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ISBN-10 : 0133557219
ISBN-13 : 9780133557213
Author: Arthur Aron
emphasizing meaning and concepts, not just symbols and numbers. Statistics for Psychology, 6th edition places definitional formulas center stage to emphasize the logic behind statistics and discourage rote memorization. Each procedure is explained in a direct, concise language and both verbally and numerically. MyStatLab is an integral part of the Statistics course. MyStatLab gives students practice with hundreds of homework problems. Every problem includes tools to help students understand and solve each problem – and grades all of the problems for instructors. MyStatLab also includes tests, quizzes, eText, a Gradebook, a customizable study plan, and much more.
Statistics for Psychology 6th Table of contents:
Chapter 1 Displaying the Order in a Group of Numbers Using Tables and Graphs
Chapter Outline
The Two Branches of Statistical Methods
Some Basic Concepts
Variables, Values, and Scores
Levels of Measurement (Kinds of Variables)
Frequency Tables
An Example
How to Make a Frequency Table
Frequency Tables for Nominal Variables
Another Example
Grouped Frequency Tables
Histograms
How to Make a Histogram
Shapes of Frequency Distributions
Unimodal and Bimodal Frequency Distributions
Symmetrical and Skewed Distributions
Normal and Kurtotic Distributions
Controversy: Misleading Graphs
Failure to Use Equal Interval Sizes
Exaggeration of Proportions
Frequency Tables and Histograms in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Making a Frequency Table
Making a Histogram
Practice Problems
Set I (for Answers to Set I Problems, see pp. 680–681)
Set II
Using SPSS
Creating a Frequency Table
Creating a Histogram
Chapter Note
Chapter 2 Central Tendency and Variability
Chapter Outline
Central Tendency
The Mean
Formula for the Mean and Statistical Symbols
Additional Examples of Figuring the Mean
Steps for Figuring the Mean
The Mode
The Median
Steps for Finding the Median
Comparing the Mean, Mode, and Median
Variability
The Variance
The Standard Deviation
Formulas for the Variance and the Standard Deviation
Examples of Figuring the Variance and Standard Deviation
Computational and Definitional Formulas
The Importance of Variability in Psychology Research
The Variance as the Sum of Squared Deviations Divided by N − 1
Controversy: The Tyranny of the Mean
Central Tendency and Variability in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Figuring the Mean
Answer
Finding the Median
Answer
Figuring the Sum of Squares and the Variance
Answer
Figuring the Standard Deviation
Answer
Outline for Writing Essays on Finding the Mean, Variance, and Standard Deviation
Practice Problems
Set I (for Answers to Set I Problems, see pp. 681–682)
Set II
Using SPSS
Finding the Mean, Mode, and Median
Finding the Variance and Standard Deviation
Chapter Notes
Chapter 3 Some Key Ingredients for Inferential Statistics Z Scores, the Normal Curve, Sample versus Population, and Probability
Chapter Outline
Z Scores
What Is a Z Score?
Z Scores as a Scale
Formula to Change a Raw Score to a Z Score
Steps to Change a Raw Score to a Z Score
Formula to Change a Z Score to a Raw Score
Steps to Change a Z Score to a Raw Score
Additional Examples of Changing Z Scores to Raw Scores and Vice Versa
The Mean and Standard Deviation of Z Scores
The Normal Curve
Why the Normal Curve Is So Common in Nature
The Normal Curve and the Percentage of Scores Between the Mean and 1 and 2 Standard Deviations from the Mean
The Normal Curve Table and Z Scores
Steps for Figuring the Percentage of Scores Above or Below a Particular Raw Score or Z Score Using the Normal Curve Table
Examples
Figuring Z Scores and Raw Scores from Percentages Using the Normal Curve Table
Examples
Sample and Population
Why Psychologists Study Samples Instead of Populations
Methods of Sampling
Statistical Terminology for Samples and Populations
Probability
Interpretations of Probability
Figuring Probabilities
Steps for Finding Probabilities
Range of Probabilities
Probabilities Expressed as Symbols
Probability Rules
Probability, Z Scores, and the Normal Distribution
Probability, Samples, and Populations
Controversies: Is the Normal Curve Really So Normal? And Using Nonrandom Samples
Is the Normal Curve Really So Normal?
