To transform or not transform skewed data for psychometric analysis: that is the question!

Area of Science:

• Psychometrics
• Statistical analysis
• Data science

Background:

• Data transformation is commonly recommended for skewed data in psychometric analyses.
• However, the actual benefits and costs of transforming skewed data are not well-established.
• This study specifically examines the impact on Cronbach alpha and Pearson product-moment correlation.

Purpose of the Study:

• To investigate the necessity and impact of data transformation on psychometric statistics.
• To evaluate the effects of transforming skewed data on Cronbach alpha and Pearson product-moment correlation.
• To determine if data transformation is always advisable for skewed item responses.

Main Methods:

• Utilized data from 758 immigrants using a Russian version of the Symptom Checklist-90-Revised (SCL-90-R).
• Calculated Cronbach alpha and Pearson product-moment correlation for original and transformed (square root, log) item responses.
• Manipulated sample size, transformation type, and transformation method (sum then transform vs. transform then sum).

Main Results:

• Neither Cronbach alpha nor Pearson product-moment correlation consistently differed between original and transformed data across various sample sizes.
• An exception was observed when items were transformed before summing for Pearson correlation, yielding inconsistent results.
• The findings indicate that transformation's impact is minimal for these specific psychometric measures.

Conclusions:

• Data transformation may not be necessary or beneficial when calculating Cronbach alpha or Pearson product-moment correlation with skewed item responses.
• The study suggests a re-evaluation of the routine application of data transformation in psychometric research.
• Careful consideration is advised before applying transformations to skewed data in similar analyses.