Performance Validation Research Articles

Pass/failure on the memory validity profile: one size does not fit all

ABSTRACT This study sought to determine the influence of various demographic variables on pass/failure on a pediatric performance validity test, the Memory Validity Profile (MVP) in a mixed clinical sample (n = 393; 62% male, 62% White, median age 11 years). Children who failed the MVP (n = 72, 18%) according to a uniform cutoff of ≤30/32 correct were younger and were more likely to have a special education history than those who passed it (n = 321, 82%). There were no statistically significant group differences on other variables such as sex, race, parental education, history of treatment for ADHD or other psychiatric disorder. Possible false positive findings were relatively most common in children under the age of 10 years and in children who received special education services under the Physical/Other Health Impairment or Speech & Language Impairment qualifications. We conclude that a uniform cutoff for pass/failure on the MVP across any and all ages and diagnostic categories is ill-advised. Instead, we recommend the development of various adjusted cutoffs for this test that maintain 90% specificity at both ends of the age spectrum as well as with different medical or developmental conditions.

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images

Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and can save lives. BT segmentation from Magnetic Resonance Images (MRI) is considered a big challenge owing to the difficulty of BT tissues, and segmenting them from the healthier tissue is challenging when manual segmentation is done through radiologists. Among the recent proposals for the brain segmentation method, the BT segmentation method based on machine learning (ML) and image processing could be better. Thus, the DL-based brain segmentation method is extensively applied, and the convolutional network has better brain segmentation effects. The deep convolutional network model has the problem of a large loss of information and a large number of parameters in the encoding and decoding processes. With this motivation, this article presents a new Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique on MRI images. The DTLSS-MIA technique aims to segment the affected BT area in the MRI images. At first, the presented method utilizes a Median filtering (MF) approach to optimize the quality of MRI images and remove the noise. For the semantic segmentation method, the DTLSS-MIA method follows DeepLabv3 + with a backbone of the EfficientNet model for determining the affected brain region. Moreover, the CapsNet architecture is employed for the feature extraction process. Lastly, the crayfish optimization (CFO) technique with diffusion variational autoencoder (D-VAE) architecture is used as a classification mechanism, and the CFO technique effectively tunes the D-VAE hyperparameter. The simulation analysis of the DTLSS-MIA technique is validated on a benchmark dataset. The performance validation of the DTLSS-MIA technique exhibited a superior accuracy value of 99.53% over other methods.

Examining the Discrepancy between Subjective Cognitive Complaints and Processing Speed Performance in Military Personnel with Traumatic Brain Injury.

To examine correlates of the discrepancy between subjective cognitive complaints and processing speed performance in a sample of military personnel with and without traumatic brain injury (TBI). About 235U.S. military service members (31 noninjured controls [NIC], 69 injured controls [IC], 70 uncomplicated mild TBI [mTBI], and 65 complicated mild/moderate/severe TBI [sTBI]) prospectively enrolled in a longitudinal TBI study completed neuropsychological testing, performance validity tests, and self-report measures of cognitive complaints and psychological symptoms. Service members were categorized as "Accurate Estimators," "Underestimators," and "Overestimators" based on discrepancies between their subjective cognition and processing speed performance. The NIC group was less likely to underestimate their cognitive abilities than the mTBI group (p < .05). Discrepancy groups significantly differed in processing speed scores (p < .001), with underestimators demonstrating the best objective cognitive performance. Spearman correlations revealed significant positive correlations between unadjusted discrepancy scores and psychological symptoms in the NIC, IC, and sTBI groups (ps < 0.05) but not the mTBI group (ps > 0.05). In contrast, discrepancy scores adjusted for premorbid intelligence were consistently and positively correlated with psychological symptoms across all injury groups (ps < 0.05). Findings suggest that mTBI injuries may increase the likelihood of a patient underestimating their cognitive performance. Further, premorbid cognitive functioning is an important factor in evaluating discrepancies in self-reported cognitive complaints and processing speed performance.

Design and Uncertainty Evaluation of a Calibration Setup for Turbine Blades Vibration Measurement

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as an advanced alternative to traditional measurement methods, capturing blade dynamics by detecting deviations in blade tip arrival times through sensors mounted on the stator casing. This research focuses on developing an analytical model to quantify the uncertainty budget involved in designing a calibration setup for BTT systems, ensuring targeted performance levels. Unlike existing approaches, the proposed model integrates both operational variability and sensor performance characteristics, providing a comprehensive framework for uncertainty quantification. The model incorporates various operating and measurement scenarios to create an accurate and reliable calibration tool for BTT systems. In the broader context, this advancement supports the use of BTT for qualification processes, ultimately extending the lifespan of turbomachinery through condition-based maintenance. This approach enhances performance validation and monitoring in power plants and aircraft engines, contributing to safer and more efficient operations.

Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers

Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.

Validation of ACR TI-RADS performance in transition age: results from a multicenter retrospective study by the TALENT study group.

Although thyroid nodules are less common in the pediatric population, the risk of malignancy is higher than in adult patients. The aim of this study was to evaluate the ultrasonographic predictive factors of malignancy in thyroid nodules and to validate American College of Radiologists (ACR) TI-RADS performance in transition age patients. One hundred forty-two patients aged between 14 and 21 years referred to the participating centers for FNA biopsy of a thyroid nodule between 2007 and 2022 were included and ultrasound reports and sonographic images were retrospectively analyzed. Nodule features were defined according to the ACR-TIRADS lexicon. Two reference standards were applied: FNA cytology and surgical histology. The diagnostic performance of single sonographic features was estimated. Significant predictors were then included in a multivariate regression model. Nodules included in ACR-TIRADS categories TR4 or TR5 had 10-fold increased risk of indeterminate or suspicious/malignant cytology [p < 0.001]. In univariate analysis, solid composition [p = 0.016] and presence of hyperechoic foci [p = 0.040] significantly increased the likelihood of malignant histology. In multivariate regression analysis, irregular margins [p = 0.011] and hyperechoic foci [p = 0.019] were independent predictors of indeterminate or suspicious/malignant cytology. Nodules included in ACR-TIRADS categories TR4 or TR5 had 10-fold increased risk of indeterminate or suspicious/malignant cytology in transition age. ACR-TIRADS was not able to rule-out malignancy compared to FNAB alone, suggesting the need to reconsider recommendations in the transition age group.

Planning falsifiable confirmatory research.

Falsifiable research is a basic goal of science and is needed for science to be self-correcting. However, the methods for conducting falsifiable research are not widely known among psychological researchers. Describing the effect sizes that can be confidently investigated in confirmatory research is as important as describing the subject population. Power curves or operating characteristics provide this information and are needed for both frequentist and Bayesian analyses. These evaluations of inferential error rates indicate the performance (validity and reliability) of the planned statistical analysis. For meaningful, falsifiable research, the study plan should specify a minimum effect size that is the goal of the study. If any tiny effect, no matter how small, is considered meaningful evidence, the research is not falsifiable and often has negligible predictive value. Power ≥ .95 for the minimum effect is optimal for confirmatory research and .90 is good. From a frequentist perspective, the statistical model for the alternative hypothesis in the power analysis can be used to obtain a p value that can reject the alternative hypothesis, analogous to rejecting the null hypothesis. However, confidence intervals generally provide more intuitive and more informative inferences than p values. The preregistration for falsifiable confirmatory research should include (a) criteria for evidence the alternative hypothesis is true, (b) criteria for evidence the alternative hypothesis is false, and (c) criteria for outcomes that will be inconclusive. Not all confirmatory studies are or need to be falsifiable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep semantic segmentation and skin disease classification fromdermoscopic images based on a modernized deep learning network with multi-feature extraction

ABSTRACT In this research, an advanced deep learning model is proposed to segment and classify skin diseases. The skin images required for classification purposes are collected from public databases. Skin lesion segmentation is initially performed over collected images using Multi-head Attention-based MobileUNet, which helps locate lesions in dermoscopic images. The segmented images are fed to the feature extraction stage to get more representative features from the images to improve the classification performance. Features like texture, shape, and colour are extracted in this stage. The extracted features are given to the Adaptive Deep Capsule Network to classify the images. The segmented images are directly given to the Adaptive MobileNet to provide classification results. The final decision is made by averaging the classified outcome using the Adaptive Hybrid Network. The performance of classification is further enhanced by tuning the parameters from DCNet and MNet using the Enhanced Secretary Bird Optimisation. The performance validation finally shows the accurate performance in the skin disease classification model.

Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.