Using Nonrandom Samples
Z Scores, Normal Curves, Samples and Populations, and Probabilities in Research Articles
Advanced Topic: Probability Rules and Conditional Probabilities
Addition Rule
Multiplication Rule
Conditional Probabilities
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Changing a Raw Score to a Z Score
Answer
Changing a Z Score to a Raw Score
Answer
Outline for Writing Essays Involving Z Scores
Figuring the Percentage Above or Below a Particular Raw Score or Z Score
Answer
Figuring Z Scores and Raw Scores From Percentages
Answer
Outline for Writing Essays on the Logic and Computations for Figuring a Percentage from a Z Score and Vice Versa
Finding a Probability
Answer
Practice Problems
Set I (for Answers to Set I Problems, see p. 682)
Set II
Using SPSS
Changing Raw Scores to Z Scores
Chapter Notes
Chapter 4 Introduction to Hypothesis Testing
Chapter Outline
A Hypothesis-Testing Example
The Core Logic of Hypothesis Testing
The Hypothesis-Testing Process
Step 1: Restate the Question as a Research Hypothesis and a Null Hypothesis About the Populations
Step 2: Determine the Characteristics of the Comparison Distribution
Step 3: Determine the Cutoff Sample Score on the Comparison Distribution at Which the Null Hypothesis Should Be Rejected
Step 4: Determine Your Sample’s Score on the Comparison Distribution
Step 5: Decide Whether to Reject the Null Hypothesis
Implications of Rejecting or Failing to Reject the Null Hypothesis
Summary of Steps of Hypothesis Testing
A Second Example
One-Tailed and Two-Tailed Hypothesis Tests
Directional Hypotheses and One-Tailed Tests
Nondirectional Hypotheses and Two-Tailed Tests
Determining Cutoff Scores with Two-Tailed Tests
When to Use One-Tailed or Two-Tailed Tests
Example of Hypothesis Testing with a Two-Tailed Test
Controversy: Should Significance Tests Be Banned?
Hypothesis Tests in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Answer
Outline for Writing Essays for Hypothesis-Testing Problems Involving a Single Sample of One Participant and a Known Population
Practice Problems
Set I (for Answers to Set I Problems, see pp. 682–684)
Set II
Chapter Notes
Chapter 5 Hypothesis Tests with Means of Samples
Chapter Outline
The Distribution of Means
Building a Distribution of Means
Determining the Characteristics of a Distribution of Means
Rule 1: The mean of a distribution of means is the same as the mean of the population of individuals.
Rule 2a: The variance of a distribution of means is the variance of the population of individuals divided by the number of individuals in each sample.
Rule 2b: The standard deviation of a distribution of means is the square root of the variance of the distribution of means.
Rule 3: The shape of a distribution of means is approximately normal if either (a) each sample is of 30 or more individuals or (b) the distribution of the population of individuals is normal.
Summary of Rules and Formulas for Determining the Characteristics of a Distribution of Means
Rule 1: The mean of a distribution of means is the same as the mean of the population of individuals:
Rule 2a: The variance of a distribution of means is the variance of the population of individuals divided by the number of individuals in each sample:
Rule 2b: The standard deviation of a distribution of means is the square root of the variance of the distribution of means:
Rule 3: The shape of a distribution of means is approximately normal if either (a) each sample is of 30 or more individuals or (b) the distribution of the population of individuals is normal.
Example of Determining the Characteristics of a Distribution of Means
Rule 1: The mean of a distribution of means is the same as the mean of the population of individuals.
Rule 2a: The variance of a distribution of means is the variance of the population of individuals divided by the number of individuals in each sample.
Rule 2b: The standard deviation of a distribution of means is the square root of the variance of the distribution of means.
Rule 3: The shape of a distribution of means is approximately normal if either (a) each sample is of 30 or more individuals or (b) the distribution of the population of individuals is normal.
