Behavioral Research and Analysis

ISBN-10: 1439818029

ISBN-13: 9781439818022

Edition: 4th 2011 (Revised)

List price: $81.00
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This text provides an overview of basic statistical methods used in behavioral research, experimental design, and report writing. It uniquely integrates statistics within the context of experimental design and simplifies the process of planning, conducting, analyzing, and preparing an experimental or research study report. Reflecting the changes of the APA guidebook and including SAS statistical software in the end-of-chapter exercises, this updated fourth edition presents new statistical procedures and new examples in sport science, public health, gerontology, and biomedicine.
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Book details

List price: $81.00
Edition: 4th
Copyright year: 2011
Publisher: CRC Press LLC
Publication date: 10/19/2011
Binding: Hardcover
Pages: 299
Size: 7.50" wide x 10.50" long x 0.75" tall
Weight: 1.540
Language: English

About the Authors
Overview of Scientific Research
What Is Science?
Scientific Method
Identify Problem
Formulate Hypothesis
Conduct Pilot Study
Collect Data
Participant (Subject) Sampling
Experimental and Control Groups
Independent and Dependent Variables
Describing Collected Data
Test Hypothesis
Generalize Results
Replicate Experiment
Goals, Principles, and Assumptions of Science
Goals of Science
Principles of Science
Empirical Verification
Assumptions of Science
Limited Causality
Contiguity of Events
Five Basic Approaches to Scientific Research
Correlation Approach
Establishing Validity
Using Multiple Predictors
Establishing Test Reliability
Developing Homogeneous Subgroups
Case History Approach
Solving Personal Problems
Predicting and Subgrouping
Field Study Approach
Experimental Approach
Advantages of Experimental Approach
Disadvantages of Experimental Approach
Purposes of Experimentation
Experimentation Versus Demonstration
Manipulation Versus Selection of Independent Values
Quasi-Experimental Approach
Time Series Design
Nonequivalent Control Group Design
Keyword Definitions
Exercise Answers
Methods of Describing Data
Samples and Populations
Consideration of Numbers in Statistics
Continuous Versus Discrete Data
Four General Scales of Measurement
Scaling Behavioral Dimensions
Graphical Methods of Description
Univariate Frequency Distribution
Determine Range
Determine Number and Size
Set Up Frequency Distribution
Tally Scores
Post Tallies
Add f Column
Graphing Results
Frequency Polygon
Other Types of Graphs
Cumulative Frequency Distribution
Univariate Descriptive Statistics
Measures of Central Tendency
Averaging Means
When to Use Different Measures of Central Tendency
Centiles and Quartiles
Measures of Dispersion, Variability, or Spread
Semi-Interquartile Range
Average Deviation
Standard Deviation
Interpretation of Standard Deviation
Standard Score
Measures of Distribution Skewness
Measures of Distribution Kurtosis
Keyword Definitions
Exercise Answers
Bivariate Descriptive Statistics
Bivariate Frequency Distributions
Graphing Relationship Between Two Variables
Shapes of Bivariate Frequency Distributions
Correlation: The Pearson r
Nature of Correlation Coefficients
Pearson Product-Moment Correlation (r)
Computation of Pearson r
Effect of Range on Value or Coefficient
Interpretation of Correlation Coefficients
Interpretation of r<sub>2</sub> (Coefficient of Determination)
Other Correlation Coefficients
Point Biserial r<sub>pb</sub>
Computation of Point Biserial
Assumptions Underlying Point Biserial
Biserial r
Computation of Biserial r<sub>b</sub>
Assumptions Underlying Biserial r
Interpretation of Biserial r
Spearman Rank Order Correlation Coefficient (Rho)
Calculation of Spearman Rho
