Validity (statistics) | EPFL Graph Search

Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below. In psychometrics, validity has a particular application known as test validity: "the degree to which evidence and theory support the interpretations of test scores" ("as entailed by proposed uses of tests"). It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating t

Ecological momentary assessment of emotional processing: An exploratory analysis comparing daily life and a psychotherapy analogue session

Hélène Beuchat, Mehdi Gholam, Loris Grandjean

Background: Emotional processing has been studied in psychotherapy as a state-dependent, sequential process of change. So far, no studies have applied this conceptualisation of emotional processing to the assessments of emotion in daily life. This is particularly important in the light of the pertinence of day-by-day fluctuations of emotions for understanding mental health and for monitoring the impact of prevention and psychotherapy programmes. This study examined the internal and ecological validity of a state-dependent conceptualisation of emotional processing in daily life, in comparison with an experiential-psychodynamic psychotherapy analogue session. Methods: In total, N = 42 university students participated in an experiential-psychodynamic session, completed symptom measures and responded to a one-week period of ecological momentary assessment (EMA) using a smartphone. Emotional processing in the session was assessed using the valid observer-rated measure Classification of Affective Meaning States (CAMS), and emotional responses in daily life were assessed using newly developed theory-consistent items self-rated via an interactive smartphone program. Results: Internal validity was generally satisfactory across the subscales used in EMA. Correspondence between EMA and in-session emotional processing was generally low, but specific relationships were found between self-rated fear, rejecting anger, hurt/grief or loneliness and the observer-rated productive emotions in the psychotherapy analogue session. Relationships between maladaptive emotional processing and intensity in symptoms were found. Conclusions: This is the first study to have examined the validity of a state-dependent conception of emotional processing in daily life, in direct comparison with a psychological session. We recommend using this assessment schedule to develop or complement integrative prevention or intervention programmes.

WILEY2021

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high-fidelity approximations of PDEs often result in a large number of degrees of freedom, the need for iterative evaluations for numerical optimizations and rapid feedback is computationally challenging.The first part of this thesis is devoted to conserving the high-dimensional equation's invariants, symmetries, and structures during the reduction process. Traditional reduction techniques are not guaranteed to yield stable reduced systems, even if the target problem is stable. In the context of fluid flows, the skew-symmetric structure of the problem entails the preservation of the kinetic energy of the system. By preserving the same structure at the level of the reduced model, we obtain enhanced stability, and accuracy and the reduced model acquires physical significance by preserving a surrogate of the energy of the original problem. Next, we focus on Hamiltonian systems, which, being driven by symmetry, are a source of great interest in the reduction community. It is well known that the breaking of these symmetries in the reduced model is accompanied by a blowup of the system energy and flow volume. In this thesis, geometric reduced models for Hamiltonian systems are further developed and combined with the dynamically orthogonal methods, addressing the poor reducibility in time of advection-dominated problems. The reduced solution is expressed as a linear combination of a finite number of modes and coincides with the symplectic projection of the high-fidelity Hamiltonian problem onto the tangent space of the approximating manifold. An error surrogate is used to monitor the approximation ability of the reduced model and make a change in the rank of the approximating system if necessary. The method is further developed through a combination of DEIM and DMD to reduce non-polynomial nonlinearities while preserving the symplectic structure of the problem and applied to the Vlasov-Poisson system.In the second part of the thesis, we consider several data-driven methods to address the poor accuracy in the under-resolved regime for Galerkin reduced models via a closure term. The closure term is developed systematically from the Mori-Zwanzig formalism by introducing projection operators on the spaces of resolved and unresolved scales, thus resulting in an additional memory integral term. The interaction between different scales turns out to be nonlocal in time and dominated by a high-dimensional orthogonal dynamics equation, which cannot be solved precisely and efficiently. Several classical methods in the field of statistical mechanics are used to approximate the memory term, exploiting the finiteness of the memory kernel support. We conclude this thesis by showing through numerical experiments how long short-term memory networks, i.e., machine learning structures characterized by feedback connections, represent a valid tool for approximating the additional memory term.

EPFL2022

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

EPFL2022

Ecological momentary assessment of emotional processing: An exploratory analysis comparing daily life and a psychotherapy analogue session

Hélène Beuchat, Mehdi Gholam, Loris Grandjean

WILEY2021