Hybrid energy management strategy based on neural networks robust to fluctuations in operational load

As global regulations on carbon neutrality tighten, the maritime industry, led by the IMO, has set a goal of achieving net-zero emissions by 2050. The electrification of ship propulsion systems is consequently accelerating. Hybrid electric propulsion ships, which combine internal combustion engines with auxiliary power sources, offer a practical solution by improving fuel efficiency and reducing emissions. However, heuristic or pre-trained energy management strategies designed for fixed conditions often struggle under fluctuating loads. This study addresses these limitations by proposing a hybrid energy management strategy that generates probabilistic operational profiles based on representative load data. A neural network controller, trained on nine global control solutions for newly generated operation cycles using Markov chain methods, maintains approximately 37% engine thermal efficiency even under variable loads. A performance validation showed that the proposed strategy resulted in 0.56% higher fuel consumption compared to the global solution, while still supporting implementation with each time step calculation taking approximately 0.072 ms, achieving near real-time performance. This small deviation highlights the trade-off between time-efficient applicability and near-optimal efficiency. By overcoming the limitations of fixed-condition strategies, this research offers a robust and adaptive approach for hybrid propulsion systems, making it suitable for immediate use in eco-friendly ships.

Comparative assessment of commercial ELISA kits for the screening of chloramphenicol residues in meat and aquaculture products according to European Regulation (EU) 2021/808 and to the new Reference Point for Action (Commission Regulation (EU) 2019/1871)

This study covered the evaluation of performance characteristics and validation of five commercial ELISA kits for the detection of a banned antimicrobial, chloramphenicol (CAP), in muscle and aquaculture products. CAP has been banned in the European Union since 1994, but is still authorised in some countries in the world. In 2019, the European Union set a new Reference Point for Action (RPA), decreasing the acceptable limit of CAP in animal tissues for human consumption from 0.30 µg/kg to 0.15 µg/kg. Validations were performed according to the European Regulation EC/2021/808 and to the European Guideline on Screening Method Validation (2023). The detection capabilities CCβ were all determined under the RPA, but were 3 to 15 times higher than the announced limit of detection (LOD). False negative rates were satisfactory for all the kits (≤ 5%) and false positive rates were acceptable. All of them were found out to be applicable to aquaculture products and meat at a common CCβ.

Construct Validity of Adaptive Performance: A Case of Malaysian Lecturers

The virtual mode of the classroom that changes from the traditional education model poses challenges among the educators especially the inability to cope with the adaption of educational technologies. Online literacy is the basic skill to exercise an outstanding performance during changing environments. However, the conceptualization of adaptive performance seeks further exploration mainly because the past studies focus on the specific context. This study aims to present the validity and reliability of the adaptive performance construct related to online teaching platforms among Malaysian public university lecturers. The questionnaire was designed using Google Form and the adopted questionnaire was emailed to lecturers of Malaysian Research Universities (henceforth, RUs) via their institution’s email address. The data were then analyzed using the SmartPLS software through the measurement model analysis. The analysis involved a second-order approach where the outer loading values, the convergent validity of Average Variance Extracted (henceforth, AVE), and reliability analysis of the adaptive performance construct. The findings suggest that the validity and reliability of the construct were established for the context of the study. The Cronbach Alpha and composite reliability values scored above the threshold values. This study confirmed the adaptive performance instrument among the lecturers in Malaysia and can provide insight for future research. The limitation includes the sample of the study that was rather homogenous which it only focused on lecturers from Malaysian RUs.

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier

The fast improvement of cyberattacks in the area of the Internet of Things (IoT) presents novel safety challenges to zero-day attacks. Intrusion detection systems (IDS) are generally focused on exact attacks to defend the use of IoT. However, the attacks were unidentified, for IDS still signifies tasks and concerns about consumers’ data privacy and safety. Anomaly-detection models are generally based on machine learning (ML) models. Conventional ML-based models have been recognized to have low estimate excellence and recognition rates. DL-based models, particularly convolutional neural networks (CNN) with regularization techniques, direct this problem, offer a superior prediction value with unidentified data, and prevent over-fitting. This manuscript presents a Binary Snake Optimizer with DL-Enabled Zero-Day Attack Detection and Classification (BSODL-ZDADC) method. The objective of the BSODL-ZDADC method is to employ metaheuristics with the DL method for enhanced recognition and classification of zero-day attacks. For data normalization, the BSODL-ZDADC method uses a Z-score normalization approach. To reduce the high dimensionality issue and improve the classification results, the BSODL-ZDADC technique designs a BSO method to choose a set of related features. Besides, the attention-based bidirectional gated recurrent unit (ABi-GRU) method helps recognize zero-day attacks. Since the hyperparameters play a vital part in the classification performance, the BSODL-ZDADC technique employs an improved sparrow search algorithm (ISSA). The experimental validation of the BSODL-ZDADC technique is verified by utilizing the ToN-IoT dataset. The performance validation of the BSODL-ZDADC technique portrayed a superior accuracy value of 98.28% over other models.

