Evaluation Of Bias Research Articles

Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future

Abstract Spinal cord disease is important in most people with multiple sclerosis (MS) but assessment remains less emphasized in patient care, basic and clinical research, and therapeutic trials. The North American Imaging in Multiple Sclerosis (NAIMS) Spinal Cord Interest Group formed to determine and present the contemporary landscape of MS spinal cord evaluation, further existing and advanced spinal cord imaging techniques, and foster collaborative work. Important themes arose: 1) Multiple Sclerosis Spinal Cord Lesions (differential diagnosis, association with clinical course); 2) Spinal Cord Radiological-Pathological Associations; 3) “Critical” spinal cord lesions; 4) Multiple Sclerosis Topographical Model; 5) Spinal Cord Atrophy; 6) Automated and Special Imaging Techniques. Distinguishing multiple sclerosis from other myelopathic etiology is increasingly refined by imaging and serological studies. Postmortem spinal cord findings and MRI-pathological correlative studies demonstrate MRI’s high sensitivity detecting microstructural demyelination and axonal loss. Spinal leptomeninges include immune inflammatory infiltrates, some in B-cell lymphoid-like structures. “Critical” demyelinating lesions along spinal cord corticospinal tracts are anatomically consistent with and may be disproportionately associated with motor progression. Multiple Sclerosis topographical model implicates the spinal cord as an area where threshold impairment associates with multiple sclerosis disability. Progressive spinal cord atrophy and “silent” multiple sclerosis progression may be emerging as an important multiple sclerosis prognostic biomarker. Manual atrophy assessment is complicated by rater bias, while automation (e.g. Spinal Cord Toolbox), and artificial intelligence may reduce this. Collaborative research by NAIMS and similar groups with experts combining distinct strengths is key to advancing assessment and treatment of people with multiple sclerosis spinal cord disease.

Rating pome fruit quality traits using deep learning and image processing.

Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale -omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

Artificial intelligence for automatic and objective assessment of competencies in flexible bronchoscopy.

Bronchoscopy is a challenging technical procedure, and assessment of competence currently relies on expert raters. Human rating is time consuming and prone to rater bias. The aim of this study was to evaluate if a bronchial segment identification system based on artificial intelligence (AI) could automatically, instantly, and objectively assess competencies in flexible bronchoscopy in a valid way. Participants were recruited at the Clinical Skills Zone of the European Respiratory Society Annual Conference in Milan, 9th-13th September 2023. The participants performed one full diagnostic bronchoscopy in a simulated setting and were rated immediately by the AI according to its four outcome measures: diagnostic completeness (DC), structured progress (SP), procedure time (PT), and mean intersegmental time (MIT). The procedures were video-recorded and rated after the conference by two blinded, expert raters using a previously validated assessment tool with nine items regarding anatomy and dexterity. Fifty-two participants from six different continents were included. All four outcome measures of the AI correlated significantly with the experts' anatomy-ratings (Pearson's correlation coefficient, P value): DC (r=0.47, P<0.001), SP (r=0.57, P<0.001), PT (r=-0.32, P=0.02), and MIT (r=-0.55, P<0.001) and also with the experts' dexterity-ratings: DC (r=0.38, P=0.006), SP (r=0.53, P<0.001), PT (r=-0.34, P=0.014), and MIT (r=-0.47, P<0.001). The study provides initial validity evidence for AI-based immediate and automatic assessment of anatomical and navigational competencies in flexible bronchoscopy. SP provided stronger correlations with human experts' ratings than the traditional DC.

