Physical description 
1 volume (various pagings) : illustrations (some colour) ; 27 cm 
Bibliography 
Includes bibliographical references (page R1  R10) and index. 
Contents 
1 An Introduction to Statistics and Research Design: The Elements of Statistical Reasoning  Two Branches of Statistics: Growing Our Knowledge about Human Behavior  Descriptive Statistics: Organizing, Summarizing, and Communicating Numerical Information  Inferential Statistics: Using Samples to Draw Conclusions about a Population  Distinguishing Between a Sample and a Population  Variables: Transforming Observations into Numbers  Independent and Dependent Variables: The Main Ingredients of Statistical Thinking  Putting Variables to Work: Independent, Dependent, and Confounding Variables  Developing and Assessing Variables: The Reliability and Validity of Tests  An Introduction to Hypothesis Testing: From Hunch to Hypothesis  Types of Research Designs: Experiments, NonExperiments, and QuasiExperiments  Experiments and Causality: Control the Confounding Variables  Research Designs Other than Experiments: NonExperiments and QuasiExperiments  One Goal, Two Strategies: Betweensubjects Designs vs. Withinsubjects Designs  Curiosity, Joy, and the Art of Research Design  Digging Deeper Into the Data: Variations on Standard Research Designs  Outlier Analyses: Does the Exception Prove the Rule?  Archival Studies: When the Data Already Exist  Chapter 2 Descriptive Statistics: Organizing, Summarizing, and Graphical Individual Variables  Organizing Our Data: A First Step in Identifying Patterns  Distributions: Four Different Ways to Describe Just One Variable  Applying Visual Depictions of Data: Generating Research Questions  Central Tendency: Determining the Typical Score  Need for Alternative Measures of Central Tendency: Bipolar Disorder  Mean: The Arithmetic Average  Median: The Middle Score  Mode: The Most Common Score  Effect of Outliers on Measures of Central Tendency  An Early Lesson in Lying With Statistics: Which Central Tendency is "Best?"  Measures of Variability: Everyone Can't Be "Typical"  Range: From the Lowest to the Highest Score  Variance: The First Step in Calculating Standard Deviation  Standard Deviation: Variation from the Mean  Shapes of Distributions: Applying the Tools of Descriptive Statistics  Normal Distributions: The Silent Power Behind Statistics  Skewed Distributions: When Our Data Are Not Symmetrical  Bimodal and Multimodal Distributions: Identifying Distinctive Populations  Kurtosis and Distributions: Tall and Skinny Versus Short and Wide  Digging Deeper into the Data: Alternate Approaches to Descriptive Statistics  Interquartile Range: An Alternative to the Range  Statistics that Don't Focus on the Mean: Letting the Distribution Guide our Choice of Statistics  Chapter 3 Visual Displays of Data: Graphs That Tell a Story  Uses of Graphs: Clarifying Danger, Exposing Lies, and Gaining Insight  Graphing in the Information Age: A Critical Skill  "The Most Misleading Graph Ever Published": The Cost and Quality of Higher Education  "The Best Statistical Graph Ever Created": Napoleon's Disastrous March to Moscow  Common Types of Graphs: A Graph Designer's Building Blocks  Scatterplots: Observing Every Data Point  Line Graphs: Searching for Trends  Bar Graphs: An Efficient Communicator  Pictorial Graphs: Choosing Clarity over Cleverness  Pie Charts: Are Pie Charts Passé?  How to Build a Graph: Dos and Don'ts  APA Style: Graphing Guidelines for Psychologists  Choosing the Type of Graph: Understanding Our Variables  Limitations of Graphic Software: Who is Responsible for the Visual Display?  Creating the Perfect Graph: General Guidelines  Graphing Literacy: Learning to Lie Versus Creating Knowledge  Lying with Statistics and Graphs: Eleven Sophisticated Techniques  Future of Graphs: Breaking the Fourth Wall  Uses and Misuses of Statistics: It's Not Just What You Draw, It's How You Draw It  Digging Deeper into the Data: The Box Plot  Chapter 4 Probabilities and Research: The Risks and Rewards of Scientific Sampling  Samples and Their Populations: Why Statisticians Are Stingy!  