Classification challenges in perfectionism.

Area of Science:

• Psychology
• Psychometrics

Background:

• High performance expectations are key to perfectionism, yet difficult to measure due to widespread endorsement of high standards.
• Existing measurement tools for perfectionism, like the Almost Perfect Scale-Revised (APS-R), face challenges with score range and distribution skew.
• Accurate screening for perfectionists is crucial for research and clinical practice.

Purpose of the Study:

• To address measurement and classification issues in perfectionism research.
• To test strategies for broadening score range and reducing skew on the APS-R Standards subscale.
• To identify distinct perfectionist profiles using factor mixture modeling and criterion indicators.

Main Methods:

• 506 participants completed the APS-R under standard, normal distribution, or visual analog scale conditions.
• Statistical transformations were applied to address score skew.
• Factor mixture modeling analyzed perfectionism scores alongside emotion regulation, perceived stress, and depression.

Main Results:

• The visual analog scale improved score differentiation but did not fully resolve range restrictions or skew.
• A three-class model of perfectionism was supported, with more balanced class distributions than previously reported.
• Perfectionists were distinguished by higher standards; maladaptive perfectionists showed higher self-criticism, suppression, stress, and depression, while adaptive perfectionists utilized reappraisal.

Conclusions:

• The study supports the value of differentiating between adaptive and maladaptive perfectionism.
• Measurement modifications, while helpful, require further refinement for optimal score distribution.
• Integrating criterion indicators like emotion regulation and distress is valuable for classifying perfectionist profiles.