Publicacions de la Universitat Jaume I és una editorial membre de l'une, cosa que en garanteix la difusió de les obres en els àmbits nacional i internacional. www.une.es Reconeixement-CompartirIgual CC BY-SA Aquest text està subjecte a una llicència Reconeixement-CompartirIgual de Creative Commons, que permet copiar, distribuir i comunicar públicament l'obra sempre que s'especifique l'autor i el nom de la publicació fins i tot amb objectius comercials i també permet crear obres derivades, sempre que siguen distribuïdes amb aquesta mateixa llicència. http://creativecommons.org/licenses/by-sa/3.0/legalcode Los motivos por los que se ha hecho este texto son de tipo docente, y tienen como función facilitar el aprendizaje de los alumnos sobre aspectos básicos de la regresión, para ello, se ha seguido un procedimiento paso a paso, indicando:• Cómo plantear una relación entre las variables con una formulación funcional y en una forma de ecuación estadística.• Una descripción de cómo se hace para pasar de una variable categórica a otra variable formada por un bloque de variables ficticias. • Cómo introducir un bloque de variables ficticias en un programa estadístico informático, el SPSS, con el objetivo de conseguir su significación estadística correspondiente.• Cómo ajustar un modelo con el programa estadístico SPSS.• Cómo escribir la mejor ecuación global ajustada a los datos.• La ecuación general puede ser desglosada en el correspondiente número de grupos que contenga; se expone cómo hacerlo y cómo escribir una ecuación de pronóstico para cada grupo diferente.• Cómo interpretar la ecuación general y cada una de las ecuaciones correspondiente a cada grupo.• Cómo guardar los valores pronosticados de la variable dependiente.• Cómo hacer una representación gráfica de conjunto, con una única figura para todos los grupos de la investigación. En el desarrollo de esta unidad indicaremos cómo identificar cada grupo de una determinada variable mediante la creación de variables dummy (variables dicotómicas con valores 1 y 0). El motivo de esta codificación (1 y 0) es que el pronóstico de valores y la interpretación resultan mucho más fáciles que si se utiliza cualquier otra.Si tomamos la variable género, las mujeres se pueden identificar mediante el valor de 1, por lo que los hombres pasarían a tener el valor de 0 (recuérdese que estos valores se usan para identificar a los grupos). No hace falta crear otra variable que identifique con 1 a los hombres y 0 a las mujeres (solo con una de las dos es suficiente).Hay un principio básico en la creación de variables dummy: se necesitan tantas variables dummy distintas como grupos haya en esa variable, menos una: n.º Vs. dummy para una variable = (n.º grupos) -1 Así, para la variable 'género' se necesita una sola variable dummy: 2 -1 = 1. Si se tiene una variable con 8 grupos, el número de variables dummy para identificarla será de 7 (8 -1). IMPORTANTE:El grupo identificado con el valor 0 en las categorías dummy es el llamado 'Grupo de Referencia'. ÍndiceSi se tiene una variable con l...
Researchers and educators raise the question of whether pupils' academic performance can be improved through parental involvement in academic activities. The main objective of the following study is to verify whether parental involvement in school activities and family socioeconomic status are associated with children's academic achievement. 150 Spanish seventh grade pupils completed intelligence tests, and their teachers assessed parents' involvement in the school and estimated parents' cultural levels. To measure academic achievement the pupil's overall grade was taken from the Pupils' Final Evaluation Registers. The education and professional level of the mother and father and home size were obtained from the Pupil Personal Register; these variables define the family socioeconomic status. The data, analyzed through application of structural equations, suggest that academic achievement is directly influenced by the cultural level of the family and the child's intelligence but is indirectly influenced by parental involvement in school activities and the socioeconomic status of the child's family.
Procesos metacognitivos: implicaciones en las dificultades de aprendizaje de las matemáticas El concepto de metacognición fue definido por Flavell [8] como el conocimiento acerca de los propios procesos o productos NEW TRENDS IN THE EVALUATION OF MATHEMATICS LEARNING DISABILITIES. THE ROLE OF METACOGNITION Summary. Introduction. The current trends in the evaluation of mathematics learning disabilities (MLD), based on cognitive and empirical models, are oriented towards combining procedures involving the criteria and the evaluation of cognitive and metacognitive processes, associated to performance in mathematical tasks. Aims. The objective of this study is to analyse the metacognitive skills of prediction and evaluation in performing maths tasks and to compare metacognitive performance among pupils with MLD and younger pupils without MLD, who have the same level of mathematical performance. Likewise, we analyse these pupils' desire to learn. Subjects and methods. We compare a total of 44 pupils from the second cycle of primary education (8-10 years old) with and without mathematics learning disabilities. Results. Significant differences are observed between pupils with and without mathematics learning disabilities in their capacity to predict and assess all of the tasks evaluated. As regards their 'desire to learn', no significant differences were found between pupils with and without MLD, which indicated that those with MLD assess their chances of successfully performing maths tasks in the same way as those without MLD. Finally, the findings reveal a similar metacognitive profile in pupils with MLD and the younger pupils with no mathematics learning disabilities. Conclusions. In future studies we consider it important to analyse the influence of the socioaffective belief system in the use of metacognitive skills.
Introduction: Time series analysis is particularly useful, especially in disciplines that require close longitudinal monitoring. Despite their great usefulness, its use is not common in fields such as psychology. For this reason, the authors of this work used time series to carry out a study on tobacco addiction. The results showed that tobacco behaviour followed an AR (2)(7) 8 model, that is, the sample had a 56-day memory. The objective of the present work is to verify the statistical power and the effect size of the model that they found. Method: Given the absence of information in the previous references, an analysis of time series was performed a posteriori imitating the models founded in the previous studies. It was calculated using G*Power software if our sample size is large enough to obtain a model with statistical power and a good effect size. Results: The output indicates that a minimum of 17 subjects are needed, with 63 data each day (a total of 1071 data) to obtain a model with a good statistical power and effect size. Conclusion: To sum up, we conclude with the affirmation that time series analysis has a poor statistical power, so samples for this type of analysis should be quite large. Furthermore, the ideal number of subjects to obtain an adequate statistical power and an effect size should be checked by a previous study or, if that is not possible, a posteriori analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.