5.2 Sampling techniques and power calculations

Sampling techniques and power calculations are crucial for conducting effective impact evaluations. They ensure that researchers can draw valid conclusions about larger populations while minimizing costs and time. Proper sampling aligns with research design, enhances validity, and supports different evaluation approaches.

Probability sampling methods like simple random, stratified, and cluster sampling help researchers select representative participants. Sample size calculations determine the number of participants needed to detect significant effects. Researchers must also consider representativeness and potential biases when designing their sampling strategy.

Importance of sampling in evaluations

Enhancing validity and efficiency

Top images from around the web for Enhancing validity and efficiency

• 

  Frontiers | Optimization of Skewed Data Using Sampling-Based Preprocessing Approach View original

  Is this image relevant?

• 

  Sampling Techniques | OER Commons View original

  Is this image relevant?

• 

  EEP impacts 1 - Framework for impact evaluation - HAMK Unlimited View original

  Is this image relevant?

• 

  Frontiers | Optimization of Skewed Data Using Sampling-Based Preprocessing Approach View original

  Is this image relevant?

• 

  Sampling Techniques | OER Commons View original

  Is this image relevant?

1 of 3

Top images from around the web for Enhancing validity and efficiency

• 

  Frontiers | Optimization of Skewed Data Using Sampling-Based Preprocessing Approach View original

  Is this image relevant?

• 

  Sampling Techniques | OER Commons View original

  Is this image relevant?

• 

  EEP impacts 1 - Framework for impact evaluation - HAMK Unlimited View original

  Is this image relevant?

• 

  Frontiers | Optimization of Skewed Data Using Sampling-Based Preprocessing Approach View original

  Is this image relevant?

• 

  Sampling Techniques | OER Commons View original

  Is this image relevant?

1 of 3

• Sampling draws conclusions about larger populations based on subsets of individuals
• Proper techniques ensure validity and reliability of impact evaluation results
• Minimizes bias and increases generalizability of findings
• Reduces costs and time associated with data collection
• Makes studies more feasible and efficient
• Directly affects statistical power and precision of impact estimates
• Enables control of confounding variables to isolate true intervention impacts

Aligning with research design

• Sampling strategies must align with evaluation design and research questions
• Ensures meaningful and accurate results
• Supports different types of impact evaluation designs (randomized controlled trials, quasi-experimental designs)
• Allows for comparison between treatment and control groups
• Facilitates subgroup analysis to identify differential impacts

Sampling techniques and applications

Probability sampling methods

• Simple random sampling selects participants completely at random from the population
• Stratified sampling divides the population into subgroups (strata) before random selection
  • Useful when subgroups may respond differently to interventions (urban vs rural areas)
• Cluster sampling randomly selects groups (clusters) rather than individuals
  • Often used for community-level interventions or when individual sampling is impractical (schools, villages)
• Systematic sampling selects every nth individual from a list
• Multi-stage sampling combines multiple techniques for complex, large-scale evaluations
  • Example: First selecting districts, then schools within districts, then students within schools

Non-probability sampling methods

• Convenience sampling selects easily accessible participants
  • Limited use in impact evaluations due to high potential for bias
• Purposive sampling selects participants based on specific characteristics
  • Can be useful for qualitative components of mixed-methods evaluations
• Quota sampling sets quotas for different subgroups to ensure representation
  • Used in quasi-experimental designs but requires careful consideration of selection biases

Factors influencing sampling technique selection

• Evaluation design (experimental vs quasi-experimental)
• Population characteristics and distribution
• Resource constraints (budget, time, personnel)
• Desired level of precision and generalizability
• Logistical considerations (geographical spread, access to participants)
• Need for subgroup analysis or stratification

Sample size calculation

Power calculation fundamentals

• Determines appropriate sample size to detect statistically significant intervention effects
• Key components include:
  • Significance level (α) typically set at 0.05
  • Statistical power (1-β) often set at 0.80 or 0.90
  • Expected effect size based on previous studies or pilot data
  • Variability in the outcome measure
• Minimum detectable effect size (MDES) represents smallest true effect detectable with given sample size and power
• Larger sample sizes increase power to detect smaller effect sizes
• Balances statistical rigor with practical constraints (budget, time, feasibility)

Advanced considerations in sample size calculation

• Clustering effects in multi-level designs require larger sample sizes
  • Intraclass correlation coefficient (ICC) measures within-cluster similarity
• Account for expected attrition rates to ensure adequate final sample size
• Adjust for multiple comparisons to control overall Type I error rate
• Consider differential effects across subgroups when planning sample size
• Use power analysis software (G*Power, Stata, R) for complex calculations
• Conduct sensitivity analyses to assess impact of changing assumptions on required sample sizes

Sample representativeness and bias

Assessing representativeness

• Compare key demographic and socioeconomic variables between sample and population
• Use statistical tests (chi-square, t-tests) to check for significant differences
• Consider practical significance of any differences found
• Assess coverage of important subgroups within the sample
• Evaluate geographic distribution of the sample relative to the population

Identifying and mitigating sources of bias

• Selection bias occurs when sample systematically differs from population
  • Address through random selection and stratification
• Non-response bias arises when non-participants differ systematically from participants
  • Implement strategies to maximize response rates (follow-ups, incentives)
• Attrition bias in longitudinal studies when dropouts are non-random
  • Use tracking methods and analyze characteristics of attritors
• Sampling frame errors lead to under- or overcoverage of population
  • Carefully review and update sampling frames before selection
• Measurement bias from inconsistent or inaccurate data collection
  • Standardize protocols and train enumerators thoroughly

Techniques for bias mitigation and analysis

• Weighting adjusts for known differences between sample and population
• Imputation methods handle missing data to reduce bias from non-response
• Propensity score matching balances treatment and control groups on observed characteristics
• Sensitivity analyses quantify potential impact of unobserved biases on results
• Triangulation with multiple data sources to cross-validate findings
• Transparent reporting of potential biases and limitations in evaluation reports