehensive introductory statistics text  April 20, 2011

This may be the single best textbook I have ever used, or it may just seem that way because of the difficulty most stat textbooks have in explaining their concepts. The author stresses the use of computers discusses several statistics programs available. He also thoughtfully provides exercise data for input to several a computer programs. Although I have tried using other introductory texts, I have yet to find a textbook as comprehensive and as clearly written as this. I rate the seller 5 stars and I plan to buy from ecampus again in the future.

Fundamental Statistics For The Behavioral Sciences: 5 out of 5 stars based on 1 user reviews.


David Howell's practical approach focuses on the context of statistics in behavioral research, with an emphasis on looking before leaping; investigating the data before jumping into a test. This provides you with an understanding of the logic behind the statistics: why and how certain methods are used rather than just doing techniques by rote.

Formulas used in the book are definitional rather than for purposes of calculation, as Howell believes formulas are there to help students define the concepts. This textbook offers hundreds of exercises, most of which are based on data from published research, promote student interest and provide the context of statistics in behavioral research.

Learn faster and understand more because Howell's texts moves you beyond number crunching, allowing you to discover the meaning of statistical results and how they relate to the research questions being asked.

Table of Contents

The importance of Context
Basic Terminology
Selection among Statistical Procedures
Using Computers
Basic Concepts
Scales of Measurement
Random Sampling
Displaying Data
Plotting Data
Stem-and-Leaf Displays
Reading Graphs
Alternative Methods of Plotting Data
Describing Distributions
Using Computer Programs to Display Data
Measures of Central Tendency
The Mode
The Median
The Mean
Relative Advantages of the Mode, the Median, and the Mean
Obtaining Measures of Central Tendency Using SPSS
A Simple Demonstration-Seeing Statistics
Measures of Variability
Interquartile Range and Other Range Statistics
The Average Deviation
The Variance
The Standard Deviation
Computational Formulae for the Variance and the Standard eviation
The Mean and the Variance as Estimators
Boxplots: Graphical Representations of Dispersion and Extreme Scores
A Return to Trimming
Obtaining Measures of Dispersion Using SPSS
A Final Worked Example
Seeing Statistics
The Normal Distribution
The Normal Distribution
The Standard Normal Distribution
Setting Probable Limits on an Observations
Measures Related to z
Seeing Statistics
Basic Concepts of Probability
Basic Terminology and Rules
The Application of Probability to Controversial Issues
Writing Up the Results
Discrete versus Continuous Variables
Probability Distributions for Discrete Variables
Probability Distributions for Continuous Variables
Sampling Distributions and Hypothesis Testing
Two Simple Examples Involving Course Evaluations and Rude Motorists
Sampling Distributions
Hypothesis Testing
The Null Hypothesis
Test Statistics and Their Sampling Distributions
Using the Normal Distribution to Test Hypotheses
Type I and Type II Errors
One- and Two-Tailed Tests
Seeing Statistics
A Final Worked Example
Back to Course Evaluations and Rude Motorists
Scatter Diagrams
The Relationship Between Pace of Life and Heart Disease
The Covariance
The Pearson Product-Moment Correlation Coefficient (r)
Correlations with Ranked Data
Factors that Affect the Correlation
Beware Extreme Observations
Correlation and Causation
If Something Looks Too Good to be True, Perhaps it Is
Testing the Significance of a Correlation Coefficient
Intercorrelation Matrices
Other Correlation Coefficients
Using SPSS to Obtain Correlation Coefficients
Seeing Statistics
A Final Worked Example
The Relationship Between Stress and Health
The Basic Data
The Regression Line
The Accuracy of Prediction
The Influence of Extreme Values
Hypothesis Testing in Regression
Computer Solutions using SPSS
Seeing Statistics
Multiple Regression
A Different Data Set
The Visual Representation of Multiple Regression
Hypothesis Testing
Refining the Regression Equation
A Second Example: Height and Weight
A Third Example: Psychological Symptoms in Cancer Patients
Hypothesis Testing Applied to Means: One Sample
Sampling Distribution of the Mean
Testing Hypotheses about Means When ?? is Known
Testing a Sample Mean When ?? is Unknown (The One-Sample t)
Factors that Affect the Magnitude of t and the Decision about H0
A Second Example: the Moon Illusion
How Large is Our Effect?
Confidence Limits on the Mean
Using SPSS to Run One-Sample t tests
A Final Worked Example
Seeing Statistics
Hypothesis Tests Applied to Means: Two Related Samples
Related Samples
Student's t Applied to Difference Scores
A Second Example: the Moon Illusion Again
Advantages and Disadvantages of Using Related Samples
How Large an Effect Have We Found?
Confidence Limits on Changes
Using SPSS for t Tests on Related Samples
Writing Up the Results
Hypothesis Tests Applied to Means: Two Independent Samples
Distribution of Differences Between Means
Heterogeneity of Variance
Nonnormality of Distributions
A Second Example with Two Independent Samples
Effect Sizes Again
Confidence Limits on ??1 ?V ??2
Writing Up the Results
Use of Computer Programs for Analysis of Two Independent Sample Means
A Final Worked Example
Seeing Statistics
The Basic Concept
Factors that Affect the Power of a Test Effect Size
Power Calculations for the One-Sample t Test Power Calculations for Differences Between Two Independent Means
Power Calculations for the t Test for Related Samples
Power Considerations in Terms of Sample Size
You Don't Have to Do it by Hand
Seeing Statistics
One-Way Analysis of Variance
The General Approach
The Logic of the Analysis of Variance
Calculations for the Analysis of Variances
Unequal Sample Sizes
Multiple Comparison Procedures
Violations of Assumptions
The Size of the Effects
Writing Up the Results
The Use of SPSS for a One-Way Analysis of Variance
A Final Worked Example
Seeing Statistics
Factorial Analysis of Variance Factorial Designs
The Extension of the Eysenck Study
Simple Effects
Measures of Association and Effect Size
Reporting the Results
Unequal Sample Sizes
A Second Example: Maternal Adaptation Revisited
Using SPSS for Factorial Analysis of Variance
Seeing Statistics
Repeated-Measures Analysis of Variance
An Example: Depression as a Response to an Earthquake
Multiple Comparisons
Effect Size
Assumptions involved in Repeated-Measures Designs
Advantages and Disadvantages of Repeated-Measures Designs
Using SPSS to Analyze Data in a Repeated-Measures Design
Writing Up the Results
A Final Worked Example
One Classification Variable: the Chi-Square Goodness of Fit Test
Two Classification Variables: Analysis of Contingency Tables
Possible Improvements on Standard Chi-Square
Chi-Square for Larger Contingency Tables
The Problem of Small Expected Frequencies
The Use of Chi-Square as a Test of Proportions
Nonindependent Observations
SPSS Analysis of Contingency Tables
Measures of Effect Size
A Final Worked Example
Writing Up the Results
Seeing Statistics
Nonparametric and Distribution-Free Statistical Tests
The Mann-Whitney Test Wilcoxon's Matched-Pairs Signed-Ranks Test
Kruskal-Wallis One-Way Analysis of Variance
Friedman's Rank Test for k Correlated Samples
Measures of Effect Size
Writing Up the Results
Choosing the Appropriate Analysis
Exercises and Examples
Arithmetic Review
Symbols and Notation
Basic Statistical Formulae
Statistical Tables
Answers to Selected Exercises
Table of Contents provided by Publisher. All Rights Reserved.

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