On the Implementation of Computerized Adaptive Observations for Psychological Assessment

The use of observational tools in psychological assessment has decreased in recent years, mainly due to its personnel and time costs, and researchers have not explored methodological innovations like adaptive algorithms in observational assessment. In the present study, we introduce the behavior-driven observation procedure to develop, test, and implement observational adaptive instruments. In Study 1, we use a preexisting observational checklist to evaluate nonverbal behaviors related to psychotic symptoms and to specify the adaptive algorithm’s model. We fit the model to observational data collected from 114 participants. The results support the model’s goodness of fit. In Study 2, we use the estimated model parameters to calibrate the adaptive procedure and test the algorithm for accuracy and efficiency in adaptively reconstructing 58 nonadaptively collected response patterns. The results show the algorithm’s good accuracy and efficiency, with a 40% average reduction in the number of administered items. In Study 3, we used real raters to test the adaptive checklist built with behavior-driven observation. The results indicate adequate intrarater agreement and good consistency of the observed response patterns. In conclusion, the results support the possibility of using behavior-driven observation to create accurate and affordable (in terms of resources) observational assessment tools.

A novel approach for solving CNOPs and its application in identifying sensitive regions of tropical cyclone adaptive observations

In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbations (CNOPs), called the adaptive cooperative coevolution of parallel particle swarm optimization (PSO) and the Wolf Search algorithm (WSA) based on principal component analysis (ACPW). Taking Fitow (2013) and Matmo (2014), two tropical cyclone (TC) cases, CNOPs solved by the ACPW algorithm are used to investigate the sensitive regions identified by TC adaptive observations with the fifth-generation Mesoscale Model (MM5). Meanwhile, the 60 and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ method) is also applied to solve CNOPs, and the result is used as a benchmark. To evaluate the advantages of the ACPW algorithm, we run the PSO, WSA and ACPW programs 10 times and then compare the maximum, minimum and mean objective values as well as the RMSEs. The analysis results prove that the hybrid strategy and cooperative coevolution are useful and effective. To validate the ACPW algorithm, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. The results of our study may be summarized as follows: The ACPW algorithm can capture similar CNOP patterns as the ADJ method, and the patterns of TC Fitow are more similar than TC Matmo. At the 120 km resolution, similarities between the CNOPs of the ADJ method and the ACPW algorithm are more than those at the 60 km resolution. Compared to the ADJ method, although the CNOPs of the ACPW method produce lower energies, they can have improved benefits gained from the reduction of the CNOPs not only across the entire domain but also in the identified sensitive regions. The sensitive regions identified by the CNOPs from the ACPW algorithm have the same influence on the improvements of the skill of TC-track forecasting as those identified by the CNOPs from the ADJ method. The ACPW method is more efficient than the ADJ method. All conclusions prove that the ACPW algorithm is a meaningful and effective method for solving CNOPs and can be used to identify sensitive regions of TC adaptive observations.

A Novel Approach for Solving CNOP and its Application in Identifying Sensitive Regions of Tropical Cyclone Adaptive Observations

In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbation (CNOP), named it adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm (ACPW) based on principal component analysis. Taking Fitow (2013) and Matmo (2014) as two tropical cyclone (TC) cases, CNOP solved by ACPW is used to investigate the sensitive regions identification of TC adaptive observations with the fifth-generation mesoscale model (MM5). Meanwhile, the 60 km and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ-method) is also applied to solve CNOP, and the result is used as a benchmark. To validate the validity of ACPW, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. (1) The ACPW can capture similar CNOP patterns with the ADJ-method, and the patterns of TC Fitow are more similar than TC Matmo. (2) When using the 120 km resolution, similarities between CNOPs of the ADJ-method and ACPW are higher than those using the 60 km. (3) Compared to the ADJ-method, although the CNOPs of ACPW produce lower energies, they can obtain better benefits gained from the reduction of CNOPs, not only in the entire domain but also in the sensitive regions identified. (4) The sensitive regions identified by CNOPs-ACPW has the same influence on the improvements of the TC tracks forecast skills with those identified by CNOPs-ADJ-method. (5) The ACPW has a higher efficiency than the ADJ-method. All conclusions prove that ACPW is a meaningful and effective method for solving CNOP and can be used to identify sensitive regions of TC adaptive observations.