QQM Topic 2

• Types of variables:

  • Quantitative
  • Measured on a numeric scale
  • Example:
  • Ages of employees at a company
  • Qualitative
  • Classified into categories
  • Example:
  • College major of each student in a class
• Longitudinal Data

  Data values observed over time
• Cross Section Data

  Data values observed at a fixed point in time
• Population

  A population is the collection of all items of interest or under investigation.
• Sample


  A sample is an observed subset of the population.

  If we examine every single one, we conduct a census
• Why sample?

  • Less time consuming than a census
  • Less costly to administer than a census
  • Well-designed sampling strategy can result in a representative sample of the same population at far less cost
• Sampling is unnecessary if all unites in population are identical.
• Representative sample

  The distribution of characteristics among elements of the sample is the same as the distribution among the total population.
• Unrepresentative sample

  Some characteristics are overrepresented or underrepresented.
• Simple Random Sampling

  • Every individual or item from the population has an equal change of being selected.
  • Ways of identifying cases:
  • Random number table
  • Random digit dialling (RDD)
• Systematic Sampling

  • Decide on sample size: n.
  • Divide frame of N individuals into groups of k individuals: k= N/n.
  • Randomly select one individual from the 1st group.
  • Select every kth individual thereafter.
  • May not be random if sequence has periodicity.

  
• Cluster Sampling

  • Population is divided into several "clusters", each representative of the population.
  • A simple random sample of clusters is selected.
  • All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique.
  • Useful when sampling frame is not available.
  • Sampling error is greater

  
• Data Types

  A) Longitude Data

  B) Cross Section Data

  
• Population vs. Sample

  A) Population

  B) Sample

  
• Stratified Sampling

  • Population divided into subgroups (called strata)
  • Ensures that various groups within the sampling frame will be included
  • Simple random sample selected from each subgroup
  • Samples from subgroups are combined into one

  
• Stratified Random Sampling

  • Proportionate stratified sampling
  • Disproportionate stratified sampling
  • Commonly used to ensure that cases from smaller strata are included sufficiently.

  
• Non-Probability Sampling

  • Items of the sample are not chosen based on known or calculable probabilities, but using a subjective (non-random) method.
  • Convenience
  • Quota
  • Purposive
  • Snowball
• Availability or Convenience Sampling

  • Elements are selected on the basis of convenience.
  • Useful in a new setting or in exploratory studies.
  • Often masquerades as a more rigorous form of research.
• Quota Sampling

  • May be representative on quota characteristics but no other way.
  • Must know relevant characteristics of entire population.
  • If a random sample cannot be drawn, it is better to use a quota sample than no quota.
• Purposive Sampling

  • Elements are selected for a purpose, usually because of their unique position.
  • Informants should be:
  • Knowledgeable
  • Willing to talk
  • Representative
  • Must pass completeness and saturation tests
  • What you hear provides an overall sense of the meaning of a concept, theme or process.
  • You gain confidence that you are learning little that is new from subsequent interviews.
• Snowball Sampling

  • Elements are selected as successive informants or interviewees identify them
  • Used for hard-to-reach or hard-to-identify interconnected populations
  • Normally cannot be confident that sample represents total population of interest
• Probability sampling methods allow researchers to use laws of chance to draw samples.

  • Simple random
  • Stratified
  • Systematic
  • Cluster
• Nonprobability methods are best to in-depth understand a small group.