Para hacer una buena recopilación de datos se necesitan buenas muestras. Pero las muestras pueden elegirse de muchas maneras. Pueden ser selecciones fortuitas o convenientes de personas, registros, redes u otras unidades, pero uno se cuestiona la calidad de tales muestras, especialmente cómo se relacionan estos métodos de selección con el hecho de sacar buenas conclusiones sobre una población tras la recopilación y el análisis de los datos. Las muestras pueden seleccionarse con más cuidado según el criterio del investigador, pero entonces uno se pregunta si ese criterio puede estar sesgado por factores personales. Las muestras también pueden extraerse de forma estadísticamente rigurosa y meticulosa, utilizando métodos de selección y control aleatorios para proporcionar una sólida representación y costos controlados. Este último tipo de muestras es el que se analizará en este curso. Examinaremos el muestreo aleatorio simple que puede utilizarse para el muestreo de personas o registros, el muestreo por conglomerados que puede utilizarse para el muestreo de grupos de personas, registros o redes, la estratificación que puede aplicarse a las muestras aleatorias simples y por conglomerados, la selección sistemática y las muestras estratificadas multietapa. El curso concluye con una breve descripción de cómo estimar y resumir la incertidumbre del muestreo aleatorio.
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There are 6 modules in this course
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10 videos10 readings
What's included
7 videos7 readings1 assignment1 peer review
What's included
10 videos6 readings1 assignment1 peer review
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6 videos6 readings1 assignment
What's included
5 videos4 readings1 peer review
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6 videos9 readings1 assignment
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Universitat Autònoma de Barcelona
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