Review of the Three Kinds of Distributions
Hypothesis Testing with a Distribution of Means: The Z Test
The Distribution of Means as the Comparison Distribution in Hypothesis Testing
Figuring the Z Score of a Sample’s Mean on the Distribution of Means
Example
A Second Example
Controversy: Marginal Significance
Hypothesis Tests About Means of Samples (Z Tests) and Standard Errors in Research Articles
Advanced Topic: Estimation, Standard Errors, and Confidence Intervals
Estimating the Population Mean When It Is Unknown
Range of Possible Means Likely to Include the Population Mean
The 95% and 99% Confidence Intervals
Steps for Figuring Confidence Limits
The Subtle Logic of Confidence Intervals
Confidence Intervals and Hypothesis Testing
Advanced Topic Controversy: Confidence Intervals versus Significance Tests
Advanced Topic: Confidence Intervals in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Figuring the Standard Deviation of the Distribution of Means
Answer
Hypothesis Testing with a Sample of More Than One: The Z Test
Answer
Outline for Writing Essays for Hypothesis-Testing Problems Involving a Single Sample of More Than One and a Known Population (Z Test)
Advanced Topic: Finding Confidence Intervals
Answer
Advanced Topic: Outline for Writing Essays for Finding Confidence Intervals
Practice Problems
Set I (for Answers to Set I Problems, see pp. 684–685)
Set II
Chapter Notes
Chapter 6 Making Sense of Statistical Significance Decision Errors, Effect Size, and Statistical Power
Chapter Outline
Decision Errors
Type I Error
Type II Error
Relationship Between Type I and Type II Errors
Summary of Possible Outcomes of Hypothesis Testing
Effect Size
Figuring Effect Size
Effect Size Conventions
Meta-Analysis
Statistical Power
Determining Statistical Power
What Determines the Power of a Study?
Effect Size
Determining Power from Predicted Effect Sizes
Sample Size
Figuring Needed Sample Size for a Given Level of Power
Other Influences on Power
Summary of Influences on Power
The Role of Power When Planning a Study
The Role of Power When Interpreting the Results of a Study
When a Result Is Statistically Significant: Statistical Significance versus Practical Significance
Role of Power When a Result Is Not Statistically Significant
Summary of the Role of Power When Evaluating Results of a Study
Controversy: Statistical Significance Versus Effect Size
Decision Errors, Effect Size, and Power in Research Articles
Advanced Topic: Figuring Statistical Power
Steps for Figuring Power
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Figuring the Effect Size
Answer
Find the Predicted Mean from an Effect Size
Answer
Outline for Writing Essays on Effect Size and Power for Studies Involving a Sample of More Than One Individual and a Known Population
Advanced Topic: Figuring Power
Answer
Practice Problems
Set I (for Answers to Set I Problems, see pp. 685–688)
Set II
Chapter Notes
Chapter 7 Introduction to t Tests Single Sample and Dependent Means
Chapter Outline
The t Test for a Single Sample
Basic Principle of the t Test: Estimating the Population Variance from the Sample Scores
Degrees of Freedom
The Standard Deviation of the Distribution of Means
The Shape of the Comparison Distribution When Using an Estimated Population Variance: The t Distribution
The Cutoff Sample Score for Rejecting the Null Hypothesis: Using the t Table
The Sample Mean’s Score on the Comparison Distribution: The t Score
Deciding Whether to Reject the Null Hypothesis
Summary of Hypothesis Testing When the Population Variance Is Not Known
Another Example of a t Test for a Single Sample
Summary of Steps for a t Test for a Single Sample
The t Test for Dependent Means
Difference Scores
Population of Difference Scores with a Mean of 0
Example of a t Test for Dependent Means
Summary of Steps for a t Test for Dependent Means
A Second Example of a t Test for Dependent Means
t Test for Dependent Means with Scores from Pairs of Research Participants
Review and Comparison of Z Test, t Test for a Single Sample, and t test for Dependent Means
