Fundamental Statistics For The Behavioral Sciences

by Howell, David C.

• ISBN13: 9780495811251
• ISBN10: 0495811254
• Edition: 7th
• Format: Hardcover
• Copyright: 2010-01-25
• Publisher: Wadsworth Publishing

Summary

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

Introduction

The importance of Context

Basic Terminology

Selection among Statistical Procedures

Using Computers

Summary

Exercises

Basic Concepts

Scales of Measurement

Variables

Random Sampling

Notation

Summary

Exercises

Displaying Data

Plotting Data

Stem-and-Leaf Displays

Histograms

Reading Graphs

Alternative Methods of Plotting Data

Describing Distributions

Using Computer Programs to Display Data

Summary

Exercises

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

Summary

Exercises

Measures of Variability

Range

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

Summary

Exercises

The Normal Distribution

The Normal Distribution

The Standard Normal Distribution

Setting Probable Limits on an Observations

Measures Related to z

Seeing Statistics

Summary

Exercises

Basic Concepts of Probability

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

Summary

Exercises

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

Summary

Exercises

Correlation

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

Summary

Exercises

Regression

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

Summary

Exercises

Multiple Regression

Overview

A Different Data Set

Residuals

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

Summary

Exercises

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

Summary

Exercises

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

Summary

Exercises

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

Summary

Exercises

Power

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

Summary

Exercises

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

Summary

Exercises

Factorial Analysis of Variance Factorial Designs

The Extension of the Eysenck Study

Interactions

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

Summary

Exercises

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

Summary

Exercises

Chi-Square

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

Summary

Exercises

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

Summary

Exercises

Choosing the Appropriate Analysis

Exercises and Examples

Arithmetic Review

Symbols and Notation

Basic Statistical Formulae

Dataset

Statistical Tables

Glossary

References

Answers to Selected Exercises

Index

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