Visual Analogue scales (VASs) are increasingly popular in psychological, social, and medical research. However, VASs can also be more demanding for respondents, potentially leading to quicker disengagement and a higher risk of careless responding. Existing mixture modeling approaches for careless response detection have so far only been available for Likert-type and unbounded continuous data but have not been tailored to VAS data. This study introduces and evaluates a model-based approach specifically designed to detect and account for careless respondents in VAS data. We integrate existing measurement models for VASs with mixture item response theory models for identifying and modeling careless responding. Simulation results show that the proposed model effectively detects careless responding and recovers key parameters. We illustrate the model's potential for identifying and accounting for careless responding using real data from both VASs and Likert scales. First, we show how the model can be used to compare careless responding across different scale types, revealing a higher proportion of careless respondents in VAS compared to Likert scale data. Second, we demonstrate that item parameters from the proposed model exhibit improved psychometric properties compared to those from a model that ignores careless responding. These findings underscore the model's potential to enhance data quality by identifying and addressing careless responding.
Careless responding, in which survey participants fail to attend to item content, is a well-recognized threat to the quality of self-report data. Although prevalence estimates commonly fall between 8 and 12% in student samples, the extent to which careless responding distorts the psychometric properties of attitude measures has received limited attention, particularly outside Western samples. The present study investigated the prevalence and psychometric consequences of careless responding in a paper-and-pencil sample of 1,112 Turkish university students who completed the sustainable development awareness scale. An instructed response item embedded within the scale identified 126 respondents (11.33%) as careless, and two post-hoc indicators (longstring and even-odd consistency) converged with this classification. Parallel analyses on the unscreened and screened samples were conducted to evaluate effects on internal consistency, confirmatory factor analysis, multigroup measurement invariance, criterion-related validity correlations, and an item-level Composite Sensitivity Index (CSI). Internal consistency was higher in the screened sample, with the gains concentrated in the two subscales containing reverse-coded items. Confirmatory factor analysis indices trended in the direction of better fit after screening, and criterion-validity correlations with related constructs were modestly larger in the screened sample at both the manifest and latent levels. Multigroup measurement invariance testing across attentive and careless responders supported metric but not scalar invariance, pointing to systematic intercept differences consistent with acquiescent responding among careless respondents. The composite sensitivity index combining changes in item means, item-total correlations, and factor loadings was used to rank items by vulnerability to careless responding, with results robust to an alternative standardized aggregation. All six reverse-coded items appeared among the ten most sensitive items, despite constituting only one-sixth of the scale, and the item immediately following the attention check showed a notably elevated sensitivity pattern that we interpret as a tentative hypothesis worth further investigation. These findings underscore the importance of routine screening for careless responding and offer practical guidance on the placement of attention checks and the use of reverse-coded items.
One key challenge in ambulatory assessment (AA)-the collection of intensive longitudinal data in daily life-is careless responding, which can compromise data quality. However, research on its prevalence and predictors in AA, particularly in clinical populations, remains limited. Using multilevel latent class analysis, we examined momentary careless responding and individual differences therein 233 individuals with chronic pain and 168 without, over a 14-day AA phase with five daily assessments. We tested whether the groups differed in the type, extent, and predictors of careless responding. Across both groups, three Level 1 profiles emerged: careful responding, fast and invariable responding, and inconsistent responding. At Level 2, we identified four respondent classes: careful, infrequently careless, and two frequently careless types. Multigroup multilevel latent class analysis indicated partial measurement invariance at Level 1, reflecting group-specific shifts in three indicator means, while the substantive interpretation of profiles remained stable. Full measurement invariance at Level 2 showed that individuals with and without chronic pain did not differ in the type or overall likelihood of careless responding. Additionally, higher stress and poorer sleep were associated with membership in the frequently careless classes. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
The experience sampling method (ESM) has become a popular tool in psychology. However, the intensive nature of ESM raises concerns about careless responding, where participants respond without paying sufficient attention. This study investigated the temporal dynamics of carelessness across three common sample types (community, student, clinical). We leveraged four careless responding indicators-response time, within-beep standard deviation, an inconsistency index, and occasion-person correlation-and used univariate and multivariate multilevel models to examine their temporal trajectories. Our results demonstrate that careless responding is not a stable phenomenon but changes over time, with evidence for increases across days and non-stationarity within days across the different samples. The presence of few and small associations among the indicators implies either that they flag distinct kinds of carelessness or that some of them do not capture carelessness at all. Overall, our findings underscore the importance of considering the temporal dynamics of carelessness in ESM studies.