Assumptions of the t Test for a Single Sample and the t Test for Dependent Means
Effect Size and Power for the t Test for Dependent Means
Effect Size
Power
Planning Sample Size
Controversy: Advantages and Disadvantages of Repeated Measures Designs
Single Sample t Tests and Dependent Means t Tests in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
t Test for a Single Sample
Answer
t Test for Dependent Means
Answer
Outline for Writing Essays for a t Test for a Single Sample
Outline for Writing Essays for a t Test for Dependent Means
Practice Problems
Set I (for Answers to Set I Problems, see pp. 688–690)
Set II
Using SPSS
t Test for a Single Sample
t Test for Dependent Means
Chapter Notes
Chapter 8 The t Test for Independent Means
Chapter Outline
The Distribution of Differences Between Means
The Overall Logic and a Visual Picture of the Distribution of Differences Between Means
Mean of the Distribution of Differences Between Means
Estimating the Population Variance
Figuring the Variance of Each of the Two Distributions of Means
The Variance and Standard Deviation of the Distribution of Differences Between Means
Steps to Find the Standard Deviation of the Distribution of Differences Between Means
The Shape of the Distribution of Differences Between Means
The t Score for the Difference Between the Two Actual Means
Hypothesis Testing with a t Test for Independent Means
Example of a t Test for Independent Means
Summary of Steps for a t Test for Independent Means
A Second Example of a t Test for Independent Means
Assumptions of the t Test for Independent Means
Effect Size and Power for the t Test for Independent Means
Effect Size
Power
Planning Sample Size
Review and Comparison of the Three Kinds of t Tests
Controversy: The Problem of Too Many t Tests
The t Test for Independent Means in Research Articles
Advanced Topic: Power for the t Test for Independent Means When Sample Sizes Are Not Equal
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Figuring the Standard Deviation of the Distribution of Differences Between Means
Answer
Hypothesis Testing Using the t Test for Independent Means
Answer
Advanced Topic: Finding Power When Sample Sizes Are Unequal
Answer
Outline for Writing Essays for a t Test for Independent Means
Practice Problems
Set I (for Answers to Set I Problems, see pp. 690–692)
Set II
Using SPSS
t Test for Independent Means
Chapter Notes
Chapter 9 Introduction to the Analysis of Variance
Chapter Outline
Basic Logic of the Analysis of Variance
Estimating Population Variance from Variation Within Each Sample
Estimating the Population Variance from Variation Between the Means of the Samples
When the Null Hypothesis Is True
When the Null Hypothesis Is Not True
Comparing the Within-Groups and Between-Groups Estimates of Population Variance
The F Ratio
The F Distribution and the F Table
An Analogy
Carrying Out an Analysis of Variance
Figuring the Within-Groups Estimate of the Population Variance
Figuring the Between-Groups Estimate of the Population Variance
Figuring the F Ratio
The F Distribution
The F Table
Hypothesis Testing with the Analysis of Variance
Another Example
Summary of Steps for Hypothesis Testing with the Analysis of Variance
Assumptions in the Analysis of Variance
Planned Contrasts
Figuring Planned Contrasts
An Example
A Second Example
The Bonferroni Procedure
Post Hoc Comparisons
The Scheffé Test
Effect Size and Power for the Analysis of Variance
Effect Size
Power
Planning Sample Size
Controversy: Omnibus Tests versus Planned Contrasts
Analyses of Variance in Research Articles
Advanced Topic: The Structural Model in the Analysis of Variance
Principles of the Structural Model
Dividing Up the Deviations
Summing the Squared Deviations
From the Sums of Squared Deviations to the Population Variance Estimates
Relation of the Structural Model Method to the Method You Learned Earlier in the Chapter
An Example
Analysis of Variance Tables
Summary of Procedures for an Analysis of Variance Using the Structural Model
