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Understanding Validity Measurement Methods and Analysis

Learn about methods and calculations for evaluating validity in statistics, including t-tests, correlations, Bland-Altman analysis, Spearman's rho, and criterion-referenced tests. Explore sample and population measurements to ensure accurate interpretation. Reflect on the role of statistics in human judgment.

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Understanding Validity Measurement Methods and Analysis

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Presentation Transcript

  1. Chapter 4 Calculating and Evaluating Validity

  2. When We Have Validity . . . We have an acceptably accurate measurement. We are measuring what we intend to measure. We are interpreting and applying the measurement appropriately.

  3. Methods for Evaluating Validity and Their Calculations

  4. Population and Sample Population: The designated group being measured Sample: A representative subgroup of the population Inferential statistics: Generalization about a population based on what is learned about the sample

  5. Using t-tests to Compare Sample Means Purpose of t-tests: Compare two sample means Determine if a sample represents a population Types: Paired vs. independent One-tailed vs. two-tailed

  6. Results of a t-testfor the VO2max-12-minute run example

  7. Interpreting t-tests p value: The probability that the difference between two means is a coincidence of random sampling Sometimes a t-test comparison of means doesn’t give us everything we need to know; we need to compare individual differences in scores.

  8. Correlations Defined: Measurement of the strength of the relationship between two variables. Pearson Product-Moment Correlation: Compares individual differences between two methods of measurement

  9. Results of a PearsonProduct-Moment Correlationfor the VO2max-12-minute run example

  10. Methods for Evaluating a Correlation r value (or correlation coefficient) Trendline R2 (or coefficient of determination)

  11. Slope of a Trendline Defined: The change in y value per unit change in x value. y value = the slope * the x value + the y intercept (point where y = 0) Or y = mx + b

  12. Scatter Plot Showing Trendline

  13. Bland-Altman Analysis A simple approach for evaluating criterion validity—through error analysis: Data taken from an unusually diverse group of subjects will show an artificially high correlation. Analysis focuses attention on error scores.

  14. Spreadsheet for a Bland-Altman Analysis

  15. Bland-Altman Analysis

  16. Evaluating Validity of Ranked Data Ordinal numbers: Ranked numbers that give a place in line but no information about distances between numbers; place holders. Interval numbers: Numbers that are separated by equal intervals; scalar numbers.

  17. Spearman’s rho A special correlation used in cases where one variable is an ordinal number Calculating Spearman’s rho: Excel doesn’t offer the calculation Some websites can do it; for example, www.wessa.net/rankcorr.wasp

  18. Evaluating the Validity of Criterion-Referenced Tests Validity ratio: The ratio of number of scores classified correctly to the total number of scores. Validity ratio = (CR+NCR)/(CR+CW+NCR) where CR = competent and classified correctly NCW = not competent and wrongly classified CW = competent but wrongly classified and NCR = not competent and classified correctly

  19. Example Diagram for Evaluating the Validity of a Criterion-Referenced Measurement

  20. Your Viewpoint After reading the chapter, go back and read the chapter-opening quote from Henry Clay: “Statistics are no substitute for human judgment.” Think also about the famous quote, “There are three kinds of lies: lies, damned lies, and statistics.” Do you agree with the sentiments expressed by these authors? Why or why not?

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