Magnetic MXene chitosan-lignosulfonate composite (Fe3O4@ MCLS) for the reductive removal of Cr(VI) and other heavy metals from water

In this study, magnetic MXene (Ti3C2Tx) chitosan-lignosulfonate composite (Fe3O4@MCLS), was synthesized based on the facile integration of Fe3O4, chitosan-lignosulfonate (CLS) nanospheres and delaminated (DL) Ti3C2Tx. This composite was designed to integrate the biocompatibility of CLS and selective adsorption of MXene with the benefit of magnetic separation. Characterization confirmed the successful stabilization of the magnetic chitosan-lignosulfonate on the MXene surface, resulting in multiple surface functionalization groups and a high specific surface area. Fe3O4@MCLS was initially tested for the removal of Cr(VI) in a batch-system, achieving 90% efficiency and a capacity of 42.5 mg/g at neutral pH. Adsorption kinetics followed the Pseudo-second-order model, and equilibrium data fit the Langmuir isotherm, indicating a monolayer adsorption mechanism. The composite demonstrated high selectivity towards Cr(VI) ions and improved magnetic recovery from the media. The results suggested prevalent adsorption mechanisms included electrostatic interactions, complexation, surface intercalation, and reduction of toxic Cr(VI) to Cr(III) on the composite adsorbent. Further validation of the composite's performance was carried out through a competitive adsorption study in a multi-metal system. The results showed that the composite effectively removed heavy metals, exhibiting varying affinities for different metal ions, following the trend: Cr(VI) > Ni(II) > Cu(II) ≈ Co(II) under neutral pH conditions. Overall, the present study demonstrates the facile preparation of a new composite material, which exhibits sustainable characteristics due to the incorporation of chitosan-lignosulfonate and iron oxide. This eco-friendly and recyclable composite shows significant potential for application in water treatment.

Optimal battery management in PV + WT micro-grid using MSMA on fuzzy-PID controller: a real-time study

In modern energy systems, managing energy within a microgrid (MG) poses significant challenges due to the unpredictable nature of renewable energy sources. This article introduces a novel approach for optimal battery management in a photovoltaic–wind microgrid using a Modified Slime Mould Algorithm (MSMA) combined with a fuzzy-PID controller. The microgrid comprises a wind turbine (WT) generator, solar photovoltaic (PV) generator, and a battery energy storage system (BESS). The BESS plays a crucial role in meeting high power demand during outages, while the fuzzy-PID controller ensures accurate prediction of the battery’s state of charge (SOC). The proposed method’s performance is evaluated by comparing the MSMA-based fuzzy-PID controller with a PSO-based fuzzy-PID controller to establish its effectiveness. The optimal energy management of the BESS in the microgrid is achieved by fine-tuning the fuzzy-PID controller using the MSMA algorithm. Simulation results demonstrate that the battery management system (BMS) effectively optimizes charging and discharging based on renewable energy availability and load demand. The fuzzy-PID controller adjusts battery operation by minimizing the error between the desired and actual battery voltage. Performance validation has been conducted in RTS-lab using five distinct load scenarios—45 kW, 35 kW, 75 kW, 4.5 kW, and 12.5 kW, which confirming the effectiveness of the proposed control strategy for energy management.

Once is enough! An analogue study on repeated validity assessment in adults with ADHD

Performance validity tests (PVTs) can be seen as gatekeepers for valid neuropsychological assessment, by marking cognitive test scores that may not reflect true ability levels. The present study explored the significance of repeated validity testing of adults with attention-deficit/hyperactivity disorder (ADHD), by exploring the potential value of performance consistency across assessments. The operational definition of performance consistency was determined by calculating the mean variation in a participant’s PVT scores across three separate assessments. Neuropsychological test data of 24 individuals diagnosed with ADHD were complemented by an analogue study involving 69 typically developing individuals who were allocated to either a control group or a simulation group instructed to feign ADHD. All individuals were assessed with embedded and stand-alone PVTs three times with one-month intervals between each assessment. The rate of failed validity testing remained rather stable across assessments. Significant differences in neuropsychological performance scores occurred between individuals with ADHD and experimental simulators, however, mostly nonsignificant effects of small size emerged when considering performance consistency. Our data demonstrate that the consistency of cognitive performance over repeated assessments may be no effective approach to complement validity assessment. Replication is needed in independent research on larger samples.

Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics

Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.