Decision Making: The Risks and Rewards of Sampling  Random Sampling: An Equal Chance of Being Selected  Variations on Random Sampling: Cluster Sampling and Stratified Sampling  Convenience Sampling: Readily Available Participants  Random Assignment: An Equal Chance of Being Assigned to a Condition  Variations on Random Assignment: Block Design and Replication  Sampling in the Behavioral Sciences: Why Sampling is Both an Art and a Science  Neither Random Selection, Nor Random Assignment: A Study of Torture  Random Assignment, But Not Random Selection: A Study of Expert Testimony  Random Selection, But Not Random Assignment: A Study of Children's Literature  Probability Theory: Distinguishing Between Mere Coincidence and Real Connections  Coincidence and Probability: Why Healthy Skepticism Is Healthy  Beyond Confirmation Biases: The Dangers of Groupthink  Probability Theory: The Basics  Expected RelativeFrequency Probability: The Probability of Statistics  Independence and Probability: The Gambler's Fallacy  Statistician Sleuths: The Case of Chicago's Cheating Teachers  Statistics and Probability: The Logic of Inferential Statistics  Dead Grandmothers: Using Probability to Make Decisions  Consideration of Future Consequences: Developing Hypotheses  Consideration of Future Consequences: Making a Decision about Our Hypotheses  Type I and Type II Errors: Statistical Inferences Can Be Wrong  Type I Errors: Sins of Commission  Type II Errors: Sins of Omission  Statistics in Everyday Life: Tying It All Together  Case of Lush: Testimonial to a Moisturizer  Understanding the Meaning of Proof: Statistical Literacy in Consumer Research  Digging Deeper into the Data: The Shocking Prevalence of Type I Errors  Estimating Type I Error in the Medical Literature  Medical Findings and Our Own Confirmation Biases  Chapter 5 Correlation: Quantifying the Relation between Two Variables  Correlation: Assessing Associations between Variables  Need for Standardization: Putting Two Different Variables on the Same Scale  Z Score: Transforming Raw Scores into Standardized Scores  Pearson Correlation Coefficient: Quantifying a Linear Association  Everyday Correlation Reasoning: Asking Better Questions  Calculation of the Pearson Correlation Coefficient: Harnessing the Power of z Scores  Misleading Correlations: Considering the Stories behind the Numbers  Correlation is Not Causation: Invisible Third Variables  A Restricted Range: When the Values of One Variable Are Limited  Effect of an Outlier: The Influence of a Single Data Point  Reliability and Validity: A Correlation Coefficient Is Only as Good as Our Data  Reliability and Validity: Correlation in Test Construction  Correlation, Psychometrics, and a SuperHeated Job Market: Creating the Measures behind the Research  Reliability: Using Correlation to Create a Consistent Test  Validity: Using Correlation to Determine Whether We Are Measuring What We Intend to Measure  Digging Deeper into the Data: Partial Correlation  Chapter 6 Regression: Tpols for Predicting Behavior  Regression: Building on Correlation  Difference between Regression and Correlation: Prediction Versus Relation  Linear Regression: Calculating the Equation for a Line using z Scores Only  Reversing the Formula: Transforming z Scores to Raw Scores  Linear Regression: Calculating the Equation for a Line by Converting Raw Scores to z Scores  Linear Regression: Calculating the Equation for a Line with Raw Scores  Drawing Conclusions from a Regression Equation: Interpretation and Prediction  Regression: Now Think Again (Realistically)!  What Correlation Can Teach Us about Regression: Correlation Still Isn't Causation  Regression to the Mean: The Patterns of Extreme Scores  Effect Size for Regression: Proportionate Reduction in Error  Multiple Regression: Predicting from More than One Variable  Multiple Regression: Understanding the Equation  Stepwise Multiple Regression and Hierarchical Multiple Regression: A Choice of Tactics  Digging Deeper Into the Data: Structural Equation Modeling (SEM)  Chapter 7 Power of Standardization: From Description to Inference  Normal Curve: 

