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Kay Chansiri

As seen in: The Startup, Towards Data Science
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Articles by Kay Chansiri

Hierarchical Linear Modeling: A Step by Step Guide

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By Kay Chansiri
| Towards Data Science
Utilize R for your mixed model analysis In most cases, data tends to be clustered. Hierarchical Linear Modeling (HLM) enables you to explore and understand your data and decreases Type I error rates. This tutorial uses R to demonstrate the basic steps of HLM in social science research. Credit: https://pixabay.com/users/geralt-9301/ from Pixabay Before beginning the analysis, let’s briefly talk about what is HLM first.
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Representation, Big Data, and algorithmic bias in Social Data Science

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By Kay Chansiri, Anna Carbone, Parvathi A. Subbiah, Vikrant Dogra
| Towards Data Science
Pushing the boundaries of Social Data Science Photo by NASA on Unsplash The buzz-phrase of our time, “Artificial intelligence,” has inspired all sorts of musings: from visions of utopic — and dystopic — futures freed from human labour, to more focused critiques on algorithmic discrimination. The reality is the ‘good, bad and the ugly’ of AI drives some of the most fundamental philosophical and societal questions we face today — and consequently much of our evolving tech legislation and policy.
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Data Screening

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By Kay Chansiri
| The Startup Verified
The first important step before analyzing your data is getting to know the data. This tutorial uses R and focuses on basic data screening principles, such as checking outliers and testing general linear model assumptions before beginning data analysis. Credit: https://www.toppr.com/guides/economics/presentation-of-data/textual-and-tabular-presentation-of-data/ Case study: The data for this tutorial is fictitious and about psychosocial traits and abusive relationships.
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