Scales Norms and Score Comparability

Scales Norms and Score Comparability essay assignment

Scales Norms and Score Comparability essay assignment

Introduction

One of the most useful qualities of a well-designed test is that it allows you to compare an individual to a population. For instance, when you took the SAT, you found your percentile relative to other college-bound high school seniors. A test of depression could show how an examinee’s depression compares to the general U.S. population, to mental health outpatients, or to persons diagnosed with major depressive disorder. A test of job aptitude could compare a job applicant to successful and unsuccessful job candidates.

This week, you explore how to evaluate the appropriate use of tests for different populations based on scales, norms, and score comparability. You also calculate mean, standard deviation, percentile, z-score, and T-score for a dataset.

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Objectives

Students will:

· Evaluate appropriateness of tests in terms of scales, norms, and score comparability for different populations

· Calculate mean, standard deviation, percentile, z-score, and T-score

 

Readings

· Anastasi, A., & Urbina, S. (1997). Psychological testing (7th ed.). Upper Saddle River, NJ: Prentice Hall.

. Chapter 3, “Norms and the Meaning of Test Scores”

· American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

. Chapter 5, “Scores, Scales, Norms, Score Linking, and Cut Scores”

· Blanton, H., & Jaccard, J. (2006). Arbitrary metrics in psychology. American Psychologist, 61(1), 27–41.  Retrieved from the Walden Library databases.

· Gibbons, R. D., Weiss, D. J., Kupfer, D. J., Frank, E., Fagiolini, A., Grochocinski, V. J., Bhaumik, D. K., Stover, A., Bock, R. D., & Immekus, J. C. (2008). Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatric Services, 59(4), 361–368. Retrieved from the Walden Library databases.

·  Thurstone, L. L. (1925). A method of scaling psychological and educational tests. Journal of Educational Psychology, 16(7), 433–451. Retrieved from the Walden Library databases.

· Young, F. W. (1984). Scaling. Annual Review of Psychology, 35(1), 55–81. Retrieved from the Walden Library databases.

 

Week 9 Discussion

 

Norming and Test Use

The normative sample used to develop a test has implications regarding the populations to which the normed scores can be generalized. Consider the following excerpt from the Mental Measurements Yearbook review of the Infant-Toddler Development Assessment:

There is no discussion of the types of children who represented the group used for development of both the Provence Profile and the complete IDA. Without such information, it is impossible to determine whether or not the IDA is representative along characteristics such as gender, family experience, and ethnicity. Without this information one could question the appropriateness of the IDA for all children.

When deciding whether a test may be appropriately used with a particular population, it is important to keep in mind the normative sample collected during the test’s development. Selective factors and other influences related to norming must be considered when implementing a test or scoring it.

To prepare for this Discussion, select one specific test instrument and a population. Research the test instrument you selected in the Walden Library, paying particular attention to the group used to develop or norm the instrument and considering how this may affect the populations with which the test is able to be used.

Reference: Provence, S., Erikson, J., Vater, S., & Palmeri, S. (1995). Infant-toddler developmental Assessment. Mental measurements yearbook (12th ed.). Lincoln, NE: Buros Institute of Mental Measurements.

With these thoughts in mind:

Post by Day 4 the title and a brief description of the test and population you selected. Explain whether or not this test can be appropriately used with this population. Use the current literature to support your response.

Be sure to support your postings and responses with specific references to the Learning Resources.

 

Quiz

Knowledge Assessment

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 standard deviation of a score, you can calculate that 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 2 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 are considered intellectually deficient (70 or lower). 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 Knowledge Assessment, you consider how raw test scores can be converted into more meaningful standardized scores, allowing you to meaningfully compare tests and to compare individuals.

In the provided dataset, you previously created a Risk-Taking scale by adding items R1 through R6.

 

Descriptio

Instructions

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 standard deviation of a score, you can calculate that 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 2 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 are considered intellectually deficient (70 or lower). 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 Knowledge Assessment, you consider how raw test scores can be converted into more meaningful standardized scores, allowing you to meaningfully compare tests and to compare individuals. In the provided dataset, you previously created a Risk-Taking scale by adding items R1 through R6.