Statistics Chapter 2: Data Collection

Introduction to Data Collection

• "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write" - H.G. Wells
• The importance of data collection in research

Learning Outcomes

• Distinguish between primary and secondary data sources
• Examine various sources of primary data
• Appreciate the art of questionnaire design
• Distinguish between probability and non-probability samples
• Conduct a sample
• Distinguish between different methods of data collection

Methods of Data Collection

• Depends on the nature of the problem, time available, money available, data source, and degree of accuracy required
• Primary data: new data collected from scratch for a specific research purpose
• Secondary data: already existing data collected by someone else earlier

Primary vs Secondary Data

• Primary data: first-hand data gathered by the researcher themselves, expensive, specific to the researcher's needs, and collected in real-time
• Secondary data: data already collected by someone else, inexpensive, might not be specific to the researcher's needs, and collected in the past

Primary Data Sources

• Investigating or experimenting
• Observation
• Focus groups
• Conducting surveys using questions

Methods of Primary Data Collection

• Experiment: deliberately impose some treatment on individuals or objects
• Observation: watching or listening carefully without any interaction
• Focus groups: a small sample of the target group discusses a topic
• Surveys: asking questions using a questionnaire

Survey Methods

• Personal interviews: face-to-face, allows for detailed feedback
• Telephone interviews: eliminates travel time and costs, but may not allow for detailed feedback
• Mail questionnaires: inexpensive, but may have a low response rate
• Electronic questionnaires: quick and inexpensive, but may not allow for detailed feedback

Questionnaire Design

• A good questionnaire should have a clear and concise introduction, be easy to understand, and have a clear and concise format
• Questions should be short, unbiased, and not emotive
• Questions should be closed or open-ended, and should cover all possible answers
• Confidentiality should be assured

Sampling

• Selecting a reasonable portion of the population to make inferences
• Advantages: reduces cost, collection time, and improves accuracy
• Sampling laws: law of statistical regularity, law of inertia of large numbers

Types of Sampling Strategies

• Non-random/non-probability sampling methods:
  • Convenience sampling: selecting readily available participants
  • Judgement sampling: selecting participants based on experience or judgement
  • Voluntary response sampling: selecting participants who volunteer
  • Snowballing sampling: selecting participants based on referrals
  • Quota sampling: selecting a predetermined number or proportion of units
• Random/probability sampling methods:
  • Simple random sampling: every participant has an equal chance of being selected
  • Stratified sampling: selecting a sample with similar characteristics to the population
  • Systematic sampling: selecting every nth participant
  • Cluster sampling: selecting groups that represent the population

Learn about the importance of statistical thinking and how to collect data. Understand the difference between primary and secondary data sources and explore various sources of primary data.