Behavior Research Methods最新文献

Sleep and physical activity are vital to the health, development, and well-being of young children. To effectively promote these behaviours at the population level, better tools for objectively quantifying them are needed. This hypothesis-generating mixed-methods study explored the potential usability of two wearable sensors to measure physical activity and sleep in young children over multiple days, drawing on physiological measurements. A longitudinal within-case design was employed, in which families with children aged 4-36 months from the North East of England were recruited through playgroups and social networks. Parents and children tested two wearable devices in a structured play setting and at home over a period of 1 week. Data on sleep, movement, and heart rate were collected using the Bittium Faros 180 heart rate monitor and the NAPPA sleep monitoring system. Usability was assessed through researcher observations and parent feedback using ethnographic methods. Wear time, heart rate variability during naps, and ultradian respiration cycles during sleep were analysed. Seven children participated and completed the study. While parents were initially enthusiastic, usability challenges arose. The heart rate monitor was considered uncomfortable, its large size hindered activity, and electrodes were detached by parents and accidently, leading to significant data loss. The NAPPA was easier to use, discreet, and comfortable, but disrupted sleep routines. Additional challenges related to non-parental caregiving resulted in non-wear and/or data loss. These results indicate that wearable devices for young children hold potential but face significant design challenges for longitudinal home use at scale. Co-creation of child-friendly, practical hardware and software is essential for effective, large-scale health monitoring in young children.

The Mirror Tracing Task (MTT) is a method used to study visuomotor skills learning. It is traditionally evaluated by counting the number of times a person draws outside of the borders of a figure, typically a star, while looking at its mirror reflection. While insightful for overall performance, this metric lacks a precise analysis of the tracing, such as details on errors in specific regions. We propose a new MTT analysis method that studies the drawing as a function of the angle around the figure's center. Two new variables are introduced: residuals, which measure deviation from the ideal drawing, and density, which measures how often a specific path is retraced. These variables are defined per angle or region, allowing a more detailed analysis, highlighting the most challenging parts of the drawing for each person, and enabling comparison across trials or finding common patterns between individuals. We applied this approach to the first MTT trial of 210 participants using age as a variable of interest. Residuals and density were summarized as a single value and compared with the traditional approach, providing similar results. When analyzed as a function of the angle, these variables enabled the identification of specific regions of the star where the errors are age-sensitive. Additionally, a time series-like approach enabled us to cluster drawings and quantify their similarity. The code used for this new method has been made openly accessible to make it easier for its applications in new research or the reanalysis of previous projects.

Synesthesia is a neurological phenomenon in which healthy individuals experience additional, automatic, and consistent perceptions unrelated to veridical sensory input. For most (but not all) synesthetes, this additional experience is a color: for example, grapheme-color synesthetes experience colors for letters of the alphabet. Measuring these color associations is of central importance to synesthesia research, but there is no standard color picker "tool" that researchers can adapt to use in their own experiments: each researcher must code their own. This is a barrier to entry for synesthesia research, and additionally creates potential methodological confounds because different researchers make color pickers with different properties. SynesthesiaColorPicker is an open-source, mobile-friendly color picker tool that can be integrated with two popular online experiment platforms (Qualtrics and lab.js/Open Lab) without any prior programming knowledge. The templates, underlying JavaScript code, and detailed instructions are available for download on a GitHub repository. Furthermore, a comparison between data collected with SynesthesiaColorPicker and with the Synesthesia Battery shows that two methodological design choices in SynesthesiaColorPicker overcome measurable confounds in existing color picker methodology.

Examining cross-group latent differences in various research contexts has brought measurement invariance/equivalence into the spotlight. One major limitation of measurement invariance testing through multiple-group confirmatory factor analysis (MGCFA) has been recognized yet has not received much attention: the selection of a referent observed variable. Recently, multiple-indicator multiple-cause (MIMIC)-interaction modeling with the all-other anchor method (i.e., a constrained baseline approach) has been used to identify referent variables in a repetitive manner. This study proposed an iterative search strategy in MIMIC-interaction models to improve the accuracy of referent variable selection, particularly when the proportion of noninvariance increases. A Monte Carlo simulation design was used to evaluate the effects of the proportion of noninvariance, magnitude of noninvariance, magnitude of latent variable differences, and sample size. The accuracy rate was used to assess the performance of selecting referent variables from among truly invariant observed variables in the population. Results showed that the iterative strategy generally outperformed the repetitive strategy for locating credible referent variables across nearly all conditions, suggesting that the iterative strategy is a reliable and practical approach for referent variable selection in applications. The superiority of the iterative strategy over the repetitive strategy became substantial in the conditions of high proportions of noninvariance, large latent variable differences, or a combination of both. We present an illustrative example to demonstrate the applicability of the iterative strategy for referent variable selection in MIMIC-interaction modeling.

Joint attention (JA), the shared focus between two individuals on an object or event, plays a pivotal role in social communication, cognitive development, and language acquisition during early childhood. However, JA is frequently impaired in children with autism spectrum disorder (ASD), highlighting the need for precise assessment to support early diagnosis and intervention. This narrative review explores the evolution of JA assessment methods, tracing the shift from human-mediated techniques to technology-driven approaches, including artificial intelligence (AI). The study analyzes research indexed in major bibliographic databases between 2002 and 2024, categorizing findings into human-mediated and technology-assisted methods. Key aspects such as target populations, data collection processes, and validation strategies are examined. By highlighting the strengths and limitations of existing approaches, the review identifies future research directions that can advance JA assessment and inform early intervention strategies, ultimately benefiting children with ASD and their families.