Careless respondents inject noise into data which can distort research findings and compromise model fit. To address this, factor mixture modeling (FMM) has been widely used to identify careless respondents. Traditionally, researchers have relied on reverse-worded questions in FMM to facilitate the detection of careless responding. With the rise of online data collection platforms, response time has appeal as a means for understanding careless behavior. We introduce a Bayesian FMM that leverages this rich source of information to identify careless respondents. By jointly modeling responses and response time, this approach effectively identifies careless individuals rushing through the questionnaire without providing responses that reflect the to-be-measured traits. Our simulation studies demonstrate that this model accurately estimates parameters and classifies respondents as either attentive or careless, while maintaining error rates within acceptable limits. Furthermore, integrating response time enhances model convergence and the precision of classification and estimation. Using mediation models as an example, we illustrate how social science researchers can use this FMM approach to address careless responding in substantive research. An empirical study further tests the applicability of the proposed model in real-world scenarios, comparing its conclusions with traditional methods. To support its use, we provide an R function to streamline implementation.
This study evaluated the reliability and validity of the Self-Report Symptom Inventory (SRSI) in adolescents, aiming to determine whether the SRSI effectively distinguishes between valid and distorted symptom reports under different instructed response conditions. Eighty-eight adolescents, aged 12 to 17 years, from the Dutch-speaking general population completed the SRSI twice: once under honest instructions, and once under one of four randomly assigned conditions: honest (n = 24), feigning depression (n = 23), feigning pain (n = 22), or careless responding (n = 19). Internal consistency, test-retest reliability, and discriminative validity were analyzed. Exploratory analyses examined the impact of careless responding on SRSI scores and the utility of consistency items (i.e., positive health items) in differentiating between careless and feigned responding. The SRSI demonstrated high discriminative validity between honest and feigned responses, with specificity of .90 and sensitivity of .91 at the standard cutoff (> 9). When careless responses were combined with feigned responses - both reflecting distorted symptom presentations - sensitivity decreased to .80. Internal consistency was high for both the genuine (α = .91) and pseudosymptoms (α = .81) scales, and test-retest reliability was high (n = 24; r = .93 and .88, respectively). Among careless responders, approximately 50% scored above the SRSI pseudosymptoms standard cutoff. Exploratory findings suggest that endorsement of multiple consistency items after failing the SRSI may reflect careless rather than feigned responding. These initial findings support the potential utility of the SRSI as a symptom validity measure for adolescents. The lower specificity at the standard cutoff than that found in experimental feigning studies in adults (.90 vs. ≥ .95) suggests that the cutoff may need to be raised in youth samples. Further validation in clinical populations is necessary before recommending its use in applied settings.
