Table 2.

Quality Standards for Psychometric Measure Evaluation

Quality Standard	Description
1. Describe the measure	Settings and population for which the measure was designed (Stone, Coonrod, & Ousley, 2000). Goal and purpose of the measure (Friberg, 2010)
2. Describe the examiner qualifications	Characteristics of the examiners and raters, including level of experience (Kottner et al., 2011). Assessor training required to administer the measure reliably. Disclose if raters were blind to the child’s diagnosis (Stone et al., 2000).
3. Explain test procedures	Adequate procedural description for clinician to replicate testing environment (Friberg, 2010).
4. Describe the sample population	Sample population used to obtain psychometric data and determine appropriateness of the test for particular children; include: geographic region, age, gender, ethnicity, socioeconomic status, and diagnostic/disability categories. Recruitment method and justification of sample size. A sample size of at least 100 children per relevant population group (e.g. gender, race, etc 2010).
5. Examine and interpret psychometric properties of reliability and validity	Reliability Internal consistency Guidelines for interpreting Cronbach’s coefficient alpha: ≤.70 = unacceptable, .70–.79 = fair, .80–.89 = good, ≥ .90 = excellent (Cicchetti and Sparrow, 1990). Type and purpose of the measure considered when interpreting reliability (Bracken, 1987) Test-retest and Interrater reliability Guidelines for interpreting statistical significance for both test-retest and interrater reliability, typically reported using Cohen’s Kappa (Cohen, 1960; for nominal data) or an Intraclass Correlation Coefficient (ICC; for ordinal, interval, or ratio data): <.40 = poor, .40–.59 = fair, .60–.74 = good, .75–1.00 = excellent (Cicchetti and Sparrow, 1981). A test-retest interval of 2–4 weeks recommended for young children (Bracken, 1987; Cicchetti, 1994). Validity Concurrent and Divergent validity Correlation of 1.00 or −1.00 not expected or desired; new test should not be an exact replica of the other measure (Cicchetti, 1994; Crocker & Algina, 1986).
6. Examine the dimensionality of the measure	Factor structure of the measure considered when evaluating subscales. Construct validity supported with item total correlations greater than .30, inter-item correlations between .30 and .70, and factor analysis with factor loadings greater than .40 (DeVon et al., 2007).