42 Determining Sample Size

If you read a journal article title that said, “Study finds that college students, on average, have incomes of $1000+ a month”, you would be shocked. However, when you examine the article further you read that they interviewed 10 students from one college in an economically successful area. While this study does represent a portion of the population, the sample size does not reflect the study’s claim because the sample was not representative of all college students. In Qualitative research, there is one key factor to keep in mind when determining sample size.

• Data Saturation
  • Data saturation is when a researcher continues to take in information until the research reveals no new results. Think of the researcher as a sponge. They absorb more and more information (data) until they are full and there is nothing new to take in.
  • EXAMPLE
    • If you are gathering information about how people feel about yogurt, you would continue gathering people’s responses until there are no new responses.

Like in the example mentioned in the intro of this reading, sample size is important in validating your study. In quantitative research, the larger your sample size is, the more representative of your population your study will be.

• Confidence Level
  • A statistical estimate used to gauge the reliability of a sample.
  • You want this as close to 100% as possible. The most rigorous studies often report Confidence Levels of 95% or more!
  • In practical terms, this means that if you did the same study 100 times, the results from 95 of those attempts would accurately reflect the population you are studying (plus or minus a small margin of error).
• Statistical Power
  • The probability that your study will reveal a statistically significant effect when it occurs.
  • This is the test that tells you whether or not you should have done the study (if we circle back to that NULL hypothesis thing).