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Produktbild: Essential Statistics for the Behavioral Sciences - Internati
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Essential Statistics for the Behavioral Sciences - Internati Student Editio

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.04.2018

Verlag

Sage Publications

Maße (L/B/H)

20,7/25,4/3,2 cm

Gewicht

1360 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-1-5443-2801-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.04.2018

Verlag

Sage Publications

Maße (L/B/H)

20,7/25,4/3,2 cm

Gewicht

1360 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-1-5443-2801-0

EU-Ansprechpartner

Zeitfracht Medien GmbH
Ferdinand-Jühlke-Straße 7|99095|Erfurt|DE

Herstelleradresse

SAGE Publications
1 Oliver's Yard 55 City Road|EC1Y 1SP|London|GB

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  • Produktbild: Essential Statistics for the Behavioral Sciences - Internati
  • PART I: Introduction and Descriptive Statistics
    Chapter 1:Introduction to Statistics
    1.1 The Use of Statistics in Science
    1.2 Descriptive and Inferential Statistics
    MAKING SENSE-Populations and Samples
    1.3 Research Methods and Statistics
    MAKING SENSE-Experimental and Control Groups
    1.4 Scales of Measurement
    1.5 Types of Variables for Which Data Are Measured
    1.6 Research in Focus: Evaluating Data and Scales of Measurement
    1.7 SPSS in Focus: Entering and Defining Variables
    Chapter 2: Summarizing Data: Frequency Distributions in Tables and Graphs
    2.1 Why Summarize Data?
    2.2 Frequency Distributions for Grouped Data
    2.3 Identifying Percentile Points and Percentile Ranks
    2.4 SPSS in Focus: Frequency Distributions for Quantitative Data
    2.5 Frequency Distributions for Ungrouped Data
    2.6 Research in Focus: Summarizing Demographic Information
    2.7 SPSS in Focus: Frequency Distributions for Categorical Data
    2.8 Graphing Distributions: Continuous Data
    2.9 Graphing Distributions: Discrete and Categorical Data
    MAKING SENSE- Deception Due to the Distortion of Data
    2.10 Research in Focus: Frequencies and Percents
    2.11 SPSS in Focus: Histograms, Bar Charts, and Pie Charts
    Chapter 3: Summarizing Data: Central Tendency
    3.1 Introduction to Central Tendency
    3.2 Measures of Central Tendency
    MAKING SENSE-Making the Grade
    3.3 Characteristics of the Mean
    3.4 Choosing an Appropriate Measure of Central Tendency
    3.5 Research in Focus: Describing Central Tendency
    3.6 SPSS in Focus: Mean, Median, and Mode
    Chapter 4: Summarizing Data: Variability
    4.1 Measuring Variability
    4.2 The Range and Interquartile Range
    4.3 Research in Focus: Reporting the Range
    4.4 The Variance
    4.5 Explaining Variance for Populations and Samples
    4.6 The Computational Formula for Variance
    4.7 The Standard Deviation
    4.8 What Does the Standard Deviation Tell Us?
    MAKING SENSE-Standard Deviation and Nonnormal Distributions
    4.9 Characteristics of the Standard Deviation
    4.10 SPSS in Focus: Range, Variance, and Standard Deviation
    PART II: Probability and the Foundations of Inferential Statistics
    Chapter 5: Probability, Normal Distributions, and z Scores
    5.1 Introduction to Probability
    5.2 Calculating Probability
    5.3 Probability and the Normal Distribution
    5.4 Characteristics of the Normal Distribution
    5.5 Research in Focus: The Statistical Norm
    5.6 The Standard Normal Distribution and z Scores
    5.7 A Brief Introduction to the Unit Normal Table
    5.8 Locating Proportions
    5.9 Locating Scores
    MAKING SENSE-Standard Deviation and the Normal Distribution
    5.10 SPSS in Focus: Converting Raw Scores to Standard z Scores
    Chapter 6: Characteristics of the Sample Mean
    6.1 Selecting Samples From Populations
    6.2 Selecting a Sample: Who's In and Who's Out?
    6.3 Sampling Distributions: The Mean
    6.4 The Standard Error of the Mean
    6.5 Factors That Decrease Standard Error
    6.6 SPSS in Focus: Estimating the Standard Error of the Mean
    6.7 APA in Focus: Reporting the Standard Error
    6.8 Standard Normal Transformations With Sampling Distributions
    Chapter 7: Hypothesis Testing: Significance, Effect Size, and Power
    7.1 Inferential Statistics and Hypothesis Testing
    7.2 Four Steps to Hypothesis Testing
    MAKING SENSE-Testing the Null Hypothesis
    7.3 Hypothesis Testing and Sampling Distributions
    7.4 Making a Decision: Types of Error
    7.5 Testing for Significance: Examples Using the z Test
    7.6 Research in Focus: Directional Versus Nondirectional Tests
    7.7 Measuring the Size of an Effect: Cohen's d
    7.8 Effect Size, Power, and Sample Size
    7.9 Additional Factors That Increase Power
    7.10 SPSS in Focus: A Preview for Chapters 8 to 14
    7.11 APA in Focus: Reporting the Test Statistic and Effect Size
    PART III: Making Inferences About One or Two Means
