Quantitative and Statistical Research Methods E-Book

William E. Martin

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Research Methods for the Social Sciences
• Sprache: Englisch

Seitenzahl: 530

Veröffentlichungsjahr: 2012

Beschreibung

Quantitative and Statistical Research Methods

This user-friendly textbook teaches students to understand and apply procedural steps in completing quantitative studies. It explains statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The research problems used in the book reflect statistical applications related to interesting and important topics. In addition, the book provides a Research Analysis and Interpretation Guide to help students analyze research articles.

Designed as a hands-on resource, each chapter covers a single research problem and offers directions for implementing the research method from start to finish. Readers will learn how to:

• Pinpoint research questions and hypotheses
• Identify, classify, and operationally define the study variables
• Choose appropriate research designs
• Conduct power analysis
• Select an appropriate statistic for the problem
• Use a data set
• Conduct data screening and analyses using SPSS
• Interpret the statistics
• Write the results related to the problem

Quantitative and Statistical Research Methods allows students to immediately, independently, and successfully apply quantitative methods to their own research projects.

Leseprobe

CONTENTS

Tables and Figures

Preface

The Authors

Chapter 1: Introduction and Overview

Review of Foundational Research Concepts

Review of Foundational Statistical Information

The Normal Distribution

Chapter 2: Logical Steps of Conducting Quantitative Research

Hypothesis-Testing Process

Chapter 3: Maximizing Hypothesis Decisions Using Power Analysis

Balance between Avoiding Type I and Type II Errors

Chapter 4: Research and Statistical Designs

Formulating Experimental Conditions

Reducing the Imprecision in Measurement

Controlling Extraneous Experimental Influences

Internal Validity and Experimental Designs

Choosing a Statistic to Use for an Analysis

Chapter 5: Introduction to IBM SPSS 20

The IBM SPSS 20 Data View Screen

Naming and Defining Variables in Variable View

Entering Data

Examples of Basic Analyses

Examples of Modifying Data Procedures

Chapter 6: Diagnosing Study Data for Inaccuracies and Assumptions

Research Example

Chapter 7: Randomized Design Comparing Two Treatments and a Control Using a One-Way Analysis of Variance

Research Problem

Study Variables

Research Design

Stating the Omnibus (Comprehensive) Research Question

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hypothesis Testing Step 6: Make Decision Regarding the H0 and Interpret Post Hoc Effect Sizes and Confidence Intervals

Formula Calculations of the Study Results

Chapter 8: Repeated-Treatment Design Using a Repeated-Measures Analysis of Variance

Research Problem

Study Variables

Research Design

Stating the Omnibus (Comprehensive) Research Question

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hypothesis Testing Step 6: Make Decision Regarding the H0 and Interpret Post Hoc Effect Sizes and Confidence Intervals

Formula Calculations of the Study Results

Chapter 9: Randomized Factorial Experimental Design Using a Factorial ANOVA

Research Problem

Study Variables

Research Design

Stating the Omnibus (Comprehensive) Research Questions

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hypothesis Testing Step 6: Make Decision Regarding the H0 and Interpret Post Hoc Effect Sizes and Confidence Intervals

Formula Calculations of the Study Results

Chapter 10: Analysis of Covariance

Research Problem

Study Variables

Research Design

Stating the Omnibus (Comprehensive) Research Question

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hypothesis Testing Step 6: Make Decision Regarding the H0 and Interpret Post Hoc Effect Sizes and Confidence Intervals

Formula ANCOVA Calculations of the Study Results

ANCOVA Study Results

Chapter 11: Randomized Control Group and Repeated-Treatment Designs and Nonparametics

Research Problem

Study Variables

Research Design

Stating the Omnibus (Comprehensive) Research Question

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hypothesis Testing Step 6: Make Decision Regarding the H0 and Interpret Post Hoc Effect Sizes

Formula Calculations

Nonparametric Research Problem Two: Friedman’s Rank Test for Correlated Samples and Wilcoxon’s Matched-Pairs Signed-Ranks Test

Chapter 12: Bivariate and Multivariate Correlation Methods Using Multiple Regression Analysis

Research Problem

Study Variables

Research Method

Stating the Omnibus (Comprehensive) Research Question

Hypothesis Testing Step 1: Establish the Alternative (Research) Hypothesis (Ha)

