Efnisyfirlit

• Cover Page
• Title Page
• Copyright Page
• Preface
• New to This Edition
• Matching the Text to Your Syllabus
• To the Student
• Ancillaries
• Acknowledgments
• About the Authors
• Chapter 1. Introduction to Statistics
  • 1-1. Statistics and Behavioral Sciences
    • Definitions of Statistics
    • Populations and Samples
    • Variables and Data
    • Parameters and Statistics
    • Descriptive and Inferential Statistical Methods
    • Statistics in the Context of Research
  • 1-2. Observations, Measurement, and Variables
    • Observations and Measurements
    • Constructs and Operational Definitions
    • Discrete and Continuous Variables
    • Scales of Measurement
  • 1-3. Three Data Structures, Research Methods, and Statistics
    • Data Structure 1. One Group with One or More Separate Variables Measured for Each Individual: Descriptive Research
    • Relationships Between Variables
    • Data Structure 2. One Group with Two Variables Measured for Each Individual: The Correlational Method
    • Data Structure 3. Comparing Two (or More) Groups of Scores: Experimental and Nonexperimental Methods
    • Experimental and Nonexperimental Methods
    • The Experimental Method
    • Nonexperimental Methods: Nonequivalent Groups and Pre-Post Studies
  • 1-4. Statistical Notation
    • Scores
    • Summation Notation
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 1.1
  • SPSS®
  • Problems
• Chapter 2. Frequency Distributions
  • 2-1. Frequency Distributions and Frequency Distribution Tables
    • Frequency Distribution Tables
    • Proportions and Percentages
    • Percentile and Percentile Ranks
    • Cumulative Frequency and Cumulative Percentage
  • 2-2. Grouped Frequency Distribution Tables
    • Real Limits and Frequency Distributions
  • 2-3. Frequency Distribution Graphs
    • Graphs for Interval or Ratio Data
    • Graphs for Nominal or Ordinal Data
    • Graphs for Population Distributions
    • The Shape of a Frequency Distribution
  • 2-4. Stem and Leaf Displays
    • Comparing Stem and Leaf Displays with Grouped Frequency Distributions
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 2.1
  • Demonstration 2.2
  • SPSS®
  • Problems
• Chapter 3. Central Tendency
  • 3-1. Overview
  • 3-2. The Mean
    • Alternative Definitions for the Mean
    • The Weighted Mean
    • Computing the Mean from a Frequency Distribution Table
    • Characteristics of the Mean
  • 3-3. The Median
    • Finding the Median for Simple Distributions
    • Finding the Precise Median for a Continuous Variable
    • A Formula for the Median with Continuous Variables
    • The Median, the Mean, and the Middle
  • 3-4. The Mode
  • 3-5. Central Tendency and the Shape of the Distribution
    • Symmetrical Distributions
    • Skewed Distributions
  • 3-6. Selecting a Measure of Central Tendency
    • When to Use the Median
    • When to Use the Mode
    • Presenting Means and Medians in Graphs
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 3.1
  • SPSS®
  • Problems
• Chapter 4. Variability
  • 4-1. Introduction to Variability
    • The Range
    • The Interquartile Range
  • 4-2. Defining Variance and Standard Deviation
    • A Note about Rounding
  • 4-3. Measuring Variance and Standard Deviation for a Population
    • The Sum of Squared Deviations (SS)
    • Final Formulas and Notation
  • 4-4. Measuring Variance and Standard Deviation for a Sample
    • The Problem with Sample Variability
    • Formulas for Sample Variance and Standard Deviation
    • Sample Variability and Degrees of Freedom
  • 4-5. Sample Variance as an Unbiased Statistic
    • Biased and Unbiased Statistics
  • 4-6. More about Variance and Standard Deviation
    • Presenting the Mean and Standard Deviation in a Frequency Distribution Graph
    • Transformations of Scale
    • Standard Deviation and Descriptive Statistics
    • Variance and Inferential Statistics
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 4.1
  • SPSS®
  • Problems
• Chapter 5. z-Scores: Location of Scores and Standardized Distributions
  • 5-1. Introduction
  • z-Scores and Locations in a Distribution
