Theoretical Foundations and Clinical Implications of Formal Psychological Assessment
Authors: Andrea Spoto, Gioia Bottesi, Ezio Sanavio, Giulio Vidotto
Journal: Psychotherapy and Psychosomatics (2013)

The quality of the initial assessment represents one of the predictors of positive therapeutic outcomes, thus an increasing amount of research focuses on the development of more accurate assessment tools [1].In clinical practice, the most widely adopted tools for assessment are clinical interview, semistructured interviews and psychological testing. Clinicians follow two main criteria in developing the sequence of questions to ask: (1) choosing questions to collect information for both diagnosis and case formulation and (2) trying to reduce redundancy. These criteria are not satisfied by self-report questionnaires because of methodological problems deriving from adopting a psychometric approach in psychodiagnosis. Since questionnaires mainly provide quantitative information (scores), they are unsuitable for collecting structured qualitative information. Furthermore, psychometric theory emphasizes the importance of obtaining reliable measures even through redundancy [2].Thus, having an assessment methodology that is as informative as a clinical interview, as time consuming as a psychological test, able to collect information useful for case formulation, reliable, valid, adaptive and nonredundant, is needed.Clinimetrics is an attempt to address this request, focusing on the quality of clinical measurements [3]. Formal psychological assessment (FPA) [4] can integrate the principles of clinimetrics through formal representation. FPA is the formal conjunction and the clinical application of two mathematical psychology theories: formal concept analysis [5] and knowledge space theory [6].The present paper briefly introduces FPA foundations and explores the main implications following from its use in clinical assessment.Consider item i2 (table 1) of the Maudsley Obsessional-Compulsive Questionnaire-Reduced (MOCQ-R) [7]. In FPA, i2 is an 'object', defined on the basis of a given theoretical framework, which could be arbitrarily chosen without effect on the application of the methodology, thus resulting in extreme flexibility. If the DSM-IV-TR [8] is selected, one could check the diagnostic criteria for a disorder investigated through i2. Each criterion is then defined as an 'attribute'. In the case at hand, the diagnostic criterion for obsessive-compulsive disorder a8 (table 1) can represent an attribute investigated by i2. Starting from a set of objects (MOCQ-R items) and a set of attributes (the DSM-IV-TR criteria for obsessive-compulsive disorder), a Boolean matrix ('formal context') is built. From a deterministic perspective, whenever an item investigates the specific symptom, 1 is reported in the cell that, otherwise, would contain 0. Defining the whole set of items as the 'domain' of the formal context, the 'clinical state' of a patient is the subset of the domain he/she affirmatively answers. Each clinical state is uniquely defined by a subset of attributes. Therefore, even if two different clinical states may score the same (2 different patients affirmatively answer the same number of items), their attribute configurations systematically differ.The formal context is used to build the 'clinical structure', i.e. the set representation of the implications among the items of the domain [4,9], including all the 'admissible response patterns' (i.e. the clinical states) and their respective set of attributes. An admissible response pattern is a set of items consistent with the implications postulated by a theoretical framework. For example, answering 'true' to item 20 ('I get into a lot of physical fights') of the Personality Diagnostic Questionnaire-4+ [10] reasonably implicates answering 'true' to item 12 ('Sometimes I get upset'), whereas the inverse implication may not occur.To summarize, FPA both deterministically maps sets of items to sets of attributes, by investigating the cognitive processes involved in answering behavior, and identifies and uses implications among items to implement an adaptive methodology. Thus, FPA offers the possibility to adaptively collect the same information of a clinical interview through the administration of self-report questionnaires. Nonetheless, a deterministic approach is inadequate in clinical practice, since neither the observed answers necessarily convey the exact information about the patient nor do all clinical states have the same probability to occur. Therefore, a probabilistic model taking into account both these issues is needed. The basic local independence model [6] addresses them by introducing 'false negative' (β) and 'false positive' (η) rates for each item.The application of FPA to obsessive-compulsive disorder, as described by the DSM-IV-TR and assessed by the 'cleaning' subscale of the MOCQ-R [7], is used to illustrate its implications in clinical assessment.The model was tested on 4,412 subjects (3,775 males and 637 females), aged 21-65 years. Participants gave written informed consent to enter the study.The first step of the procedure was to construct the deterministic model, i.e. the formal context. The list of items and attributes is displayed in table 1.The second step of the procedure involved the construction of the clinical structure from the context. An iterative expectation-maximization procedure [11] was used to estimate parameters (β, η, attribute probabilities) and fit indexes of the model. Results (table 1) showed an acceptable model fit [χ2(212) = 340.74; bootstrap-p > 0.10]. All of the available indexes provide information potentially usable to improve both items and model, thus representing a strong potential of FPA.FPA aims to formally fill the existing gap between questionnaire scores and collections of symptoms producing them; otherwise, no formal objective methods to detect the clinical elements presented by patients answering a questionnaire are available. As argued by Tomba and Bech [3], 2 patients, even though they may obtain different scores to an instrument, may be diagnosed with the same disorder. Furthermore, 2 patients may score the same, but belong to different clinical states. This second issue is what FPA focuses on, by referring to the microanalysis [3] conducted on the symptoms involved in the affirmatively answered items. Such analysis is formally guaranteed by the link between the formal context (attributes assignment for each item), and the observed response pattern. Thus, answering items (a) and (b) rather than (c) and (d) generates two different symptom configurations even if resulting in the same score. FPA allows the use of self-report information to detect differences among patients, thus providing an in-depth evaluation of the investigated construct and of the cues potentially useful in subsequent treatment phases leading to more targeted interventions. Furthermore, FPA offers the possibility to move beyond the concept of cutoff scores, which is relevant for recovery: treatment outcomes may be evaluated in terms of 'loss of attributes'. Lastly, these structures can be transferred into an algorithm for adaptive psychological assessment, allowing for both the collection of wide-spectrum information and the performance of an accurate investigation of different disorders and comorbidities. The correctness of the inferences carried out by such an algorithm is ensured by the mathematical structure of FPA.Despite FPA's strengths, to date some enhancements are still required in regard to understandability of FPA mathematical foundations, improvement of number and quality of fit indexes to test the models, and extension of FPA to polytomous items.The authors would like to thank Giorgio Bertolotti and Anna Maria Zotti for their support and encouragement.