Hypothesis Testing Step 2: Establish the Null Hypothesis (H0)

Hypothesis Testing Step 3: Decide on a Risk Level (Alpha) of Rejecting the True H0 Considering Type I and II Errors and Power

Hypothesis Testing Step 4: Choose Appropriate Statistic and Its Sampling Distribution to Test the H0 Assuming H0 Is True

Hypothesis Testing Step 5: Select Sample, Collect Data, Screen Data, Compute Statistic, and Determine Probability Estimates

Hand Calculations of Statistics

Chapter 13: Understanding Quantitative Literature and Research

Interpretation of a Quantitative Research Article

References

Index

Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved.

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Library of Congress Cataloging-in-Publication Data

Martin, William E. (William Eugene), 1948–

Quantitative and statistical research methods : from hypothesis to results / William E. Martin, Krista D. Bridgmon.— First edition.

pages cm.— (Research methods for the social sciences; 42)

Includes bibliographical references and index.

ISBN 978-0-470-63182-9 (pbk.); ISBN 978-1-118-22075-7 (ebk.); ISBN 978-1-118-23457-0 (ebk.); ISBN 978-1-118-25908-5 (ebk.)

1. Psychology—Methodology. 2. Social sciences—Methodology. 3. SPSS (Computer file) I. Bridgmon, Krista D., 1979-II. Title.

BF38.5.M349 2012

150.72’7—dc23

2012010748

TABLES AND FIGURES

TABLES

Table 1.1

Values Used to Illustrate Measures of Central Tendency and Variability

Table 1.2

Frequency Distribution of Scores of Depressive Symptoms

Table 1.3

Scores and Difference Measures for Dependent

t

Analysis

Table 3.1

Decision Balance between Type I and Type II Errors

Table 3.2

Cohen’s Strength of

d

Effect Sizes

Table 3.3

Cohen’s Strength of η

2

Effect Sizes

Table 3.4

Cohen’s Strength of

r

Effect Sizes

Table 4.1

Threats to Internal Validity: THIS MESS DREAD

Table 5.1

Frequencies Table of Ethnicity

Table 5.2

Descriptive Statistics of Status

Table 5.3

An Independent

t

-Test Analysis

Table 5.4

Correlation Matrix of Age, COSE Confidence in Executing Microskills, and COSE Dealing with Difficult Client Behaviors

Table 6.1

Mean and Standard Deviation of MAAS Scores

Table 6.2

Frequencies of MAAS Scores

Table 6.3

Missing Case for TotalMAAS

Table 6.4

Skewness and Kurtosis Values with Standard Errors of the Dependent Variable for Both Conditions

Table 6.5

Shapiro-Wilk Statistic Results to Assess Normality

Table 6.6

Results of Levene’s Test of Homogeneity of Variance

Table 6.7

One-Way ANOVA Results before Log10 Data Transformation

Table 6.8

Skewness and Kurtosis Values after Log10 Transformation

Table 6.9

Shapiro-Wilk Statistics after Log10 Transformation

Table 6.10

Levene’s Test of Homogeneity of Variance after Log10 Transformation

Table 6.11

One-Way ANOVA Results for the Log10 Transformed Data

Table 6.12

Data Diagnostics Study Example

Table 7.1

Descriptive Statistics of Depressive Symptoms by Condition Group

Table 7.2

Highest ±

z

-Scores by Condition Group

Table 7.3

Skewness, Kurtosis, and Standard Error Values by Group

Table 7.4

Skewness

z

-Scores by Condition Group

Table 7.5

Kurtosis

z

-Scores by Condition Group

Table 7.6

Shapiro-Wilk Statistics by Condition Group

Table 7.7

Levene’s Test of Homogeneity of Variance

Table 7.8

One-Way Analysis Results

Table 7.9

HSD Post Hoc Analysis

Table 7.10

ANOVA Summary Table Specifications

Table 7.11

ANOVA Summary Table

Table 7.12

Matrix of Mean Differences

Table 7.13

One-Way Analysis of Variance Data

Table 8.1

Descriptive Statistics of Weight Loss by Condition Group

Table 8.2

Highest ±

z

-Scores by Condition Group

Table 8.3

Skewness, Kurtosis, and Standard Error Values by Condition Group

Table 8.4

Skewness

z

-Scores by Condition Group

Table 8.5

Kurtosis

z

-Scores by Condition Group

Table 8.6

Shapiro-Wilk Statistics by Condition Group

Table 8.7

Mauchly’s Test of Sphericity

Table 8.8

RM-ANOVA Results for the Omnibus Null Hypothesis

Table 8.9

Post Hoc Comparisons Using the Fisher’s Protected Least Significant Differences (PLSD) Statistic

