Validity implies precise and exact results acquired from the data collected. In technical terms, a measure can lead to a proper and correct conclusions to be drawn from the sample that are generalizable to the entire population.
1.Internal validity: When the relationship between variables is causal. This type refers to the relationship between dependent and independent variables. It is associated with the design of the experiment and is only relevant in studies that try to establish a causal relationship. For example, it can be used for the random assignment of treatments.
2.External validity: When there is a causal relationship between the cause and effect that can be transferred to people, treatments, variables, and different measurement variables which differ from the other.
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3.Statistical conclusion validity: The conclusion reached or inference drawn about the extent of the relationship between the two variables. For instance, it can be found when we aim at finding the strength of relationship between any two variables that have been under observation and analysis. If we do reach the correct conclusion, then it is said to be statistical conclusion validity. There are two types of statistical conclusion validity. They are as follows:
a. Type one error: Type one error is when we conclude that there is a relationship between two variables and we reject a true null hypothesis when in reality, there is no relationship between the two variables. This is in fact very dangerous.
b.Type two errors: If we fail to reject a false null hypothesis that is true it is called type two error.
In statistical conclusion validity, the method of power analysis is used to detect the relationship. Several problems crop up while making a statistical conclusion. For instance, if a small sample size is used, then there is the possibility that the result will not be correct. To avoid this, the sample size should be of considerable size. Statistical validity is also threatened by the violation of statistical assumptions. The results may not be accurate, however, if values in analysis are biased and the wrong statistical test is approved.
4.Construct validity: Extent that a measurement actually represents the construct it is measuring. For instance, in structural equation modeling, when we draw the construct, then we presume that the factor loading for the construct is greater than .7. To draw construct validity, Cronbach’s alpha is used. For exploratory purposes .60 is accepted, for confirmatory purposes .70 is accepted, and .80 is considered good. If the construct satisfies the above presumption and expectation, then the construct would be helpful in predicting the relationship for dependent variables. Convergent/divergent validation and factor analysis are also used to test construct validity.
Relationship between reliability and validity: There is no way that a test that is unreliable is valid. Again, any test that is valid must be reliable. By this statement we are able to derive that validity plays a significant role in analysis as it ensures the conclusion of accurate results.
1.Insufficient data collected to make valid conclusion
2.Measurement done with too few measurement variables
3.Too much variation in data or outliers in data
4.Wrong selection of samples
5.Inaccurate measurement method taken for analysis
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421-458.
Brinkman, W. -P., Haakma, R., & Bouwhuis, D. G. (2009). The theoretical foundation and validity of a component-based usability questionnaire. Behaviour & Information Technology, 28(2), 121-137.
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Thousand Oaks, CA: Sage Publications. View
Cronbach, L. J. (1971). Test validation. In R. L. Thorndike (Ed.), Educational measurement (2nd ed., pp. 443-507). Washington, DC: American Council on Education.
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Guilford, J. P. (1946). New standards for test evaluation. Educational and Psychological Measurement, 6(5), 427-439.
Krause, M. S. (1972). The implications of convergent and discriminant validity data for instrument validation. Psychometrika, 37(2), 179-186.
Lieberman, D. Z. (2008). Evaluation of the stability and validity of participant samples recruited over the internet. CyberPsychology & Behavior, 11(6), 743-746.
Lozano, L. M., Carcía-Cueto, E., & Muñoz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology, 4(2), 73-79.
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Education measurement (3rd ed., pp. 13-103). Washington, DC: American Council on Education.
Moret, M., Reuzel, R., van der Wilt, G. J., & Grin, J. (2007). Validity and reliability of qualitative data analysis: Interobserver agreement in reconstructing interpretative frames. Field Methods, 19(1), 24-39.
Rosenbaum, P. R. (1989). Criterion-related construct validity. Psychometrika, 54(4), 625-659.
Shepard, L. A. (1993). Evaluating test validity. Review of Research in Education, 19, 405-450.
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