Evaluating Drawings’ Creativity: Synchrony Effects on Rater Bias and the Mediating Role of Emotional Arousal

ABSTRACT Creativity evaluation, particularly rating the creativity of products, is an unstable construct subjective to the influence of various individual states and characteristics. However, little research has been conducted on whether and how participants’ physiological states affect rating products. This experimental study aimed to explore how synchrony (i.e. whether individuals’ evaluation time synchronizes with their peak circadian arousal periods during a day) affects the rating of creativity in drawings, whether these effects vary according to drawings’ different creativity levels, and the influence of emotional state on synchrony and creativity rating. A sample of 191 university students rated several drawings of low, medium, and high creativity levels at their optimal or non-optimal times of day, and their subjective emotional valence and arousal were recorded. The results showed that participants exhibited a more positive rater bias when rating low- and medium-creativity-level drawings during their optimal times of day. Emotional arousal, but not valence, mediated the relationship between synchrony and rater bias. These findings highlight the role of synchrony and emotional arousal in rating creative products, contributing to our understanding of how raters’ synchrony states influence creativity evaluation.

Personality and rater bias: How personality traits influence rater bias and training proficiency

Personality and rater bias: How personality traits influence rater bias and training proficiency

Use of a large language model with instruction-tuning for reliable clinical frailty scoring.

Frailty is an important predictor of health outcomes, characterized by increased vulnerability due to physiological decline. The Clinical Frailty Scale (CFS) is commonly used for frailty assessment but may be influenced by rater bias. Use of artificial intelligence (AI), particularly Large Language Models (LLMs) offers a promising method for efficient and reliable frailty scoring. The study utilized seven standardized patient scenarios to evaluate the consistency and reliability of CFS scoring by OpenAI's GPT-3.5-turbo model. Two methods were tested: a basic prompt and an instruction-tuned prompt incorporating CFS definition, a directive for accurate responses, and temperature control. The outputs were compared using the Mann-Whitney U test and Fleiss' Kappa for inter-rater reliability. The outputs were compared with historic human scores of the same scenarios. The LLM's median scores were similar to human raters, with differences of no more than one point. Significant differences in score distributions were observed between the basic and instruction-tuned prompts in five out of seven scenarios. The instruction-tuned prompt showed high inter-rater reliability (Fleiss' Kappa of 0.887) and produced consistent responses in all scenarios. Difficulty in scoring was noted in scenarios with less explicit information on activities of daily living (ADLs). This study demonstrates the potential of LLMs in consistently scoring clinical frailty with high reliability. It demonstrates that prompt engineering via instruction-tuning can be a simple but effective approach for optimizing LLMs in healthcare applications. The LLM may overestimate frailty scores when less information about ADLs is provided, possibly as it is less subject to implicit assumptions and extrapolation than humans. Future research could explore the integration of LLMs in clinical research and frailty-related outcome prediction.

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine.We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures.We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data.In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

A pilot study of the outcomes of human trafficking survivors assessment with girls who have experienced commercial sexual exploitation

PurposeThis study utilized the Outcome of Human Trafficking Survivors (OHTS) to monitor the progress of female victims of CSE over a year while they were enrolled in a comprehensive treatment program. MethodsSixty-seven girls (M age 17.70 years) who were identified as confirmed victims of CSE (46 %) or at risk for CSE (54 %) and presented for treatment were administered the OHTS upon intake into the program and again at 90 day intervals resulting in four data points. Ratings were provided by staff members who interact with the member in each area assessed. A repeated-measures ANOVA was conducted comparing mean outcome differences across all four timepoints. ResultsResults indicate that significant changes were found in the area of Housing and Education, with gains in Education over time. For this sample, Parenting and Immigration were domains of less relevance and data was not consistently obtained in these areas. Other categories assessed by the OHTS did not demonstrate significant changes over time. ConclusionThe OHTS can be used to track progress of clients enrolled in treatment programming, but the goals of the program should align with the categories that are assessed in the OHTS. Repeated administration may be difficult due to high dropout rates in treatment and there may be rater bias.