Table 8.10

Trends of Weight Loss Means Across the Condition Groups

Table 8.11

RM-ANOVA Summary Table Specifications MS

T

/MS

E

Table 8.12

Study Data with Column and Row Means by Subject and Condition

Table 8.13

RM-ANOVA Summary Table Specifications

Table 8.14

Repeated-Measures Analysis of Variance Data

Table 9.1

2 × 2 Factorial Design Matrix

Table 9.2

Descriptive Statistics of Treatment Retention by Treatment Condition × Treatment Status Groups

Table 9.3

Highest ±

z

-Scores by Group

Table 9.4

Skewness, Kurtosis, and Standard Error Values by Group

Table 9.5

Skewness

z

-Scores by Condition Group

Table 9.6

Kurtosis

z

-Scores by Condition Group

Table 9.7

Shapiro-Wilk Statistics by Condition Group

Table 9.8

Levene’s Test Comparing Variances of the Treatment Condition Groups (SC vs. SC + CM)

Table 9.9

Levene’s Test Comparing Variances of the Treatment Status Groups (0–1 vs. ≥2)

Table 9.10

Descriptive Statistics by Conditions

Table 9.11

Levene’s Test of Equality of Error Variancesa

Table 9.12

Two-Way Analysis of Variance Results

Table 9.13

Treatment Condition at Each Treatment Status Level Results

Table 9.14

Treatment Status at Each Level of Treatment Condition Results

Table 9.15

Decisions and Conclusions Regarding Null Hypotheses of Main Effects and Interaction Effect

Table 9.16

Decisions and Conclusions Regarding Null Hypotheses of Simple Effects

Table 9.17

CI.

99

for Mean Difference of Treatment Retention by Treatment Condition

Table 9.18

CI.

99

for Mean Difference of Treatment Retention by Treatment Status

Table 9.19

Two-Way ANOVA Summary Table Specifications

Table 9.20

Study Data with Column and Row Means by Subject and Condition

Table 9.21

Two-Way ANOVA Summary Table

Table 9.22

Simple Effects Summary Table

Table 9.23

Two-Way Analysis of Variance Data

Table 10.1

Highest ±

z

-Scores for the Covariate Age and the Dependent Variable Longest Duration of Abstinence

Table 10.2

Skewness, Kurtosis, and Standard Error Values by Group

Table 10.3

Skewness

z

-Scores by Treatment Condition Group

Table 10.4

Kurtosis

z

-Scores by Substance Treatment Group

Table 10.5

Shapiro-Wilk Statistics by Substance Treatment Condition

Table 10.6

Levene’s Test of Homogeneity of Variance for CovAge and DVLDA

Table 10.7

Homogeneity of Regression (Slope)

Table 10.8

Test of Homogeneity of Variance of DVLDA Including CovAge and IVTreatmentCondition

Table 10.9

ANCOVA Results

Table 10.10

Estimated Marginal Means

Table 10.11

Confidence Interval (.99) for the Mean Difference between SC and SC + CM

Table 10.12

Data and Summary Statistics for LDALDA DV (

Y

)

Table 10.13

Data and Summary Statistics for Age

Table 10.14

Summary of Previous Calculations

Table 10.15

Summary of ANCOVA Results

Table 10.16

Analysis of Covariance Data

Table 11.1

K-W–MWU Data

Table 11.2

Descriptive Statistics of Pain Improvement by Electric Simulation Condition

Table 11.3

Three Highest ±

z

-Scores of Pain Improvement by Electric Stimulation Condition

Table 11.4

Skewness, Kurtosis, and Standard Error Values by Group

Table 11.5

Skewness

z

-Scores by Condition Group

Table 11.6

Kurtosis

z

-Scores by Condition on Pain Improvement Scores

Table 11.7

Shapiro-Wilk Statistics by Conditions

Table 11.8

Levene’s Test of Homogeneity of Variance

Table 11.9

Mean Ranks of Pain Improvement by Conditions

Table 11.10

K-W Results

Table 11.11

Mean Ranks of the Low Electric Stimulation Condition Compared to the Placebo Condition

