Background

Various psychometric statistical methods have been used in a paper by Simon et al1 in this issue of the Journal. These notes are intended to provide some additional explanation of the methods employed in developing a health measurement scale. [See Box 1 for a glossary of terms used in this article.]

What statistical methods are used in scale development?

A health measurement scale is a tool designed for a particular purpose: to quantify some attitude (say ‘acceptability’ of sterilisation), or to screen for those with a high-risk status (say, practising unsafe sex) in order to offer them additional counselling, or to assess cancer knowledge (as in the Simon et al.1 article).Any such tool needs to be fit for purpose or, preferably, best for purpose.2 The main statistical methods available for application in psychometric work have subtly differing objectives, and they are couched in terms of concepts such as reliability, validity and ‘responsiveness to change’. In general the statistical methods (and designs) that are used in psychometric work are adaptations of the well-known standard statistical approaches, but with ‘bespoke’ labels reflecting their psychometric focus:

• (1) Cronbach's alpha (α)

• (2) Test-retest reliability (rt-r)

• (3) Item-total correlation (ri-t).

When/why are psychometric statistical methods useful?

Psychometric statistical methods are useful first …