This study examined how careless and inconsistent reporting affects adolescent suicidality prevalence and sex differences, a methodological issue often overlooked in self-report epidemiological research. I used data from two nationally representative surveys of secondary-school students conducted in 2010 (n = 7640; 49.3 % female) and 2014 (n = 5592; 52.6 % female). Both surveys assessed depressive symptoms, suicidal ideation, suicide plans, suicide attempts, attempt recognition, and attempt disclosure. Three methods of prevalence computation were used: unadjusted estimates (M1); excluding fictitious drug endorsers and treating inconsistencies as missing (M2); and excluding all careless and inconsistent reporters (M3). About 19 % of respondents were identified as careless or inconsistent. Compared to M1, M2 and M3 yielded lower prevalence estimates for most indicators. The largest reductions involved, on average, reports of unnoticed suicide attempts (-73.8 %), talking to no one about an attempt (-73.3 %), and reporting six or more suicide attempts (-35.9 %). Most sex differences were unaffected, except for the 'six or more suicide attempts' category and attempt recognition and disclosure items. Overlooking misreporting may inflate adolescent suicidality prevalence and distort sex-difference estimates. Incorporating validity checks and data-cleaning procedures can improve the accuracy of epidemiological findings and the effectiveness of prevention programs.
With the increasing publication of self-report online studies, concerns are growing about the quality of the data collected through these methods. This study focused on response bias, a major threat to data quality, by analyzing data from a real-world study on adult temperament conducted in two different countries. The sample included 1,497 participants aged 18-80 years from the United States (n = 598) and Lithuania (n = 899). The primary objectives were to determine the prevalence of response bias and to evaluate its impact on psychometric outcomes. Indicators of biased responding included patterns suggestive of potentially careless responding (e.g., invariant and random response patterns) and those flagged by internal validity checks or clinical controls (e.g., social desirability and ratings-perception discrepancies). Results indicated that the inclusion of data reflecting potentially careless responding reduced internal consistency and distorted factor structure, whereas its exclusion improved these psychometric indicators. In contrast, with regard to clinical controls, removing flagged data resulted in a decline in psychometric quality. Additionally, higher rates of careless responding were observed in the sample subjected to forced answering. These findings highlight the importance of mitigating response bias in online self-report research and raise broader questions about the integrity of data in existing survey-based datasets. By jointly evaluating careless responding and clinical threats in real-world, cross-national samples, this study extends prior work and demonstrates the applied value of post-hoc screening for improving psychometric quality.
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Online questionnaires are widely used for large-scale screening. However, careless responding (CR) from participants can compromise the reliability of screening outcomes. Prior studies have focused on the effects of individual and environmental factors on CR, but the effect of questionnaire type remains underexplored. This study investigates the individual factors influencing CR in online mental health screening and assesses how the effect of these factors varies across different psychological questionnaires. This study analyzed data from 24,367 participants across 4 questionnaires (PHQ-9 [Patient Health Questionnaire-9], PSS [Perceived Stress Scale], ISI [Insomnia Severity Index], and GAD-7 [Generalized Anxiety Disorder-7 Scale]). CR was defined as the proportion of items completed in less than 2 seconds per item. We used a multiple linear regression model to examine the effect of individual factors (sex, age, education, smoking, and drinking) on CR across 4 questionnaires. In addition, response times were visualized to identify patterns between careless and careful responders. Females demonstrate lower levels of CR than males when completing the PHQ-9 (β=-.172, 95% CI -0.104 to -0.089; P<.001), PSS (β=-.234, 95% CI -0.162 to -0.14; P<.001), ISI (β=-.207, 95% CI -0.13 to -0.114; P<.001), and GAD-7 (β=-.177, 95% CI -0.108 to -0.093; P<.001). Older participants demonstrated lower levels of CR on the PHQ-9 (β=-.036, 95% CI -0.007 to -0.003; P<.001), ISI (β=-.036, 95% CI -0.007 to -0.003; P<.001), and GAD-7 (β=-.053, 95% CI -0.009 to -0.005; P<.001), but their age was unrelated to CR on the PSS. Interestingly, compared with participants with an associate-level education, those with a high education (bachelor's, master's, or doctoral degree) demonstrated higher levels of CR, especially those with a master's degree (PHQ-9: β=.098, 95% CI 0.136 to 0.188; P<.001 and GAD-7: β=.091, 95% CI 0.125 to 0.178; P<.001). Smokers exhibited varied patterns, with current smokers demonstrating lower levels of CR on the PHQ-9 (β=-.022, 95% CI -0.064 to -0.016; P=.001) and GAD-7 (β=-.014, 95% CI -0.051 to -0.002; P=.03), whereas occasional smokers demonstrated higher levels of CR on the PSS (β=.019, 95% CI 0.010 to 0.050; P=.003) than nonsmokers. Drinkers demonstrated lower levels of CR than nondrinkers, with the strongest effect among occasional drinkers on the PHQ-9 (β=-.163, 95% CI -0.103 to -0.087; P<.001). Analysis of response times revealed that participants tended to spend less time on PHQ-9 and GAD-7 surveys, and CR on PSS and ISI surveys was characterized by skipping questions. The effect of individual factors on CR varies across questionnaire types. These findings offer valuable insights for questionnaire designers and administrators, highlighting the need for targeted intervention.