Table 11.12

MWU Results Comparing Low Electric Stimulation to Placebo

Table 11.13

Mean Ranks of the Placebo Condition Compared to the High Electric Stimulation Condition

Table 11.14

MWU Results Comparing Placebo to High Electric Stimulation

Table 11.15

Formula Kruskal-Wallis and Mann-Whitney

U

Calculations of the Study Results

Table 11.16

Low Electric Stimulation Condition Compared to Placebo Condition on Pain Improvement

Table 11.17

High Stimulation Condition Compared to Placebo Condition on Pain Improvement

Table 11.18

Friedman-Wilcoxon Data

Table 11.19

Descriptive Statistics of Pain Improvement by High Electric Conditions

Table 11.20

Mean Ranks of Pain Improvement by High Electric Conditions

Table 11.21

Friedman’s Statistic of Pain Improvement by High Electric Conditions

Table 11.22

Wilcoxon Results

Table 11.23

Friedman’s Rank Test

Table 11.24

Wilcoxon’s Matched-Pairs Signed-Ranks Test: First Treatment Scores Compared to Removed Treatment Scores

Table 11.25

Wilcoxon’s Matched-Pairs Signed-Ranks Test: Restored Treatment Scores Compared to Removed Treatment Scores

Table 12.1

Highest ±

z

-Scores for DSI, SPIPract, and SPIScient

Table 12.2

Largest (Maximum) Mahalanobis Distance Value

Table 12.3

Bivariate Correlation Coefficients between the Study Variables

Table 12.4

Multicollinearity Measures of Tolerance and Variance Inflation Factor (VIF)

Table 12.5

Model Summary of Sequential MRA

Table 12.6

Analysis of Variance of the Two Sequential MRA Models

Table 12.7

Significance Values of Each Predictor Variable

Table 12.8

Matrix of Correlation Coefficients, Means, and Standard Deviations

Table 12.9

Sequential MRA Data

Table 13.1

Comparisons of Effect Sizes of the Mozart Effect

FIGURES

Figure 1.1

Bar Chart of Scores of Depressive Symptoms

Figure 1.2

Histogram of Scores of Depressive Symptoms

Figure 1.3

Normal Curve Superimposed on Histogram of Scores of Depressive Symptoms

Figure 1.4

Q-Q Plot of Scores of Depressive Symptoms

Figure 1.5

The Normal Distribution and Standardized Scores

Figure 3.1

G*Power First Page

Figure 3.2

A Priori Power Analysis for the Example

Figure 4.1

Issues in Choosing a Statistic to Use for an Analysis

Figure 5.1

IBM SPSS 20 Initial Screen

Figure 5.2

Data View Screen of IBM SPSS 20

Figure 5.3

Variable View Screen of IBM SPSS 20

Figure 5.4

Variables Named and Defined in Variable View

Figure 5.5

Example Data in Data View

Figure 5.6

Bar Chart of Ethnicity

Figure 5.7

Scatter Plot of

Age

and

COSE Dealing with Difficult Client Behaviors

Figure 5.8

COSE Composite Sum Variable

Figure 5.9

COSE Composite Mean Variable

Figure 6.1

Histogram of Mindfulness Attention Awareness Scores for the Treatment Group

Figure 6.2

Histogram of Mindfulness Attention Awareness Scores for the Control Group

Figure 6.3

Q-Q Plot to Assess Normality of Treatment Condition Scores

Figure 6.4

Q-Q Plot to Assess Normality of Control Condition Scores

Figure 6.5

Histograms of the Dependent Variable by Condition Groups after Log10 Data Transformation

