Survey Data Collection and Analytics 專項課程

Este curso te proporcionará una visión general de los productos de datos existentes y una buena comprensión del panorama de recopilación de datos. Con la ayuda de varios ejemplos, aprenderás cómo identificar qué fuentes de datos probablemente coincidan con tu pregunta de investigación, cómo convertir tu pregunta de investigación en piezas medibles y cómo pensar en un plan de análisis. Además, este curso te proporcionará un marco general que no solo te permitirá comprender cada paso requerido para una recopilación y un análisis de datos exitosos, sino que también te ayudará a identificar los errores asociados con diferentes fuentes de datos. Aprenderás algunas métricas para cuantificar cada error potencial y, por lo tanto, tendrás herramientas disponibles para describir la calidad de una fuente de datos. Finalmente, presentaremos diferentes esfuerzos de recopilación de datos a gran escala realizados por agencias gubernamentales y del sector privado, y revisaremos los conceptos aprendidos a través de estos ejemplos. Este curso es adecuado tanto para principiantes como para aquellos que conocen una fuente de datos en particular, pero no otras, y buscan un marco general para evaluar productos de datos.

This course presents research conducted to increase our understanding of how data collection decisions affect survey errors. This is not a “how–to-do-it” course on data collection, but instead reviews the literature on survey design decisions and data quality in order to sensitize learners to how alternative survey designs might impact the data obtained from those surveys.

The course reviews a range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e. as web surveys). Mixed mode designs are also covered as well as several hybrid modes for collecting sensitive information e.g., self-administering the sensitive questions in what is otherwise a face-to-face interview. The course also covers newer methods such as mobile web and SMS (text message) interviews, and examines alternative data sources such as social media. It concentrates on the impact these techniques have on the quality of survey data, including error from measurement, nonresponse, and coverage, and assesses the tradeoffs between these error sources when researchers choose a mode or survey design.

This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.