Introduction to Quantitative Analysis for International Educators (Springer Texts in Education)

✍ Scribed by Melissa Whatley

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 248
• Category: Library

✦ Synopsis

​This textbook introduces international education scholars, professionals and graduate students to quantitative research methods. It provides them with the most foundational and useful concepts in quantitative analysis, using international education themes for all examples and practice problems. Readers will have a foundation to pursue more advanced methods and analyses, whether through formal training or future individual study.

The book assumes no prior knowledge of quantitative analysis and is written for individuals who are new to statistics. Practice problems are found at the end of all substantive chapters, and datasets are provided so that readers can practice the analyses that are presented. Moreover, in each chapter, different statistical approaches are illustrated with real examples from international education research. These examples provide context for each statistical method and represent a wide variety of contexts in international education research, covering a variety of international education topics and a variety of regions of the world. From the instructor perspective, these example studies could be used as additional course reading. Moreover, supplemental material provides slides that instructors can modify and use in conjunction with the text.

✦ Table of Contents

Preface
Overview of the Book
A Note on Statistical Software
Acknowledgements
Data Dictionaries for Sample Datasets
Sample Dataset #1
Sample Dataset #2
Sample Dataset #3
Equations Summary
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Contents
1 Introduction to Quantitative Data
1.1 Collecting Quantitative Data
1.2 Surveys
1.3 Populations and Samples
1.4 Discussing Quantitative Data: Types of Variables
1.5 Descriptive and Inferential Statistics
Recommended Reading
2 Measures of Central Tendency
2.1 Mode, Median, and Mean
2.1.1 Mode
2.1.2 Median
2.1.3 Mean
2.2 Skewed Distributions
2.3 The Normal Distribution (Part 1)
2.4 Examples from Sample Data and the Literature
2.5 Practice Problems
Recommended Reading
3 Measures of Variability
3.1 Range, Variance, and Standard Deviation
3.1.1 Range
3.1.2 Variance
3.1.3 Standard Deviation
3.2 The Normal Distribution (Part 2)
3.3 Examples from Sample Data and the Literature
3.4 Practice Problems
Recommended Reading
4 Hypothesis Testing
4.1 Sampling Distribution
4.1.1 Expected Value of the Mean and Standard Error
4.1.2 The t-Distribution
4.2 Hypothesis Testing with One Sample
4.2.1 One-Sample t-tests
4.3 Other t-Tests
4.3.1 Two-Samples t-tests
4.3.2 Dependent Samples t-test
4.4 Example from the Literature
4.5 Practice Problems
Recommended Reading
5 One-Way ANOVA and the Chi-Square Test of Independence
5.1 One-Way Analysis of Variance (ANOVA)
5.1.1 Step One: Between-Group Variation
5.1.2 Step Two: Within-Group Variation
5.1.3 Step Three: The F-distribution
5.1.4 Example from the Literature
5.2 Chi-Square Test of Independence
5.2.1 Calculating Expected Frequencies
5.2.2 Comparing Observed and Expected Frequencies
5.2.3 The Chi-Square (χ2) Distribution
5.2.4 Example from the Literature
5.3 Looking Forward
5.4 Practice Problems
Recommended Reading
6 Correlation
6.1 Correlation: Main Ideas
6.2 Calculating Pearson’s Correlation Coefficient
6.3 Additional Correlation Calculations
6.3.1 The Coefficient of Determination
6.3.2 Significance Testing
6.4 Examples from Sample Data and the Literature
6.5 Correlation and Causation
6.6 Practice Problems
Recommended Reading
7 Ordinary Least Squares Regression
7.1 Simple Linear Regression
7.1.1 Functional Form
7.1.2 Ordinary Least Squares
7.1.3 Calculating a Simple Linear Regression
7.1.4 Regression Residuals and Error
7.2 Multiple Linear Regression
7.2.1 Partialling Out
7.2.2 Full Function for Multiple Regression
7.2.3 Hypothesis Testing for Regression Coefficients
7.2.4 Model Fit
7.3 Example from the Literature
7.4 Another Note on Correlation and Causation
7.5 Practice Problems
Recommended Reading
8 Additional Regression Topics
8.1 Categorical Predictors
8.2 Interactions Between Variables
8.3 Functional Form
8.3.1 Quadratic Functional Form
8.4 Binary Outcome Variables
8.4.1 Logistic Regression
8.4.2 Odds Ratio
8.4.3 Average Marginal Effects
8.4.4 Pseudo-R2
8.5 Example from the Literature
8.6 Looking Forward
8.7 Practice Problems
Recommended Reading
9 Introduction to Experimental and Quasi-Experimental Design
9.1 Experimental Design: Randomized Control Trials
9.1.1 Treatment and Control Groups
9.1.2 Randomization and Selection Bias
9.1.3 Outcomes
9.1.4 Threats to Validity
9.1.5 Example from the Literature
9.2 Nonexperimental Contexts
9.2.1 Propensity Score Matching
9.2.2 Example from the Literature
9.3 Quasi-Experimental Design
9.3.1 Regression Discontinuity
9.3.2 Difference-In-Differences
9.4 Future Quantitative Research in International Education
Recommended Reading
10 Writing About Quantitative Research
10.1 Scholarly Writing
10.1.1 Introduction
10.1.2 Theoretical Framework and Literature Review
10.1.3 Method
10.1.4 Results
10.1.5 Limitations
10.1.6 Discussion and Conclusion
10.2 Writing for External Audiences
Practice Problems Answer Key
A Critical Values of the t Distribution
B Critical Values of the F Distribution at α = 0.05
C Critical Values of the Chi-Square Distribution
D Current International Education (and Related) Journals
Glossary
Bibliography