Basic Research Methods and Statistics An Integrated Approach

ISBN-10: 0155071629
ISBN-13: 9780155071629
Edition: 2000
List price: $325.95 Buy it from $148.90
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Description: This book was written in response to the needs for a growing number of schools that are teaching an integrated research methods/statistics course. Basic Research Methods and Statistics has detailed, comprehensive and even-handed coverage of the  More...

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Book details

List price: $325.95
Copyright year: 2000
Publisher: Wadsworth
Publication date: 11/3/1999
Binding: Hardcover
Pages: 752
Size: 7.75" wide x 9.50" long x 1.25" tall
Weight: 3.146
Language: English

This book was written in response to the needs for a growing number of schools that are teaching an integrated research methods/statistics course. Basic Research Methods and Statistics has detailed, comprehensive and even-handed coverage of the fundamental issues in research design and data analysis, and is written in a conversational style that students can easily comprehend. The text is comprehensive in its coverage of basic and intermediate topics, however, the modular format allows professors to skip or rearrange the order of chapters without loss of continuity. Therefore, the text is appropriate for either a one-semester or two-semester course.

Preface
An Introductory Overview
Ways of Knowing
Intuition and Reasoning Versus Empirical Observation: An Example
The Scientific Method
Characteristics of Scientific Observations
Rival Explanations
Defining the Terms
Replication
Internal and External Validity
Conducting a Research Study
Exercises
Ethics in Research
Guidelines for Psychologists
The Basic Ethical Dilemma
The Six General Ethical Principles
Specific Ethical Issues in Research with Humans
Risk or Freedom From Harm
Informed Consent
Debriefing
Privacy
Ethical Treatment of Animals as Research Subjects
Special Issues About the Ethics of Research
Who Decides What Is "Right?"
Ethics of Funding
Ethics and Statistics
What Becomes of What You Find?
A Final Note for New Researchers
Exercises
Variables
Variables Versus Constants: Definitions and Examples
How to Identify Variables Versus Constants
Types of Variables
Types of Relationships
No Relationship
Correlation
Causality
Necessary, Sufficient, and Contributory Causes
Simple Versus Multiple Causation
Exercises
Measuring Variables
Data-Gathering Techniques
Behavioral Observations
Self-Reports
Behavioral Ratings
Archival Records
Physical Trace Approach
Measurement
Operational Definitions: Measuring Variables
Operational Definitions: Establishing Research Conditions
Reliability and Validity
Reliability
Validity
Levels (or Scales) of Measurement
Sensitivity of Measurements
Exercises
Descriptive Statistics
Populations and Samples
About the Computations in This Text
Frequency
Graphing Frequencies
Grouped Frequencies
Probability
Central Tendency
Mode
Median
Mean
Means Versus Medians: The Case of Outliers
Distributions of Scores
The Normal Distribution
Skewed Distributions
Variability
Variance and Standard Deviation
Median Absolute Deviation
Standard Deviations Versus the Median Absolute Deviation: The Case of Outliers
Range
Number of Categories/Values
The Variation Ratio
Selecting Appropriate Descriptive Statistics
Simple Data Transformations
z-Scores
z-Scores as Inferential Statistics: Areas Under the Normal Curve
Exercises
Hypothesis Testing
Representativeness and Sampling Procedures
Random Sampling
Stratified Random Sampling
Available Samples and Convenience Sampling
Statistical Hypotheses
The Null Hypotheses (H[subscript 0])
Research or Alternate Hypothesis (H[subscript 1])
Sampling Distributions
The Shape of the Sampling Distribution
The Average of the Sampling Distribution
Variability of Sampling Distributions
Probabilities of Samples
Making the Decision
Significance Levels ([alpha])
Critical Values
Tables of Critical Values
Have We Made the Correct Decision?
Threats to the Validity of Hypothesis-Testing: Pitfalls to Avoid
