lesson 2

• Data


  Raw facts and observations
• Methods of Collecting Data

  • Direct Observation
  • Experiments
  • Surveys
• Surveys


  Solicits information from people
• Questionnaire Design Principles


  • Keep the questionnaire as short as possible
  • Ask short, simple, and clearly worded questions
  • Start with demographic questions
  • Use dichotomous and multiple choice questions
  • Use open-ended questions cautiously
  • Avoid using leading-questions
  • Pretest the questionnaire
  • Think about how the data will be used
• Sampling


  Selecting a sub-set of a whole population
• Sampling Plans


  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Sampling
• Simple Random Sampling


  A sample selected in such a way that every possible sample of the same size is equally likely to be chosen
• Stratified Random Sampling


  Obtained by separating the population into mutually exclusive sets (strata) and then drawing simple random samples from each stratum
• Cluster Sampling


  A simple random sample of groups or clusters of elements
• The larger the sample size, the more accurate the sample estimates can be expected to be
• Sampling Error


  Differences between the sample and the population that exist only because of the observations that happened to be selected for the sample
• Non-Sampling Error


  More serious errors due to mistakes made in the acquisition of data or due to the sample observations being selected improperly
• Types of Non-Sampling Error

  • Errors in data acquisition
  • Nonresponse errors
  • Selection bias
• Errors in Data Acquisition


  Incorrect measurements, mistakes in transcription, misinterpretation of terms, or inaccurate responses
• Nonresponse Error


  Error or bias introduced when responses are not obtained from some members of the sample
• Selection Bias


  Occurs when the sampling plan is such that some members of the target population cannot possibly be selected for inclusion in the sample