Construct validation process

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... 

By using multiple sources for data collection, repeatedly confronting the data with multiple actors (operators, managers, engineers) in the operational process and updating the data, triangulation takes place to address the construct validity and to validate the retrieved data. 

As the initial step, a thorough and complete conceptualization of the process, and the steps that will be taken to achieve the desired results should be defined in a protocol. This document is to be used as the basis of communication with design and mechanical engineers in developing specifications for an effective and workable design, to be constructed, validated, and operated within existing cost constraints. 

The cleanroom should be conceptualized, designed, constructed, validated, operated, and maintained in a manner that supports the aseptic process. A construction plan, including reasonable time frames for the acquisition of labor and materials should be developed, followed, and updated. All work and materials should comply with local building codes and safety ordinances. 

The development of an alternative method begins with the creation of a test followed by generation of a database that supports its utility. This work provides the preliminary evidence that a method is reproducible and has predictive capability. Once available, this information can be used to construct a prediction model that describes how to convert the results from an alternative method into predictions of toxicity in vivo. When a method has been technically advanced to this point, it may be assessed in the validation process. 

So, two questions. First, what exactly is the construct validation validating Is it some claim about intelligence, and if so what Second, is it possible the whole process could misleadingly appear to reify a notion of intelligence that is not in fact as rich or broad as that people apply in everyday life ... 

Presently, validity is represented as a process whereby we determine the degree of confidence we can place on inferences we make about people based on their scores from that scale. Some different types of validity, a discussion of which is beyond the scope of this chapter, are called content validity, criterion validity, and construct validity. 

The development of a qualified down-scale model of a process module is integral to the approach of process validation using bench-scale experiments, as described earlier. We have developed down-scale models of process steps ranging from various types of process chromatography for protein purification to separation by precipitation and filtration. These down-scale models have been utilized to evaluate the effects of relevant process parameters on product-quality attributes. The normal logical sequence of process development, of course, is bench scale to pilot scale to full scale. However, for many plasma protein purification processes, a reverse order needs to be followed. As licensed full-scale processes already exist, the full-scale process steps need to be scaled down to construct small process models in order to evaluate the robustness of process parameters on the product without impacting full-scale production. These models can also be utilized to evaluate process changes, improvements, and optimizations easily and economically. 

A further step in the model validation process is to assess the stability of the model parameter estimates over the sample space. If the model has been constructed using observations taken over a long time span, one can test the stability of the estimated parameters by fitting the model to data concerning shorter time spans and determining the pattern of successive estimates of the parameters. If the estimated model parameters were not stable and, for example, showed trend-like patterns, use of the parameter estimates obtained fi om fitting all the data for predicting concentrations would be unwise. 

As previously discussed, the preliminary scope defines the major components of the API facility. The scope will have identified key processing steps, all associated equipment, and any facility requirements. The preliminary scope will be a key document in communicating to outside design firms the intent of the facility(s) and the overall process intent of the project. The API manufacturer must then decide how they want to manage critical steps of the design, construction, validation, and start-up process. 

Secondly, this study provides the adaptation reports of two well-known instruments (i.e., RTOP and mFSMAS) with respect to their reliability and construct validity measures. The adaptation process refers to context-based, Physical Chemistry, and language-based, Turkish, modifications. 

Aljer and Devienne [5] consider the use of a formal specification language as the foundation of real validation process. They propose architecture based upon stepwise refinement of a formal model to achieve controllable implementation. Partitioning, fault tolerance, and system management are seen as particular cases of refinement in order to conceptualize systems correct by proven construction. The methodology based on the refinement paradigm is described. To prove this approach, the B-HDL tool based on a combination of VHDL and B method formal language has been developed. 

As we have seen the safety system project requires strong emphasis on QA and validation processes. There is a risk that project time and construction pressures will work against doing the job in a nice methodical and orderly manner. Hence the planning of the installation and commissioning work is a line of defense against these pressures. 

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