Statistical Versus Practical and Psychological Significance
The Relevant Error Rate and "Accepting" The Null Hypothesis
The Arbitrary Cut-off Point Between "Rare" and "Common" Events
Proposed Alternatives to the Null-Hypothesis-Testing Procedure
A Call for Compromise: Using a Combination of Approaches
Exercises
General Research Methods
The Experimental Method
The Logic of Experiments
An Overview of the Experimental Method
An Alternative to Random Assignment: Repeated Measures
Research Settings for Experiments: Laboratory Versus Field Experiments
Quasi-Experimental Research Methods
Non-Equivalent Groups Designs
Time-Series Designs
Two Approaches to Analyzing the Results
Summary of Quasi-Experimental Methods
Correlational Methods
Naturalistic Observation
Summary of the Correlational Method
Exercises
Correlation Coefficients
Magnitude
Direction
Graphing the Relationship Between Two Variables
Selecting the Appropriate Correlation Coefficient
Pearson Product-Moment Correlation Coefficient
The Pearson r as an Inferential Statistic: Testing the Null Hypothesis
Spearman Rank-Order Correlation Coefficient
The Spearman r as an Inferential Statistic: Testing the Null Hypothesis
Tied Ranking Procedure
Correlating Nominal Data
Phi Coefficient
Testing the Difference Between Two Correlations
Exercises
Introduction to Regression Analysis
The Logic Behind Simple Regression Analysis
The Regression Equation
About the Regression Equation
Assumptions and Limitations of the Least-Squares Method of Regression
Linearity
Normal Distributions and Homoscedasticity
Evaluating Y': How Accurate Are Our Predictions?
A Short-Cut for Computing the Standard Error of the Estimate
z-Scores and Regression Analysis
Coefficient of Determination
Using Venn Diagrams to Illustrate r[superscript 2]
The Basic Concepts of Multiple Regression
Using Venn Diagrams to Illustrate Multiple Regression and R[superscript 2]
Multicollinearity
Exercises
Designing Experiments and Quasi-Experiments
One-way Designs
Factorial Designs and the Concept of Interaction: "It Depends"
Multiple Determinants (or Predictors) of Behavior
Contingencies Among Determinants: The Essence of Interaction
Terminology and Notation Systems for Factorial Designs
Factorial Designs and Confounds
The Research Questions Addressed in Factorial Designs
Selecting Only the Necessary Independent Variables
Selecting the Necessary Levels of the Independent Variables
No-Treatment Control Group
Placebo Control Groups
Research Designs
Comparisons Between Groups Versus Repeated Measures
Testing Participants Repeatedly in Within-Subjects Designs
Advantages of Within-Subjects Designs
Disadvantages of Within-Subjects Designs
Counterbalancing
Summary of Within-Subjects Designs
Advantages and Disadvantages of Between-Subjects Designs
Matching Designs
Some General Confounds: Threats to Internal Validity
Maturation
History
Regression Toward the Mean
Instrumentation
Mortality
Sensitization
Pretest-Posttest Designs: The Need for a Control Group
Selecting Within-Subjects Factors for Mixed Designs
The Special Case of Age as an Independent Variable
Steps in Designing an Experiment or Quasi-Experiment
Exercises
The z-Test and t-Test: Analyzing Data from One-and Two-Group Designs
The z-Test: When the Population Standard Deviation ([sigma]) Is Known
z-Test: Application 1: When the Population Mean ([mu]) Is Known
z-Test Application 2: When the Population Mean ([mu]) Is Being Tested
Requirements for the z-Test
The t-Test: When the Population Standard Deviation ([sigma]) Is Unknown
One-Sample t-Test
Two-Sample t-Test: Independent Samples From a Between-Subjects Design
Two-Sample t-Test: Related Samples From a Within-Subjects or Matching Design
The Limited Applicability of z- and t-Tests
Exercises
Analysis of Variance
Sources of Variation
Between-Subjects Designs
Within-Subjects Designs
Factorial Designs
Mixed Designs
Computing Sums of Squares
One-way BS-ANOVA
Two-way BS-ANOVA
One-way RM-ANOVA
Degrees of Freedom
Mean Squares
The F-Ratio
Testing the Significance of F
