I have been working for >10 years on missing data.
My passion for data science mainly comes from its transversality: as a statistician, we can interact with so many scientific fields!
With missing data the same is true but within statistics, as it can pop up in all its branches.
When I first meet a scientist for a new project, I always start the conversation by asking “Show me the data!” to understand the underlying challenges.
So far, I have never been shown a complete dataset... (of course there might be some bias!).
With @imkemay, Aude Sportisse, @nj_tierney and @Natty_V2, we created the Rmistatic platform rmisstastic.netlify.app, to organize all the resources (courses, tutorials, articles, software, etc.) and implement analysis pipelines with missing data in R/Python.
@imkemay@nj_tierney@Natty_V2 Feel free to use it!
We welcome any contribution or suggestions.
Tomorrow, I’ll give personal feedback and advice on how to handle missing data.
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Missing Data, a thread ⬇️
Missing values are everywhere! We have listed more than 150 R packages in cran.r-project.org/web/views/Miss…
So let us give few pointers:
The method of handling missing data depends on the purpose of the analysis: estimation, completion, prediction, etc.
1) For inference with missing values, estimating as well as possible a parameter and giving a confidence interval, consider likelihood approaches (using EM algorithms) or multiple imputation
2) Single Imputation/Matrix completion aims at completing (predicting the missing entries) a dataset as best as possible. Multiple imputation aims at estimating parameters and their variability, taking into account the uncertainty due to missing values
I’m going to begin today with a bold claim: Being an applied statistician is a lot like being an ethnographer.
I say this both based upon years of experience working in collaborative projects and consulting and based on my experience studying ethnography. (Recall: before my PhD in statistics, I started and quit a PhD in sociology).
Very often a question asked is not the ‘real’ question at hand. Typically, the person asking has a sense of the problem, but may not know exactly how to ask the question.
Yesterday I tweeted about nested data, with multi-level models (MLM) versus OL + cluster-robust variance estimation (CRVE). This made me think about another confusion that arise, between what are called fixed versus random effects.
Let’s begin with a simple relationship between a covariate X and Y in nested data, e.g. students i nested in school j. We are interested in understanding the relationship between X and Y at the student level.
Approach 1: Assume the schools are fixed, but that students are a random sample within these schools. Assume the relationship between X and Y is the same in all schools. This often amounts to including a dummy variable for each school in the model. Here I use OLS to estimate β_1.
I work primarily with nested data. One example is in experiments, with students nested in schools. Another is meta-analysis, with effect sizes nested in studies. In this thread, I’ll focus on students nested in schools, but this applies more generally.
Question 1: Do you need to take nesting into account in your analysis? Our world is naturally nested – students in classrooms in teachers in schools in districts and so on. Does this mean we need to take all of these levels into account? No.
Nesting only needs to be accounted for if it is part of how our sample of data is generated – either how the data is selected (sampled) or the who gets an intervention being studied (assignment).
Hello everyone – I’m so excited (and nervous!) to get to tweet with you all this week. I’ll start by telling you some general things about myself.
I’m an Associate Professor of Statistics at Northwestern University and a Faculty Fellow at the Institute for Policy Research. I also Co-Direct the Statistics for Evidence-Based Policy and Practice Center. For more info see here: bethtipton.com
I call my field “Social Statistics” and I much of what I study has to do with the role of statistics in the creation and use of evidence for decision making, particularly in the field of education research.
The #DataFeminism book also made me look inward and examine my own biases, which I am exceedingly grateful for.
Namely, it forced me to reckon with some of my fundamental operating assumptions as a statistician & data scientist.
Examples threaded below...
In chapter 3, the authors discuss the role of emotion in data visualization, specifically calling out giants in the field like Edward Tufte and Alberto Cairo (no snitch tagging, please) for what is presented as an anti-emotion stance.
On Tufte: "Any ink devoted to something other than the data themselves ... is a suspect and intruder to the graphic. Visual minimalism, according to this logic, appeals to reason first. ... Decorative elements ... are associated with messy feelings ... and emotional persuasion."