Introduction to Quantitative Research Methods

Quantitative Research Methods: An Introduction

This introductory course provides a comprehensive overview of quantitative research methods, designed for researchers and postgraduate students embarking on or planning quantitative research. You'll gain a solid foundation in the core components of the research process, equipping you with the skills to design and execute effective studies.

Course Highlights:

• Research Process Fundamentals: Understand the entire quantitative research process, from formulating research questions and research hypotheses to designing the study, collecting data, and performing data analysis.
• Research Design: Explore the key elements of research design, including ethical considerations. Learn about various experimental designs (completely randomized, randomized block, factorial) and the principles of experimental design (replication, control, randomization, blinding, lurking variables).
• Data Collection Methods: Master a range of data collection methods, with a focus on survey sampling. Learn to apply both probability sampling techniques (simple random, systematic, cluster, stratified, multi-stage) and non-probability sampling techniques (voluntary response, convenience, quota, judgmental, snowball). Understand the strengths and weaknesses of each method and when to apply them.
• Sampling Size Calculation: Learn the crucial factors influencing sampling size calculations, including desired confidence levels, margin of error, expected effect sizes, and data analysis complexity.
• Data Analysis Overview: Gain familiarity with major data analysis methods and statistical methodologies. This course provides an overview, not hands-on training. You'll be introduced to key groups of techniques:
  • Exploratory Data Analysis: Explore data structures using summary statistics and visualizations (pie charts, line charts, box plots, histograms, etc.).
  • Dependence Analysis Techniques: Understand relationships between variables using methods like regression analysis, ANOVA, linear models, generalized linear models, discriminant analysis, structural equation modeling, conjoint analysis, hierarchical linear models, canonical correlations, decision trees, artificial neural networks, and support vector machines.
  • Inter-Dependence Techniques: Discover techniques for uncovering hidden relationships among variables, objects, or individuals, including cluster analysis, principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and artificial neural networks.
  • Predictive Analysis Techniques: Explore data analysis methods applicable to predictive modeling, such as regression analysis, linear models, generalized linear models, discriminant analysis, decision trees, artificial neural networks, and support vector machines.
• Real-World Applications: Analyze examples from peer-reviewed research articles to see how these techniques are applied in practice. Learn by example and validate your learning.

Course Conclusion:

The course concludes with a review of key concepts and an introduction to related advanced courses:

• Applied Linear Regression Analysis (Expected Mid-2021)
• Applied Logistic Regression Analysis

Important Update (September 5th, 2021):

• Feedback on Practice Exercises: Feedback on practice questions and exercises is available for current students and those who enrolled before October 1st, 2021. Due to increasing enrollment, feedback will no longer be provided to new students enrolling from October 1st, 2021.
• Course Price Adjustment: The course price will be slightly reduced for new students enrolling on or after October 1st, 2021, due to the discontinuation of feedback on practice exercises. However, new students are encouraged to continue completing the practice exercises for self-directed learning.