It's Everywhere!  Standardization, z Scores, and the Normal Curve: Discovering Reason behind the Randomness  Standardization: Comparing z Scores  Putting z Scores to Work: Transforming z Scores to Percentiles  Central Limit Theorem: How Sampling Creates a Less Variable Distribution  Creating a Distribution of Means: Understanding Why It Works  Characteristics of the Distribution of Means: Understanding Why It's So Powerful  

How to Take Advantage of the Central Limit Theorem: Beginning With z Scores  Creating Comparisons: Applying z Scores to a Distribution of Means  Estimating Population Parameters from Sample Statistics: Connecting Back  Digging Deeper into the Data: The History of the Normal Curve  Chapter 8 Hypothesis Testing With z Tests: Making Fair Comparisons  Versatile z Table: Raw Scores, z Scores, and Percentages  From z Scores to Percentages: The Benefits of Standardization  From Percentages to z Scores: The Benefits of Sketching the Normal Curve  Z Table and Distributions of Means: The Benefits of Unbiased Comparisons  Hypothesis Tests: An Introduction  Assumptions: The Requirements to Conduct Analyses  Six Steps of Hypothesis Testing  Hypothesis Tests: The Single Sample z Test  Z Test: When We Know the Population Mean and the Standard Deviation  Z Test: The Six Steps of Hypothesis Testing  Effect of Sample Size: A Means to Increase the Test Statistic  Increasing Our Test Statistic through Sample Size: A Demonstration  Effect of Increasing Sample Size: What's Going On  Digging Deeper in the Data: What to Do with Dirty Data  Chapter 9 Hypothesis Testing with t Tests: Making Fair Comparisons between Two Groups  T Distributions: Distributions of Means When the Parameters Are Not Known  Using a t Distribution: Estimating a Population Standard Deviation from a Sample  Calculating a t Statistic for the Mean of a Sample: Using the Standard Error  When t and z Are Equal: Very Large Sample Sizes  T Distributions: Distributions of Differences Between Means  Hypothesis Tests: The Single Sample t Test  Single Sample t Test: When We Know the Population Mean, But Not the Standard Deviation  T Table: understanding Degrees of Freedom  T Test: The Six Steps of Hypothesis Testing  Hypothesis Tests: Tests for Two Samples  Paired Samples t Test: Two Sample Means and a WithinGroups Design  Independent Samples t Test: Two Sample Means and a BetweenGroups Design  Digging Deeper into the Data: Exploring Two Group Comparisons  Difference Scores: Are All Differences Created Equal?  Graphing Two Samples: Visualizing Two Sets of Scores  Chapter 10 Hypothesis Testing Using OneWay ANOVA: Comparing Three or More Groups  When to Use the F Distribution: Working With More than Two Samples  A Mnemonic for When to Use a t Distribution or the F Distribution: 't' for Two  F Distribution: Analyzing Variability to Compare Means  Relation of F to t (and z): F as a Squared t for Two Groups and Large Samples  Analysis of Variance (ANOVA): Beyond t Tests  Problem of Too Many t Tests: Fishing for a Finding  Assumptions for ANOVA: Naming the Ideal Conditions for the Perfect Study  OneWay BetweenGroups ANOVA: Applying the Six Steps of Hypothesis Testing  Everything ANOVA but the Calculations: The Six Steps of Hypothesis Testing  F Statistic: Logic and Calculations  Bringing It All Together: What Is the ANOVA Telling Us to Do About the Null Hypothesis?  Why the ANOVA is Not Sufficient: PostHoc Tests  Digging Deeper into the Data: PostHoc Tests to Determine Which Groups Are Different  Planned and A Priori Comparisons: When Comparisons between Pairs Are Guided by Theory  Tukey HSD: An Honest Approach  Bonferroni Test: A More Stringent PostHoc Test  Chapter 11 TwoWay ANOVA: Understanding Interactions  TwoWay ANOVA: When the Outcome Depends on More Than One Variable  Why Use a TwoWay ANOVA: The Practicalities and Aesthetics  More Specific Vocabulary of TwoWay ANOVAs: Name That ANOVA Part II  Two Main Effects and an Interaction: Three F Statistics and Their Stories  Layers of ANOVA: Understanding Interactions  Interactions and Public Policy: Using TwoFactor ANOVA to Improve Planning  Interpreting Interactions: Understanding Complexity  Visual Representations of Main Effects and Interactions: Bar Graphs  Expanded Source Table: Conducting a TwoWay Between Subjects ANOVA  TwoWay ANOVA: The Six Steps of Hypothesis Testing  TwoWay ANOVA: Identifying Four Sources of Variability  Interactions: A More Precise Interpretation  Interpreting Interactions: Towards a More Precise Statistical Understanding  Residuals: Separating the Interaction from the Main Effect  Digging Deeper into the Data: More Sophisticated Versions of ANOVA  WithinGroups and Mixed Designs: When the Same Participants Experience More than One Condition  MANOVA, ANCOVA, and MANCOVA: Multiple Dependent Variables and Covariates  Chapter 12 Beyond Hypothesis Testing: Confidence Intervals, Effect Size, and Power  Beyond Hypothesis Testing: Reducing Misinterpretations  Men, Women, and Math: An Accurate Understanding of Gender Differences  Beyond Hypothesis Testing: Enhancing Our Samples' Stories  Confidence Intervals: An Alternative to Hypothesis Testing  Interval Estimation: A Range of Plausible Means  z Distributions: Calculating Confidence Intervals  t Distributions: Calculating Confidence Intervals  Effect Size: Just How Big is the Difference?  