Your Statistical Consultant Answers to Your Data Analysis Questions

ISBN-10: 1412997593
ISBN-13: 9781412997591
Edition: 2nd 2013
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Description: Discussing the issues that surround a range of statistical questions and controversies, the Second Edition reveals divergent perspectives on these issues and offers practical advice and examples for conducting statistical analyses that reflect the  More...

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Book details

List price: $79.00
Edition: 2nd
Copyright year: 2013
Publisher: SAGE Publications, Incorporated
Publication date: 9/4/2012
Binding: Paperback
Pages: 384
Size: 7.25" wide x 9.00" long x 1.00" tall
Weight: 1.672

Discussing the issues that surround a range of statistical questions and controversies, the Second Edition reveals divergent perspectives on these issues and offers practical advice and examples for conducting statistical analyses that reflect the authors' interpretation of the consensual wisdom of the field. It is a compendium of statistical knowledge - some theoretical, some applied - that addresses those questions most frequently asked by students and colleagues struggling with statistical analyses.

Kjell Erik Rudestam is Professor of Psychology at Fielding Graduate University, Santa Barbara, California, having served for many years as Associate Dean of Academic Affairs. He was previously a psychology professor at York University, Toronto. He received his PhD in psychology (clinical) from the University of Oregon. He is author of Your Statistical Consultant, Second Edition (also with Rae R. Newton), Handbook of Online Learning (with Judith Schoenholtz-Read), and eight other books, as well as numerous articles in professional journals on such topics as suicide, psychotherapy, and family and organizational systems. He is a Fellow of the American Psychological Association (Division 12), a Diplomate of the American Board of Examiners in Professional Psychology (Clinical), and a Diplomate of the American Academy of Experts in Traumatic Stress.

Rae R. Newton is Professor of Sociology Emeritus at California Statenbsp;University, Fullerton. He recently joined the faculty of the School ofnbsp;Psychology at Fielding Graduate University where he serves as a researchnbsp;consultant and statistical advisor to doctoral students and faculty. Henbsp;received his PhD in sociology from the University of California, Santanbsp;Barbara, and completed postdoctoral training in mental health measurementnbsp;at Indiana University. His primary interests include longitudinalnbsp;modeling of outcomes for high risk youth and foster care populations,nbsp;family violence and statistics education. He is author, with Kjell Eriknbsp;Rudestam, of Your Statistical Consultant: Answers to Your Data Analysisnbsp;Questions, now in its second edition and numerous articles in professionalnbsp;journals on topics including family violence, child maltreatment, andnbsp;measurement. In semi-retirement he enjoys traveling with his wife in theirnbsp;RV and surfing throughout Mexico and Central America.