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Overall Analysis of Variance
Answer
Planned Contrasts
Answer
Bonferroni Procedure
Answer
Post Hoc Comparisons Using the Scheffé Method
Answer
Figuring Effect Size for an Analysis of Variance
Answer
Advanced Topic: Figuring an Analysis of Variance Using the Structural Model Method
Answer
Outline for Writing Essays for a One-Way Analysis of Variance
Practice Problems
Set I (for Answers to Set I Problems, see pp. 692–695)
Set II
Using SPSS
Figuring a One-Way Analysis of Variance
Figuring a Planned Contrast for a One-Way Analysis of Variance
Post Hoc Tests for a One-Way Analysis of Variance
Chapter Notes
Chapter 10 Factorial Analysis of Variance
Chapter Outline
Basic Logic of Factorial Designs and Interaction Effects
An Example
Factorial Research Design Defined
Interaction Effects
Some Terminology
Recognizing and Interpreting Interaction Effects
Identifying Interaction Effects in Words and Numbers
Some Examples
Result A
Result B
Result C
Result D
Result E
Result F
More Examples
Result A
Result B
Result C
Result D
Result E
Result F
Identifying Interaction Effects Graphically
Relation of Interaction and Main Effects
Basic Logic of the Two-Way Analysis of Variance
The Three F Ratios in a Two-Way Analysis of Variance
Logic of the F Ratios for the Column and Row Main Effects
Logic of the F Ratio for the Interaction Effect
Assumptions in the Factorial Analysis of Variance
Extensions and Special Cases of the Analysis of Variance
Three-Way and Higher Analysis of Variance Designs
Repeated Measures Analysis of Variance
Controversy: Dichotomizing Numeric Variables
Factorial Analysis of Variance in Research Articles
Advanced Topic: Figuring a Two-Way Analysis of Variance
The Structural Model for the Two-Way Analysis of Variance
Steps for the Two-Way Analysis of Variance
Degrees of Freedom in a Two-Way Analysis of Variance
Degrees of Freedom for Between-Groups Variance Estimates for the Main Effects
Degrees of Freedom for the Interaction Effect Variance Estimate
Degrees of Freedom for the Within-Groups Population Variance Estimate
Total Degrees of Freedom
Table for a Two-Way Analysis of Variance
Example
Advanced Topic: Power and Effect Size in the Factorial Analysis of Variance
Effect Size
Power
Planning Sample Size
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Recognizing and Interpreting Main Effects and Interaction Effects
Answers
Bar Graphs and Figuring a Two-Way Analysis of Variance
Answers
Outline for Writing Essays for a Two-Way Analysis of Variance
Practice Problems
Set I (for Answers to Set I Problems, see pp. 695–697)
Set II
Using SPSS
Figuring a Two-Way Analysis of Variance
Making a Bar Graph of the Results of a Two-Way Analysis of Variance
Chapter Notes
Chapter 11 Correlation
Chapter Outline
Graphing Correlations: The Scatter Diagram
How to Make a Scatter Diagram
An Example
Patterns of Correlation
Linear and Curvilinear Correlations
No Correlation
Positive and Negative Linear Correlations
Strength of the Correlation
Importance of Identifying the Pattern of Correlation
The Correlation Coefficient
Logic of Figuring the Linear Correlation
Interpreting the Correlation Coefficient
Formula for the Correlation Coefficient
Steps for Figuring the Correlation Coefficient
An Example
A Second Example
Significance of a Correlation Coefficient
An Example
Assumptions for the Significance Test of a Correlation Coefficient
Correlation and Causality
Three Possible Directions of Causality
Ruling Out Some Possible Directions of Causality
Correlational Statistical Procedures versus Correlation Research Methods
Issues in Interpreting the Correlation Coefficient
The Correlation Coefficient and the Proportionate Reduction in Error or Proportion of Variance Accounted For
Restriction in Range
Unreliability of Measurement
Influence of Outliers
What If There Is Some Curvilinearity? The Spearman Rho
Effect Size and Power for the Correlation Coefficient
Planning Sample Size
Controversy: What Is a Large Correlation?
