Abstract Linear mixed-effects models (LMEMs) are used to account for variation within factors with multiple observations, such as participants, trials, items, channels, etc (for an earlier approach, see Clark, 1973). This variation is modelled in terms of random intercepts (e.g., overall variation per participant) as well as random slopes for the fixed effects (e.g., treatment effect per participant). These measures help reduce false positives and false negatives (Barr, Levy, Scheepers, & Tily, 2013), and the resulting models tend to be robust to violations of assumptions (Schielzeth et al.
Las aplicaciones web nos ayudan a facilitar el uso de nuestro trabajo, ya que no requieren programación para utilizarlas. Crear estas aplicaciones en R, mediante paquetes como "shiny" o "flexdashboard", ofrece múltiples ventajas. Entre ellas destaca la reproducibilidad, tal como veremos en torno a una aplicación para la simulación de datos (https://github.com/pablobernabeu/Experimental-data-simulation).
In a highly recommendable presentation available on Youtube, Michael Frank walks us through R Markdown. Below, I loosely summarise and partly elaborate on Frank's advice regarding collaboration among colleagues, some of whom may not be used to R Markdown (see relevant time point in Frank's presentation).
The first way is to use GitHub, which has a great version control system, and even allows the rendering of Markdown text, if the file is given the extension ‘.
This document is part of teaching materials created for the workshop 'Open data and reproducibility v2.1: R Markdown, dashboards and Binder', delivered at the CarpentryCon 2020 conference. The purpose of this specific document is to practise R Markdown, including basic features such as Markdown markup and code chunks, along with more special features such as cross-references for figures, tables, code chunks, etc. Since this conference was originally going to take place in Madison, let's look at some open data from the City of Madison.
This open-source, R-based web application is suitable for educational or research purposes in experimental sciences. It allows the **creation of varied data sets with specified structures, such as between-group or within-participant variables, that …
Dashboard with open data from a study by Prudic et al. (2018), that compares citizen science with traditional methods in butterfly sampling. Coding tasks included long-transforming, merging, and as ever, wrangling with a table.
This project offers free activities to learn and practise reproducible data presentation. Pablo Bernabeu organises these events in the context of a Software Sustainability Institute Fellowship. Programming languages such as R and Python offer free, powerful resources for data processing, visualisation and analysis. Experience in these programs is highly valued in data-intensive disciplines. Original data has become a public good in many research fields thanks to cultural and technological advances. On the internet, we can find innumerable data sets from sources such as scientific journals and repositories (e.g., OSF), local and national governments, non-governmental organisations (e.g., data.world), etc. Activities comprise free workshops and datathons.
Event-related potentials (ERPs) offer a unique insight in the study of human cognition. Let’s look at their reason-to-be for the purposes of research, and how they are defined and processed. Most of this content is based on my master’s thesis (download), which I could fortunately conduct at the Max Planck Institute for Psycholinguistics (conference paper also available).
Electroencephalography The brain produces electrical activity all the time, which can be measured via electrodes on the scalp—a method known as electroencephalography (EEG).
This app presents linguistic data over several tabs. The code combines the great front-end of Flexdashboard—based on R Markdown and yielding an unmatched user interface—, with the great back-end of Shiny—allowing users to download sections of data they select, in various formats. The hardest nuts to crack included modifying the rows/columns orientation without affecting the functionality of tables. A cool, recent finding was the reactable package. A nice feature, allowed by Flexdashboard, was the use of quite different formats in different tabs.
Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set those yourself based on whatever prior information or purposes you had. The 'naive' approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables.
The single dependent variable, RT, was accompanied by other variables which could be analyzed as independent variables. These included Group, Trial Number, and a within-subjects Condition. What had to be done first off, in order to take the usual table? The trials!
We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of consecutive trials was manipulated in order to produce an Auditory-to-visual switch condition, a Haptic-to-visual switch condition, and a Visual-to-visual, no-switch condition. Event-Related Potentials (ERPs) were time-locked to the onset of the first word (property) in the target trials so as to measure the effect online and to avoid a within-trial confound. A switch effect was found, characterized by more negative ERP amplitudes for modality switches than no-switches. It proved significant in four typical time windows from 160 to 750 milliseconds post word onset, with greater strength in the Slow group, in posterior brain regions, and in the N400 window. The earliest switch effect was located in the language brain region, whereas later it was more prominent in the visual region. In the N400 and Late Positive windows, the Quick group presented the effect especially in the language region, whereas the Slow had it rather in the visual region. These results suggest that contextual factors such as time resources modulate the engagement of linguistic and embodied systems in conceptual processing.
Racism has long been ingrained in human societies. Ancient Greek Aristotle already claimed that non-Greeks were slaves by nature, as they easily submitted to despotic government (Reilly, Kaufman, & Bodino, 2002). This study focuses on racism in the United States, which extends from the foundation of the country, when black people were generally born into slavery, and were at any rate regarded as an inferior people. US racism stands out globally for two reasons. First, the country has played a hegemonic part in the World since soon after its foundation. Second, the US is regarded as the most advanced society technology-wise, as it sets the minutes for the technology sector worldwide. In spite of these advantages, the country has long suffered the plague of widespread racism. Indeed, the abolition of slavery in the mid-nineteenth century did not grant equal citizen rights to the black population. Over time, the black population started to confront this situation. Especially the mid-nineteenth century saw large uprisings and a patent division of different societal sectors, as reflected in literary works such as Ellison’s 'Invisible Man' (1952). Inequality and confrontation about racism has extended to date, and the costs thereof have been large in terms of lives and otherwise (Feagin, 2004).