Missing data are a common challenge in multilevel designs, and multiple imputation (MI) is often used for handling them. Past research has shown that multilevel MI provides an effective treatment of missing data, so long as the imputation model takes the multilevel structure and the intended analyses into account, and modern methods have been developed that can accommodate even complex types of analyses. However, multilevel MI can be difficult to apply in practice, where the multilevel structure is often not very pronounced or not of immediate interest in the analysis. In these applications, existing methods can become unstable and often struggle to provide reliable results. In this article, we introduce a fully conditional specification (FCS) approach to multilevel MI that combines single-level imputation methods with group means (GM) or adjusted group means (AGM) to accommodate the multilevel structure. Based on theoretical investigations and multiple simulation studies, we evaluated the performance of these methods across balanced and unbalanced designs and with larger numbers of variables. Our findings suggest that the AGM approach - though not the GM approach - performs well across most scenarios we investigated and can even outperform conventional multilevel MI approaches in challenging applications. We also provide an illustrative example of implementing these methods in a simulated setting and discuss the implications of our findings for practice.

Individuals typically employ multiple cognitive strategies rather than relying on a single approach in decision-making scenarios or problem-solving tasks. With recent advancements in measurement technology, the collection of process data has become increasingly common, with response times (RTs) and eye movement fixation counts (FCs) emerging as critical indicators of cognitive processing. Analysis of RTs and FCs can reveal problem-solving strategies that may not be discernible from response patterns alone. To enhance diagnostic accuracy and provide deeper insights into the cognitive processes underlying strategy selection, this study developed a multi-strategy cognitive diagnosis modeling framework that integrates individual RTs and FCs into a unified framework to define strategy selection (MS-CDM-RTFC). The empirical study utilized data from Raven's Advanced Progressive Matrices (APM), a widely used measure of nonverbal reasoning and fluid intelligence, to evaluate the practical applicability of the MS-CDM-RTFC model. Simulation results based on the empirical analysis indicate that the MS-CDM-RTFC achieves higher parameter recovery and attribute classification accuracy, demonstrating significantly better performance than traditional multi-strategy models.

Body-object interaction (BOI)-the degree to which a person physically interacts with objects or entities-has been shown to affect language processing, although studies have reported inconsistent results depending on the language or context. Moreover, no large-scale BOI database exists for Japanese. To address this gap, we collected BOI ratings for 5,637 Japanese words from 1,267 native Japanese speakers. The ratings demonstrated notable levels of intra-item and inter-rater reliability and cross-linguistic validity. We further examined the relationship between BOI ratings and normative data from a lexical decision task for Japanese words. A hierarchical multiple regression analysis controlling for various lexical and semantic variables revealed that BOI was a significant predictor of lexical decision task performance. However, unlike the original BOI effect, higher BOI ratings were associated with longer response times and lower accuracy. This finding aligns with recent results from large-scale studies on BOI data collection, highlighting the need to reevaluate the impact of BOI on lexical processing.

In a series of Monte Carlo simulation studies, we evaluated the power and Type I error rates of resampling-based procedures for comparing effect latencies between groups in the visual world paradigm (VWP). Resampling-based methods, while versatile, are known to fail in certain cases. Therefore, validation of such methods through simulation is crucial. We compared permutation- and bootstrapping-based tests combined with different methods for measuring effect latency while manipulating sample size and true effect size. Alongside previously used latency measures, we tested new measures involving the application of an effect size threshold. Simulations were based on existing VWP datasets representing different effect types (preferential looks triggered by lexical vs. grammatical cues, cohort competitor effects in word recognition) and data collection methods (infrared- vs. webcam-based eye tracking). A total of 156,000 simulations were conducted across five studies, involving 548 million resampled datasets. The main findings are as follows: (1) With sufficient sample sizes, tests were effective in detecting latency differences of 200-300 ms in sentence processing tasks, and as small as 100 ms in word recognition. (2) The permutation test and bootstrapped percentile CIs exhibited the highest overall power without inflation of Type I error rates. (3) Applying an effect size threshold in latency estimation led to consistent increases in statistical power. (4) Resampling by participant was robust to increases in cross-subject variability;in contrast, bootstrapping within participants and time bins led to elevated Type I error rates. Based on these results, we offer recommendations for using non-parametric resampling-based procedures to compare group latencies in VWP experiments.

Single-case experimental designs (SCEDs) can be used for identifying effective interventions via the intensive study of one or a few individuals in different conditions, actively manipulated by the researcher. The assessment of SCED data entails both judging whether there is sufficient evidence of a functional relation (i.e., a causal effect of the intervention on the target behavior) and quantifying the magnitude of the effect. In the current text, the focus is on assessing the presence of a functional relation, considering all the attempts to demonstrate an effect that SCEDs include. Specifically, the aim is to review several freely available websites, which require no additional software to be installed, and offer graphical representations of the data, visual aids, and quantifications. Several data analytical steps are outlined for performing the assessment, both dealing with each basic effect separately and evaluating the consistency of effects. Software that is useful for carrying out these steps is reviewed, including the way in which the data files should be specified and the few clicks required by applied researchers to achieve the desired output. The interpretations of the output are illustrated with real data.