Applied Statistics I: Basic Bivariate Techniques

577 Pages·2020·405.077 MB·English

by  Rebecca M. Warner

Basic Bivariate Techniques
REBECCA M. WARNER ©
Applied Statistics I
Third Edition
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Applied Statistics I
Basic Bivariate Techniques
Third Edition
Rebecca M. Warner
ProfessorEmerita, University
ofNewHampshire
© SAGE
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Singapore | Washington DC | Melbourne
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BriefContents
Preface
Acknowledgments
Aboutthe Author
Chapter1 - Evaluating Numerical
Information
Chapter2 - Basic Research Concepts
Chapter3 - Frequency Distribution Tables
Chapter4 - Descriptive Statistics
Chapter5 - Graphs: Bar Charts, Histograms,
and Boxplots
Chapter6 - The Normal Distribution and z
Scores
Chapter7 - Sampling Error and Confidence
Intervals
Chapter8 - The One-Samplet Test:
Introduction to Statistical Significance Tests
Chapter9 Issuesin Significance Tests: Effect
Size, Statistical Power, and Decision Errors
Chapter 10 - Bivariate Pearson Correlation
Chapter11 - Bivariate Regression
Chapter 12 - The Independent-Samplest Test
Chapter 13 - One-Way Between-Subjects
Analysis ofVariance
Chapter14 - Paired-Samplest Test
Chapter 15 - One-Way Repeated-Measures
Analysis ofVariance
Chapter16 - FactorialAnalysis ofVariance
Chapter 17 - Chi-Square Analysis of
ContingencyTables
Chapter18 - Selection ofBivariate Analyses
and Review ofKey Concepts
Appendices
Glossary
References
Index
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1.8 Biases of Information Consumers
1.8.1 Confirmation Bias (A
Detailed Contents
Social Influence and
Consensus
Preface
1.9 Ethical Issues in Data Collection and
Acknowledgments
Analy:
Aboutthe Author
1.9.1 Ethical Guidelines for
Chapter1 - Evaluating Numerical
Researchers: DataCollection
Information
1.9.2 Ethical Guidelines for
1.1 Introduction
Statisticians: Data Analysis and
1.2 Guidelines for Numeracy
Reporting
1.3 SourceCredibility
1.10 Lying With Graphs and Statistics
1.3.1 Self-Interest or Bias
1.11 Degreesof Belief
1.3.2 Bias and “Cherry-Picking”
1.12 Summary
1.3.3 Primary, Secondary, and
Chapter 2 - Basic Research Concepts
Third-Party Sources
2.1 Introduction
1.3.4 Communicator Credentials
2.2 Types ofVariables
andSkills
2.2.1 Overview
1.3.5 Track Record for Truth-Telling
2.2.2 Categorical Variables
1.4 Message Content
2.2.3 Quantitative Variables
1.4.1 Anecdotal Versus Numerical
2.2.4 Ordinal Variables
Information
2.2.5 Variable Type and Choice of
1.4.2 Citation of Supporting
Analysis
Evidence
2.2.6 Rating Scale Variables
1.5 Evaluating Generalizability
2.2.7 Scores That Represent Counts
1.6 Making Causal Claims
2.3 Independent and Dependent
1.6.1 The “Post Hoc, Ergo Propter
Variables
Hoc”Fallacy
2.4 Typical Research Questions
1.6.2 Correlation (by Itself) Does Not
2.4.1 AreX and Correlated?
Imply Causation
2.4.2 Does X Predict Y?
1.6.3 Perfect Correlation Versus
2.4.3 Does X Cause Y?
Imperfect Correlation
2.5 Conditionsfor Causal Inference
1.6.4 “Individual Results Vary”
2.6 Experimental Research Design
1.6.5 Requirementsfor Evidence of
2.7 Nonexperimental Research Design
Causal Inference
2.8 Quasi-Experimental Research
1.7 Quality Control Mechanisms in
Designs
Science
2.9 Other Issues in Design and Analysis
1.7.1 Peer Review
2.10 Choice ofStatistical Analysis
1.7.2 Replication and Accumulation
Preview
of Evidence
2.11 Populations and Samples: Ideal
1.7.3 Open Science and Study
Versus Actual Situations
Preregistration
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2.11.1 Ideal Definition of Population 3.8.2 Evaluation of Score Location
and Sample Using Cumulative Percentage
2.11.2 Two Real-World Research 3.8.3 Grouped or Binned Frequency
Situations Similar to the Ideal Distributions
Population and Sample Situation 3.9 Frequency Tables for Categorical
2.11.3 Actual Research Situations Versus Quantitative Variables
That Are Not Similar to Ideal 3.10 Reporting Data Screening for