    • The z-Score Formula for a Population
    • Determining a Raw Score (X) from a z-Score
    • Computing z-Scores for Samples
  • 5-3. Other Relationships between z, X, the Mean, and the Standard Deviation
  • 5-4. Using z-Scores to Standardize a Distribution
    • Population Distributions
    • Sample Distributions
    • Using z-Scores for Making Comparisons
  • 5-5. Other Standardized Distributions Based on z-Scores
    • Transforming z-Scores to a Distribution with a Predetermined Mean and Standard Deviation
  • 5-6. Looking Ahead to Inferential Statistics
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 5.1
  • Demonstration 5.2
  • SPSS®
  • Problems
• Chapter 6. Probability
  • 6-1. Introduction to Probability
    • Defining Probability
    • Random Sampling
    • Probability and Frequency Distributions
  • 6-2. Probability and the Normal Distribution
    • The Unit Normal Table
    • Probabilities, Proportions, and z-Scores
  • 6-3. Probabilities and Proportions for Scores from a Normal Distribution
    • Finding Proportions/Probabilities Located between Two Scores
  • 6-4. Percentiles and Percentile Ranks
    • Finding Percentiles
    • Quartiles
  • 6-5. Looking Ahead to Inferential Statistics
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 6.1
  • SPSS®
  • Problems
• Chapter 7. Probability and Samples: The Distribution of Sample Means
  • 7-1. Samples, Populations, and the Distribution of Sample Means
    • The Distribution of Sample Means
    • Characteristics of the Distribution of Sample Means
  • 7-2. Shape, Central Tendency, and Variability for the Distribution of Sample Means
    • The Central Limit Theorem
    • The Shape of the Distribution of Sample Means
    • The Mean of the Distribution of Sample Means: The Expected Value of M
    • The Standard Error of M
    • Three Different Distributions
  • 7-3. z-Scores and Probability for Sample Means
    • A z-Score for Sample Means
  • 7-4. More about Standard Error
    • Sampling Error and Standard Error
  • 7-5. Looking Ahead to Inferential Statistics
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 7.1
  • SPSS®
  • Problems
• Chapter 8. Introduction to Hypothesis Testing
  • 8-1. The Logic of Hypothesis Testing
    • The Elements of a Hypothesis Test
    • The Four Steps of a Hypothesis Test
    • A Closer Look at the z-Score Statistic
  • 8-2. Uncertainty and Errors in Hypothesis Testing
    • Type I Errors
    • Type II Errors
    • Selecting an Alpha Level
  • 8-3. More about Hypothesis Tests
    • A Summary of the Hypothesis Test
    • Factors That Influence a Hypothesis Test
    • Assumptions for Hypothesis Tests with z-Scores
  • 8-4. Directional (One-Tailed) Hypothesis Tests
    • The Hypotheses for a Directional Test
    • The Critical Region for Directional Tests
    • Comparison of One-Tailed versus Two-Tailed Tests
  • 8-5. Concerns about Hypothesis Testing: Measuring Effect Size
    • Measuring Effect Size
  • 8-6. Statistical Power
    • Calculating Power
    • Power and Sample Size
    • Power and Effect Size
    • Another Look at Sample Size and Effect Size
    • Other Factors That Affect Power
    • A Note on the Direction of the Treatment Effect
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 8.1
  • Demonstration 8.2
  • Demonstration 8.3
  • SPSS®
  • Problems
• Chapter 9. Introduction to the t Statistic
  • 9-1. The t Statistic: An Alternative to z
    • The Problem with z-Scores
    • Introducing the t Statistic
    • Degrees of Freedom and the t Statistic
    • The t Distribution
    • The Shape of the t Distribution
    • Determining Proportions and Probabilities for t Distributions
  • 9-2. Hypothesis Tests with the t Statistic
    • Using the t Statistic for Hypothesis Testing
    • Hypothesis Testing Example
    • Assumptions of the t Test
    • The Influence of Sample Size and Sample Variance
  • 9-3. Measuring Effect Size for the t Statistic
    • Estimated Cohen’s d
    • Measuring the Percentage of Variance Explained, r 2
    • Confidence Intervals for Estimating μ
    • Constructing a Confidence Interval
