Applied Demographic Data Analysis
Preface
Why a book on statistics for demographers?
Demographers have always been a mixture of sociologists, economists, statisticians, health researchers, and other broad sub-disciples of social science. As such, we bring with us a large amount of baggage from our respective academic life courses, and we often are trained by a wide variety of mentors and professors. It’s my perspective that our interdisciplinary experience is one of our greatest strengths as a group. Given that our training is often in one of a core set of home disciplines, we often have methodological training from said discipline, and this may not be a broad enough perspective to firmly ground us in the types of methods that demographers commonly employ. This is not the fault of the departments that trained us, it’s just a historical fact. So, why am I writing a book on statistics and data analysis aimed at demographers? I will give you three reasons:
Demographers have to go beyond the sample. This is to say that our results and research is generally representative of a larger national or international population, and we do this explicitly in our models.
We demographers don’t use random samples for our analysis. Statistics books the world over are based on assumptions of random sampling and independence, while the data that we often have to, or desire to use, comes more than likely from a data source that was collected using a complex survey design. This is a big deal and we have to have training materials that instill this in our students early on in their careers.
Weird data. As demographers, we often use data from lots of different places and if you were trained up to this point to believe that the linear model is the end-all be-all of statistical inference, I’ve got news for you friends, you’ve been misled. Categorical outcomes, counts, hierarchically structured, longitudinally collected, spatially referenced, just to name a few of such oddities, are ubiquitous in our field, and part of what makes our discipline so cool and interesting to newcomers.
My goal for this book is to take the lessons I’ve learned teaching statistics to a diverse and often cursorily trained group of students who have problems they care about, that they need to bring demographic data to bear upon. This is a challenge, and I have always been a stalwart proponent of teaching statistics and data analysis in a very applied manner. As such, this book won’t be going into rigorous proofs of estimators or devoting pages to expositions of icky algebra; instead it will focus on exploring modern methods of data analysis that in used by demographers every day, but not always taught in our training programs.
As someone who has learned much more of these methods by personal exploration than by formal study, I find that many of these methods are absent from the canon of social science statistics, but are both in great demand from people who hire us, and absolutely necessary to the demographer’s analytic toolkit. It’s a major goal of this book to de-mystify the process and to make it accessible to a wide audience, so I will always strive to illustrate the key aspects of the methods described herein, and ground the discussion of methods in applications.
Broader picture of what i’ll cover
What’s a demographer?
A demographer is a person who studies the size, structure, and distribution of human populations, and how they change over time due to births, deaths, migration, and aging. Demographers use statistical methods to analyze data on population dynamics and to make predictions about future population trends. They often work in fields such as public health, social science, economics, and government policy.
What is applied demography?
Applied demography is the application of demographic methods and techniques to real-world problems and issues. It involves using demographic data and analysis to inform policy decisions, program planning, and resource allocation in various fields, such as public health, urban planning, and social services. Applied demographers often work with large datasets and use statistical software to analyze and interpret demographic data, with the goal of providing insights and recommendations for decision-makers.
Why R is a good option for applied demography?
R is a powerful and flexible programming language that is widely used in the field of applied demography for several reasons:
Open Source: R is free and open-source, making it accessible to a wide range of users, including those in academia, government, and non-profit organizations.
Statistical Packages: R has a rich ecosystem of packages specifically designed for statistical analysis and data visualization. This includes packages for handling complex survey data, which is often used in demography.
Data Visualization: R excels at data visualization, allowing demographers to create informative and aesthetically pleasing graphics to communicate their findings effectively.
Reproducibility: R promotes reproducible research practices through the use of scripts and R Markdown, enabling demographers to document their analysis workflows and share their code with others.
Community Support: R has a large and active user community, providing a wealth of resources, tutorials, and forums for users to seek help and share knowledge.
Why write code as a demographer?
As a demographer, writing code is essential for several reasons:
Efficiency: Writing code allows demographers to automate repetitive tasks and analyze large datasets more efficiently than manual methods.
Reproducibility: Code enables demographers to document their analysis workflows, making it easier to reproduce results and share methods with others.
Flexibility: Coding provides the flexibility to customize analyses and create tailored solutions for specific research questions.
Collaboration: Code can be easily shared and collaborated on with other researchers, facilitating teamwork and knowledge exchange.
Integration: Writing code allows demographers to integrate various data sources and analytical tools, enhancing the overall analysis process.