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Cómo combinar y analizar datos complejos

Richard Valliant, Ph.D.

En este curso, aprenderás a usar las ponderaciones de las encuestas para estimar estadísticas descriptivas (como medias y totales) y cantidades más complejas (como parámetros de modelos para regresiones lineales y logísticas). Se explicarán las capacidades de software, haciendo especial hincapié en R®. El curso también abarcará nociones básicas sobre vinculación de registros y búsqueda de coincidencias estadísticas, dos procesos que son cada vez más importantes para combinar datos de fuentes distintas. En el curso, también se exploran los problemas éticos que suscrita la combinación de conjuntos de datos. Es posible que haga falta obtener el consentimiento informado de las personas para que permitan la vinculación de sus datos. Conocerás las diferencias entre los requisitos legales de distintos países.

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What's inside

Syllabus

Estimación básica
Tras completar los módulos 1 y 2 de este curso, entenderás cómo estimar estadísticas descriptivas, generales y de subgrupos de los datos de encuestas. Revisaremos el software usado para la estimación (R, Stata o SAS) con ejemplos sobre cómo estimar determinados valores, como medias, proporciones y totales. También aprenderán a estimar parámetros en modelos lineales, logísticos y de otro tipo, y conocerán las opciones de software, haciendo especial hincapié en R. En los módulos 3 y 4, se hablará sobre cómo agregar datos a los análisis. Esto supone conocer de técnicas de vinculación de registros y saber qué se requiere a fin de obtener los permisos necesarios para vincular datos.
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Modelos
En el módulo 2, se explica cómo estimar parámetros de modelos lineales y logísticos mediante datos de encuestas. Tras completar este módulo, entenderás cómo los métodos utilizados son distintos de los que se usan en datos que no son de encuestas. También hablaremos sobre las características de los conjuntos de datos de encuestas que deben tenerse en cuenta cuando se estiman errores estándar de parámetros de modelos estimados.
Vinculación de registros
El módulo empieza con el debate actual en torno al uso de más registros administrativos (vinculados) en el Sistema Estadístico Federal de los EE. UU. y una motivación general para vincular registros. Se ofrecerán numerosos ejemplos que explican por qué es útil vincular datos. Se analizarán los desafíos de la vinculación de registros. También se incluye un resumen breve sobre las técnicas clave de vinculación.
Ética
En este módulo, se mencionarán los problemas clave a la hora de obtener el consentimiento para la vinculación de registros. La falta de consentimiento puede provocar sesgos en las estimaciones. Se darán ejemplos de investigaciones actuales, así como sugerencias prácticas sobre cómo obtener el consentimiento de vinculación.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Para estudiantes que buscan reforzar sus conocimientos básicos en el uso de ponderaciones de encuestas
Para estudiantes que buscan aplicar técnicas de vinculación de registros y emparejamiento estadístico en sus investigaciones
Para estudiantes que buscan desarrollar sus habilidades en el análisis de datos y la estimación de modelos estadísticos

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Cómo combinar y analizar datos complejos with these activities:
Review Mathematics of Probability
Reviewing probability concepts will help to fortify your understanding of survey statistics.
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  • Examine basic rules of probability.
  • Review concepts of conditional probability and Bayes' theorem.
Brush up on Basic Statistics
Reviewing basic statistical concepts will provide a strong base for understanding survey statistics.
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  • Revise central tendency and dispersion measures.
  • Review sampling distributions and the Central Limit Theorem.
Solve practice problems on sampling distributions
Solving practice problems on sampling distributions will help you apply concepts learned in the course.
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  • Find the mean and standard deviation of a sampling distribution.
  • Use the Central Limit Theorem to approximate probabilities.
Three other activities
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Create infographics on survey design
Creating infographics on survey design will help you visualize and understand the key concepts.
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  • Identify the key elements of survey design.
  • Design an infographic that visually represents these elements.
Follow tutorials on data visualization
Following tutorials on data visualization will provide you with practical skills to present survey results.
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  • Explore different data visualization tools and techniques.
  • Learn how to create effective visualizations that convey insights clearly.
Contribute to an open-source data visualization project
Collaborating on data visualization projects will broaden your understanding and practical skills.
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  • Identify an open-source data visualization project that aligns with your interests.
  • Join the project community and contribute your skills.