Anomalous survey responses, including random, careless, extreme, acquiescent, straightline, and alternating responding, threaten the validity of survey-based research. Machine learning (ML) algorithms offer flexible, model-agnostic alternatives to traditional detection methods, yet their relative effectiveness across anomaly types remains poorly understood. This study evaluated 11 unsupervised anomaly detection algorithms spanning four paradigms (distance-based, density-based, reconstruction-based, and tree/boundary-based) against six simulated anomaly types embedded in a realistic survey dataset (N = 3,000). Results revealed pronounced differential sensitivity: globally deviant patterns (random, extreme, alternating) were universally detectable, whereas careless and acquiescent responding required reconstruction- or boundary-based methods, and straightline responding resisted detection by all algorithms (maximum area under the receiver operating characteristic curve [AUC-ROC] < .70). No single algorithm dominated across all types. These findings argue for multimethod approaches combining ML algorithms with traditional response quality indicators, and provide a framework for selecting detection methods based on anticipated anomaly types.
The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations, this is not the case. This work introduces the multiple observed classification local independence model (MOCLIM), which extends the BLIM by allowing the above probabilities to vary across populations. In the MOCLIM, each individual is characterized by proficiency, careless and guessing classes, which are observed and determine the probabilities of knowledge states, careless errors and lucky guesses of a population. Given a particular class type (proficiency, careless, or guessing), the probabilities are the same for populations with the same class but may vary between populations with different classes. Algorithms for maximum likelihood estimation of the MOCLIM parameters are provided. The results of a simulation study suggest that the true parameter values are well recovered by the estimation algorithm and that the true model can be uncovered by comparing the goodness-of-fit of alternative models. The results of an empirical application to data from Raven-like matrices suggest that the MOCLIM effectively discriminates between situations where group differences are expected and those where they are not.
The Big Five Inventory-2 (BFI-2; Soto & John, 2017a) was developed to improve on the limitations of the original BFI by balancing the number of positively and negatively worded items and establishing a hierarchical structure for the Big Five traits. However, as the BFI-2 employs a Likert format with agree-disagree options, it suffers from common problems of the Likert format, including acquiescence bias and method effects due to the negatively worded items. In this research, we converted the BFI-2 into three alternative formats: Expanded, Item-Specific-Full, and Item-Specific-Light. These formats have tailored response options for each item and avoid the use of negatively worded items, thereby addressing the issues associated with the Likert format. Across two studies (N = 1,335 and N = 1,451), we randomly assigned Canadian undergraduate students to complete the BFI-2 in the original Likert format or one of the three alternative formats. Results showed that the Likert and alternative formats exhibit similar predictive validity. However, the alternative formats-particularly the Expanded format-showed better psychometric properties, including enhanced factor structure, increased reliability, and possibly reduced careless responding. We recommend that researchers consider adopting the BFI-2 in these alternative formats and adapting other Likert scales to these alternative formats.
