Research Sampling Techniques Quiz

Sampling Techniques

• A sample is a part of a population chosen to represent the population's variables.
• Sampling is crucial in research because comprehensive studies are often impractical.

Advantages of Sampling

• Lower cost
• Faster data collection
• Increased amount of information gleaned
• Sampling is essential when a comprehensive study isn't possible
  • For example, studying fish, animals, or nomadic populations, or testing the quality of industrial products (like matches).
  • Examining patient blood is another example.

Types of Sampling

• Non-Probability (Non-Random) Samples
  • Purposive Samples: Chosen based on the researcher's judgment, not randomly, and results aren't generalizable.
  • Pre-test or Pilot Study: Used to test the study, identify missing parameters, and exclude unnecessary factors to save time, resources, and personnel.
  • Quota Sample: Used, frequently in the USA, for pre-voting surveys by institutions like Gallup. Researchers are asked to gather information from specific individuals in carefully categorized groups, yet this method is uncommon in community or clinical medicine.
  • Convenience Sample: A sample created from easily accessible people. It's one of the primary kinds of non-probability sampling.
• Probability (Random) Samples
  • Characteristics:
    • Selects population individuals with known probability.
    • Calculates standard error on results.
    • Generalizes sample results to the entire population.
  • Types
    • Simple random sample
    • Systematic random sample
    • Stratified random sample
    • Multistage random sample
    • Cluster sample

Simple Random Sample

• Methods:

  • Ideal Bowl Method: Create a frame, use paper cards for each population member with unique serial numbers from the frame, and mix them in a bowl. Randomly select from the bowl.
  • Coin toss: Randomly choosing elements from a group by flipping a coin.
  • Random ball draws: Drawing randomly from a container after thorough mixing.
  • Computer-generated random numbers

• Advantages:

  • Simple random samples are the foundational type.
  • Every member in the population has an equal chance of being selected.

• Disadvantages:

  • Creating an accurate population frame can be difficult if it's large.
  • The sample's members may be concentrated in a single sector (e.g., all female, or all from one specific region).
  • In populations with high variability, simple random samples aren't optimal; the standard deviation is high and precision is low.

Systematic Random Sample

• Methods: Select a random starting point and then take every 'nth' member of the frame (population). -If population = 100 and a sample size of 10 is needed, randomly select a number (e.g., 4) between 0 and 10. Select the fourth member, then every 10th member (e.g. 4, 14, 24, 34, etc).

• Advantages:

  • Easy to select.
  • Well-distributed across diverse populations.
  • More precise than simple random samples.

• Disadvantages:

  • Difficult to create the frame if population is large.
  • Sample size may be smaller than expected if the selected start number results in a less than expected sample

Stratified Random Sample

• Characteristics: Ideal for populations with substantial variability (high SD values).
• Methods: Classify the population into subgroups (strata) based on certain traits. Randomly select members from each strata proportionally to its size.
  • Example: Divide a population by education level (illiterate, read/write, basic education, etc) or by socioeconomic status (high, middle, low).
• Advantages:
  • Accurately represents all subgroups within a population.
  • Proportional representation from sampling fractioms.
• Disadvantages:
• A complete frame for each subgroup is required. - Stratification can be complicated if many variables are involved.

Multistage Random Sample

• Used when populations are huge and geographically dispersed or have complex hierarchies (like provinces, districts, talukas/towns/villages).

  • Select a sample of areas (e.g. governorates), then areas within those areas (e.g. districts), then villages/towns within those areas. Finally, select individuals from the sampled villages to make the sample for study.

• Advantages

  • Easy to select samples from far larger populations than other options.
  • Economical approach as researchers only survey a subset of the population.
  • Disadvantages:
  • Not as precise as simple random sampling.

Cluster Sample

• Groups the population into clusters (e.g., geographic areas, schools, etc.).
• Methods:
  • Randomly selecting clusters.
  • Studying each individual within the selected clusters (in depth)
• Advantages:
  • Reduced costs and travel expenses to conduct research.
  • Simple to implement.
• Disadvantages:
  • Less accurate than simpler sampling methods.

Summary of Method Advantages and Disadvantages

• The most important consideration is what the researcher needs from the collected information and how accurate the results need to be.
• Advantages of one method may be limitations of another method.

Test your knowledge on the techniques and concepts of sampling in research. This quiz covers the purpose of sampling, advantages over comprehensive surveys, and key definitions related to sampling methods. Perfect for students and researchers looking to solidify their understanding of sampling principles.