The phrase "a sound theoretical framework" is correct and usable in written English.
It can be used when discussing the basis or structure of a theory that is well-founded and reliable in academic or professional contexts.
Example: "In order to support our research findings, we need to establish a sound theoretical framework that underpins our hypotheses."
Alternatives: "a solid theoretical foundation" or "a robust theoretical structure".
The proposed methodology is rooted on a sound theoretical framework.
Therefore, designing tools is most successfully and efficiently accomplished by using a sound theoretical framework, informed by cognitive science and usability engineering.
Since JavaDON is rooted in a sound theoretical framework, it is well-suited for building even complex expert system applications, both stand-alone and Web-based ones.
He describes the two neuroimaging techniques used (positron emission topography, or PET, and functional magnetic resonance imaging, or fMRI), but makes it clear that techniques and machines do not provide interesting data without a sound theoretical framework.
Readers will be provided with a set of applicable guidelines in which to design, implement and evaluate highly usable digital environments within the context of a sound theoretical framework.
This research has important practical implications in that it highlights the value of developing an intervention based on a sound theoretical framework and which is aligned with the needs and beliefs of personnel within a particular organisation.
Instruments should be based on a sound, theoretical model of what they measure.
In particular, comparison is made between numerical results and some analytical prediction formulae available for fatigue damage evaluation; amongst all expressions, a new proposed original approach is shown to give sound theoretical framework and improved results.
The most sound theoretical framework for model comparison in a Bayesian framework is calculation of the Bayes factor (BF): B F = p (D | M 1 ) p (D | M 2 ) where p(D| M) is the marginal likelihood of model M, averaged over the model parameter values θ: p (D | M ) = ∫ P r (D | θ, M ) p (θ | M ) d θ So the BF is the ratio of the marginal likelihoods of the two models.
While all of the measures appear to have been well-validated and based on sound theoretical frameworks, some practical issues remain.