Pedestrians usually sustain more severe injuries in traffic crashes due to the lack of protection. Although previous studies have analyzed crash-influencing factors, the causal relationships between injury severity and factors remain limited. Thus, the study aims to investigate the causal effects of factors, particularly the hazardous driving behaviors, on pedestrian injury severity. It employs causal machine learning and the Shapley Additive exPlanations to analyze the marginal contribution of the feature variables on crash injury severity. Furthermore, the causal tree model is used to reveal the heterogeneous causal effects. The results indicate that 1) the pedestrian injury severity is influenced by various factors, such as pedestrian age, speed limit, and vehicle type, 2) hazardous driving behaviors can deteriorate pedestrian injury severity, with 'failing to yield right of way' showing the highest mean conditional average treatment effect (CATE) (0.768), followed by 'careless driving' (0.626) and 'violating traffic signs or signals' (0.590), and 3) hazardous driving behaviors, road speed limits, and pedestrian age jointly contribute to the pedestrian injury severity. The study reveals heterogeneity in the causal relationship between hazardous driving behaviors and the injury severity of pedestrian-vehicle crashes, which serves to develop differential intervention strategies to mitigate pedestrian injury.
Background: Distinguishing functional dystonia (FD) from idiopathic dystonia (ID) remains a major clinical challenge because both conditions are diagnosed primarily on clinical grounds and may be accompanied by non-motor psychiatric symptoms. Although personality abnormalities have been described in functional neurological disorders, their relevance in the differential diagnosis of dystonia remains insufficiently studied, and comparative data on FD and ID are lacking, particularly in the Russian population. Patients and Methods: A total of 178 patients with idiopathic dystonia (focal and segmental dystonia, ID) and 32 patients with functional dystonia (FD) were observed. A clinical interview by a psychiatrist was conducted; the SCID-II-PD questionnaire and the Five-Factor Personality Questionnaire (5-PFQ) were used to assess PD. Results: Patients with FD more often than patients with ID had such PD as dependent, paranoid, passive-aggressive, borderline, schizoid and schizotypal (p < 0.001), as well as obsessive-compulsive (p < 0.013) and avoidant (p < 0.049) according to SCID-II-PD. In FD, personalities of the eccentric cluster A predominate; patients with FD are characterized in personality terms by significantly greater introversion, detachment, naturalness (irresponsibility, impulsivity, carelessness), emotional restraint and practicality (conservatism, low sensitivity, rigidity) according to 5-PFQ. Conclusions: Patients with FD differ from patients with ID in both categorical and dimensional personality characteristics. The predominance of cluster A personality pathology and the identified pattern of personality-related variables may have potential value as adjunctive markers in the clinical differentiation of FD from ID. Further external validation is required before these findings can be incorporated into diagnostic algorithms.
Experience sampling method (ESM) research, relying on real-time data collection via mobile devices, provides unique insights into adolescents' daily lives. However, concerns about digital distraction and overstimulation have led to shifting societal norms and consequently, increased restrictions on smartphone use-both institutionally (e.g., school bans) and informally (e.g., parental rules, self-regulation). These constraints raise questions about the feasibility and ecological validity of using ESM in adolescent samples. In this study, 195 adolescents (Mage = 16.12) participated in a 17-day ESM protocol, completing six prompts daily. Most adolescents reported facing school-based (88%) and parental (56%) smartphone restrictions. Despite these constraints, compliance was moderate to high (M = 78%), and analyses of nonresponse patterns revealed when and why prompts were most likely to be missed. Early morning prompts were often missed due to sleep, late morning prompts due to school, and evening prompts due to work-highlighting the importance of context-sensitive sampling strategies. Moreover, data quality was high: careless responding was rare, and participants reported high levels of integrity and motivation. Most participants evaluated the study as positive, with financial incentives, scientific contribution, and social connection as key motivators. These findings underscore that adolescent ESM studies remain feasible and ecologically valid when protocols are flexibly aligned with real-world constraints. Given that societal norms on digital well-being are in flux and smartphone restrictions intensify, aligning ESM design with adolescents' everyday realities becomes increasingly essential to preserve both feasibility and ecological validity in research on adolescence.
