What Did We Miss? Analysis of Military Personnel Responses to an Open-Ended Question in a Post-Deployment Health Survey

ABSTRACT Introduction Post-deployment health surveys completed by military personnel ask about a range of deployment experiences. These surveys are conducted to determine if there are links between experiences and poor health. Responses to open-ended questions in these surveys can identify experiences that might otherwise go unreported. These responses may increase knowledge about a particular deployment and inform future surveys. This study documented deployment experiences described by Australian Defence Force personnel who were deployed to the Middle East. Materials and Methods A survey completed by 14,032 personnel examined health outcomes and over 100 experiences relating to their Middle East deployment. Responses to two open-ended questions captured additional experiences. Descriptive statistics reveal the characteristics of those who did and did not describe additional experiences, and a content analysis details the nature and frequency of the experiences reported. The study was approved by an Institutional Review Board. Results Five percentage (n = 692) of personnel who completed the survey described additional deployment experiences. The most frequently reported experiences were specific Navy experiences; experiences of poor leadership; administrative or organizational issues; the anthrax vaccine; and traumatic events/potentially morally injurious experiences. Conclusions The findings suggest that post-deployment health surveys should have questions about certain deployment experiences tailored by military service (i.e., Air Force, Army, and Navy). Researchers could consider including questions about personnel experiences of leadership for its impact on health and about potentially morally injurious experiences that may help explain adverse mental health. Clear wording of open-ended questions and participant instructions may improve response rates and reduce response biases.

Scene Context Impairs Perception of Semantically Congruent Objects

Visual scene context is well-known to facilitate the recognition of scene-congruent objects. Interestingly, however, according to predictive-processing accounts of brain function, scene congruency may lead to reduced (rather than enhanced) processing of congruent objects, compared with incongruent ones, because congruent objects elicit reduced prediction-error responses. We tested this counterintuitive hypothesis in two online behavioral experiments with human participants ( N = 300). We found clear evidence for impaired perception of congruent objects, both in a change-detection task measuring response times and in a bias-free object-discrimination task measuring accuracy. Congruency costs were related to independent subjective congruency ratings. Finally, we show that the reported effects cannot be explained by low-level stimulus confounds, response biases, or top-down strategy. These results provide convincing evidence for perceptual congruency costs during scene viewing, in line with predictive-processing theory.

Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests

The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model.

Confidence as a noisy decision reliability estimate

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgments. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subject's estimate of the reliability of their decision. The quality of this estimate is limited by the subject's uncertainty about the uncertainty of the variable that informs their decision ("meta-uncertainty"). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, revealing that meta-uncertainty varies across subjects, is stable over time, generalizes across some domains, and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

Cultural factors weaken but do not reverse left-to-right spatial biases in numerosity processing: Data from Arabic and English monoliterates and Arabic-English biliterates

Directional response biases due to a conceptual link between space and number, such as a left-to-right hand bias for increasing numerical magnitude, are known as the SNARC (Spatial-Numerical Association of Response Codes) effect. We investigated how the SNARC effect for numerosities would be influenced by reading-writing direction, task instructions, and ambient visual environment in four literate populations exemplifying opposite reading-writing cultures—namely, Arabic (right-to-left script) and English (left-to-right script). Monoliterates and biliterates in Jordan and the U.S. completed a speeded numerosity comparison task to assess the directionality and magnitude of a SNARC effect in their numerosity processing. Monoliterates’ results replicated previously documented effects of reading-writing direction and task instructions: the SNARC effect found in left-to-right readers was weakened in right-to-left readers, and the left-to-right group exhibited a task-dependency effect (SNARC effect in the smaller condition, reverse SNARC effect in the larger condition). Biliterates’ results did not show a clear effect of environment; instead, both biliterate groups resembled English monoliterates in showing a left-to-right, task-dependent SNARC effect, albeit weaker than English monoliterates’. The absence of significant biases in all Arabic-reading groups (biliterates and Arabic monoliterates) points to a potential conflict between distinct spatial-numerical mapping codes. This view is explained in terms of the proposed Multiple Competing Codes Theory (MCCT), which posits three distinct spatial-numerical mapping codes (innate, cardinal, ordinal) during numerical processing—each involved at varying levels depending on individual and task factors.

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Forced choice (FC) is one of the most used forms measurement for non-cognitive assessments, which can effectively resist faking and some other response biases compared to the Likert-types scales, and has been a popular topic in the field of industrial organizational psychology in recent years. Inspired by Lee et al., (2019) study, the present study proposed a 2PL-RANK model as a variant of the GGUM-RANK for fitting dominance RANK items. To improve the efficiency of parameter estimation, the authors apply the stEM algorithm to the 2PL-RANK model, which greatly improves the efficiency of parameter estimation in joint estimation. What’s more, we derived information functions for this model based on the logic of Joo et al., (2018). Then, simulation studies were conducted to examined the recovery of model's parameters with RANK triplet responses, which manipulated four factors, with sample size, the number of dimensions, the number of blocks measured in each dimension, and the correlation between dimensions. Results show that the 2PL-RANK model performed well in estimating item and trait parameters. Finally, the utility of 2PL-RANK and Thurstonian IRT model (TIRT) in a 24-dimensional FC personality test was compared. An empirical study was then conducted based on a 24-dimensional FC personality test to illustrate the practical use of the model.

Using Constrained Factor Mixture Analysis to Validate Mixed-Worded Psychological Scales: The Case of the Rosenberg Self-Esteem Scale in the Dominican Republic

A common method to collect information in the behavioral and health sciences is the self-report. However, the validity of self-reports is frequently threatened by response biases, particularly those associated with inconsistent responses to positively and negatively worded items of the same dimension, known as wording effects. Modeling strategies based on confirmatory factor analysis have traditionally been used to account for this response bias, but they have recently become under scrutiny due to their incorrect assumption of population homogeneity, inability to recover uncontaminated person scores or preserve structural validities, and their inherent ambiguity. Recently, two constrained factor mixture analysis (FMA) models have been proposed by Arias et al. (2020) and Steinmann et al. (2021) that can be used to identify and screen inconsistent response profiles. While these methods have shown promise, tests of their performance have been limited and they have not been directly compared. Thus the objective of the current study was to assess and compare their performance with data from the Dominican Republic of the Rosenberg Self-Esteem Scale (N = 632). Additionally, as this scale had not yet been studied for this population, another objective was to show how using constrained FMAs could help in the validation of mixed-worded scales. The results indicated that removing the inconsistent respondents identified by both FMAs (≈8%) reduced the amount of wording effects in the database. However, whereas the Steinmann et al. method only cleaned the data partially, the Arias et al. (2020) method was able to remove the great majority of the wording effects variance. Based on the screened data with the Arias et al. method, we evaluated the psychometric properties of the RSES for the Dominican population, and the results indicated that the scores had good validity and reliability properties. Given these findings, we recommend that researchers incorporate constrained FMAs into their toolbox and consider using them to screen out inconsistent respondents to mixed-worded scales.