It is important to understand that validity is a broad term that encompasses many components of the assessment. Content validity refers to the extent to which the assessment measures the various aspects of the particular construct question (Aliakbari, Parvin, Heidari, & Haghani, 2015). The term investigates whether the questions constituting the instrument really measure the construct in question or the respondents’ behaviors are influenced by other factors outside of the controlled variable. STEADI, the instrument chosen for this assignment, demonstrates a high content validity established through expert judgment.
STEADI is composed out of three close-ended questions, each measuring the knowledge of the content domain (falls in geriatric patients) of which it was designed to measure. In particular, the first question is related to the current experience with falls. The second question refers to the likelihood of falling for the next year. The third question assesses the person’s attitudes toward falling, summarizing the item questionnaire. These three criteria adequately and representatively sample the questionnaire to be evaluated.
To establish content validity, first, the purpose of the test and the construct will be defined and measured repeatedly. Second, the multi-faceted content domain will be composed to represent the construct via identifiable, agreed-upon dimensions that may appear more relevant to the topic of geriatric falls than others (Casey et al., 2017). It is important to note that statistical methods are not primary in determining the content validity of the test. The establishment of content validity as such will be made through external expert judgment (Lohman, Crow, DiMilia, Nicklett, Bruce, & Batsis, 2017). Professionals will be asked to assess the degree to with the test’s outline adequately equips the content domain, as well to which degree questionnaire items are proportionally sampled.
References
Aliakbari, F., Parvin, N., Heidari, M., & Haghani, F. (2015). Learning theories application in nursing education. Journal of Education and Health Promotion, 4, 3-11. Web.
Casey, C., Parker, E., Winkler, G., Liu, X., Lambert, G., & Eckstrom, E. (2017). Lessons learned from implementing CDC’s STEADI falls prevention algorithm in primary care. The Gerontologist, 57(4), 787–796. Web.
Lohman, M., Crow, R., DiMilia, P., Nicklett, E., Bruce, M., & Batsis, J. (2017). Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample. Journal of Epidemiology and Community Health, 71(12), 1191–1197. Web.