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In mathematics, an argument is valid if its conclusion is logically entailed by its premises. In the social sciences, empirical research proceeds analogously from data to the scientific theories or answers research questions. However, social researchers tackle many more uncertainties than mathematicians do. For once, the social sciences are inductive and theorize statistical phenomena, hence social theories need to embrace probabilistic notions of validity. Moreover, if data are of questionable validity, their methodologically conclusive analysis is not likely to yield valid conclusions.
Validity should not be confused with Reliability. Data are reliable to the extent to which the process of generating them is replicable. Replicability says nothing about what data are about. By contrast, data are valid to the extent to which they accurately represent the phenomena of analytical interest. A theory is valid to the extent to which it is corroborated by independently obtained evidence. In Measurement Theory, a test is valid to the extent it measures what it claims it measure.
Four major kinds of validity can be distinguished. Logical validity concerns the conclusiveness of propositions derived from known premises. Logic has little to do with what resides outside its discourse but informs scientific argumentation and writing. Face validity is obvious or common truth. Researchers invoke face validity when they accept data, methods, and conclusions because they make sense to them without feeling the need to give reasons for their assessments. The use of face validity in research is more common than seen. Social validity, also called “pragmatic validity,” is the quality of research results to speak to current public concerns or contribute to prevailing social issues (Riffe et al. 1998). Justifications of research projects to funding agencies almost always rely on claims of their potential value and social impacts. Empirical validity is the primary concern of all sciences. It is the degree to which independently available evidence supports various stages of the research process and withstands the challenges of additional data, competing theories, and alternative experiments or measurements.
Several methods have been used to assess empirical validity. Based on a recommendation by the American Psychological Association (1999), we can distinguish the following: (1) content validity refers to phenomena that must be read or interpreted in order to become data. Most theories in social research are based on written documents, communications, and representations, including assertions by interviewees. To capture their meanings in the form of analyzable data requires culturally competent observers or readers. The results obtained from such data need to be interpreted as well – unlike meter readings; (2) sampling validity responds to media biases, and institutional reasons for selectively preserving records of analytical interests; (3) semantic validity responds to whether the categories of an analysis are commensurate with the categories of the object of inquiry; (4) construction and use is concerned with how a research process – its algorithms and networks of analytical steps – relates to or models structures in the object of an analysis. Simulation studies, for example, define a dynamics that takes off from how variables in its object of attention are related; (5) structural validity relies on demonstrating the structural correspondence of the research methods and the way the phenomena represented by the data relate to claims made by research results; (6) functional validity is the degree to which a method of analysis is vindicated by repeated successes rather than validated by structural correspondences (Janis 1965).
Correlative validity is established by correlating the research results with variables concurrent with but extraneous to the way these results were obtained. Its purpose is to confer validity to the research results from measures known to be valid. An important systematization of correlative validity is the multitrait-multimethod technique (Campbell & Fiske 1959). It distinguishes: (1) convergent validity, the degree to which research results correlate with other variables known to measure the same or closely related phenomena; (2) discriminant validity, the degree to which research results are distinct from or unresponsive to unrelated phenomena and hence do not correlate with the research results; and (3) predictive validity, the degree to which research results pertain to anticipated but not yet observed phenomena, whether at a future point in time or elsewhere.
- American Educational Research Association, American Psychological Association, and National Council on Measurement in Education (1999). Standards for educational and psychological testing. Washington, DC: American Psychological Association.
- Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.
- Headland, T. N., Pike, K. L., & Harris, M. (eds.) (1990). Emics and etics: The insider/outsider debate. Newbury Park, CA: Sage.
- Janis, I. L. (1965). The problem of validating content analysis. In H. D. Lasswell, N. Leites et al. (eds.), Language of politics. Cambridge, MA: MIT Press, pp. 55–82.
- Krippendorff, K. (2013). Content analysis: An introduction to its methodology, 3rd edn. Thousand Oaks, CA: Sage.
- Riffe, D., Lacy, S. & Fico, F. G. (1998). Analysing media messages: Using quantitative content analysis in research. Mahwah, NJ: Lawrence Erlbaum.