Standardizing Measurement Scores
Information about test norms allows you to equate scores across different tests of the same construct and lets you compare individuals to each other. Once you have the mean and standard deviation of a score, you can calculate an individual’s z-score. Z-scores, also known as standard scores, tell you how many standard deviations away from the mean an individual is. Scores that are two standard deviations away from the mean represent the most extreme 5% of the population and often are considered to be unusual enough to warrant special consideration, such as a clinical diagnosis. For instance, IQ scores that are two standard deviations above the mean (130 or greater) are considered in a “gifted” range, and scores two standard deviations below the mean (70 or lower) are considered intellectually deficient. Scores on measures of depression that are two standard deviations above the mean often are considered to represent clinical depression.
T-scores are another kind of standard score; the MMPI is the best-known example of a test that uses T-scores. (Note that T-scores have nothing to do with t-tests.) Z-scores have a mean of 0 and a standard deviation of 1; T-scores have a mean of 50 and a standard deviation of 10. Thus, the average score on a test would be assigned a T-score of 50 and a z-score of zero. A score that was one standard deviation below average would be assigned a T-score of 40 and a z-score of -1.
For this Assignment, you will practice converting raw test scores into more meaningful standardized scores.
To prepare for this assignment:
Read the assigned pages in the SPSS Instructions on how to transform raw scores into standardized scores.
Use the table from the SPSS Instructions to properly organize, format, and present your standardized scores.
Submit a 2-page paper (not including references) that addresses the following:
Using the OCEAN SPSS data file, convert the OCEAN measures to T-scores, percentile scores, and z-scores. Then, split the file based on gender and compute the means and standard deviations for the male and female respondents in the sample, and summarize these in an APA Style table. Finally, compute a t-test to compare the T-scores based on gender. Summarize these analyses in an APA Style table. Then, explain insights you gained from your analyses about the impact and interpretation of transformed scores.
Note: Support your Assignment with specific references to all resources used in its preparation. You are to provide a reference list for all resources, including those in the Learning Resources for this course.
LEARNING RESOURCES:
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
Ferguson, C. J. (2016). An effect size primer: A guide for clinicians and researchers. In Methodological issues and strategies in clinical research., 4th ed. (pp. 301–310). American Psychological Association.
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137–152.
Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., Eisman, E. J., Kubiszyn, T. W., & Reed, G. M. (2001). Psychological testing and psychological assessment. A review of evidence and issues. The American Psychologist, 56(2), 128–165.
Pek, J., & Flora, D. B. (2018). Reporting effect sizes in original psychological research: A discussion and tutorial. Psychological Methods, 23(2), 208–225.
Funder, D. C. (2013, February 1). Does (effect) size matter? [Blog post]. Retrieved from https://funderstorms .wordpress.com/2013/02/01/does-effect-size-matter/
Funder, D. C., & Ozer, D. J. (1983). Behavior as a function of the situation. Journal of Personality and Social Psychology, 44, 107–112.
Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78.
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45(12), 1304–1312.
Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003. Schmitt, N., & Oswald, F. L. (2006). The impact of corrections for faking on the validity of noncognitive measures in selection settings. Journal of Applied Psychology, 91(3), 613–621.
