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Case study: Estimating Dynamic Cross-National Opinion Using Existing Survey Data #195

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Case study: Estimating Dynamic Cross-National Opinion Using Existing Survey Data #195

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Expand Up @@ -31,6 +31,28 @@ for code, Creative Commons for text)</span>; authors retain all copyright.
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## Estimating Dynamic Cross-National Opinion Using Existing Survey Data
<small> This case study describes a method for estimating dynamic, cross-national, latent public opinion using existing survey data. It outlines the model and its coding in Stan, explains how to set the model up in R and run it using cmdstanr, then walks through an example where the model is used to estimate public support for democracy in 144 countries from 1988 to 2020.

[View](case-studies/estimating-dynamic-latent-opinion.html) <span
class="note">(HTML)</span>

Authors
: Christopher Claassen

Keywords
: latent variable models, public opinion, longitudinal model

Source Repository
: [example-models/knitr/dynamic-opinion](https://github.com/joonho112/example-models/tree/master/knitr/dynamic_opinion/estimating-dynamic-latent-opinion.html)
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I'm still getting an error even with this new link. It doesn't look like there's a dynamic-opinion folder inside joonho112/example-models/tree/master/knitr/.

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I've changed the url to my repo - maybe the path of least resistance?

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Sorry for the delay on this. The current URL I see is

https://github.com/joonho112/example-models/tree/master/knitr/dynamic_opinion/estimating-dynamic-latent-opinion.html

but this still gives me a 404 - page not found error. Is the URL right? Or is this a private repository maybe?

<span class="note">(GitHub)</span>

Dependencies
: <tt style="font-size: 90%"> tidyverse, dplyr, bayesplot, ggplot2, arm, cmdstanr</tt>

License
: BSD (3 clause), CC-BY</small>

## Instrumental Variables Analysis of Randomized Experiments with One-Sided Noncompliance
<small> In this document, we demonstrate how to implement Bayesian inference for causal effects in randomized experiments with one-sided noncompliance using Stan. Specifically, we aim to replicate the analysis presented in Imbens and Rubin (1997). We present Stan models with and without the exclusion restriction assumption, showcasing a significant advantage of the Bayesian model-based approach.

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