Oxcarbazepine for the treatment of bipolar and depressive disorders in the outpatient setting: A retrospective chart review

Oxcarbazepine is often utilized off-label for bipolar and depressive disorders in outpatient settings despite limited evidence. We performed a retrospective chart review on 38 adult outpatients diagnosed with bipolar and depressive disorders (ICD-10 codes F30-39), treated with oxcarbazepine by psychiatrists and psychiatric nurse practitioners between 2015 and 2021. Primary outcomes were Clinical Global Impression Severity (CGI-S) and Improvement (CGI-I) scores, assigned retrospectively from clinical documentation. Patients were predominantly female (70%), aged 20–76 (mean 36), with a mean of 1.8 DSM diagnoses (range 1–4) and 1.7 (range 0–5) concurrent psychotropic medications. A starting mean oxcarbazepine dose of 489 mg/day, titrated to a mean final dose of 663 mg/day, was associated with a CGI-I of 2.5 [2.25, 2.75] and a pre-to-post treatment decrease in CGI-S from 3.4 to 2.4. Overall response and remission rates were 52% and 29%, respectively. Limitations of this study include potential sample bias, documentation bias and rater bias among other limitations inherent to retrospective study designs.

Service employees’ STARA awareness and proactive service performance

PurposeThis study aims to explore the role of frontline service employees’ (FSEs) awareness that their job can be substituted by smart technology, artificial intelligence, robotics and algorithms (STARA) in their job autonomy and proactive service performance and when these relationships can be buffered. Drawing on the cognitive appraisal theory of stress, the study examined the mediating relationship between FSEs’ STARA awareness, job autonomy and proactive service performance and the moderating effects of self-efficacy and resilience on this relationship.Design/methodology/approachThe authors administered two-wave online surveys to 301 South Korean FSEs working in various service sectors (e.g. retailing, food/beverage, hospitality/tourism and banking). The Time 1 survey measured respondents’ STARA awareness, self-efficacy, resilience and job autonomy, and the Time 2 survey assessed their proactive service performance.FindingsFSEs’ STARA awareness negatively affected their subsequent proactive service performance through decreased job autonomy. The negative association between STARA awareness and job autonomy was weaker when FSEs’ self-efficacy was high than when it was low. While the authors observed no significant moderation of resilience, the author found a marginally significant three-way interaction between STARA awareness, self-efficacy and resilience. Specifically, STARA awareness was negatively related to job autonomy only when both self-efficacy and resilience were low. When either self-efficacy or resilience was high, the association between STARA awareness and job autonomy became nonsignificant, suggesting the buffering roles of the two personal resources.Research limitations/implicationsGiven that the measurement of variables relied on self-reported data, rater biases might have affected the findings of the study. Moreover, the simultaneous measurement of STARA awareness, self-efficacy, resilience and job autonomy could preclude causal inferences between these variables. The authors encourage future studies to use a more rigorous methodology to reduce rater biases and establish stronger causality between the variables.Practical implicationsService firms can decrease FSEs’ STARA awareness through training in the knowledge and skills necessary to work with these technologies. To promote FSEs’ proactive service performance in this context, service firms need to involve them in decisions related to STARA adoption and allow them to craft their jobs. Service managers should provide FSEs with social support and exercise empowering and supportive leadership to help them view STARA as a challenge rather than a threat.Originality/valueDistinct from prior research on STARA awareness and employee outcomes, the study identified proactive service performance as a key outcome in the STARA context. By presenting self-efficacy and resilience as crucial personal resources that buffer FSEs from the deleterious impact of STARA awareness, the study provides practitioners with insights that can help FSEs maintain their job autonomy and proactive service performance in times of digitalization and automation.

