Incremental Clinical Utility of ADHD Assessment Measures With Latino Families

Objective: This study examined the incremental clinical utility of parent and teacher reports of ADHD symptomatology and functional impairment in Latino youth, as well as parent and teacher agreement with the final clinical judgment on a diagnostic structured interview. Method: Participants included 70 Latino youth (47 males, 23 females; M age = 8.13 years, SD = 2.51 years) and their parents and teachers; 60 participants were diagnosed with ADHD. Correlations, percent agreement, kappas, and regressions were utilized. Results: Results demonstrated that teachers agreed with the final clinical judgment more often than did parents. Results additionally demonstrated that functional impairment did not statistically significantly improve diagnostic models already including ADHD symptoms; follow-up analyses were run and are discussed. Finally, results demonstrated that teacher reports statistically significantly improved diagnostic models already including parent reports. Conclusion: The current findings suggest the importance of including both parent and teacher reports of both ADHD symptomatology and functional impairment when assessing ADHD in Latino youth

Exploring Technology and Task Adaptation Among Individual Users of Mobile Technology

IS research often focuses on the contributions of information systems to organizational productivity. The task-technology fit theory proposes a positive contribution to performance when there is a good fit between the technology and the task. The fit appropriation model further proposes that the relationship between fit and performance is moderated by the user’s appropriation of the technology. This research extends these theories by investigating the role of adaptation in the fit between task and technology. Prior research has modeled adaptation as a single construct encompassing task, technology and the individual, which does not allow for an understanding of the relationship among these items. We propose that adaptation should be measured as two distinct constructs. We develop and validate a scale to measure task adaptation and technology adaption and find support for task adaption as a mediator of the effects of technology adaptation on performance

Development of a Brief Measure of Career Development Influences Based on the Systems Theory Framework

This paper documents the initial development and validation of a brief quantitative measure of career development influences based on the Systems Theory Framework of career development (McMahon & Patton, 1995; Patton & McMahon, 1997, 1999, 2006). Initial exploratory factor analyses of pilot study data revealed a six factor structure based on 20 of the 28 influences. A subsequent confirmatory factor analysis procedure using SEM revealed a fundamentally stable factor structure across the two different populations tested, although some further modifications were made to the scale. The final 19 item scale identified five correlated factors, of which three were within the framework’s individual system, one was within the social system, and one was within the environmental-societal system. In the final section of the paper, the theoretical implications of this factorial structure and the importance of the 'world of work knowledge' influence are addressed. The utility of the career development influences scale as a brief measure to contextualise more targeted measures in large scale quantitative career development studies is discussed

Suite of simple metrics reveals common movement syndromes across vertebrate taxa

ecause empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging)

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Antecedents and consequences of effectuation and causation in the international new venture creation process

The selection of the entry mode in an international market is of key importance for the venture. A process-based perspective on entry mode selection can add to the International Business and International Entrepreneurship literature. Framing the international market entry as an entrepreneurial process, this paper analyzes the antecedents and consequences of causation and effectuation in the entry mode selection. For the analysis, regression-based techniques were used on a sample of 65 gazelles. The results indicate that experienced entrepreneurs tend to apply effectuation rather than causation, while uncertainty does not have a systematic influence. Entrepreneurs using causation-based international new venture creation processes tend to engage in export-type entry modes, while effectuation-based international new venture creation processes do not predetermine the entry mod

EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data -- e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools

Where do statistical models come from? Revisiting the problem of specification

R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to be: specification, estimation and distribution. Since then the problem of statistical model specification has received scant attention in the statistics literature. The paper traces the history of statistical model specification, focusing primarily on pioneers like Fisher, Neyman, and more recently Lehmann and Cox, and attempts a synthesis of their views in the context of the Probabilistic Reduction (PR) approach. As argued by Lehmann [11], a major stumbling block for a general approach to statistical model specification has been the delineation of the appropriate role for substantive subject matter information. The PR approach demarcates the interrelated but complemenatry roles of substantive and statistical information summarized ab initio in the form of a structural and a statistical model, respectively. In an attempt to preserve the integrity of both sources of information, as well as to ensure the reliability of their fusing, a purely probabilistic construal of statistical models is advocated. This probabilistic construal is then used to shed light on a number of issues relating to specification, including the role of preliminary data analysis, structural vs. statistical models, model specification vs. model selection, statistical vs. substantive adequacy and model validation.Comment: Published at http://dx.doi.org/10.1214/074921706000000419 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org