preface

Introduction R Foundation

1. RStudio Structure
1) R package
2) Four windows of RStudio
2. RStudio Basics I
1) Install and update R and RStudio
2) Specify the working directory
3) R data types and features
3. RStudio Basics II
1) Creating and saving R objects
2) Dataframe
3) Others

Chapter 1: Fundamentals of Quantitative Research

1.
Variables and Scales
1) Types of variables
2) Type of scale
2.
Sampling method
1) Probabilistic sampling
2) Non-probability sampling
3.
Descriptive and inferential statistics
1) Statistics and probability theory
2) Differences between descriptive and inferential statistics
3) Descriptive statistics
4.
R example
1) Data entry and frequency analysis
2) Central tendency values ​​and dispersion
3) Descriptive statistics by group
4) Interquartile deviation and box plot
5) Standard score
· Practice problems

Chapter 2: Experimental Design and Validity Threats

1.
Experimental design and internal validity threats
1) Experimental design and causal inference
2) Random sampling and random assignment, true experimental design and quasi-experimental design
3) Threats to internal validity
4) Quasi-experimental design and threats to internal validity
2.
Threats to external validity and construct validity
1) Threats to external validity
2) Threat factors to recruitment validity
3.
Relationships between internal, external, and construct validity threats
1) Relationship between internal validity and external validity
2) Relationship between internal validity and recruiting validity
3) Relationship between internal validity and external validity
4.
Advanced experimental design
1) Forward-looking design and retrospective design
2) Propensity score matching
3) Causal inference and experimental design considerations
· Practice problems

Chapter 3: Fundamentals of Statistical Analysis

1.
Statistical hypothesis testing
1) Principles of statistical hypothesis testing
2) Null and alternative hypotheses in two-sided/one-sided tests
3) Type I error, Type II error, confidence interval, power
4) Criticism of statistical hypothesis testing
2.
random variable
1) Characteristics of random variables
2) Expected value and variance
3.
Central limit theorem
1) Distribution of sample consensus
2) Distribution of sample means
4.
Basic probability distribution
1) Normal distribution
2) Binomial distribution
· Practice problems

Chapter 4 Z-test and t-test

1.
Inference statistics
1) Point estimation and interval estimation
2) Significance level, significance probability, rejection threshold, statistical power
2.
single-sample test
1) Hypothesis testing for variables following the Z-distribution: Z-test
2) Hypothesis testing for variables following a t-distribution: t-test
3) Statistical power
4) R Example: One-Sample t-Test
3.
independent sample test
1) When the population variance is known or the central limit theorem can be used: Z-test
2) When the parent variance is unknown but homoscedasticity is assumed: t-test
3) When the parent variance is unknown and homoscedasticity cannot be assumed: Welch-Aspin test
4) R Example: Independent Samples t-Test
4.
Paired sample test
1) Statistical model and assumptions
2) R Example: Paired Samples t-Test
· Practice problems

Chapter 5 Correlation Analysis and Reliability

1.
Correlation analysis
1) Covariance
2) Correlation coefficient
3) Statistical assumptions and testing of the Pearson correlation coefficient
4) Partial correlation coefficient and partial correlation coefficient
5) R example
2.
Reliability
1) Statistical assumptions and formulas
2) Reliability calculation method
3) Standard error of measurement
4) Factors affecting reliability
5) R example
· Practice problems

Chapter 6 Regression Analysis I: Simple Regression Analysis

1.
Statistical models and assumptions
2.
Checking Statistical Assumptions: Residual Analysis
1) Independence
2) Regularity
3) Equal variance
3.
Hypothesis testing
1) Significance test of regression coefficients
2) Sum of squares decomposition and F-test
3) Coefficient of determination
4.
Diagnosing Regression Models
5.
R example
1) Pre- and post-tests of the experimental and control groups
2) School grades and college entrance exam scores
· Practice problems

Chapter 7 Regression Analysis II: Multiple Regression Analysis

1.
Statistical models and assumptions
2.
Hypothesis testing
1) Sum of squares decomposition and F-test
2) Model parsimony and modified coefficient of determination
3.
Variable selection and multicollinearity
1) Variable selection
2) Multicollinearity
4.
Other Key Concepts
1) Create dummy variables
2) Standardization coefficient
3) Centralization
5.
R example
1) CSAT Korean language section score
2) Cyber ​​flight experience
· Practice problems

Chapter 8 ANOVA I: One-Way ANOVA

1.
Statistical models and assumptions
2.
Hypothesis testing
1) Checking statistical assumptions: Residual analysis
2) Sum of squares decomposition and F-test
3.
Contrast and post-comparison
1) Contrast
2) Post-comparison
4.
Other Key Concepts
1) Variables and levels
2) Multiple testing error
3) Effect size
5.
R example
1) When the assumption of equal variance is met
2) When the assumption of equal variance is not met
· Practice problems

Chapter 9 ANOVA II: Two-Way ANOVA

1.
Statistical models and assumptions
2.
Hypothesis testing
1) Checking statistical assumptions: Residual analysis
2) Sum of squares decomposition and F-test
3.
Main effects and interaction effects
1) Main effect
2) When there is no interaction effect
3) When there is an interaction effect
4.
Example of interaction effects
1) If there is no interaction effect
2) When there is an interaction effect
5.
R example
1) When the interaction effect is not significant: Private education and math grades
2) If the interaction effect is significant: Rope jumping record
· Practice problems

Chapter 10 ANCOVA (Analysis of Covariance)

1.
Statistical models and assumptions
1) Statistical model
2) Statistical assumptions
2.
Hypothesis testing
3.
Key Concepts
1) Covariates
2) EMM
3) ANCOVA effect size
4. Things to keep in mind when conducting ANCOVA studies
1) ANCOVA and homogeneous groups
2) t-test on pre-test scores
3) ANCOVA when there is a large difference in pre-test scores between groups
4) ANCOVA and sample size by group
5) Mixing of experimental treatments and experimental units
5.
R example
1) Pre- and post-test
2) Academic enthusiasm by department
· Practice problems

Chapter 11 rANOVA (Repeated Measures Analysis of Variance)

1.
Essential Terminology
1) Intrinsic design vs. cross-design
2) Wireless effect vs. fixed effect
3) Section
2.
Statistical models and assumptions
1) Experimental design features
2) Statistical model
3) Statistical assumptions
3.
Hypothesis testing, sum of squares decomposition, F-test
4.
caution
1) ANCOVA vs. rANOVA
2) Advantages and limitations of rANOVA
5.
R example
1) Repeated measurement data and data format
2) Pre- and post-test
3) Pre-, post-, and follow-up inspections
· Practice problems

Chapter 12 Categorical Data Analysis (Chi-Square Test)

1.
Cross-tabulation
1) Joint probability, marginal probability, conditional probability
2) Independence test
3) Black statistics and statistical assumptions
4) Ozbi
5) R example
2.
Logistic regression model
1) Statistical model
2) Statistical assumptions
3) R example
· Practice problems

Chapter 13 Nonparametric Tests

1.
independent sample test
2.
Paired sample test
1) Two groups
2) Two or more groups
3.
R example
1) Independent sample test
2) Paired sample test
· Practice problems

Practice Problem Answers and Solutions

Appendix 493
〈Appendix 1〉 Distribution Table (Z-distribution, t-distribution, F-distribution, chi-square distribution)
Appendix 2: Classification Criteria for US WWC Papers
Appendix 3: APA Citation Format Rules and Examples

R, analysis data, tables, figures, and in-depth
References
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