Graphics for uncertainty

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data

An assertion language for constraint logic programs

In an advanced program development environment, such as that discussed in the introduction of this book, several tools may coexist which handle both the program and information on the program in different ways. Also, these tools may interact among themselves and with the user. Thus, the different tools and the user need some way to communicate. It is our design principie that such communication be performed in terms of assertions. Assertions are syntactic objects which allow expressing properties of programs. Several assertion languages have been used in the past in different contexts, mainly related to program debugging. In this chapter we propose a general language of assertions which is used in different tools for validation and debugging of constraint logic programs in the context of the DiSCiPl project. The assertion language proposed is parametric w.r.t. the particular constraint domain and properties of interest being used in each different tool. The language proposed is quite general in that it poses few restrictions on the kind of properties which may be expressed. We believe the assertion language we propose is of practical relevance and appropriate for the different uses required in the tools considered

ObStruct: A method to objectively analyse factors driving population structure using Bayesian ancestry profiles

Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin) correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses. © 2014 Gayevskiy et al

The Feeling of Numbers: emotions in everyday engagements with data and their visualisation

This paper highlights the role that emotions play in engagements with data and their visualisation. To date, the relationship between data and emotions has rarely been noted, in part because data studies have not attended to everyday engagements with data. We draw on an empirical study to show a wide range of emotional engagements with diverse aspects of data and their visualisation, and so demonstrate the importance of emotions as vital components of making sense of data. We nuance the argument that regimes of datafication, in which numbers, metrics and statistics dominate, are characterised by a renewed faith in objectivity and rationality, arguing that in datafied times, it is not only numbers but also the feeling of numbers that is important. We build on the sociology of a) emotions and b) the everyday to do this, and in so doing, we contribute to the development of a sociology of data

The Sleeping Brain's Influence on Verbal Memory: Boosting Resistance to Interference

Memories evolve. After learning something new, the brain initiates a complex set of post-learning processing that facilitates recall (i.e., consolidation). Evidence points to sleep as one of the determinants of that change. But whenever a behavioral study of episodic memory shows a benefit of sleep, critics assert that sleep only leads to a temporary shelter from the damaging effects of interference that would otherwise accrue during wakefulness. To evaluate the potentially active role of sleep for verbal memory, we compared memory recall after sleep, with and without interference before testing. We demonstrated that recall performance for verbal memory was greater after sleep than after wakefulness. And when using interference testing, that difference was even more pronounced. By introducing interference after sleep, this study confirms an experimental paradigm that demonstrates the active role of sleep in consolidating memory, and unmasks the large magnitude of that benefit

The Effect of Student-Directed Transition Planning With a Computer-Based Reading Support Program on the Self-Determination of Students With Disabilities

The purpose of this study was to investigate the impact of student-directed transition planning instruction (Whose Future Is It Anyway? curriculum) with a computer-based reading support program (Rocket Reader) on the self-determination, self-efficacy and outcome expectancy, and transition planning knowledge of students with disabilities. This study employed a pre- and postmeasure design with 168 middle school students with disabilities who were assigned to an experimental group (n = 86) and control group (n = 82). The results of the study demonstrated that self-determination, self-efficacy, and outcome expectancy for education planning improved through the application of Rocket Reader . Avenues are discussed for promoting middle school students’ self-determination in their transition planning, as are implications for future research.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

The Communicability of Graphical Alternatives to Tabular Displays of Statistical Simulation Studies

Simulation studies are often used to assess the frequency properties and optimality of statistical methods. They are typically reported in tables, which may contain hundreds of figures to be contrasted over multiple dimensions. To assess the degree to which these tables are fit for purpose, we performed a randomised cross-over experiment in which statisticians were asked to extract information from (i) such a table sourced from the literature and (ii) a graphical adaptation designed by the authors, and were timed and assessed for accuracy. We developed hierarchical models accounting for differences between individuals of different experience levels (under- and post-graduate), within experience levels, and between different table-graph pairs. In our experiment, information could be extracted quicker and, for less experienced participants, more accurately from graphical presentations than tabular displays. We also performed a literature review to assess the prevalence of hard-to-interpret design features in tables of simulation studies in three popular statistics journals, finding that many are presented innumerately. We recommend simulation studies be presented in graphical form

The Glasgow Norms:Ratings of 5,500 words on nine scales

The Glasgow Norms are a set of normative ratings for 5,553 English words on nine psycholinguistic dimensions: arousal, valence, dominance, concreteness, imageability, familiarity, age of acquisition, semantic size, and gender association. The Glasgow Norms are unique in several respects. First, the corpus itself is relatively large, while simultaneously providing norms across a substantial number of lexical dimensions. Second, for any given subset of words, the same participants provided ratings across all nine dimensions (33 participants/word, on average). Third, two novel dimensions—semantic size and gender association—are included. Finally, the corpus contains a set of 379 ambiguous words that are presented either alone (e.g., toast) or with information that selects an alternative sense (e.g., toast (bread), toast (speech)). The relationships between the dimensions of the Glasgow Norms were initially investigated by assessing their correlations. In addition, a principal component analysis revealed four main factors, accounting for 82% of the variance (Visualization, Emotion, Salience, and Exposure). The validity of the Glasgow Norms was established via comparisons of our ratings to 18 different sets of current psycholinguistic norms. The dimension of size was tested with megastudy data, confirming findings from past studies that have explicitly examined this variable. Alternative senses of ambiguous words (i.e., disambiguated forms), when discordant on a given dimension, seemingly led to appropriately distinct ratings. Informal comparisons between the ratings of ambiguous words and of their alternative senses showed different patterns that likely depended on several factors (the number of senses, their relative strengths, and the rating scales themselves). Overall, the Glasgow Norms provide a valuable resource—in particular, for researchers investigating the role of word recognition in language comprehension