Non-hierarchical cluster analysis is the next step to a hierarchical cluster model. It allows the partitioning of the similar matrices into equal numbers of clusters. It also creates a list of the partitions from the similar matrix generated in the hierarchical cluster.
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.
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.
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.
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.
Hierarchical clustering uses methods to segregate the texts according to the similar vocabularies and then similar words or context are clustered together.
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.
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.