Figure 6.6

Normal Q-Q Plots after Log10 Data Transformation

Figure 7.1

A Priori Power Analysis of ANOVA Problem

Figure 7.2

Normal Q-Q Plot of Depressive Symptoms for CBT Group

Figure 7.3

Normal Q-Q Plot of Depressive Symptoms for IPT Group

Figure 7.4

Normal Q-Q Plot of Depressive Symptoms for Control Group

Figure 7.5

Matrix Scatterplot to Assess Independence

Figure 7.6

Hypothesis Testing Graph—One-Way ANOVA

Figure 8.1

Repeated-Treatment Design with One Group for Study Example

Figure 8.2

Same Participants Measured Repeatedly over Time

Figure 8.3

Same Participants Measured under Different Conditions

Figure 8.4

Matched Pairs of Participants Measured under Different Conditions

Figure 8.5

Power Analysis for the RM-ANOVA Problem

Figure 8.6

Histograms of Weight Loss by Weight Loss Intervention

Figure 8.7

Normal Q-Q Plots of Weight Loss by Weight Loss Intervention Conditions

Figure 8.8

Profile Plot of Means of Weight Loss by Condition Groups

Figure 8.9

Hypothesis Testing Graph RM-ANOVA

Figure 9.1

Power Analysis for Treatment Condition of the Factorial ANOVA Problem

Figure 9.2

Power Analysis for Treatment Status of the Factorial ANOVA Problem

Figure 9.3

Power Analysis for Treatment Condition × Treatment Status Interaction of the Factorial ANOVA Problem

Figure 9.4

Normal Q-Q Plot by Condition Groups

Figure 9.5

Matrix Scatter Plot to Assess Independence on Treatment Retention Across the Condition Groups

Figure 9.6

Estimated Marginal Means of Treatment Retention

Figure 9.7

Hypothesis Testing Graph Factorial ANOVA

Figure 10.1

G*Power Screen Shots for ANCOVA Problem

Figure 10.2

Normal Q-Q Plots of CovAge by Groups

Figure 10.3

Normal Q-Q Plots of DVLDA by Groups

Figure 10.4

Matrix Scatter Plot to Assess Independence

Figure 10.5

Profile Plot

Figure 10.6

Hypothesis Testing Graph ANCOVA

Figure 11.1

Randomized Pretest-Posttest Control Group Design

Figure 11.2

A Priori Power Analysis Results for Low Electric Stimulation Versus Placebo

Figure 11.3

A Priori Power Analysis Results for High Electric Stimulation Versus Placebo

Figure 11.4

Histogram of the Low Electric Stimulation Condition on Pain Improvement

Figure 11.5

Histogram of the Placebo Condition on Pain Improvement

Figure 11.6

Histogram of the High Electric Stimulation Condition on Pain Improvement

Figure 11.7

Normal Q-Q Plot of Pain Improvement Scores for the Low Electric Stimulation Condition

Figure 11.8

Normal Q-Q Plot of Pain Improvement Scores for the Placebo Condition

Figure 11.9

Normal Q-Q Plot of Pain Improvement Scores for the High Electric Stimulation Condition

Figure 11.10

Post Hoc Effect Size and Power Analysis for Low Electric Stimulation versus Placebo

Figure 11.11

Post Hoc Effect Size and Power Analysis for High Electric Stimulation versus Placebo

Figure 11.12

A Priori Power Analysis Results for High Electric Stimulation at First Treatment (or Restored Treatment) versus Removed Treatment

Figure 11.13

Post Hoc Effect Size and Power Analysis for High Electric Stimulation at First Treatment versus Removed Treatment

Figure 11.14

Post Hoc Effect Size and Power Analyses Using G*Power 3.1 for High Electric Stimulation at First Treatment versus Restored Treatment

Figure 11.15

Post Hoc Effect Size and Power Analysis for High Electric Stimulation at Removed Treatment versus Restored Treatment

