When analyzing public opinion data, I find myself asking, “whose opinion counts?” The perspective of immigrants is understudied in public opinion literature on immigration, despite representing an increasingly salient electoral demographic. My research breaks down generalizations by comparing immigration attitudes within and across native-born and immigrant populations, and how factors such as partisanship, race, and social identity accounts for variance in such attitudes. My findings highlight the limitations of treating “immigrants" as a homogenous group and emphasize the need for more nuanced and targeted approaches to understanding and engaging with public opinion research. Understanding the complexity and diversity within this key demographic is crucial for delivering targeted political information and fostering more inclusive and informed public discourse on immigration.

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The primary goal of this project is to analyze the natural groupings within the Iris dataset using clustering techniques. The objective is to uncover hidden patterns, validate the biological classifications of the dataset, and compare different clustering methods to understand their effectiveness and implications.

Distribuição espacial das escolas públicas de Juiz de Fora/MG, com uso dos pacotes geobr e leaflet do R.