    • Factors Affecting the Width of a Confidence Interval
  • 9-4. Directional Hypotheses and One-Tailed Tests
    • The Critical Region for a One-Tailed Test
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 9.1
  • Demonstration 9.2
  • SPSS®
  • Problems
• Chapter 10. The t Test for Two Independent Samples
  • 10-1. Introduction to the Independent-Measures Design
  • 10-2. The Hypotheses and the Independent-Measures t Statistic
    • The Hypotheses for an Independent-Measures Test
    • The Formulas for an Independent-Measures Hypothesis Test
    • Calculating the Estimated Standard Error
    • Pooled Variance
    • Estimated Standard Error
    • The Final Formula and Degrees of Freedom
  • 10-3. Hypothesis Tests with the Independent-Measures t Statistic
    • Directional Hypotheses and One-Tailed Tests
    • Assumptions Underlying the Independent-Measures t Formula
    • Hartley’s F-Max Test
  • 10-4. Effect Size and Confidence Intervals for the Independent-Measures t
    • Cohen’s Estimated d
    • Explained Variance and r 2
    • Confidence Intervals for Estimating μ 1 - μ 2
    • Confidence Intervals and Hypothesis Tests
  • 10-5. The Role of Sample Variance and Sample Size in the Independent-Measures t Test
    • Error and the Role of Individual Differences
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 10.1
  • Demonstration 10.2
  • SPSS®
  • Problems
• Chapter 11. The t Test for Two Related Samples
  • 11-1. Introduction to Repeated-Measures Designs
  • 11-2. The t Statistic for a Repeated-Measures Research Design
    • Difference Scores: The Data for a Repeated-Measures Study
    • The Hypotheses for a Repeated-Measures t Test
    • The Repeated-Measures t Statistic
  • 11-3. Hypothesis Tests for the Repeated-Measures Design
    • Directional Hypotheses and One-Tailed Tests
    • Assumptions of the Related-Samples t Test
  • 11-4. Effect Size, Confidence Intervals, and the Role of Sample Size and Sample Variance for the Repeated-Measures t
    • Effect Size for the Repeated-Measures t
    • Confidence Intervals for Estimating μ D
    • Descriptive Statistics and the Hypothesis Test
    • Sample Variance and Sample Size in the Repeated-Measures t Test
  • 11-5. Comparing Repeated- and Independent-Measures Designs
    • Repeated-Measures versus Independent-Measures Designs
    • Time-Related Factors and Order Effects
    • The Matched-Subjects Design
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 11.1
  • Demonstration 11.2
  • SPSS®
  • Problems
• Chapter 12. Introduction to Analysis of Variance
  • 12-1. Introduction: An Overview of Analysis of Variance
    • Terminology in Analysis of Variance
    • Statistical Hypotheses for ANOVA
    • Type I Errors and Multiple-Hypothesis Tests
    • The Test Statistic for ANOVA
  • 12-2. The Logic of Analysis of Variance
    • Between-Treatments Variance
    • Within-Treatments Variance
    • The F-Ratio: The Test Statistic for ANOVA
  • 12-3. ANOVA Notation and Formulas
    • ANOVA Formulas
    • Analysis of Sum of Squares (SS)
    • The Analysis of Degrees of Freedom (df)
    • Calculation of Variances (MS) and the F-Ratio
  • 12-4. Examples of Hypothesis Testing and Effect Size with ANOVA
    • The Distribution of F-Ratios
    • The F Distribution Table
    • An Example of Hypothesis Testing and Effect Size with Anova
    • Measuring Effect Size for ANOVA
    • An Example with Unequal Sample Sizes
    • Assumptions for the Independent-Measures ANOVA
  • 12-5. Post Hoc Tests
    • Posttests and Type I Errors
    • Tukey’s Honestly Significant Difference (HSD) Test
    • The Scheffé Test
  • 12-6. More about ANOVA
    • A Conceptual View of ANOVA
    • The Relationship Between ANOVA and t Tests
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 12.1
  • Demonstration 12.1
  • SPSS®
  • Problems
• Chapter 13. Two-Factor Analysis of Variance
  • 13-1. An Overview of the Two-Factor, Independent-Measures ANOVA
    • Main Effects and Interactions
    • Main Effects
    • Interactions
    • More about Interactions
    • Independence of Main Effects and Interactions
  • 13-2. An Example of the Two-Factor ANOVA and Effect Size
