Prevalence norms for 40,777 Catalan words: An online megastudy of vocabulary size.

In this study, we present word prevalence data (i.e., the number of people who know a given word) for 40,777 Catalan words. An online massive visual lexical decision task involving more than 200,000 native speakers of this language was carried out. The characteristics of the participants as well as those of the words which mostly influence word knowledge were examined. Regarding the participants, the analysis of the data revealed that their age was the main factor influencing vocabulary size, followed by their educational level and other variables such as the number of languages spoken and their level of proficiency in Catalan. Concerning the words, by far the most determining factor was lexical frequency, with a minor influence of both length and the size of the orthographic neighborhood. These data mainly agree with those reported in other languages in which the same variables have been analyzed (Dutch, English, and Spanish, thus far). Therefore, the list is increased with Catalan, a language which, due to its use in an essentially bilingual context, is of special interest to researchers interested in the field of bilingualism and second language acquisition.

The predictors of general knowledge: Data from a Spanish megastudy.

Studies on sociodemographic data and crystallized intelligence have often struggled to recruit enough participants to achieve sufficient validity. However, the advent of the internet now allows this problem to be solved through the creation of megastudies. Yet, this methodology so far has only been used in studies on vocabulary size, while general knowledge, another key component of crystallized intelligence, remains unexamined. In the present study, regression models were used to examine the impact of sociodemographic variables-gender, age, years of study and socioeconomic status-on general knowledge scores. The sample comprised 48,234 participants, each of whom answered 60 general knowledge questions, their data being fully available online. Men were found to score higher than women in general knowledge. Years of study and socioeconomic status acted as strong and weak positive predictors, respectively. Age acted as a strong positive predictor until the age of 50, where it became progressively detrimental. These results are discussed relative to other studies on crystallized intelligence, highlighting the need to study each of its components individually.

SUBTLEX-CAT: Subtitle word frequencies and contextual diversity for Catalan.

SUBTLEX-CAT is a word frequency and contextual diversity database for Catalan, obtained from a 278-million-word corpus based on subtitles supplied from broadcast Catalan television. Like all previous SUBTLEX corpora, it comprises subtitles from films and TV series. In addition, it includes a wider range of TV shows (e.g., news, documentaries, debates, and talk shows) than has been included in most previous databases. Frequency metrics were obtained for the whole corpus, on the one hand, and only for films and fiction TV series, on the other. Two lexical decision experiments revealed that the subtitle-based metrics outperformed the previously available frequency estimates, computed from either written texts or texts from the Internet. Furthermore, the metrics obtained from the whole corpus were better predictors than the ones obtained from films and fiction TV series alone. In both experiments, the best predictor of response times and accuracy was contextual diversity.

Clustering words to match conditions: an algorithm for stimuli selection in factorial designs / Agrupar palabras para igualar condiciones: un algoritmo para la selección de palabras en diseños factoriales

With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables is time consuming and error prone. To assist experimenters in this thankless task, we present a simple method to perform it with little effort. The method is based on Kmeans clustering as a way to detect small and tight clusters of words that match in the desired variables. We have formalized the procedure into an algorithmic format, that is, a series of easy-to-follow steps. In addition, we also provide an SPSS syntax that helps in choosing the correct size of the clustering. After reviewing the theory, we present a worked example that will guide the reader through the complete procedure. The dataset of the worked example is available as a supplementary material to this paper (AU)

Con el creciente refinamiento de los modelos de procesamiento del lenguaje y los nuevos hallazgos sobre qué variables pueden modular dichos procesos, la selección de palabras para experimentos de diseño factorial se está convirtiendo en una tarea cada vez más ardua. Seleccionar conjuntos de palabras que difieren en una variable pero que están igualadas en una decena de posibles variables extrañas, lleva mucho tiempo y está sujeto a errores. Para ayudar a los experimentadores en esta desagradecida tarea, presentamos un método sencillo que permite realizarla con poco esfuerzo. El método se basa en el agrupamiento de Kmedias para identificar conjuntos pequeños y compactos de palabras igualadas en las variables deseadas. El procedimiento ha sido formalizado en un algoritmo, esto es, una serie de pasos concretos y sencillos de seguir. Además, también aportamos la sintaxis en SPSS para ayudar en la selección del número adecuado de agrupaciones. Tras una revisión de la teoría, presentamos un ejemplo práctico que guiará al lector a través del procedimiento completo. El conjunto de datos del ejemplo se encuentra disponible como material complementario a este artículo (AU)

NIM: A Web-based Swiss army knife to select stimuli for psycholinguistic studies.

NIM is Web-based software developed to help experimenters with some of the usual tasks carried out in psycholinguistic studies. It allows the user to search for words according to several variables, such as length, matching substrings, lexical frequency, or part of speech, in English, Spanish, and Catalan. NIM also provides the user with the possibilities to obtain different word metrics, such as lexical frequency, length, and part of speech; to find intralanguage and cross-language lexical neighbors; and to get control words for critical stimuli. Regardless of the language used, the program also enables the user to get the orthographic similarity between word pairs and to identify repeated items in lists of experimental stimuli. NIM is free and is publicly available at http://psico.fcep.urv.cat/utilitats/nim/ .