Signal, error, or bias? exploring the uses of scores from observation systems

AbstractScores from observational measures of teaching have recently been put to many uses within school systems, including communicating a standard of practice and providing teacher feedback, identifying teachers for professional development, monitoring system equity, and making employment decisions. In each of these uses, observation scores are interpreted as representing some aspect of the enacted instruction or teachers’ capacity to enact instruction, as seen through the observation systems lens for understanding teaching quality. The quality of these interpretations, or the extent to which observation scores are composed of a signal that accurately reflects the interpretation, has important implications for the overall validity of uses of observation systems. Starting from an explicit conceptualization of instruction, this paper combines generalizability theory and hierarchical linear modelling approaches to decompose observation scores to explore the extent to which scores from observation systems are composed of signal, error, and bias across four different uses (i.e., teacher feedback, professional development, monitoring system equity, and employment decisions) of scores. We show that the quality of observation scores may depend more on what scores are interpreted as representing (i.e., the proposed use) than on the specific observation rubric being used. Further, we show that rater errors and biases are a major threat to any attempt to interpret observation scores as capturing the observation system’s understanding of teaching quality. We discuss implications for using scores from observation systems.

Caregiver report of social-emotional functioning in infants and young children after inflicted traumatic brain injury

ABSTRACT Social-emotional difficulties are common sequelae of traumatic brain injury (TBI). Children who have experienced inflicted TBI (iTBI) may be at increased risk for social-emotional problems due to the risk factors associated with both early neurologic injury and with child maltreatment. We characterized the associations among injury severity, caregiver type (i.e., biological parents, non-kinship, kinship), and child social-emotional functioning in 41 infants and young children who had sustained iTBI and were seen in a large, regional children’s hospital. This study was a retrospective analysis, utilizing data collected from the medical record as part of routine clinical care. Social-emotional functioning was assessed with the Bayley Scales of Infant and Toddler Development-Third Edition. Children with more severe injuries were rated as having worse social-emotional functioning. Caregiver type was associated with child social-emotional scores, above and beyond injury and demographic predictors. Biological parents were more likely to report better social-emotional skills than non-kinship caregivers, with the pattern of results suggesting that rater bias plays a role in this difference. In order to ensure that children are accurately identified for supports, these relationships should be considered when interpreting caregiver report of social-emotional skills.

Measuring Backsliding with Observables: Observable-to-Subjective Score Mapping

ABSTRACTMultiple well-known democracy-rating projects—including Freedom House, Polity, and Varieties of Democracy (V-Dem)—have identified apparent global regression in recent years. These measures rely on partly subjective indicators, which—in principle—could suffer from rater bias. For instance, Little and Meng (2023) argue that shared beliefs driven by the current zeitgeist could lead to shared biases that produce the appearance of democratic backsliding in subjectively coded measures. To assess this argument and the strength of the evidence for global democratic backsliding, we propose an observable-to-subjective score mapping (OSM) methodology that uses only easily observable features of democracy to predict existing indices of democracy. Applying this methodology to three prominent democracy indices, we find evidence of backsliding—but beginning later and not as pronounced as suggested by some of the original indices. Our approach suggests that the Freedom House measure particularly does not track with the recent patterns in observable indicators and that there has been a stasis or—at most—a modest decline in the average level of democracy.

Detecting Rater Bias in Mixed-Format Assessments

ABSTRACT Mixed-format assessments made up of multiple-choice (MC) items and constructed response (CR) items that are scored using rater judgments include unique psychometric considerations. When these item types are combined to estimate examinee achievement, information about the psychometric quality of each component can depend on that of the other. For example, the presence of differential item functioning (DIF) in MC items could compromise the sensitivity of rater effect indicators, such as differential rater functioning (DRF) indices. Likewise, the presence of DRF could compromise the sensitivity of DIF indices. We used real data and a simulation study to consider the impact of DIF on DRF indices, and the impact of DRF on DIF indices. Results indicate some interaction between DIF and DRF that varies across data collection designs. We consider the implications of these results for research and practice.

