Explaining psychological statistics / Barry H. Cohen.

Format:

Book

Author/Creator:

Cohen, Barry H., 1949-

Language:

English

Subjects (All):

Psychometrics.

Psychology--Mathematical models.

Psychology.

Statistics--Study and teaching (Higher).

Statistics.

Physical Description:

1 online resource (849 pages) : illustrations, graphs

Edition:

Fourth edition.

Place of Publication:

Hoboken, New Jersey : Wiley, 2013.

Summary:

BARRY H. COHEN, PhD, is a clinical associate professor in the department of psychology at New York University, where he has been teaching statistics for more than twenty-five years. He is the coauthor of two other successful statistics books from Wiley: Introductory Statistics for the Behavioral Sciences, Seventh Edition and Essentials of Statistics for the Social and Behavioral Sciences.

Contents:

Cover

Title Page

Copyright

Contents

Preface to the Fourth Edition

Acknowledgments

Part One Descriptive Statistics

Chapter 1 Introduction to Psychological Statistics

A. Conceptual Foundation

What Is (Are) Statistics?

Statistics and Research

Variables and Constants

Scales of Measurement

Parametric Versus Nonparametric Statistics

Likert Scales and the Measurement Controversy

Continuous Versus Discrete Variables

Scales Versus Variables Versus Underlying Constructs

Independent Versus Dependent Variables

Experimental Versus Observational Research

Populations Versus Samples

Statistical Formulas

Summary

Exercises

B. Basic Statistical Procedures

Variables With Subscripts

The Summation Sign

Properties of the Summation Sign

Rounding Off Numbers

C. Analysis by SPSS

Ihno's Data

Variable View

Data Coding

Missing Values

Computing New Variables

Reading Excel Files Into SPSS

Chapter 2 Frequency Tables, Graphs, and Distributions

Frequency Distributions

The Cumulative Frequency Distribution

The Relative Frequency and Cumulative Relative Frequency Distributions

The Cumulative Percentage Distribution

Percentiles

Graphs

Real Versus Theoretical Distributions

Grouped Frequency Distributions

Apparent Versus Real Limits

Constructing Class Intervals

Choosing the Class Interval Width

Choosing the Limits of the Lowest Interval

Relative and Cumulative Frequency Distributions

Cumulative Percentage Distribution

Estimating Percentiles and Percentile Ranks by Linear Interpolation

Graphing a Grouped Frequency Distribution

Guidelines for Drawing Graphs of Frequency Distributions

Exercises.

C. Analysis by SPSS

Creating Frequency Distributions

Percentile Ranks and Missing Values

Graphing Your Distribution

Obtaining Percentiles

The Split File Function

Stem-and-Leaf Plots

Chapter 3 Measures of Central Tendency and Variability

Measures of Central Tendency

Measures of Variability

Skewed Distributions

Formulas for the Mean

Computational Formulas for the Variance and Standard Deviation

Obtaining the Standard Deviation Directly From Your Calculator

Properties of the Mean

Properties of the Standard Deviation

Measuring Skewness

Measuring Kurtosis

Summary Statistics

Using Explore to Obtain Additional Statistics

Boxplots

Selecting Cases

Key Formulas

Chapter 4 Standardized Scores and the Normal Distribution

z Scores

Finding a Raw Score From a z Score

Sets of z Scores

Properties of z Scores

SAT, T, and IQ Scores

The Normal Distribution

Introducing Probability: Smooth Distributions Versus Discrete Events

Real Distributions Versus the Normal Distribution

z Scores as a Research Tool

Sampling Distribution of the Mean

Standard Error of the Mean

Sampling Distribution Versus Population Distribution

Finding Percentile Ranks

Finding the Area Between Two z Scores

Finding the Raw Scores Corresponding to a Given Area

Areas in the Middle of a Distribution

From Score to Proportion and Proportion to Score

Describing Groups

Probability Rules

Advanced Material: The Mathematics of the Normal Distribution

Creating z Scores

Obtaining Standard Errors.

