Lecture 3: Basics of Sampling and Population in Social Sciences¶
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Learning Outcomes:
Define key terms related to sampling (e.g., unit of analysis, sample).
Explain the importance of representative samples.
Activities:
Discuss sampling methods and challenges in data representation.
Lecture 4: Sampling Methods and Bias¶
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Learning Outcomes:
Understand different sampling methods (probability, non-probability).
Identify sampling bias and its implications.
Activities:
Review case studies (Election Polling, COVID-19).
Visualize sampling designs.
Discussion 2: Sampling in Research and Its Implications¶
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Learning Outcomes:
Analyze the potential for bias in sampling.
Understand strengths and weaknesses of various sampling methods.
Activities:
Discuss ethical concerns and biases in real-world sampling
Lab 2: Sampling Techniques and Data Aggregation¶
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Jupyter Notebook on Github
Learning Outcomes:
Define key sampling terms and identify bias.
Perform data aggregation techniques using tables.
Project/Homework Part 2: Advanced Table Operations and Visualization¶
Jupyter Notebook on Github
Learning Outcomes:
Conduct data manipulation using complex table operations.
Perform data visualization techniques.
Activities:
Work with data tables to aggregate and visualize data.