Data Collection Notes

Module 1 - Section 3: Data Collection

Overview

• Presented by: Rosana Fok

Example of Data Collection

• Scenario: An ice-cream store started to advertise in a local newspaper in May.

  • Result: This advertising campaign led to a 40% increase in ice-cream sales for the following month.

• Conclusion: The advertisement was effective in increasing ice-cream sales.

Key Concepts in Sampling

Random Sampling vs. Random Allocation

• Random Sampling:

  • Definition: A method of gathering a sample by randomly selecting individuals from the population of interest.

• Random Allocation/Assignment:

  • Definition: The process of randomly assigning the subjects within the sample into different treatment groups.

Types of Inferences

• Two distinct types of inferences can be made based on sampling methodologies:

1. Population Inference

• Definition: This type of inference involves making a conclusion about the entire population based on the data obtained from a sample.

2. Causal Inference

• Definition: This inference pertains to establishing a conclusion about a cause-and-effect relationship between variables.

Matrix of Inferences

Different Types of Random Samples

• There are several methodologies for drawing random samples, each with unique characteristics:

  • SRS (Simple Random Sample):

  • Every individual has an equal chance of being selected.

  • Systematic Random Sample:

  • Selection is made according to a random starting point and a fixed, periodic interval.

  • Stratified Random Sample:

  • The population is divided into subpopulations (strata) and samples are drawn from each.

  • Cluster Random Sample:

  • The population is divided into clusters (usually geographically), and entire clusters are randomly selected for sampling.