Challenges and Opportunities of Civic Education in Colleges and Universities in Digital Environment

Abstract Civic education in colleges and universities plays a pivotal role in fostering students’ national identity and civic responsibilities. Enhanced by digital learning environments, it promises to cultivate exemplary citizens more effectively. This paper details the development of a MOOC interactive communication platform utilizing the open-source edX framework, which serves to enhance civic education. It delves into optimizing resource storage technologies on the platform. It introduces an innovative assignment mutual evaluation algorithm tailored for Civics and Political Science education, which accounts for rater bias and scoring patterns. The newly implemented resource storage and mutual assessment algorithms have notably improved the functionality of the civic education platform. Performance metrics from the platform indicate an encouraging trend in student achievement across three courses, with average scores of 78.32, 79.24, and 83.27, respectively, showcasing a consistent upward trajectory. Further analysis reveals a converging trend in the variance of data distribution, indicating a reduction in disparities in student learning outcomes as evidenced by a decrease in standard deviation values. Comprehensively, students’ overall test scores have shown significant improvement. Notably, mastery of subject matter knowledge saw an increase from 2.3% to 21.4%, and the pass rate escalated from 92.6% to 98.3%. Although there were initial discrepancies in the cognitive rating of civic education values between the experimental and control groups, the experimental group exhibited substantial gains, underscoring the efficacy of the platform and its methodologies.

The relationship of school performance with self-control and grit is strongly genetic and weakly causal

The non-cognitive skills self-control and grit are often considered predictors of school performance, but whether this relationship is causal remains unclear. We investigated the causality of this association using a twin design. Specifically, we evaluated the direct impact of self-control and grit on school performance, while controlling for genetic or environmental influences common to all three traits (i.e., confounding). Teachers of 4891 Dutch 12-year-old twin pairs (of which 3837 were complete pairs) completed a survey about school performance (school grades), self-control (ASEBA self-control scale), and the perseverance aspect of grit. Our analysis aimed to determine the direct impact of self-control and grit on school performance, while simultaneously controlling for genetic or environmental confounding. Establishing the regression relationship corrected for confounding supports the interpretation of the regression relationship as causal. In all analyses, we corrected for sex, rater bias of the teachers, and parental socioeconomic status. Initially, in the standard regression, self-control, and grit explained 28.4% of the school performance variance. However, allowing for genetic confounding (due to genetic pleiotropy) revealed that most of this association could be attributed to genetic influences that the three traits share. In the presence of genetic pleiotropy, the phenotypic regression of school performance on self-control and grit accounted for only 4.4% (i.e., the effect size association with the causal hypothesis). In conclusion, self-control and grit predict school performance primarily due to genetic pleiotropy, with a much smaller causal effect (R2 = 4.4%). This suggests that interventions targeting self-control and grit alone may yield limited improvements in school performance.

Objective and Subjective Clustering Methods for Verb Fluency Responses From Individuals With Alzheimer's Dementia and Cognitively Healthy Older Adults.

The verb fluency task has been researched using a variety of analysis methods and shown its sensitivity to declines in executive functioning and lexical retrieval abilities in various neurogenic populations. Few studies to date, however, have analyzed clusters and switches in the task, and there is a lack of robust analysis methods that preclude subjectivity and potential rater bias. The purpose of this study was to investigate the reliability when using subjective clustering methods and to determine the feasibility of using an objective clustering method to determine verb fluency performance in individuals with Alzheimer's dementia (IwDs) and cognitively healthy older adults (CHOAs). Responses from a verb fluency task were obtained from IwDs and CHOAs. Group differences were examined using an objective clustering method for multiple variables regarding clustering and switching. We also calculated the intrarater, interrater, and intermethod reliability using intraclass coefficients. Significant group differences were found when utilizing the objective clustering method in all variables except the average cluster size, with IwDs performing poorer than CHOAs. Intrarater reliability was excellent. Interrater reliability between two authors and intermethod reliability between the objective and subjective methods were variable ranging from moderate to good. The results from using the objective clustering method in this study are consistent with the previous literature, making it a viable option for clustering analyses on the verb fluency task, which naturally minimizes subjectivity and rater bias. Alternatively, employing a thoroughly validated and reliable subjective approach can also mitigate potential rater bias and improve replicability across studies. https://doi.org/10.23641/asha.24061017.

Negative first impression judgements of autistic children by non-autistic adults.

