Stronger effects of self-generated than cue-induced expectations when verifying predictions in data graphs

Expectations can come in different forms when analyzing and presenting data. Prior studies have documented stronger effects (behavioral and electrophysiological) of self-generated predictions as compared to cues. While participants presumably cannot help but use their own predictions, they might sometimes ignore cues (of low validity). In two experiments we compared the impact of cues (verbal and visual) and self-generated predictions on the performance of participants checking their current prediction against a presented data graph (linear upward or downward trend). Different from prior studies, the setup allowed for within-experiment comparison of different cue formats and ensured that cues could not be ignored. Nevertheless we found that self-generated predictions had a stronger impact than cues. Verbal cues had a stronger effect than visual cues without verbalization. Responses to graphs with a linear upward trend were faster and were influenced more strongly by predictions, than the response to graphs with a downward trend.     

An evaluation of estimation data collection to trial‐by trial data collection during discrete trial teaching

There are many data collection procedures used during discrete trial teaching including first‐trial data collection, probe data, trial‐by‐trial data collection, and estimation data. Continuous, or trial‐by‐trial data collection, consists of the interventionist collecting data on learner behavior on each trial. Estimation data consists of the interventionist estimating learner performance after a teaching session using a rating scale. The purpose of the present study was to compare trial‐by‐trial data collection to estimation data collection during discrete trial teaching to teach children expressive labels. The data collection procedures were examined in terms of accuracy of data collection, efficiency of teaching (i.e., number of trials delivered per session), and rate of child acquisition of targets. Results of the adapted alternating treatment design replicated across three participants and multiple targets found estimation data collection to be as accurate as trial‐by‐trial data collection in determining mastery of targets. Estimation data collected by the interventionist was also found to be accurate when compared to the actual trial‐by‐trial data collected after the study concluded.     

Social validity assessment of behavior data recording among human services care providers

Care providers within human services organizations have many job responsibilities and performance expectations. In the present study, we conducted social validity assessment with 78 care providers concerning their attitudes and opinions about behavior data recording with adults who had intellectual disability and lived in community group homes. Specifically, the care providers responded to a written questionnaire that inquired about the practicality, training/supervision, and value of behavior data recording in the context of service delivery. Results indicated generally high approval of behavior data recording practices, purposes, and approaches to training. We discuss implications of these findings for implementing data recording by care providers and the contribution of social validity assessment to training and performance management within human services organizations.     

Physiological responses to urban design during bicycling: A naturalistic investigation

The current research set out to measure the moderating effect that urban design may have on bicyclist physiology while in transition. Focusing on the hilly City of Wuppertal, Germany, we harnessed bicyclists with mobile sensors to measure their responses to urban design metrics obtained from space syntax, while also adjusting for known traffic, terrain, and contextual factors. The empirical strategy consisted of exploratory data analysis (EDA), ordinary least squares (OLS), and a local regression model to account for spatial autocorrelation. The latter model was robust (R² = 68%), and showed that two statistically significant (p < 0.05) urban design factors influenced bicyclist physiology. Controllability, a measure of how spatially dominated a space is, increased bicyclist responses (i.e., decreased comfortability); while integration, which is related to accessibility and connectivity, had the opposite effect. Other noteworthy covariates included one-way streets and density of parked automobiles: these exerted a negative influence on bicyclist physiology. The results of this research ultimately showed that nuanced urban designs have a moderate influence on bicycling comfort. These outcomes could be utilized by practitioners focused on implementing appropriate interventions to increase bicyclist comfort levels and this mode share.     

寻求反馈能改善绩效吗?——反馈寻求行为与个体绩效关系的元分析

利用元分析方法探讨反馈寻求行为(FSB)与个体绩效的关系以明确其能否改善个体绩效。共有62篇实证研究纳入元分析, 被试总人数达15141人。结果表明：反馈寻求行为与个体绩效呈中等程度正相关(r = 0.329), 且与创新绩效的关系(r = 0.409)强于关系绩效(r = 0.302)和任务绩效(r = 0.258); 询问式反馈寻求行为(Inquiry FSB)与个体绩效及其分维度绩效的关系均强于监控式反馈寻求行为(Monitoring FSB)。文化背景和数据收集方式调节了反馈寻求行为与个体绩效的关系, 该关系在东亚文化背景下(r = 0.393)和截面同源数据中(r = 0.433)最强, 且在纵向配对数据中仍显著正相关(r = 0.154), 充分说明反馈寻求行为能改善个体绩效; 反馈寻求行为的测量工具、反馈源、非自评绩效的主客观性和被试类型的调节效应不显著。研究结果为反馈寻求行为对个体绩效的预测提供了较精确的估计, 并为反馈寻求行为的后续研究指引了方向。     

Modelling inter-individual differences in latent within-person variation: The confirmatory factor level variability model

Psychological theories often produce hypotheses that pertain to individual differences in within-person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between-person differences in the mean level of a certain variable and the residual within-person variance. Currently, these approaches can be applied only when the data stem from a single variable. However, it is common practice in psychology to assess not just a single measure but rather several measures of a construct. In this paper we describe a model in which we combine the single-indicator model with confirmatory factor analysis. The new model allows individual differences in latent mean-level factors and latent within-person variability factors to be estimated. Furthermore, we show how the model's parameters can be estimated with a maximum likelihood estimator, and we illustrate the approach using an example that involves intensive longitudinal data.     

Standard errors of two-level scalability coefficients

For the construction of tests and questionnaires that require multiple raters (e.g., a child behaviour checklist completed by both parents) a novel ordinal scaling technique is currently being further developed, called two-level Mokken scale analysis. The technique uses within-rater and between-rater coefficients to assess the scalability of the test. These coefficients are generalizations of Mokken's scalability coefficients. In this paper we derived standard errors for the two-level coefficients and for their ratios. The coefficients, the estimates, the estimated standard errors and the software implementation are discussed and illustrated using a real-data example, and a small-scale simulation study demonstrates the accuracy of the estimates.     

计算机动态测验中问题解决过程策略的分析：多水平混合IRT模型的拓展与应用

学生在完成计算机动态测验过程中, 会产生大量带有时间标记的过程性数据。本研究基于5个国家(地区) 3196名学生在PISA2012一道交通问题解决任务上的139990条数据, 将多水平混合IRT (MMixIRT)模型进行拓展, 用于探索问题解决过程策略的类别特点。结果表明, 该模型不仅可以基于行为序列对不同国家(地区)学生在解决问题时策略使用情况的典型特征进行分析, 还可以提供个体水平的能力估计值。拓展的MMixIRT模型可用于分析过程性数据的特征。     

Tracking Fluctuations in Psychological States Using Social Media Language: A Case Study of Weekly Emotion

Personality psychologists are increasingly documenting dynamic, within-person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality-relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text-based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human-annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self-reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model-predicted emotion and self-reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology     

The Personality Panorama: Conceptualizing Personality Through Big Behavioural Data

Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant-report, and self-report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self-report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a ‘personality panopticon’ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest. © 2020 European Association of Personality Psychology