Category: Learning modules

By Avishek Majumder & Priya Chetty on October 24, 2018 No Comments

Hamlet II is an approach to quantitative text-based analytical software. This article reviews the difference between them and Hamlet II. The table below presents various text-based analytical software available commercially.

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The problem of multicollinearity arises when one explanatory variable in a multiple regression model highly correlates with one or more than one of other explanatory variables. It is a problem because it underestimates the statistical significance of an explanatory variable (Allen, 1997).

 
By Avishek Majumder & Priya Chetty on October 24, 2018 No Comments

This article talks about the application of Singular Value Decomposition (SVD) technique MDPREF using Hamlet II. It is performed on the same matrix of profiles or context units saved while performing joint frequency analysis.

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By Rashmi Sajwan & Priya Chetty on October 22, 2018 7 Comments

This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.

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By Avishek Majumder & Priya Chetty on October 22, 2018 No Comments

Correspondence analysis is a diagnostic tool which helps to present the content of a single text or the context unit profiles containing the joint frequency.

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By Avishek Majumder & Priya Chetty on October 22, 2018 No Comments

Hierarchical clustering uses methods to segregate the texts according to the similar vocabularies and then similar words or context are clustered together.

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By Rashmi Sajwan & Priya Chetty on October 16, 2018 17 Comments

Applying Granger causality test in addition to cointegration test like Vector Autoregression (VAR) helps detect the direction of causality. It also helps to identify which variable acts as a determining factor for another variable. This article shows how to apply Granger causality test in STATA.

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By Rashmi Sajwan & Priya Chetty on October 16, 2018 12 Comments

Heteroskedastic means “differing variance” which comes from the Greek word “hetero” (‘different’) and “skedasis” (‘dispersion’). It refers to the variance of the error terms in a regression model in an independent variable.

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