Although autism inclusion and acceptance has increased in recent years, autistic people continue to face stigmatization, exclusion, and victimization. Based on brief 10-second videos, non-autistic adults rate autistic adults less favourably than they rate non-autistic adults in terms of traits and behavioural intentions. In the current study, we extended this paradigm to investigate the first impressions of autistic and non-autistic children by non-autistic adult raters and examined the relationship between the rater's own characteristics and bias against autistic children. Segments of video recorded interviews from 15 autistic and 15 non-autistic children were shown to 346 undergraduate students in audio with video, audio only, video only, transcript, or still image conditions. Participants rated each child on a series of traits and behavioural intentions toward the child, and then completed a series of questionnaires measuring their own social competence, autistic traits, quantity and quality of past experiences with autistic people, and explicit autism stigma. Overall, autistic children were rated more negatively than non-autistic children, particularly in conditions containing audio. Raters with higher social competence and explicit autism stigma rated autistic children more negatively, whereas raters with more autistic traits and more positive past experiences with autistic people rated autistic children more positively. These rapid negative judgments may contribute to the social exclusion experienced by autistic children. The findings indicate that certain personal characteristics may be related to more stigmatised views of autism and decreased willingness to interact with the autistic person. The implications of the findings are discussed in relation to the social inclusion and well-being of autistic people.

Multidimensional nonadditivity in one-facet g-theory designs: A profile analytic approach.

We introduce a new method for estimating the degree of nonadditivity in a one-facet generalizability theory design. One-facet G-theory designs have only one observation per cell, such as persons answering items in a test, and assume that there is no interaction between facets. When there is interaction, the model becomes nonadditive, and G-theory variance estimates and reliability coefficients are likely biased. We introduce a multidimensional method for detecting interaction and nonadditivity in G-theory that has less bias and smaller error variance than methods that use the one-degree of freedom method based on Tukey's test for nonadditivity. The method we propose is more flexible and detects a greater variety of interactions than the formulation based on Tukey's test. Further, the proposed method is descriptive and illustrates the nature of the facet interaction using profile analysis, giving insight into potential interaction like rater biases, DIF, threats to test security, and other possible sources of systematic construct-irrelevant variance. We demonstrate the accuracy of our method using a simulation study and illustrate its descriptive profile features with a real data analysis of neurocognitive test scores. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Negative Symptom Inventory-Self-Report (NSI-SR): Initial development and validation.

Negative symptoms (i.e., anhedonia, avolition, asociality, blunted affect, alogia) are frequently observed in the schizophrenia-spectrum (SZ) and associated with functional disability. While semi-structured interviews of negative symptoms represent a gold-standard approach, they require specialized training and may be vulnerable to rater biases. Thus, brief self-report questionnaires measuring negative symptoms may be useful. Existing negative symptom questionnaires demonstrate that this approach may be promising in schizophrenia, but no measure has been devised for use across stages of psychotic illness. The present study reports initial psychometric validation of the Negative Symptom Inventory-Self-Report (NSI-SR), the self-report counterpart of the Negative Symptom Inventory-Psychosis Risk clinical interview. The NSI-SR is a novel transphasic negative symptoms measure assessing the domains of anhedonia, avolition, and asociality. The NSI-SR and related measures were administered to two samples: 1) undergraduates (n=335), 2) community participants, including: SZ (n=32), clinical-high risk for psychosis (CHR, n=25), and healthy controls matched to SZ (n=31) and CHR (n=30). The psychometrically trimmed 11-item NSI-SR showed good internal consistency and a three-factor solution reflecting avolition, asociality, and anhedonia. The NSI-SR demonstrated convergent validity via moderate to large correlations with clinician-rated negative symptoms and related constructs in both samples. Discriminant validity was supported by lower correlations with positive symptoms in both samples; however, correlations with positive symptoms were still significant. These initial psychometric findings suggest that the NSI-SR is a reliable and valid brief questionnaire capable of measuring negative symptoms across phases of psychotic illness.