Data Science: Validity (Definitions)

"An argument that explains the degree to which empirical evidence and theoretical rationales support the adequacy and appropriateness of decisions made from an assessment." (Asao B Inoue, "The Technology of Writing Assessment and Racial Validity", 2009)

[external *]: "The extent to which the results obtained can be generalized to other individuals and/or contexts not studied." (Joan Hawthorne et al, "Method Development for Assessing a Diversity Goal", 2009)

[external *:] "A study has external validity when its results are generalizable to the target population of interest. Formally, external validity means that the causal effect based on the study population equals the causal effect in the target population. In counterfactual terms, external validity requires that the study population be exchangeable with the target population." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

[internal *:] "A study has internal validity when it provides an unbiased estimate of the causal effect of interest. Formally, internal validity means that the empirical effect from the study is equal to the causal effect in the study population." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"Construct validity is a term developed by psychometricians to describe the ability of a variable to represent accurately an underlying characteristic of interest." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

[operational validity:] "is defined as a model result behavior has enough correctness for a model intended aim over the area of system intended applicability." (Sattar J Aboud et al, "Verification and Validation of Simulation Models", 2010)

"Validity is the ability of the study to produce correct results. There are various specific types of validity (see internal validity, external validity, construct validity). Threats to validity include primarily what we have termed bias, but encompass a wider range of methodological problems, including random error and lack of construct validity." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

[internal validity:] "Accuracy of the research study in determining the relationship between independent and the dependent variables. Internal validity can be assured only if all potential confounding variables have been properly controlled." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

[external *:] "Extent to which the results of a study accurately indicate the true nature of a relationship between variables in the real world. If a study has external validity, the results are said to be generalisable to the real world." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"The degree to which inferences made from data are appropriate to the context being examined. A variety of evidence can be used to support interpretation of scores." (Anne H Cash, "A Call for Mixed Methods in Evaluating Teacher Preparation Programs", 2016)

[construct *:] "Validity of a theory is also known as construct validity. Most theories in science present broad conceptual explanations of relationship between variables and make many different predictions about the relationships between particular variables in certain situations. Construct validity is established by verifying the accuracy of each possible prediction that might be made from the theory. Because the number of predictions is usually infinite, construct validity can never be fully established. However, the more independent predictions for the theory verified as accurate, the stronger the construct validity of the theory." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)