Correlation in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Making a Scatter Diagram and Describing the General Pattern of Association
Answer
Figuring the Correlation Coefficient
Answer
Figuring the Significance of a Correlation Coefficient
Answer
Outline for Writing Essays on the Logic and Figuring of a Correlation Coefficient
Practice Problems
Set I (for Answers to Set I Problems, see pp. 697–699)
Set II
Using SPSS
Creating a Scatter Diagram
Finding the Correlation Coefficient
Chapter Notes
Chapter 12 Prediction
Chapter Outline
Predictor (X) and Criterion (Y) Variables
Prediction Using Z Scores Versus Raw Scores
The Linear Prediction Rule
An Example
Another Example
The Regression Line
Slope of the Regression Line
The Intercept of the Regression Line
How to Draw the Regression Line
An Example of Drawing the Regression Line
Another Example of Drawing the Regression Line
Finding the Best Linear Prediction Rule
The Least Squared Error Principle
Finding a and b for the Least Squares Linear Prediction Rule
Issues in Prediction
The Standardized Regression Coefficient
An Example
The Standardized Regression Coefficient (β) and the Correlation Coefficient (r)
Hypothesis Testing and Prediction
Multiple Regression
Multiple Regression Prediction Rules
An Important Difference Between Multiple Regression and Bivariate Prediction
Assumptions of Prediction
Limitations of Prediction
Controversy: Unstandardized and Standardized Regression Coefficients; Comparing Predictors
Prediction in Research Articles
Advanced Topic: Error and Proportionate Reduction in Error
Proportionate Reduction in Error
Steps for Figuring the Proportionate Reduction in Error
An Example
Proportionate Reduction in Error as r2
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Using the Linear Prediction Rule
Answer
Drawing a Regression Line
Answer
Finding the Values of α and β
Answer
Figuring the Standardized Regression Coefficient
Answer
Multiple Regression Predictions
Answer
Advanced Topic: Figuring the Proportionate Reduction in Error
Answer
Outline for Writing Essays on the Logic and Figuring of Bivariate Prediction
Practice Problems
Set I (for Answers to Set I Problems, see pp. 699–702)
Set II
Using SPSS
Figuring the Bivariate Linear Prediction Rule
Chapter Notes
Chapter 13 Chi-Square Tests
Chapter Outline
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit
Steps for Figuring the Chi-Square Statistic
The Chi-Square Distribution
The Chi-Square Table
Steps of Hypothesis Testing
Another Example
The Chi-Square Test for Independence
Contingency Tables
Independence
Sample and Population
Determining Expected Frequencies
Figuring Chi-Square
Degrees of Freedom
Hypothesis Testing
Steps of Hypothesis Testing
A Second Example
Assumptions for Chi-Square Tests
Effect Size and Power for Chi-Square Tests for Independence
Effect Size
Power
Needed Sample Size
Controversy: The Minimum Expected Frequency
Chi-Square Tests in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Chi-Square Test for Goodness of Fit
Answers
Chi-Square Test for Independence
Answers
Effect Size for a 2 × 2 Chi-Square Test for Independence
Answer
Effect Size for a Chi-Square Test for Independence with a Contingency Table Greater Than 2 × 2
Answer
Outline for Writing Essays for a Chi-Square Test for Goodness of Fit
Outline for Writing Essays for a Chi-Square Test for Independence
Practice Problems
Set I (for Answers to Set I Problems, see pp. 702–704)
Set II
Using SPSS
Chi-Square Test for Goodness of Fit
Chi-Square Test for Independence
Chapter Notes
Chapter 14 Strategies When Population Distributions Are Not Normal Data Transformations and Rank-Order Tests
Chapter Outline
Assumptions in the Standard Hypothesis-Testing Procedures
Data Transformations
Legitimacy of Data Transformations
Kinds of Data Transformations
An Example of a Data Transformation
Rank-Order Tests
Overview of Rank-Order Tests
Basic Logic of Rank-Order Tests
An Example of a Rank-Order Test
The Null Hypothesis in a Rank-Order Test
Using Parametric Tests with Rank-Transformed Data
Comparison of Methods
Advantages and Disadvantages
Relative Risk of Type I and Type II Errors
Controversy: Computer-Intensive Methods
Data Transformations and Rank-Order Tests in Research Articles
Learning Aids
Summary
Key Terms
Example Worked-Out Problems
Square-Root Transformation
Answer
Rank-Order Transformation
Answer
Practice Problems
Set I (for Answers to Set I Problems, see pp. 704–705)
Set II
Using SPSS
Checking for Normal Distributions
Data Transformations
Rank-Order Tests
Chapter Notes
Chapter 15 The General Linear Model and Making Sense of Advanced Statistical Procedures in Research Articles
Chapter Outline
The General Linear Model
How the Big Four Are Special Cases of the General Linear Model
Partial Correlation
Reliability
Multilevel Modeling
Factor Analysis
Causal Modeling
Path Analysis
Mediational Analysis
Structural Equation Modeling
An Example of Structural Equation Modeling
Some Limitations of Causal Modeling
Procedures That Compare Groups
Analysis of Covariance (ANCOVA)
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)
An Example
Multivariate Analysis of Covariance
Overview of Statistical Techniques
Controversy: Should Statistics Be Controversial?
How to Read Results Using Unfamiliar Statistical Techniques
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