Behavioral Assessment and the Functional Analysis

Author(s):  
Christine Maguth Nezu ◽  
Arthur M. Nezu ◽  
William O'Brien ◽  
Stephen N. Haynes ◽  
Joseph Keawe‘aimoku Kaholokula
2003 ◽  
Vol 19 (3) ◽  
pp. 204-209 ◽  
Author(s):  
Antonio Godoy ◽  
Aurora Gavino

Summary: In behavioral assessment, the strategy theoretically most coherent for case formulation is to carry out a functional analysis aimed at discovering, among other factors, functionally relevant stimuli acting upon the problem-behavior. However, little is known about the decision-making processes involved in this task. Although many authors have suggested prescriptive models for this process, the strategies used by clinicians when gathering information seem to be left to experience and common sense. The present research is an attempt to increase the knowledge about this process of information gathering. The study was carried out with psychology students in their final year who already had enough theoretical knowledge to gather this kind of information, but still lacked practical experience. Subjects were asked to gather information aimed at checking a hypothesis about the functional role on a given behavior of either an antecedent or a subsequent (i.e., reinforcing) stimulus. The results show that information gathered to test a reinforcing stimulus hypothesis is more homogeneous than information to test a hypothesis about a functionally relevant antecedent stimulus. The strategies used to test both types of hypotheses are different. In both instances, however, subjects more frequently gathered information useful to refute or refine the hypothesis than information useful to verify it.


1998 ◽  
Vol 14 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Stephen N. Haynes

Many clinical judgments affect treatment decisions in behavior therapy. For most patients psychiatric diagnosis is insufficient for behavioral treatment design because there are important between-patient differences in causal factors. Pretreatment behavioral assessment is necessary to insure the most effective treatment strategy. Behavioral treatment programs are often based on assessment-based judgments about patient behavior problems, goals, and causal variables. These judgments and the subsequent methods of assessment, however, are affected by the tenets of the respective behavioral assessment paradigm. Such tenets include the multimodal, conditional, and dynamic nature of behavior problems; the importance of behavior in the natural environment, reciprocal determinism; and multiple, dynamic, and between-person differences in causal factors. Behavioral, as opposed to nonbehavioral, assessment methods are often lower-level, less inferential, focus on situational factors, and emphasize observation and behavioral skills in the natural environment. The functional analysis is the integration of pretreatment assessment data on a patient. It identifies the important, controllable, causal relationships applicable to specified behaviors for an individual patient. The Functional Analytic Clinical Case Model is an efficient method of organizing and communicating about the functional analysis.


2020 ◽  
Author(s):  
Jordan D Bailey ◽  
Jonathan C Baker ◽  
Mark J. Rzeszutek ◽  
Marc Lanovaz

The Questions About Behavioral Function (QABF) has a high degree of convergent validity, but a gap exists between the results of the assessment and those obtained through experimental functional analysis. Machine learning (ML) can improve the validity of instruments by using data to build a mathematical model for more accurate predictions. We used published QABF and subsequent functional analyses to train ML models to identify the function of behavior. With ML models, predictions can be made from indirect assessment results based on learning from results of past functional analyses. In study one, we compared the results of two classification algorithms to the QABF criteria using a leave-one-out cross-validation approach. Both classifiers outperformed the QABF assessment on multi-label overall accuracy, but false negatives remained an issue. In study two, we augmented the data with 1,000 artificial samples to train and test an artificial neural network. The artificial network outperformed other models on all measures of accuracy. The results indicated that ML could be used to inform conditions that should be present in a functional analysis after more data are collected from the field.


2018 ◽  
Vol 43 (2) ◽  
pp. 316-321 ◽  
Author(s):  
Glen Dunlap ◽  
Lee Kern

In the 25 years since the publication of the article reprinted in this issue of Behavioral Disorders, a tremendous amount of research and opinion has been published on the topics of functional analysis, functional assessment, and assessment-based interventions. In this commentary, we reflect on the context in which our original work was conducted and briefly consider changes that have occurred and challenges that remain.


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