Misunderstandings from Hypothesis Testing: When "Significant" Isn't Very Significant  What Effect Size Is: Standardization across Studies  Cohen's d: The Effect Size for a z Test or a t Test  R2: The Effect Size for ANOVA  Statistical Power and Sensitivity: Correctly Rejecting the Null Hypothesis  Calculation of Statistical Power: How Sensitive is a z Test?  Beyond Sample Size: Other Factors that Affect Statistical Power  Digging Deeper into the Data: MetaAnalysis  MetaAnalysis: A Study of Studies  Steps to Conduct a MetaAnalysis  File Drawer Statistic: Where Are All the Null Results?  Chapter 13 Chi Square: Quantifying the Difference between Expectations and Observations  NonParametric Statistics: When We're Not Even Close to Meeting the Assumptions  NonParametric Tests: Using the Right Statistical Tool for the Right Statistical Job  NonParametric Tests: When to Use Them  NonParametric Tests: Why to Avoid Them Whenever Possible  Chisquare Test for GoodnessofFit: When We Have One Nominal Variable  ChiSquare Test for GoodnessofFit: The Six Steps of Hypothesis Testing  A More Typical ChiSquare Test for GoodnessofFit: Evenly Divided Expected Frequencies  Chisquare Test for Independence: When We Have Two Nominal Variables  ChiSquare Test for Independence: The Six Steps of Hypothesis Testing  Cramer's Phi: The Effect Size for Chi square  Graphing Chisquare Percentages: Depicting the Relation Visually  Relative Risk: How Much Higher Are the Chances of an Outcome?  Digging Deeper into the Data: A Deeper Understanding of Chi square  Standardized Residuals: A PostHoc Test for Chi square  ChiSquare Controversies: Expectations about Expected Frequencies  Chapter 14 Beyond Chi Square: Commonly Used Nonparametric Tests with Ordinal Data  NonParametric Statistics: When the Data Are Ordinal  Hypothesis Tests with Ordinal Data: A Nonparametric Equivalent for Every Parametric Test  Examining the Data: Deciding to Use a Nonparametric Test for Ordinal Data  Spearman Rank Order Correlation Coefficient: Quantifying the Association between Two Ordinal Variables  Calculating Spearman's Correlation: Converting Interval Observations to RankOrdered Observations  EyeBalling the Data: Using Your Scientific Common Sense  Nonparametric Hypothesis Tests: Comparing Groups Using Ranks  Wilcoxon SignedRank Test for Matched Pairs: A Nonparametric Test for WithinSubjects Designs  MannWhitney U Test: Comparing Two Independent Groups Using Ordinal Data  KruskalWallis H Test: Comparing the Mean Ranks of Several Groups  Digging Deeper into the Data: Transforming Skewed Data, the Meaning of Interval Data, and Bootstrapping  Coping with Skew: Data Transformations  Controversies in NonParametric Hypothesis Tests: What Really Is an Interval Variable?  Bootstrapping: When the Data Do the Work Themselves  Chapter 15. 

Choosing a Statistical Test and Reporting the Results: The Process of Statistics  Before You Even Begin: Choosing the Right Statistical Test  Planning Your Statistics First: How to Avoid That PostData Collection Regret  Beyond the Statistical Plan: Tips for a Successful Study  Guidelines for Reporting Statistics: The Common Language of Research  Choosing the Right Statistical Test: Questions to Ask Yourself  Choosing the Right Statistical Test: Questions to Ask About the Data  Reporting the Statistics: The Results Section of an APAStyle Paper  Telling Your Story: What to Include in a Results Section  Defending Your Study: Convincing the Reader that the Results are Worth Reading  "Traditional" Statistics: The Longstanding Way of Reporting Results  

Statistics Strongly Encouraged by APA: Essential Additions to the "Traditional" Statistics  What Not to Include in a Results Section: Keeping the Story Focused  Two Excerpts from Results Sections: Understanding the Statistical Story  Unfamiliar Statistics: How to Approach Any Results Section with Confidence  Digging Deeper into the Data: Reporting More Sophisticated Statistical Analyses. 
Other author 
Heinzen, Thomas E.

Subject 
Social sciences  Statistical methods.

ISBN 
9780716750079 (hbk.) £39.99 

0716750074 (hbk.) £39.99 
