Validity
Validity refers to the state in which the researcher or the investigator can get assurance that the inferences drawn from the data are error free or accurate. If there is validity in the sample, then it is in the population from where that sample has been drawn.
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There are basically four major types of Validity. These types are Internal, External, Statistically Conclusive and Construct.
Internal Validity refers to the type where there is a causal relationship between the variables. It signifies the causal relationship between the dependent and the independent type of variable. Internal Validity refers to those factors that are the reason for affecting the dependent variable. This type is used in the case of the design of experiments where the treatments are randomly assigned.
External Validity refers to the type where there is a causal relationship between the cause and the effect. The cause and effect are those that are generalized or transferred either to different people or different treatment variables and the measurement variable.
Statistically Conclusive Validity refers to the type in which the researcher is interested about the inference on the degree of association between the two variables. For instance, in the study of the association between the two variables, the researcher reaches statistically conclusive validity only if he has performed statistical significance tests upon the hypotheses predicted by him. This type is violated when the researcher reaches two types of errors, namely type I error and type II error.
Type I error causes violation of this type of validity because in this type of error, the researcher rejects the hypothesis which was indeed true.
Type II error causes violation of this type of validity because in this type of error, the researcher accepts the hypothesis which was indeed false.
Construct Validity refers to the type in which the construct of the test is involved in predicting the relationship for the dependent type of variable. For example, construct validity can be drawn with the help of Cronbach’s alpha. In Cronbach’s alpha, it is assumed that if its value is 0.80, then it is considered good for confirmation, and if its value is 0.70, then it is adequate. So, if the construct satisfies such conditions, then the validity holds. Otherwise, it does not.
Convergent/divergent validation and factor analysis is also used to test this type of validity.
There is a strong relationship between validity and reliability. A test is said to be unreliable if it does not hold the conditions of validity. Reliability is a necessary property of the test, but is not the sufficient condition.
Thus, validity plays the significant role in making an accurate inference about the data.
There are certain things that act as a threat to validity. These are as follows:
If the researcher collects insufficient data to attain this in the inference, this is not feasible because insufficient data will not represent the population as a whole.
If the researcher measures the sample of the population with too few measurement variables, then he also cannot achieve validity of that sample.
If the researcher selects the wrong type of sample, then he too cannot achieve validity in the inference about the population.
If the researcher selects an inaccurate measurement method during analysis, then the researcher would not be able to achieve validity.