7.2 Probability and Non-probability Sampling Methods

Sampling methods are crucial in marketing research, determining how data is collected and analyzed. Probability sampling allows for statistical inferences, while non-probability sampling is quicker and more cost-effective. Each method has its strengths and weaknesses, impacting research outcomes.

Choosing the right sampling method depends on research goals, population characteristics, and available resources. Probability methods like simple random and stratified sampling offer representativeness, while non-probability methods like convenience and snowball sampling can be useful for exploratory research or hard-to-reach populations.

Sampling Methods in Marketing Research

Probability vs non-probability sampling

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• Probability sampling assigns each population element a known, non-zero chance of selection
  • Enables statistical inferences about the population from sample data
  • Preferred when representativeness and generalizability are crucial (consumer surveys, market share studies)
• Non-probability sampling selects elements based on convenience, judgment, or other non-random criteria
  • Does not support statistical inferences about the population
  • Suitable for quick, cost-effective, or exploratory research (product concept tests, focus groups)

Types of probability sampling

• Simple random sampling (SRS) gives each element an equal chance of selection
  • Requires a complete sampling frame listing all population elements
  • Can be conducted with replacement (element can be selected multiple times) or without replacement (element removed after selection)
• Systematic sampling selects elements at regular intervals from a ranked list
  • Interval $k = \frac{N}{n}$, where $N$ = population size and $n$ = sample size
  • Randomly chooses a starting point between 1 and $k$, then selects every $k$th element
• Stratified sampling divides population into mutually exclusive, exhaustive subgroups (strata)
  • Conducts SRS within each stratum to ensure representation of key subgroups (age, gender, income)
  • Can be proportionate (stratum sample size proportional to population stratum size) or disproportionate
• Cluster sampling divides population into mutually exclusive, exhaustive clusters (geographic regions, retail outlets)
  • Randomly selects a subset of clusters, then includes all elements within chosen clusters
  • Useful when a complete list of population elements is unavailable

Non-probability sampling techniques

• Convenience sampling selects elements based on their convenience and availability
  • Advantages: quick, inexpensive, easy to implement (mall intercepts, online panels)
  • Disadvantages: high risk of bias, not representative, results not generalizable
• Judgment sampling selects elements based on researcher's judgment or expertise
  • Advantages: targets specific respondents, useful for exploratory research (industry experts, trendsetters)
  • Disadvantages: prone to researcher bias, not representative, results not generalizable
• Quota sampling selects elements based on predetermined quotas for specific subgroups
  • Advantages: ensures representation of key subgroups, more structured than convenience sampling (age, gender quotas)
  • Disadvantages: non-random selection within subgroups, potential bias, results not generalizable
• Snowball sampling starts with initial respondents, then asks them to refer other potential respondents
  • Advantages: useful for hard-to-reach or hidden populations (rare disease sufferers, niche hobbyists), quickly builds large sample
  • Disadvantages: prone to bias, not representative, results not generalizable

Selection of sampling methods

1. Consider research objectives
   • Exploratory research may use non-probability methods (focus groups for new product ideas)
   • Descriptive or causal research often requires probability methods (customer satisfaction surveys)
2. Evaluate target population characteristics
   • Homogeneous populations suitable for SRS (members of a professional association)
   • Heterogeneous populations may benefit from stratified or cluster sampling (national consumer study)
   • Hard-to-reach populations may require snowball sampling (illicit drug users)
3. Assess available resources
   • Budget, time, personnel constraints may favor non-probability methods (convenience sampling for student projects)
   • Probability methods require more resources but yield more reliable results (government-funded health study)
4. Balance trade-offs between representativeness, generalizability, cost, and feasibility
   • Prioritize methods aligning with research objectives and population characteristics
   • Consider hybrid approaches combining probability and non-probability methods (stratified sampling with convenience sampling within strata)