Preface
About the Authors
Introduction
Data Collection and Exploration
Getting Started With Statistical Analysis: Where Do I Obtain Data, and How Do I Prepare Data for Statistical Analysis?
Where Data Come From
Primary Data Analysis
Secondary Data Analysis
Preparing Data for Analysis
Cases: How Do I Define a Unit of Analysis?
How Do I Use Variables and Values to Describe a Case?
Are There Rules I Should Use for Naming Variables?
What Is Data Coding?
What About Open-Ended Response Choices? How Are These Coded?
How Do I Code Open-Ended Questions?
What Are the Basic Guidelines of Data Organization?
How Do I Define and Code Missing Data?
How Do I Code Nonspecific Responses?
How Do I Deal With Blanks and Zeros?
A Detailed Example of Data Organization and Coding
The Questionnaire
The Codebook
How Do I Examine Data Prior to Analysis?
When Should I Screen Data?
How Do I Display My Data Visually?
What Is a Box and Whisker Plot?
What Is a Stem-and-Leaf Diagram?
What Is a Normal Distribution?
How Do I Prepare to Analyze Categorical Data?
How Do I Examine Two Variables at the Same Time?
How Do I Use a Crosstabulation to Examine Categorical Variables?
How Do I Use a Scatterplot to Examine Continuously Distributed Bivariate Data?
How Do I Display Bivariate Data if the Independent Variable Is Categorical and the Dependent Variable Continuous?
Simple Bar Chart for Means
Summary
The Logic of Statistical Analysis: Issues Regarding the Nature of Statistics and Statistical Tests
Traditional Approaches to Statistical Analysis and the Logic of Statistical Inference
What Is the Difference Between Descriptive and Inferential Statistics?
What Is a Statistical Generalization?
What Do We Mean by "Sampling Error"?
What Are Sampling Distributions?
Some Examples: Sampling Distributions Based on Samples of Different Sizes
What Is a Confidence Interval?
What Is a Hypothesis Test?
Rethinking Traditional Paradigms: Power, Effect Size, and Hypothesis-Testing Alternatives
What Is the Difference Between Statistical and Substantive Significance?
What Is Statistical Power?
Binomial Effect Size Displays
An Example Using Odds Ratios
What Are the Relationships Among Sample Size, Sampling Error, Effect Size, and Power?
Confidence Intervals Revisited
What Are the Problems With Statistical Significance Testing?
Has Significance Testing Outlived Its Usefulness?
What Axe the Assumptions of Statistical Testing?
What Are the Assumptions Related to Characteristics of Population Distributions?
What Are Normality and Multivariate Normality?
Is the Distribution Normal?
How Do I Examine Data for Bivariate and Multivariate Normality?
What Is Homoscedasticity?
What Are the Assumptions About Error or Disturbance Terms?
What Are the Assumptions About the Sample?
How Important Are Random Samples?
How Do I Calculate an Appropriate Sample Size?
A Reconsideration of the Sample Size Issue
What Is Independence of Observations?
What Are the Assumptions About Measurement?
What Is Measurement Error?
Explicit Recognition of the "Measurement Model"
What Are the Assumptions About the Statistical Model?
When Should I Be Concerned About Meeting the Assumptions of a Test?
An Introduction to Statistical Models: Explaining Relationship Patterns
Our Notation System
Modeling Three-Variable Relationships
The Full Mediation Model (The Intervening Effects Explanation)
The Spurious Explanation
The Joint Effects Explanation
The Interaction Effect or Moderation Explanation
The Suppressor Variable Explanation
Summary: Patterns of Relationships and Their Explanations
When Do I Control Variables?
An Example of Model Building
Model Representing the First Hypothesis
Modification of the Model to Include Hypothesis 3
Modification of the Model to Include Hypothesis 4
Modification of the Model to Include Hypothesis 5
How Do I Select the Appropriate Statistical Test?
Am I Comparing Groups or Examining Relationships?
When Should I Use Multivariate Analysis?
How Do I Select the Appropriate Statistical Test?
Major Research Questions Suggested by the Four Design Frameworks
Statistical Analyses Suggested by Each Design Framework
Design 1
Design 2
Design 3
Design 4
Describing the Techniques
Issues Related to Variables and their Distribution
How Do I Deal With Missing Values, Outliers, and Non-normality?
How Do I Deal With Missing Values?
Three Patterns of Missing Data: MAR, MCAR, and MNAR
Adjusting for MCAR Data
Adjusting for Missing, Non-MCAR Data
Case Deletion
Imputation
Regression
Adjusting for Missing Outcomes Due to Participant Attrition
Maximum Likelihood Methods
Multiple Imputation Methods
Adjusting for Missing Values: Summary
How Do I Control or Adjust for Outliers?
Identifying Outliers
Univariate Distributions
Bivariate and Multivariate Distributions
Adjusting Data for Outliers
How Do I Adjust for Non-normal Data?
Data Transformation
Power Transformations
Limitations of Data Transformation
Types of Variables and Their Treatment in Statistical Analysis
How Do I Determine Whether a Parametric or Nonparametric Test Is Best?
Parametric Tests and Their Nonparametric Alternatives
What Are Dummy Variables, and How Do I Code Them?
When, If Ever, Should I Dichotomize a Continuous Variable?
Recommendations
Understanding the Big Two: Major Questions About Analysis of Variance and Regression Analysis
Questions About Analysis of Variance
What Are the Nuts and Bolts of Analysis of Variance?
What Is an Interaction Effect, and How Do I Interpret It?
What Are the Issues in the Interpretation of Interaction?
Recommendations
How Do I Select the Best Method for Analyzing the Pretest-Posttest Design?
What Is an Analysis of Covariance?
Recommendations
What Are Planned and Post Hoc Comparisons?
What Are Planned and A Priori Contrasts?
What Are Post Hoc and A Posteriori Tests?
Recommendations
How Do I Control for Familywise Error?
What Are Familywise Error Rates?
Recommendations
When Should I Use MANOVA?
What Is the Distinction Between a MANOVA and an ANOVA?
Recommendations
Questions About Multiple Regression Analysis
What Are the Nuts and Bolts of Multiple Regression Analysis?
How Do I Determine the Appropriate Number of Subjects and Predictor Variables?
What Are Stepwise and Hierarchical Multiple Regression Procedures, and When Should I Use Each?
Describing Relationships Between Independent and Dependent Variables
Predicting the Value of the Dependent Variable
Testing a Theoretical Model
A Detailed Example: The Effect of Age and Education on Income
What Are Regression Coefficients, and How Do I Interpret Them?
How Do I Interpret Bivariate Correlation Coefficients?
How Do I Interpret Multiple Correlations?
What Are Unstandardized and Standardized Regression Coefficients?
How Do I Interpret Partial and Semipartial Correlations?
Partitioning of Variability for Bivariate Squared Correlations (r<sup>2</sup>)
Partitioning of Variability for Semipartial Squared Correlations (r<sup>2</sup>)
Partitioning of Variability for Partial Squared Correlations
How Do I Interpret an Interaction Effect?
The Bigger Picture
How Do I Understand the Relationships Among the Different Statistical Techniques?
When Should I Use Meta-Analysis?
The Pros and Cons of Meta-Analysis
An Example of Meta-Analysis
What Are Modern Robust Statistics?
Ten Tips for Success in Statistical Analysis
Get Comfortable With Your Data
Thoroughly Explore Your Data, Twice!
Sometimes Pictures Speak Louder Than Words
Replication Is Underemphasized and Overdue
Remember the Distinction Between Statistical Significance and Substantive Significance
Remember the Distinction Between Statistical Significance and Effect Size
Statistics Do Not Speak for Themselves
Keep It Simple When Possible
Use Consultants
Don't Be Too Hard on Yourself
References
Index

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