    • An Example of Hypothesis Testing with a Two-Factor ANOVA
    • Measuring Effect Size for the Two-Factor ANOVA
    • Interpreting the Results from a Two-Factor ANOVA
  • 13-3. More about the Two-Factor ANOVA
    • Testing Simple Main Effects
    • Using a Second Factor to Reduce Variance Caused by Individual Differences
    • Assumptions for the Two-Factor Anova
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 13.1
  • SPSS®
  • Problems
• Chapter 14. Correlation and Regression
  • 14-1. Introduction
    • The Characteristics of a Relationship
  • 14-2. The Pearson Correlation
    • The Sum of Products of Deviations
    • Calculation of the Pearson Correlation
    • Correlation and the Pattern of Data Points
    • The Pearson Correlation and z-Scores
  • 14-3. Using and Interpreting the Pearson Correlation
    • Where and Why Correlations Are Used
    • Interpreting Correlations
    • Correlation and Causation
    • Correlation and Restricted Range
    • Outliers
    • Correlation and the Strength of the Relationship
  • 14-4. Hypothesis Tests with the Pearson Correlation
    • The Hypotheses
    • The Hypothesis Test
  • 14-5. Alternatives to the Pearson Correlation
    • The Spearman Correlation
    • Ranking Tied Scores
    • Special Formula for the Spearman Correlation
    • The Point-Biserial Correlation and Measuring Effect Size with r 2
    • The Phi-Coefficient
  • 14-6. Introduction to Linear Equations and Regression
    • Linear Equations
    • Regression
    • The Standard Error of Estimate
    • Analysis of Regression: The Significance of the Regression Equation
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 14.1
  • SPSS®
  • Problems
• Chapter 15. The Chi-Square Statistic: Tests for Goodness of Fit and Independence
  • 15-1. Introduction to Chi-Square: The Test for Goodness of Fit
    • Parametric and Nonparametric Statistical Tests
    • The Chi-Square Test for Goodness of Fit
    • The Null Hypothesis for the Goodness-of-Fit Test
    • The Data for the Goodness-of-Fit Test
    • Expected Frequencies
    • The Chi-Square Statistic
  • 15-2. An Example of the Chi-Square Test for Goodness of Fit
    • The Chi-Square Distribution and Degrees of Freedom
    • Locating the Critical Region for a Chi-Square Test
    • A Complete Chi-Square Test for Goodness of Fit
    • Goodness of Fit and the Single-Sample t Test
  • 15-3. The Chi-Square Test for Independence
    • The Null Hypothesis for the Test for Independence
    • Observed and Expected Frequencies
    • The Chi-Square Statistic and Degrees of Freedom
    • A Summary of the Chi-Square Test for Independence
  • 15-4. Effect Size and Assumptions for the Chi-Square Tests
    • Cohen’s w
    • The Phi-Coefficient and Cramér’s V
    • Assumptions and Restrictions for Chi-Square Tests
  • Summary
  • Key Terms
  • Focus on Problem Solving
  • Demonstration 15.1
  • Demonstration 15.2
  • SPSS®
  • Problems
• Appendix A. Basic Mathematics Review
• Appendix B. Statistical Tables
• Appendix C. Solutions for Odd-Numbered Problems in the Text
• Appendix D. General Instructions for Using SPSS®
• Statistics Organizer: Finding the Right Statistics for Your Data
• Summary of Statistics Formulas
• References

UM RAFBÆKUR Á HEIMKAUP.IS

Bókahillan þín er þitt svæði og þar eru bækurnar þínar geymdar. Þú kemst í bókahilluna þína hvar og hvenær sem er í tölvu eða snjalltæki. Einfalt og þægilegt!

Rafbók til eignar
Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt.

Þú kemst í bækurnar hvar sem er
Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).

Auðvelt að fletta og leita
Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.

Glósur og yfirstrikanir
Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.

Hvað viltu sjá? / Þú ræður hvernig síðan lítur út
Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.



Fleiri góðir kostir
- Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)
- Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu
- Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir
- Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu