Measurement is a replicable and systematic process through which an object or instrument is quantified or classified as in the field of social science that deals with quantification of behaviour. In this case determining validity of the measuring instrument (questionnaire) holds utmost importance (Drost 2011). Consequently, measuring of behaviours lead to the dilemma of whether measuring what is intended to be measured.
For example, when a study is intended to measure the engagement of employees in an organisation, the survey developed to answer the motivating factors is not considered to be valid.
Although the issue of validity cannot be established with complete certainty, it is still favoured to maintain validity of the measuring instrument. The reason behind determining validity lays in the plethora of threats a research faces. This includes history, maturation, testing, instrumentation, selection, mortality, diffusion of treatment and compensatory equalisation, rivalry and demoralisation.
Importance of determining validity in a research
Traditionally, establishment of instrument validity was limited to the sphere of quantitative research. However, the concept of determination of credibility of the research is applicable to qualitative data. Rooted in the positivist approach of philosophy, quantitative research deals primarily with culmination of empirical conceptions (Winter 2000). Under such approach, validity determines whether the research truly measures what it was intended to measure. Furthermore, it also measures the truthfulness of the research results (Kothari 2012).
Construct validity and construction of initial concept
For example, the construct validity would determine whether the subject has high anxiety score in a survey. Does it actually have a high degree of anxiety?
Construct validity lays ground for construction of initial concept notion, question or hypothesis that determines the data to be collected. Furthermore, it plan the collection of data (Wainer & Braun 1988). Therefore, construct validity deals with determining the research instrument and what is intended to be measured.
Further, it uses three different parameter to check validity:
- Homogeneity; research instrument measures one construct such as anxiety levels.
- Convergence; the research instrument measures concepts which are similar to other instruments, in order to determine the convergence is results.
- Theoretical evidence; when the findings are in sync with the theoretical evidence.
Determining required variable with content validity
For example, to determine the anxiety level on different parameters the content validity helps to determine the role of every factor that contributes towards anxiety.
Content validity category determines whether the research instrument is able to cover the content with respect to the variables and tests.
Face validity is a sub-set of content validity. In face validity, experts or academicians are subjected to the measuring instrument to determine the intended purpose of the questionnaire.
Criterion validity to compare different measuring instruments
Criterion validity helps to review the existing measuring instruments against other measurements. This is to determine the extent to which different instruments measure same variable. There are three sub-sets of criterion validity; convergent, divergent and predictive .
In case of convergent, the results predict high correlation with the existing instrument i.e. they are measuring similar variables.
The second subset is the divergent, where the correlation with the measuring instrument is low. In such cases the measuring instrument should be changed.
For example, if one of the instrument measures anxiety and the other instrument measures IQ level then there will be divergence.
Finally, in case of predictive, the instrument should be able to “predict” the likelihood that IQ levels impact or predict the anxiety levels.
The following table show different validity applied in a research.
The entire research process should establish validity. This is important in order to ensure the capability of the instrument (survey, interview, etc.) in deriving the results (Drost 2011).
- Creswell, J.W. & Miller, D.L., 2000. Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3), pp.124–130.
- Drost, E.A., 2011. Validity and Reliability in Social Science Research. Education Research and Perspectives, 38(1), pp.115–123.
- Fraser, S. & Greenhalgh, T., 2001. Coping with complexity: educating for capability. BMJ, 323, pp.799–803.
- Glyn Winter, 2000. A Comparative Discussion of the Notion of “Validity” in Qualitative and Quantitative Research. The Qualitative Report, 4(4). Available at: http://www.nova.edu/ssss/QR/QR4-3/winter.html.
- Kothari, C.R., 2012. Research Methodology: An introduction. In Research Methodology: Methods and Techniques. p. 418.
- Leung, L., 2015. Validity, reliability, and generalizability in qualitative research. Journal of family medicine and primary care, 4(3), pp.324–7. Available at: http://www.ncbi.nlm.nih.gov/pubmed/26288766 [Accessed July 11, 2016].
- Lincoln, Y.S. & Guba, E.G., 1982. ESTABLISHING. DEPENDABILITY AND tl:ONFIRMABILITY IN NATURALISTIC INQUIRY THROUGH AN AUDIT. In American Educational Research Association Annual Meeting, New York. New York: U.S. Department of Education, p. 31. Available at: http://files.eric.ed.gov/fulltext/ED216019.pdf [Accessed October 21, 2015].
- Lincoln, Y.S. & Guba, E.G., 1985. Naturalistic Inquiry, Beverly Hills, CA: SAGE Publication.
- Long, T. & Johnson, M., 2000. Rigour, reliability and validity in qualitative research. Clin Eff Nurs, 4, pp.30–37.
- Maxwell, J.A., 2005. Qualitative Research Design: An Interactive Approach, SAGE Publications. Available at: https://books.google.co.in/books/about/Qualitative_Research_Design.html?id=XqaJP-iehskC&pgis=1 [Accessed May 20, 2015].
- Morse, J., Barrett, M. & Mayam, M., 2002. Verification strategies for establishing reliability validity in qualitative research. Int J Qual Res, 1, pp.1–19.
- Nahid Golafshani, 2003. Understanding Reliability and Determining Validity in Qualitative Research. The Qualitative Report, 8(4), pp.597–607.
- Noble, H. & Smith, J., 2015. Issues of validity and reliability in qualitative research. Evidence Based Nursing, 18(2), pp.34–35. Available at: http://ebn.bmj.com/lookup/doi/10.1136/eb-2015-102054 [Accessed July 11, 2016].
- Sandelowski, M., 1993. Rigor or rigor mortis: the problem of rigor in qualitative research revisited. Adv Nurs Sci, 16, pp.1–8.
- Slevin, E., 2002. Enhancing the truthfulness, consistency, and transferability of a qualitative study: using a manifold of two approaches. Nurse Res, 7, pp.79–197.
- Wainer, H. & Braun, H.I., 1988. Test validity, Hilldale, NJ: Lawrence Earlbaum Associates.
She is a true Piscean. She loves doing things to perfection with passion. She is very creative and likes to make personalized gifts for her dear ones, this is actually something that keeps her going. Shruti loves adventure sports and likes river rafting and cliff jumping.
Latest posts by Shruti Datt (see all)
- Matrices and arrays in MATLAB - November 15, 2018
- Search and help in MATLAB - September 21, 2017
- Multi-stage CRS analysis and interpretation of input orientation - September 5, 2017