A nonlinear transformation of test scores into standard scores may be required when the test data under consideration:
A) are normally distributed.
B) are not normally distributed.
C) were originally converted into A scores.
D) were not obtained under standardized conditions.

Question

Quizplus · Accepted Answer

Nonlinear transformation of test scores into standard scores (such as z-scores) is required when the test data under consideration are not normally distributed. This is done to make the scores more interpretable and comparable across different groups or time points. If the data are normally distributed, linear transformation (such as subtracting the mean and dividing by the standard deviation) is sufficient to convert them into standard scores. Choice A is incorrect because normally distributed data can be converted into z-scores without nonlinear transformation. Choice C is not relevant to the transformation of test scores into standard scores. Choice D is incorrect because standardized conditions refer to the administration and scoring of the test, not the distribution of the scores.

A Nonlinear Transformation of Test Scores into Standard Scores May