Figure 12.1

A Priori Power Analysis of MRA Problem

Figure 12.2

Histogram of Residuals of DSI Predicted by SPIScient and SPIPract

Figure 12.3

Normal P-P Plot of Residuals of DSI Predicted by SPIScient and SPIPract

Figure 12.4

Scatter Plot of Residuals of DSI Predicted by SPIScient and SPIPract

PREFACE

Working through a solution to a research problem is a stimulating process. The focus of this book is learning statistics while progressing through the steps of the hypothesis-testing process from hypothesis to results. The hypothesis-testing process is the most commonly used tool in science and entails following a logical sequence of actions, judgments, decisions, and interpretations as statistics are applied to research problems. Statistics emerged as a discipline with the purpose of developing and applying mathematical theory and scientific operations to enhance human understanding of phenomena experienced in life. For example, William Gossett developed the t-statistic while working at the Guinness Brewery in the late 1800s. He worked to explain the factors that contribute to Guinness beer remaining suitable for drinking and what fertilizers produce the best yield of barley used in brewing. Analysis of variance is the most widely used family of statistics in the world, and Sir Ronald A. Fisher developed the procedure in 1921 while researching the factors contributing to better yields of wheat and potatoes.

The research problems used in the book reflect statistical applications related to interesting and important topics. For example, research problems for students to work through include findings on the efficacy of using cognitive-behavioral therapy to treat depression among adolescents and evaluating if support partners added to weight loss treatment can improve weight loss among persons who are overweight. It is hoped that students will find the problems that they work through to be interesting and relevant to their field of study. The research problems presented are consistent with findings in the field.

The format for each chapter on a major statistic is to cover the research problem by taking the student through identifying research questions and hypotheses; identifying, classifying, and operationally defining the study variables; choosing appropriate research designs; conducting power analysis; choosing an appropriate statistic for the problem; using a data set; conducting data screening and analyses (IBM SPSS); interpreting the statistics; and writing the results related to the problem.

It is the intent of the authors to provide a user-friendly guide to students to understand and apply procedural steps in completing quantitative studies. Students will know how to plan research and conduct statistical analyses using several common statistical and research designs after completion of the book. The quantitative methodological tools learned by students can actually be applied to their own research with less oversight by faculty.

Students will develop competencies in using IBM SPSS for statistical analyses. Computer-generated statistical analysis is the primary method used by quantitative researchers. Students will have the opportunity to also calculate statistics by hand for a fuller understanding of mathematics used in computations.

Moreover, the curriculum includes having students analyze research articles in psychology using a research analysis and interpretation guide. These learning experiences allow students to enhance their understanding of consuming research using the information they have learned about statistical and research methods.

ACKNOWLEDGMENTS

The authors would like to gratefully acknowledge the outstanding editorial leadership and support provided by Andrew Pasternack, Senior Editor; Seth Schwartz, Associate Editor; and Kelsey McGee, Senior Production Editor, all of Jossey-Bass. We also wish to thank the following reviewers for their thoughtful and valuable feedback in the early stages of the manuscript: Joel Nadler, Kathryn Oleson, Richard Osbaldiston, and Joseph Taylor.

THE AUTHORS

William E. Martin Jr. is a professor of educational psychology and senior scholar in the College of Education at Northern Arizona University. His areas of teaching include intermediate, computer, and multivariate statistics; research methods; and psychodiagnostics. His research relates to person-environment psychology and psychosocial adaptation.

Krista D. Bridgmon received a PhD in educational psychology from Northern Arizona University with emphasis in counseling. She is an assistant professor of psychology at Colorado State University–Pueblo. She has taught undergraduate courses in abnormal psychology, child psychology, clinical psychology, statistics, tests and measurements, and theories of personality, and has taught graduate courses in appraisal and assessment, clinical counseling, ethics, and school counseling. Her doctoral dissertation examined the stress factors that all-but-dissertation (ABD) students encounter in the disciplines of counselor education and supervision, counseling psychology, and clinical psychology. The study created an instrument using multivariate correlational methods to measure stress factors associated with being ABD, named the BASS (Bridgmon All-But-Dissertation Stress Survey).

To my wife Susan and my children and their spouses: Neil and Jennifer, Kurt and Michelle, and Carol and Kyle To my grandchildren: Grace, Adriana, Hudson, Lillee, Uriah, Naaman, and Isaac

—W.E.M. Jr.

To Jerrad and Coltin: Thank you for always making me laugh!

—K.B

Chapter 1

INTRODUCTION AND OVERVIEW

LEARNING OBJECTIVES

Understand the purpose of the book and the structure of the book.

Review independent, dependent, and extraneous variables and their scales of measurement.

Review measures of central tendency and variability.

Review visual representations of data, including the normal distribution.