Step 2: Data Collection

Step 2: Data Collection

A. Purpose of Data Collection

  • Collect data from a sample due to difficulty or expense of gathering from the entire population.

  • Use statistical analysis to generalize findings if proper sampling procedures are followed.

B. Sampling Approaches

  1. Probability Sampling

    • Every member has a chance of being selected.

    • Reduces research bias and ensures representativeness.

    • Suitable for parametric tests.

  2. Non-probability Sampling

    • Some members are more likely to be selected based on convenience or self-selection.

    • Easier data collection but can introduce biases.

    • More appropriate for non-parametric tests, though weaker inferences result.

C. Generalization and Validity

  • External validity applies findings to those sharing characteristics with the sample.

  • Results from WEIRD (Western, Educated, Industrialized, Rich and Democratic) populations may not generalize to non-WEIRD populations.

  • Discuss limitations of generalizing findings when using parametric tests to data fromnon-probability samples.

D. Creating an Appropriate Sampling Procedure

  • Determine how to recruit participants and whether a diverse representation is feasible.

E. Experimental Example: Sampling

  • Contact schools to recruit diverse students for an experiment.

F. Correlational Example: Sampling

  • Use social media to recruit male college students from various universities.

G. Sample Size Calculation

  • Assess sample size using existing studies, statistics, or calculators.

  • Minimum of 30 units per subgroup recommended.

  • Key components to input into calculators:

    1. Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.

    2. Statistical power: the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.

    3. Expected effect size: a standardized indication of how large the expected result of your study will be, usually based on other similar studies.

    4. Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

H. Conclusion

  • Proper sampling strategies are crucial for reliable data and analyses.