Career center

Learners who complete Cómo combinar y analizar datos complejos will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians collect, analyze, interpret, and present numerical data in order to develop meaningful conclusions and solve problems. This course, Cómo combinar y analizar datos complejos, teaches how to use survey weights to estimate descriptive statistics, more complex quantities, and parameters of models for linear and logistic regressions. Students will also gain experience with statistical matching and record linkage, two increasingly important processes for combining data from disparate sources. This course can be especially helpful for those who wish to specialize in survey research or data analysis.
Data Scientist
Data scientists apply mathematical and computational techniques to analyze and interpret vast amounts of data in order to extract insights and solve problems. This course, Cómo combinar y analizar datos complejos, provides a solid foundation in statistical methods for data analysis, including estimation of descriptive statistics, model parameters, and record linkage. This knowledge is essential for data scientists who wish to succeed in the field.
Survey Researcher
Survey researchers design, conduct, and analyze surveys in order to gather data about a population. This course, Cómo combinar y analizar datos complejos, provides training in the specific methods and techniques used in survey research, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for survey researchers who wish to produce high-quality data and insights.
Market Researcher
Market researchers collect, analyze, and interpret data about markets and consumers in order to develop marketing strategies. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in market research, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for market researchers who wish to specialize in survey research or data analysis.
Public Health Analyst
Public health analysts collect, analyze, and interpret data in order to assess and improve public health. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in public health, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for public health analysts who wish to produce high-quality data and insights.
Business Analyst
Business analysts use data to analyze business processes and make recommendations for improvement. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in business analysis, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for business analysts who wish to specialize in data analysis.
Health Economist
Health economists use economic principles to analyze and improve the efficiency of healthcare systems. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in health economics, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for health economists who wish to specialize in data analysis or health policy.
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty in order to develop insurance and pension plans. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in actuarial science, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for actuaries who wish to specialize in data analysis or risk management.
Data Analyst
Data analysts use statistical methods and techniques to analyze data in order to extract insights and solve problems. This course, Cómo combinar y analizar datos complejos, provides a strong foundation in statistical methods for data analysis, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for data analysts who wish to succeed in the field or specialize in areas like survey research.
Economist
Economists use economic theories and data to analyze and solve economic problems. This course, Cómo combinar y analizar datos complejos, provides a solid foundation in the statistical methods used in economics, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for economists who wish to succeed in the field or conduct research using data.
Policy Analyst
Policy analysts use data and research to inform and develop public policy. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in policy analysis, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for policy analysts who wish to specialize in data analysis or research.
Risk Manager
Risk managers use statistical methods to identify, assess, and manage risks. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in risk management, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for risk managers who wish to specialize in data analysis or risk assessment.
Social Scientist
Social scientists use social theories and data to analyze and solve social problems. This course, Cómo combinar y analizar datos complejos, provides a solid foundation in the statistical methods used in social science research, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for social scientists who wish to succeed in the field or conduct research using data.
Survey Statistician
Survey statisticians design, conduct, and analyze surveys in order to gather data about a population. This course, Cómo combinar y analizar datos complejos, provides advanced training in the statistical methods used in survey research, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge is essential for survey statisticians who wish to produce high-quality data and insights.
Transportation Analyst
Transportation analysts use data to analyze and improve transportation systems. This course, Cómo combinar y analizar datos complejos, provides training in the statistical methods used in transportation analysis, including survey weighting, estimation of descriptive statistics and model parameters, and record linkage. This knowledge can be especially helpful for transportation analysts who wish to specialize in data analysis or transportation planning.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Cómo combinar y analizar datos complejos.
This text provides advanced topics and methods for the design and analysis of clinical trials. It is recommended as additional reading for those students with a strong background in statistics.
This text provides a more in depth examination of statistical models used in health care research. It is recommended as additional reading for those students interested in learning more.
This text provides a practical guide to the design and analysis of medical research studies. It good reference tool and is recommended reading.
This text provides a modern introduction to regression models and Bayesian statistics. It good addition to students interested in learning more about these topics.
This text provides a comprehensive overview of statistical methods used in medical research. It is well written and provides good examples. While it is not essential reading, it could serve as a good reference tool.
This text provides an overview of statistical methods used to improve health care. It is recommended as additional reading for those students interested in learning more about this topic.

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