Score Metric Explorer

How to read the scores

• This tool uses descriptive category labels with equivalent standard-score cut points as dictated in the Wechsler system of categorising scores.
• All score types represent the same relative location using different means and standard deviations.
• Z is the common reference: it expresses distance from the mean in standard deviation units.
• Percentile rank is cumulative and non-linear. Equal percentile changes do not represent equal score changes.
• A mathematically converted scaled score may contain decimals, while published test scores are commonly reported in discrete whole-number values.

For my fellow nerds!

I hope this tool is useful for converting scores, but also to visualise how descriptive categories relate to the underlying continuous distribution, particularly at the boundaries of categories where depending on the score type, small differences in the underlying score cause rounding to occur and can result in different category assignments. This is why the "SS=12 average or high average" debate arises.

To explain further, take the example of a 108 standard score. It is "Average" but mathematically rounds to scaled score 12 which is generally considered "High Average". However, in the Wechsler scales, Pearson generally treats scaled scores as rounded down rather than rounded to their nearest whole number. To evidence this, in the technical manual for the WAIS-5, scaled score 12 is listed as representing 75th percentile, as is standard score 110 (Tables 6.1 and 6.2, p. 132, 133) play around with this tool and you can 75th percentile is the point at which the scaled score 12 becomes a whole number (12.000 rather than 11.999). In the technical manual, Pearson further avoids the issue of rounding scaled scores by presenting descriptors only alongside standard scores and percentages (Table 6.3, p.135). Personally, its a good reason to be cautious about overvaluing descriptors in favour of a more continuous intepretation of any profile with close scores that happen to span categories.