Situations Quantitative Variables
2.12 Common Problems in 3.11 What We Hopeto See in Frequency
Interpretation of Results Tables for Categorical Variables
Appendix 2A: More About Levels of 3.11.1 Categorical Variables That
Measurement Represent Naturally Occurring
Appendix 2B: Justification for the Use of Groups
Likert and Other Rating Scales as 3.11.2 Categorical Variables That
Quantitative Variables (in Some Represent Treatment Groups
Situations’ 3.12 What We Hopeto See in Frequency
Chapter3 - Frequency Distribution Tables Tables for Quantitative Variables
3.1 Introduction 3.13 Summary
3.2 Use of Frequency Tablesfor Data Appendix 3A: Getting Startedin IBM
Screening SPSS” Version 25
3.3 Frequency Tablesfor Categorical 3.A.1 The Bare Minimum: Using an
Variables Existing SPSS Data File to Obtain,
3.4 Elementsof Frequency Tables Print, and Save Results
3.4.1 Frequency Counts (n or f 3.A.2 Moving Between Windowsin
3.4.2 Total Numberof Scores in a SPSS
Sample (N 3.A.3 Creating a File and Entering
3.4.3 Missing Values (ifAny Data
3.4.4 Proportions 3.A.4 Defining Variable Names and
3.4.5 Percentages Properties ofVariables
3.4.6 Cumulative Frequencies or Appendix 3B: Missing Values in
Cumulative Percentages Frequency Tables
3.5 Using SPSS to Obtain a Frequency Appendix 3C: Dividing Scores Into
Table Groups or Bins
3.6 Mode, Impossible Score Values, and Chapter4 - Descriptive Statistics
Missing Values 4.1 Introduction
3.7 Reporting Data Screening for 4.2 Questions About Quantitative
Categorical Variables Variables
3.8 Frequency Tables for Quantitative 4.3 Notation
Variables 4.4 Sample Median
3.8.1 Ungrouped Frequency 4.5 Sample Mean (M)
Distribution 4.6 An Important Characteristic ofM:
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The Sum ofDeviations From M = 0 5.1 Introduction
4.7 Disadvantage ofM: It Is Not Robust 5.2 Pie Chartsfor CategoricalVariables
Against Influence ofExtreme Scores 5.3 Bar Charts for Frequencies of
4.8 Behavior ofMean, Median, andMode CategoricalVariables
in Common Real-World Situations 5.4 GoodPractice for Construction ofBar
4.8.1 Example 1: Bell-Shaped Charts
Distribution 5.5 Deceptive Bar Graphs
4.8.2 Example 2: Bimodal or 5.6 Histogramsfor Quantitative
Polarized Distribution Variables
4.8.3 Example 3: Skewed 5.7 Obtaining a Histogram Using SPSS
Distribution 5.8 Describing and Sketching Bell-
4.8.4 Example 4: No Clear Mode Shaped Distributions
4.9 ChoosingAmongMean, Median, and 5.9 GoodPractices in SettingUp
Mode Histograms
4.10 Using SPSS to Obtain Descriptive 5.10 Boxplot (Box andWhiskers Plot:
Statistics for a Quantitative Variable 5.10.1 How to Set Up a Boxplot by
4.11 Minimum, Maximum, and Range: Hand
Variation Among Scores 5.10.2 How to Obtain a Boxplot
4.12 The Sample Variance s? Using SPSS
4.12.1 Step 1: Deviation ofEach 5.11 Telling Stories About Distributions
Score From the Mean 5.12 UsesofGraphsin Actual Research
4.12.2 Step 2: Sum ofSquared 5.13 Data Screening: Separate Bar Charts
Deviations or Histograms for Groups
4.12.3 Step 3: Degrees ofFreedom 5.14 Use ofBar Charts to Represent
4.12.4 Puttingthe Pieces Together: Group Means
Computing a Sample Variance 5.15 Other Examples
4.13 Sample Standard Deviation (S or SD! 5.15.1 Scatterplots
4.14 How a Standard Deviation Describes 5.15.2 Maps
Variation Among Scores in a Frequency 5.15.3 Historical Example
Table 5.16 Summary
4.15 Why Is There Variance? Chapter6 - The Normal Distribution and z
4.16 Reports ofDescriptiveStatistics in Scores
ournalArticles 6.1 Introduction
4.17 Additional Issues in Reporting 6.2 LocationsofIndividual Scores in
Descriptive Statistics Normal Distributions
4.18 Summary 6.3 Standardized or z Scores
Appendix 4A: OrderofArithmetic 6.3.1 First Step in Finding az Score
Operations for X: The Distance ofX From M
Appendix 4B: Rounding 6.3.2 Second Step: Divide the (X-M)
Chapter 5 - Graphs: Bar Charts, Histograms, Distance by SD to Obtain aUnit-Free
and Boxplots or Standardized Distance ofScore
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