Obtaining Areas of the Normal Distribution

Data Transformations

Part Two One- and Two-Sample Hypothesis Tests

Chapter 5 Introduction to Hypothesis Testing: The One-Sample z Test

Selecting a Group of Subjects

The Need for Hypothesis Testing

The Logic of Null Hypothesis Testing

The Null Hypothesis Distribution

The Null Hypothesis Distribution for the One-Sample Case

z Scores and the Null Hypothesis Distribution

Statistical Decisions

The z Score as Test Statistic

Type I and Type II Errors

The Trade-Off Between Type I and Type II Errors

One-Tailed Versus Two-Tailed Tests

Step 1: State the Hypothesis

Step 2: Select the Statistical Test and the Significance Level

Step 3: Select the Sample and Collect the Data

Step 4: Find the Region of Rejection

Step 5: Calculate the Test Statistic

Step 6: Make the Statistical Decision

Interpreting the Results

Assumptions Underlying the One-Sample z Test

Varieties of the One-Sample Test

Why the One-Sample Test Is Rarely Performed

Publishing the Results of One-Sample Tests

Advanced Material: Correcting Null Hypothesis Testing Fallacies

Advanced Exercises

The One-Sample z Test

Testing the Normality Assumption

Chapter 6 Interval Estimation and the t Distribution

The Mean of the Null Hypothesis Distribution

When the Population Standard Deviation Is Not Known

Calculating a Simple Example

The t Distribution

Degrees of Freedom and the t Distribution

Critical Values of the t Distribution

Calculating the One-Sample t Test

Sample Size and the One-Sample t Test

Uses for the One-Sample t Test.

Cautions Concerning the One-Sample t Test

Estimating the Population Mean

Advanced Material: A Note About Estimators

Step 1: Select the Sample Size

Step 2: Select the Level of Confidence

Step 3: Select the Random Sample and Collect the Data

Step 4: Calculate the Limits of the Interval

Relationship Between Interval Estimation and Null Hypothesis Testing

Assumptions Underlying the One-Sample t Test and the Confidence Interval for the Population Mean

Use of the Confidence Interval for the Population Mean

Publishing the Results of One-Sample t Tests

Performing a One-Sample t Test

Confidence Intervals for the Population Mean

Bootstrapping

Chapter 7 The t Test for Two Independent Sample Means

Null Hypothesis Distribution for the Differences of Two Sample Means

Standard Error of the Difference

Formula for Comparing the Means of Two Samples

Null Hypothesis for the Two-Sample Case

The z Test for Two Large Samples

Separate-Variances t Test

The Pooled-Variances Estimate

The Pooled-Variances t Test

Formula for Equal Sample Sizes

Calculating the Two-Sample t Test

Interpreting the Calculated t

Limitations of Statistical Conclusions

Step 1: State the Hypotheses

Step 3: Select the Samples and Collect the Data

Confidence Intervals for the Difference Between Two Population Means

Assumptions of the t Test for Two Independent Samples.

HOV Tests and the Separate-Variances t Test

Random Assignment and the Separate-Variances t Test

When to Use the Two-Sample t Test

When to Construct Confidence Intervals

Heterogeneity of Variance as an Experimental Result

Publishing the Results of the Two-Sample t Test

Advanced Material: Finding the Degrees of Freedom for the Separate-Variances t Test

Performing the Two-Independent-Samples t Test

Confidence Interval for the Difference of Two Population Means

Chapter 8 Statistical Power and Effect Size

The Alternative Hypothesis Distribution

The Expected t Value (Delta)

The Effect Size

Power Analysis

The Interpretation of t Values

Estimating Effect Size

Manipulating Power

Using Power Tables

The Relationship Between Alpha and Power

Power Analysis With Fixed Sample Sizes

Sample Size Determination

The Case of Unequal Sample Sizes

The Power of a One-Sample Test

Constructing Confidence Intervals for Effect Sizes

Calculating Power Retrospectively

Meta-Analysis

Advanced Material: When Is Null Hypothesis Testing Useful?

Power Calculations in SPSS

G*Power 3

Part Three Hypothesis Tests Involving Two Measures on Each Subject

Chapter 9 Linear Correlation

Perfect Correlation

Negative Correlation

The Correlation Coefficient

Linear Transformations

Graphing the Correlation

Dealing With Curvilinear Relationships

Problems in Generalizing From Sample Correlations

Correlation Does Not Imply Causation

True Experiments Involving Correlation

Summary.

Exercises.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-118-65224-X

1-118-65214-2

OCLC:

864916826