    Chapter 8: Testing Means: One-Sample t Test With Confidence Intervals
    8.1 Going From z to t
    8.2 The Degrees of Freedom
    8.3 Reading the t Table
    8.4 Computing the One-Sample t Test
    8.5 Effect Size for the One- Sample t Test
    8.6 Confidence Intervals for the One-Sample t Test
    8.7 Inferring Significance and Effect Size From a Confidence Interval
    8.8 SPSS in Focus: One-Sample t Test and Confidence Intervals
    8.9 APA in Focus: Reporting the t Statistic and Confidence Intervals
    Chapter 9: Testing Means: Two-Independent-Sample t Test With Confidence Intervals
    9.1 Introduction to the Between- Subjects Design
    9.2 Selecting Samples for Comparing Two Groups
    9.3 Variability and Comparing Differences Between Two Groups
    9.4 Computing the Two-Independent-Sample t Test
    MAKING SENSE-The Pooled Sample Variance
    9.5 Effect Size for the Two-Independent-Sample t Test
    9.6 Confidence Intervals for the Two-Independent-Sample t Test
    9.7 Inferring Significance and Effect Size From a Confidence Interval
    9.8 SPSS in Focus: Two-Independent- Sample t Test and Confidence Intervals
    9.9 APA in Focus: Reporting the t Statistic and Confidence Intervals
    Chapter 10: Testing Means: Related-Samples t Test With Confidence Intervals
    10.1 Related Samples Designs
    10.2 Introduction to the Related-Samples t Test
    10.3 Computing the Related-Samples t Test
    MAKING SENSE-Increasing Power by Reducing Error
    10.4 Measuring Effect Size for the Related-Samples t Test
    10.5 Confidence Intervals for the Related-Samples t Test
    10.6 Inferring Significance and Effect Size From a Confidence Interval
    10.7 SPSS in Focus: Related-Samples t Test and Confidence Intervals
    10.8 APA in Focus: Reporting the t Statistic and Confidence Intervals
    PART IV: Making Inferences About The Variability of Two or More Means
    Chapter 11: One-Way Analysis of Variance: Between-Subjects and Within-Subjects (Repeated-Measures) Designs
    11.1 An Introduction to Analysis of Variance
    11.2 The Between-Subjects Design for Analysis of Variance
    11.3 Computing the One-Way Between-Subjects ANOVA
    MAKING SENSE-Mean Squares and Variance
    11.4 Post Hoc Tests: An Example Using Tukey's HSD
    11.5 SPSS in Focus: The One-Way Between-Subjects ANOVA
    11.6 The Within-Subjects Design for Analysis of Variance
    11.7 Computing the One-Way Within-Subjects ANOVA
    11.8 Post Hoc Tests for the Within-Subjects Design
    11.9 SPSS in Focus: The One-Way Within-Subjects ANOVA
    11.10 A Comparison of Within-Subjects and Between-Subjects Designs for ANOVA: Implications for Power
    11.11 APA in Focus: Reporting the Results of the One-Way ANOVAs327 Chapter Summary Organized by Learning Objective
    Chapter 12: Two-Way Analysis of Variance: Between- Subjects Factorial Design
    12.1 Introduction to Factorial Designs
    12.2 Structure and Notation for the Two-Way ANOVA
    12.3 Describing Variability: Main Effects and Interactions
    MAKING SENSE-Graphing Interactions
    12.4 Computing the Two-Way Between-Subjects ANOVA
    12.5 Analyzing Main Effects and Interactions
    12.6 Measuring Effect Size for Main Effects and the Interaction
    12.7 SPSS in Focus: The Two-Way Between-Subjects ANOVA
    12.8 APA in Focus: Reporting the Results of the Two-Way ANOVAs
    PART V: Making Inferences About Patterns, Prediction, and Nonparametric Tests
    Chapter 13: Correlation and Linear Regression
    13.1 The Structure of Data Used for Identifying Patterns and Making Predictions
    13.2 Fundamentals of the Correlation
    13.3 The Pearson Correlation Coefficient
    MAKING SENSE-Understanding Covariance
    13.4 SPSS in Focus: Pearson Correlation Coefficient
    13.5 Assumptions and Limitations for Linear Correlations
    13.6 Alternatives to Pearson: Spearman, Point-Biserial, and Phi
    13.7 SPSS in Focus: Computing the Alternatives to Pearson
    13.8 Fundamentals of Linear Regression
    13.9 Using the Method of Least Squares to Find the Regression Line
    MAKING SENSE-SP, SS, and the Slope of a Regression Line
    13.10 Using Analysis of Regression to Determine Significance
    13.11 SPSS in Focus: Analysis of Regression
    13.12 A Look Ahead to Multiple Regression
    13.13 APA in Focus: Reporting Correlations and Linear Regression
    Chapter 14: Chi-Square Tests: Goodness-of-Fit and the Test for Independence
    14.1 Distinguishing Parametric and Nonparametric Tests
    14.2 The Chi-Square Goodness-of-Fit Test
    MAKING SENSE-The Relative Size of a Discrepancy
    14.3 SPSS in Focus: The Chi-Square Goodness-of-Fit Test
    14.4 Interpreting the Chi-Square Goodness-of-Fit Test
    14.5 The Chi-Square Test for Independence
    14.6 Measures of Effect Size for the Chi-Square Test for Independence
    14.7 SPSS in Focus: The Chi-Square Test for Independence
    14.8 APA in Focus: Reporting the Chi-Square Tests