Assumption Underlying Spearman Rho
Use of Spearman Rho
Kendall's Coefficient of Concordance (W)
Computation of W
Phi Coefficient (�)
Computation of Phi
Assumptions Underlying Phi
Special Uses of Phi Coefficient
Correlation Ratio (Eta)
Calculation of Correlation Ratio
Prediction and Concept of Regression
Concept of Regression
Computation of Regression Lines
Equation for Straight Line
Computation of Linear Regression Line
Relation of b<sub>yx</sub> and b<sub>xy</sub> to r
Standard Error of Estimate
Computation of SE<sub>est</sub>
Interpretation of SE<sub>est</sub>
Keyword Definitions
Exercise Answers
Simple Experimental Designs
Introduction to Inferential Statistics
Sampling Distribution of Means
Central Limit Theorem
Relationship of Sample Size to �<sub>X</sub>
Computing Standard Error of Mean �<sub>X</sub>
Sampling Distribution of Difference Between Two Means �<sub>D</sub><sub>X</sub>
Computing �<sub>D</sub><sub>X</sub>
Statistical Hypothesis Testing
One-Tailed Versus Two-Tailed Hypotheses
Type I and Type II Errors
Power of Statistical Testing
Two Randomized Groups Designs: t-Test for Independent Samples
Two Randomized Groups (Between Groups) Design
t-Test for Independent Data
Concept of Degrees of Freedom
Use of f-Test in Statistical Hypothesis Testing
Limitations of Randomized Groups Design
Two Matched Groups and Repeated Measures Designs: t-Test for Correlated Data
Two Matched Groups Design
t-Test for Correlated Data
Computation of f for Correlated Data
Repeated Measures (Within Subjects) Design
Advantages and Uses of Repeated Measures Designs
Disadvantages of Repeated Measures Designs
Counterbalancing in Repeated Measures Designs
Using t-Test With Repeated Measures Design
Nonparametric Analysis
Mann-Whitney U-Test
Assumptions of Mann-Whitney U-Test
Computation of Mann-Whitney U-Test
Explanation of U-Test
Wilcoxon Matched-Pairs Signed-Ranks Test (T)
Assumptions of Wilcoxon Test
Computation of Wilcoxon Test
Explanation of Wilcoxon Test
Chi-Square Distribution
Chi-Square Tests of Independence
Computation of Degrees of Freedom for Chi-Square Tests
Chi-Square Tests of Goodness of Fit
Chi-Square Test for Goodness of Fit to Normal
Computation of Chi-Square With Small Expected Frequencies
Testing for Significance of Correlation
Test for Significance of Phi (�)
Testing for Significance of Pearson r and Spearman Rho
Keyword Definitions
Exercise Answers
Simple Analysis of Variance
More Than Two Treatments Designs
Reasons for Using More Than Two Treatments
Using More Than Two Treatments May Yield a Different Answer
To Obtain Fairly Precise Knowledge of the IV-DM Relationship
To Study More Than Two Treatment Conditions
Types of More Than Two Treatment Designs
Single-Factor (Simple) Analysis of Variance
Concept of Analysis of Variance (ANOVA)
Rationale for F-Test
Assumptions of F-Test
Why Multiple t-Tests Should Not Be Used
ANOVA for More Than Two Randomized Groups Design
Computation of Sums of Squares
Degrees of Freedom, Mean Squares, and F-Ratio
Generalized ANOVA Summary Table
Computational Example
ANOVA for Repeated Measures Design
Computation of Sums of Squares
Degrees of Freedom, Mean Squares, and F-Ratio
Generalized ANOVA Summary Table
Computation Example
Post Hoc Analyses: Multiple Comparisons Among Means
Tukey's WSD (Wholly Significant Difference)
Neuman-Keuls Test
Bonferroni t-Test
Scheffe Test for All Possible Comparisons
Keyword Definitions
Exercise Answers
Multifactor Analysis of Variance
Rationale for Factorial Designs
Factorial Designs
Two-Factor Designs
Three-Factor Designs
Four-Factor Designs
Nested Designs
Fully Crossed Designs
Nested Designs
Limitation of Nested Designs
Types of Analysis of Variance Designs
Between-Groups Designs
Completely Within-Subjects (Repeated Measures) Designs
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