Construct-related validity

Validity relates to whether you are measuring what you want to measure. When we measure the width of a room, the question of validity usually does not arise. When we are measuring a complex process, such as aptitude to perform well in college or the ability of the safety-management system in a plant to prevent future loss, validity becomes a serious question. Scientists (Chronbach and Meeh 1955) generally define three categories of validity content-related, criterion-related, and construct-related validity. 

Because criteria are usually somewhat arbitrary, and because there is often no single, ultimate criterion, it is best to use several criteria when attempting to establish criterion-based validity. Doing so will not only make it somewhat more likely that you will establish this validity but likely increase your understanding of the measure you are testing. This brings us to the concept of construct-related validity. 

Chapter 9 discussed the construction of candidate ARs in variable density systems. Mass fractions are hence useful in broadening the use of AR theory to a wider class of systems (i.e., gas phase reactions). Mass fractions always obey a linear mixing law, and thus these constructions are valid even when the system does not obey constant density. We described a number of useful relations for converting common process variables used in AR theory, such as concentration and mole fraction, in terms of species mass fractions. These conversions are often assisted by use of an appropriate equation of state that relates the system volume to the process variables of interest. Rate expressions involving concentration are then easily viewed in mass fraction space, and the AR may be computed appropriately. 

Construct-related evidence of validity refers to the nature of the psychological construct or characteristics being measured by the instrument. How well does a measure of the construct explain differences in the behavior of individuals or their performance on a certain task There are usually three steps in obtaining the construct-related evidence of validity, i.e., (1) the variable being measured is clearly defined (2) hypotheses, based on a theory underlying the variable, are formed about how people will behave in a particular situation and (3) the hypotheses are tested both logically and empirically. 

Audit instruments can be evaluated on the basis of three different types of validity when linking the audit to safety performance. These are content-related, criterion-related, and construct-related procedures for accumulating evidence of validity (Anastasi 1988, 139). Content-related validity involves the examination of the audit content to determine whether it covers a representative sample of the behaviors to be measured. Criterion-related validity procedures indicate the effectiveness of an audit in predicting performance in specified activities. The performance on the audit is checked against a criterion that is considered a direct and independent measure of what the audit was intended to measure. The third type of validity, construct validity, is the extent to which the audit is said to be measuring a theoretical construct or trait. Construct validation requires accumulation of information from a variety of sources. 

It Is impossible to construct a valid phase diagram on the basis of the above data. The compiler s opinion is that the value A had its given meaning only if the component added is a salt. In the case of water, the relation jj q 100 - A is valid. On the basis of this assumption the compiler 2 has calculated the following values. 

Instead, Cronbach and Meehl (1955) suggested that classification should be based on a construct validation approach. Construct validation involves developing a theory about a construct that is defined by a set of interconnected laws. These laws are ideas that relate constructs to one another as well as to observable behaviors. The set of constructs, laws, and observable behaviors is called a nomological network. 

Construct validation follows a three-stage process. The first stage, theory formulation, involves specifying relationships among constructs and their relation to external variables (e.g., etiological factors). Disorder x is created by process y. For example, a theory about the construct panic disorder could specify that it is caused by the catastrophic misinterpretation of benign bodily cues (Clark, 1986) or by a faulty suffocation monitor (Klein, 1993). Internal... 

The results were not completely consistent, but these findings seem to favor a dimensional model of depression. However, this may reflect inadequate indicator selection. Taxometric power analysis suggests this is not the case, but the power analyses may have presented an overly optimistic view of indicator quality. In addition, the construct validity of the indicators used in this study is unknown. The validity of the BDI and the MMPI Scale 2 are well established, but the authors used indicators derived from these instruments, not the scales themselves. We cannot assume that the indicators assessed depression as accurately as the original scales. In fact, we don t know whether the derived scales are reliable. It is possible that the indicators actually did not tap syndromal depression, but instead they tapped a closely related factor such as negative affect, and thus are largely irrelevant to the question about the taxonicity of depression per se. 

The expectation value of H in the coherent state (7.17) can be evaluated explicitly for any Hamiltonian. However, an even simpler construction of Hd (valid to leading order in N) can be done (Cooper and Levine, 1989) by introducing intensive boson operators (Gilmore, 1981). In view of its simplicity, we report here this construction. If one divides the individual creation and annihilation operators by the square root of the total number of bosons, the relevant commutation relations become... 

Fmpirical methods can be applied in order to determine the validity of the BFT surface area. The derived standard isotherms can be obtained by normalization of the y-axis (volume adsorbed) of adsorption isotherms. It is strongly recommended that data should always be derived from standard isotherms related to a nonpor-ous sample of the same type of material. Various methods have been established like the as-method where the quantity of gas adsorbed V], is related to the value at a relative pressure of 0.4. In the t-plot, the vertical axis is normalized in relation to the average thickness of the adsorbed layer. The shape of the constructed reduced isotherms reveal the presence or absence of micropores and allows the determination of their volume [79, 80]. 

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