Poor adherence to antiretroviral therapy (ART) continues to undermine HIV treatment outcomes in sub-Saharan Africa, including Ghana. This study aimed to quantify ART treatment adherence among people living with HIV (PLHIV) at Pantang Hospital in Ghana. This study took place from July to October 2025 among people living with HIV who receive care at Pantang Hospital in Accra, Ghana. Using random sampling, data were collected from 151 participants through interviewer-administered questionnaires assessing sociodemographic characteristics and ART adherence behaviors. A hold-back validation method was used to split the final dataset, allocating 80% to training and 20% to validation. Six machine learning models were applied to identify and rank key adherence predictors, and model performance was compared. In all, 151 participants were recruited. Participants had a median age of 42 years; 75.5% were female, and 39.7% were older than 45 years. Most participants were Christian (88.7%), lived in peri-urban areas (70.9%), and were self-employed (72.2%). Non-adherence behaviors were common: 66.9% reported forgetting to take medication, 64.2% reported carelessness, and 85.4% reported stopping medication when feeling worse. Among the six models, Elastic Net regression demonstrated the best overall performance. The strongest predictors of non-adherence were not taking medication over the past weekend (utility estimate 0.3471, 95% confidence interval 0.2292-0.4650) and missing medication 6-10 times in the past week (utility estimate 0.2693, 95% confidence interval 0.1538-0.3848). The study showed poor adherence to ART among this cohort of PLHIV which increases the risk of community-level transmission. This study provides quantitative data to help develop effective interventions to address poor adherence to improve health outcomes of PLHIV.
Meticulous literature citation is a cornerstone of science, yet citations in published articles mischaracterize the cited work with concerning frequency. There appears to be limited emphasis on citation practices in graduate research training. This tutorial emphasizes the scientific importance of careful citation and the potential costs of careless citation practices. Recommendations are aimed at promoting citations that clearly and accurately represent the cited work. This article provides a resource to encourage more careful citation with the goal of bringing the behavior of scientists under tighter control of the literature and promoting dissemination of work that connects the reader with a systematic body of knowledge.
This study aimed to isolate, identify, and describe the bacteria isolated from mortality events occurred among pond-farmed Nile tilapia in different farms at Edku, Beheira province, Egypt, with special emphasis on their antimicrobial resistance profile. Specimens were collected from six private farms that experienced mortality outbreaks that occurred throughout the year between August 2023 and 2024. Clinical and postmortem examinations uncovered characteristic signs of bacterial septicemia. Forty-three bacterial isolates were recovered from the examined moribund and recently dead specimens. Based on the genetic data and evolutionary relationships derived from the 16S ribosomal RNA gene, six different isolates were resolved as Streptococcus agalactiae (46.5%), Vibrio alginolyticus (11.6%), Vibrio campbellii (14%), Vibrio owensii (9.3%), Aeromonas veronii (11.6%), and Enterococcus faecalis (7%). The antibiogram results revealed varying susceptibility patterns among the isolates. V. alginolyticus showed intermediate susceptibility to ciprofloxacin, while V. campbellii was only moderately sensitive to oxytetracycline. Of particular concern, A. veronii was found to be resistant to all tested antimicrobials. However, E. faecalis displayed sensitivity to ciprofloxacin, making it a potential drug of choice for treating diseased tilapia; this isolate was also moderately susceptible to oxytetracycline, erythromycin, and kanamycin. Finally, S. agalactiae exhibited intermediate susceptibility only to amoxycillin/clavulanic acid, oxytetracycline, and ciprofloxacin. Our findings clearly demonstrate that careless antibiotic administration in fish farming drives the emergence of antimicrobial resistance. Therefore, proactive planning is necessary, encompassing comprehensive surveillance, and the establishment of strict control and prevention measures to curb bacterial spread and safeguard productivity in Tilapia aquaculture.