Although characteristic and profile agreement are both legitimate methods of assessing self-adaptation, there is very little empirical or theoretical information on how these two forms of agreement are related. Common sense, but not as many empirical studies, would indicate that the agreement of characteristics and profile has something in common. Generally, as noted above, average values are in the same range (0.40 or more). On the other hand, there is ample evidence that these two forms of agreement have significantly different interpretations, implying that they are not interchangeable (Bernieri et al., 1994; Kenny and Winquist, 2001; Connelly and Ones, 2010). The idea that characteristic and profile agreements cannot overlap significantly is also reinforced by easy-to-build examples of an obvious separation between these two forms of agreement. It is easy to imagine an artificial example where a correlation between two personality profiles is zero. For example, although it is unlikely that scores are identical to all personality traits, it is still possible and perfectly compatible with many personality patterns (Allik et al., 2012). If this were to happen, there would be no deviations in these personality profiles, and therefore, a self-difference agreement is zero. At the same time, targets and their informants may report very similar or even identical levels of these personality characteristics, leading to high characteristic correlations. Conversely, it is possible to present correlations of characteristics that differ only significantly from zero, based on a considerable number of dyads that report similar profiles. In summary, these two approaches – person-centred and variable geometry – have sometimes shown similar and sometimes different results (Furr, 2009, p. 203).

The effects of differences in motivation for the construction of intelligence tests performed under low conditions have been examined occasionally over the past 70 years [88,89,90,91,92], but research in this area has intensified over the past 20 years [27,93]. Some contemporary lines of study have attempted to demonstrate the influence of effort on test performance when use is low inducing experimental motivation. The means of induction of effort were different between studies, but include the manipulation of motivational frameworks (for example.B. “Scores are made available to employers”; [26]), monetary policy incentives [94], public recognition of students for their test results [95] and feedback on performance [96]. Other studies have used non-experimental methods to study stress measurement, such as self-reporting motivation measurement [97], observational coding [98], filtering subjects with extreme reaction times [99] and using people`s adaptive statistics to detect unusual patterns of reaction [100].