and short term loading. Creating a nomological net can also make the observation and measurement of existing constructs more efficient by pinpointing errors. Researchers have found that studying the bumps on the human skull (phrenology) are not indicators of intelligence, but volume of the brain is. Removing the theory of phrenology from the nomological net of intelligence and adding the theory of brain mass evolution, constructs of intelligence are made more efficient and more powerful. The weaving of all of these interrelated concepts and their observable traits creates a "net" that supports their theoretical concept. For example, in the nomological network for academic achievement, we would expect observable traits of academic achievement (i.e. GPA, SAT, and ACT scores) to relate to the observable traits for studiousness (hours spent studying, attentiveness in class, detail of notes). If they do not then there is a problem with measurement (of academic achievement or studiousness), or with the purported theory of achievement. If they are indicators of one another then the nomological network, and therefore the constructed theory, of academic achievement is strengthened. Although the nomological network proposed a theory of how to strengthen constructs, it doesn't tell us how we can assess the construct validity in a study. Multitrait-multimethod matrix The multitrait-multimethod matrix (MTMM) is an approach to examining construct validity developed by Campbell and Fiske (1959). This model examines convergence (evidence that different measurement methods of a construct give similar results) and discriminability (ability to differentiate the construct from other related constructs). It measures six traits: the evaluation of convergent validity, the evaluation of discriminant (divergent) validity, trait-method units, multitrait-multimethods, truly different methodologies, and trait characteristics. This design allows investigators to test for: "convergence across different measures...of the same 'thing'...and for divergence between measures...of related but conceptually distinct 'things'. Threats to construct validity Apparent construct validity can be misleading due to a range of problems in hypothesis formulation and experimental design. Hypothesis guessing: If the participant knows, or guesses, the desired end-result, the participant's actions may change. An example is the Hawthorne effect: in a 1925 industrial ergonomics study conducted at the Hawthorne Works factory outside Chicago, experimenters observed that both lowering and brightening the ambient light levels improved worker productivity. They eventually determined the basis for this paradoxical result: workers who were aware of being observed worked harder no matter what the change in the environment. Bias in experimental design (intentional