Post Hoc Analyses
Dunn's Multiple Comparisons Procedure
Example of a One-way BS-ANOVA
Example of a 2 [times] 3 (Two-way) BS-ANOVA
Example of a One-way RM-ANOVA
Exercises
Nonparametric Tests for Experiments and Quasi-Experiments
Nominal Scales
Between-Subjects Designs: Chi-Square (X[superscript 2])
Within-Subjects Designs: Cochran's Q
Ordinal Scales
Between-Subjects Designs: The Wilcoxon Rank Sum Test and the Kruskal-Wallis H
Within-Subjects Designs: Wilcoxon Signed Ranks Test (a.k.a. the Wilcoxon W)
A Cautionary Note about When to Select the Appropriate Statistic
Exercises
Estimation and Confidence Intervals
Confidence Intervals for the Mean
Finding the Margin of Error: The Maximum Error of the Estimate
Confidence Interval: The Range of Likely Values of the Population Mean
Summary: Steps in Computing the Confidence Interval for the Mean
Confidence Intervals for Proportions (or Percentages)
Numerical Examples of Confidence Intervals for Proportions (or Percentages)
Limitations of Confidence Intervals for Proportions (or Percentages)
Confidence Intervals for Pearson Correlations
Numerical Example of Confidence Intervals for the Pearson Correlation
Minimum Differences Between Treatment Means
Minimum Differences in Two-Group Between-Subjects Designs
Minimum Differences in Two-Treatment Related-Samples Designs
Confidence Intervals Versus Significance Testing
Numerical Example of Confidence Intervals That Do Not Overlap
Numerical Example of Confidence Intervals That Overlap
Confidence Intervals and Statistical Power
Renewing the Call for Compromise: Combining the Approaches
Exercises
Single-Subject Research Design
Key Elements of Single-Subject Research
Elements of Measurement
Design Phases in Single-Subject Research Designs
Presentation of Data
Threats to the Validity of Data from Single-Subject Designs
Specific Research Designs
ABAB Designs
Multiple-Baseline Designs
The Changing Criterion Design
Alternating Treatment Designs
Mixed Designs
Single-Subject Designs for Applied and Basic Research Questions
Evaluation of Data from Single-Subject Designs
Visual Inspection
Interpreting the Data from Single-Subject Research
Potential Confounds and Problems in the Visual Interpretation of Data
Resources for Further Study
Exercises
Qualitative Research Methods and Analysis
Qualitative Methods for Gathering Data
Observation
Interviews
Textual Analysis
Transcription
Reliability and Validity in Qualitative Research
Triangulation of Methods: Increasing the Validity
A Case Study Illustrating Triangulation of Qualitative Research Methods
Statistics for Qualitative Methods
The Future of Qualitative Methods
Suggestions for Further Reading
Exercises
Appendix A
Ethical Principles of Psychologists and Code of Conduct
Introduction
Preamble
General Principles
Competence
Integrity
Professional and Scientific Responsibility
Respect for People's Rights and Dignity
Concern for Others' Welfare
Social Responsibility
Ethical Standards
General Standards
Evaluation, Assessment, or Intervention
Advertising and Other Public Statements
Therapy
Privacy and Confidentiality
Teaching, Training Supervision, Research, and Publishing
Forensic Activities
Resolving Ethical Issues
Appendix B
Statistical Tables
Appendix C
Introduction to Statistical Power
Two Variances
Using the Two Variances to Test the Null Hypothesis
Statistical Power
Maximizing the Power in a Study
Adequate Sample Size
Lower Significance ([alpha]) Levels
Selecting Designs with More Inherent Power
Power Analysis
Exercises
Appendix D
Reporting the Research
Methods of Dissemination
Presentations at Professional Meetings
Written Reports
Electronic Dissemination
General Writing Style
Some Specific Issues and Common Errors
Re-writing
Plagiarism
APA Format and Manuscript Preparation
Sections of an APA-Format Research Report
Examples of References Using the APA Format
Manuscript Headings
Sample Manuscripts
Appendix E
Answers for the Odd-Numbered Exercises
Appendix F
References
Glossary
Index

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