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In quantitative research, validity is considered when a researcher is designing a survey or conducting an experiment for collecting data about some phenomena. Validity, in terms of quantitiave research, relates to accuracy, or, in other words, it relates to how accurately or precisely a given concept is being measured by the researcher. A valid measure is one which actually or validly measures the concept it is supposed to be measuring.
Researchers use four criteria for designing their measures: face validity, content validity, predictive validity and construct validity: (1) Face validity is when ”at face” a measure appears logical. For example, when a person is measuring height, they would not ask ”How heavy are you?” (2) Content validity is when a measure covers the range of meanings of a concept. For example, to measure the concept ”generosity,” just asking the question ”how much money have you donated in the last month?” will not do. Rather, the researcher needs to ensure that a set of questions (or a single question) address all possible concepts of generosity. (3) Predictive validity examines whether a measure can predict the potential behavior or outcome that it is supposed to predict. For example, ”GRE scores have a high degree of success in predicting success in graduate schools” therefore GRE scores are considered to have good predictive validity. (4) Construct validity seeks agreement between a theoretical concept and a measure. For example, in criminology, strain theory suggests that a variety of different societal pressures cause an individual to experience strain and therefore commit crime. Contrarily, a researcher finds in his research that strain leads to success in life. In this case, his measure is not in agreement with the dominant strain theory and he needs to question his construct validity.
Bibliography:
- Creswell, J. (2009) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 3rd edn. Sage, Thousand Oaks, CA.
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