Analysis of Categorical Data Under a Full Model

The NEO-IPIP-300 is a 300-item version scale of freely available personality tests based on the OCEAN Model of 30 distinctive personality traits. The scale measures human personality preferences and groups them into five distinct factors, namely Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The scale has been translated into many languages before, but there was no translation and norms available for the Urdu language.Paper reports the translation, creation of web version, data collection (N=869), and reliability of Urdu version of NEO-IPIP-300. We also did a CFA Analysis and Measurement Invariance test as part of the paper. Full measurement invariance was met for the full model, and partial measurement invariance was met for neuroticism (metric and scalar) and extraversion (metric). In general, all models fit well and suggest that the Urdu IPIP-300-NEO aligns well with the English IPIP-300-NEO. In some cases, the Urdu inventory performed better (e.g., higher internal consistency) than the English inventory.

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Non-Mode Clustering of Categorical Data with Attributes Weighting

Journal of Software ◽

10.3724/sp.j.1001.2013.04470 ◽

2014 ◽

Vol 24 (11) ◽

pp. 2628-2641 ◽

Cited By ~ 1

Author(s):

Li-Fei CHEN ◽

Gong-De GUO

Keyword(s):

Categorical Data

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Machine Learning Based Predictive Action on Categorical Non-Sequential Data

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190417150421 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1020-1030

Author(s):

Pradeep S. ◽

Jagadish S. Kallimani

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Categorical Data ◽

Numerical Data ◽

Processing Technique ◽

Machine Learning Algorithms ◽

Sequential Data ◽

Industry Standard ◽

Robust Model ◽

Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

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