A new compact potentiometric electrode for pH monitoring built upon a glass substrate with a Ce-doped SnO2 layer.

A novel compact potentiometric electrode specifically designed for pH monitoring, featuring a good construction on a glass substrate coated with a cerium-doped tin oxide (Ce-doped SnO2) layer. The Ce-doped SnO2 thin film was created by spray-pyrolysis it on a glass substrate. Field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) were used to characterize the deposited metal oxide coating. As a miniature potentiometric electrode, the synthesized Ce-doped SnO2/glass substrate was utilized to measure a broad pH range (pH 2-12) in aqueous solutions. The electrode had a perfect near-Nernstian response (slope of -58.6 ± 0.7 mV per decade), high potential stability, mechanical durability, and great selectivity towards some common interfering cations and anions. These characteristics made it ideal for quality control and assurance purposes. The electrode's performance parameters and validation measurements were assessed using established procedures. The Ce-doped SnO2-based electrode satisfactorily monitored the pH of several genuine water, drink, and fruit juice samples, and the data compared well with those obtained using a traditional pH glass electrode. The integration of Ce-doped SnO2 as the active material marks a significant advancement, providing enhanced electrochemical stability, improved sensitivity, and a wider pH detection range compared to conventional electrodes. The compact design not only reduces the sensor's footprint but also facilitates its application in miniaturized and portable pH monitoring devices, making it highly suitable for advanced analytical and environmental sensing applications.

Do MMPI-3 Validity Scale Findings Generalize to Concurrently Administered Measures? Validation with a Forensic Disability Sample.

Research has demonstrated that over-reporting and under-reporting, when detected by the MMPI-2/-RF Validity Scales, generalize to responses to other self-report measures. The purpose of this study was to investigate whether the same is true for the Minnesota Multiphasic Personality Inventory-3 (MMPI-3) Validity Scales. We examined the generalizability of over-reporting and under-reporting detected by MMPI-3 Validity Scales to extra-test self-report, performance-based, and performance validity measures. The sample included 665 majority White, male disability claimants who, in addition to the MMPI-3, were administered several self-report measures, some with embedded symptom validity tests (SVTs), performance-based measures, and performance validity tests (PVTs). Three groups were identified based on MMPI-3 Validity Scale scores as over-reporting (n = 276), under-reporting (n = 100), or scoring within normal limits (WNL; n = 289). Over-reporting on the MMPI-3 generalized to symptom over-reporting on concurrently administered self-report measures of psychopathology and was associated with evidence of over-reporting from other embedded SVTs. It was also associated with poorer performance on concurrently administered measures of cognitive functioning and PVTs. Under-reporting on the MMPI-3 generalized to symptom minimization on collateral measures of psychopathology. On measures of cognitive functioning, we found no differences between the under-reporting and WNL groups, except for the Wisconsin Card Sorting Test-64 Card Version and Wide Range Achievement Test-Fifth Edition (each with negligible effect sizes). MMPI-3 Validity Scales can identify possible over- and under-reporting on concurrently administered measures. This can be of particular value when such measures lack validity indicators.

Neuropsychological Validity Assessment Beliefs and Practices: A Survey of North American Neuropsychologists and Validity Assessment Experts.

The present study sought to identify changes in neuropsychological validity assessment beliefs and practices relative to surveys of North American neuropsychologists conducted in 2015 and 2016, obtain a more nuanced understanding of such beliefs and practices, and examine salient validity assessment topics not addressed by previous surveys. Adult focused neuropsychologists (n = 445) and neuropsychological validity assessment experts (n = 16) were surveyed regarding their perceptions and practices related to the following topics: (i) importance of validity testing; (ii) multiple performance validity test (PVT) administration and interpretation; (iii) suspected causes of invalidity; (iv) reporting on malingering; (v) assessment of examinees of diverse language, culture, and nation of origin; (vi) terminology; and (vii) most frequently utilized validity measures. There was general agreement, if not consensus, across multiple survey topics. The vast majority of neuropsychologists and experts view validity testing as mandatory in clinical and forensic evaluations, administer multiple PVTs regardless of setting, believe validity assessment to be important in the evaluation of all individuals including older adults and culturally diverse individuals, and view evaluations with few to no validity tests interspersed throughout the evaluation as being of lesser quality. Divergent opinions were also seen among respondents and between neuropsychologists and experts on some topics, including likely causes of invalidity and assessment and formal communication of malingering. Current results highlight the necessity of formal validity assessment within both clinical and forensic neuropsychological evaluations, and findings document current trends and reported practices within the field.