Readiness Instrument Psychometric Evaluation: Readiness Estimate & Deployability Index (READI)


Name: Theresa Dresma

Rank: Lt Col, USAF

Organization: University of Maryland, Baltimore

Performance Site: Malcolm Grove Medical Center, Andrews AFB, MD; David Grant Medical Center, Travis AFB, CA; 74th Medical Group, Wright-Patterson AFB, OH; Wilford Hall Medical Center, Lackland AFB, TX

Year Published: 2000

Abstract Status: Final


A high state of readiness is essential for Active Duty Air Force (AF) nurses. Reineck (1998) developed the Readiness Estimate and Deployability Index (READI) to provide commanders with a tested instrument to identify Army nurses preparation for short-notice deployments. Modifications of Reineck's (1996, 1998) Readiness Instrument derived the Readiness Estimate and Deployability Index Revised for Air Force Nurses (READI-R-AFN). In the pilot study, 181 of 350 active duty Air Force nurses (52% response rate) completed questionnaires. The READI-R-AFN was refined based on preliminary item analysis, internal consistency (alpha coefficient > 0.70), test-retest reliability and structural equation modeling (SEM). Factor analysis confirmed the hypothesized nature of the construct using Flannery's (1994) model of Stress Resistant Persons. Significant items of the 83-item READI-R-AFN were retained for the shorter 40-item form of the READI-R-AFN [SF], subsequently tested for reliability and validity in another convenience sample of 500 active duty AF nurses with 205 nurses responding (41% response rate). Six dimensions of Individual Readiness (IR) were confirmed in both samples of active duty AF nurses. In Phase III, the READI-R-AFN [SF] was introduced into a planned field evaluation consisting of two groups of nurses (EMEDS and CCATT courses) to evaluate the instrument's sensitivity to an intervention. Thirty-four nurses in the CCATT group and thirty-two nurses in the EMEDS group provided complete data for analysis. The READI-R-AFN [SF] was introduced at baseline and following intervention. Results showed all subscales with significant change scores (p