An Introduction to Applied Multivariate Analysis with R

Everitt, Brian S.; Hothorn, Torsten

doi:10.1007/978-1-4419-9650-3

Cited by 353 publications

(259 citation statements)

References 0 publications

Supporting

Mentioning

235

Contrasting

Unclassified

Order By: Relevance

“…To get an initial overview of the correlation structure, factor scores for the four latent factors of Musical Behavior (Musical Practice, Music Professionalism, Listening Engagement, and Singing) as well as for the INMI latent factor of INMI disturbance were computed using Bartlett's method of weighted least-squares (DiStefano, Zhu, & Mîndrilȃ, 2009;Everitt & Hothorn, 2011). A correlation table was computed between these and the existing factors from the three different scales (OC, Musical Behavior, and INMI).…”

Section: Confirmatory Analysismentioning

confidence: 99%

Individual Differences Predict Patterns in Spontaneous Involuntary Musical Imagery

Müllensiefen

Fry²,

Jones

et al. 2012

Music Perception

View full text Add to dashboard Cite

INVOLUNTARY MUSICAL IMAGERY (INMI) DESCRIBESthe everyday phenomenon of having a tune stuck in the head. Research has established the ubiquity of this form of spontaneous cognition but the predictive role of individual differences is still debated. This study examines the impact of everyday musical behaviors and subclinical obsessive compulsive attributes on INMI experiences. In total 1,536 participants completed three online questionnaires; a novel inventory of musical behavior and INMI, and a standardized obsessive compulsion (OC) inventory. Exploratory factor analysis (N ¼ 512) and structural equation modelling (N ¼ 1,024) were applied. Everyday singing and music listening positively predict length and frequency of reported INMI episodes, respectively. No relationships were found with musical training. High OC was positively related to INMI frequency and disturbance, but only indirectly to INMI episode length and unpleasantness. The identified contributory factors of INMI experiences are discussed in the context of musical memory and spontaneous mental activity.

show abstract

Section: Confirmatory Analysismentioning

confidence: 99%

Individual Differences Predict Patterns in Spontaneous Involuntary Musical Imagery

Müllensiefen

Fry²,

Jones

et al. 2012

Music Perception

View full text Add to dashboard Cite

show abstract

“…The method was proposed by Hartigan and Wong (1979) and is still considered one of the most robust clustering algorithms, due to sensitivity to the presence of outliers. For Everitt and Hothorn (2011), the sensitivity is from matrix of distances used in getting the centroides. The number of groups to be composed is indicated by the letter .…”

Section: Non-hierarchical Methods (K-means)mentioning

confidence: 99%

Identifying User Profiles from Statistical Grouping Methods

Oliveira

Gomes

et al. 2018

Journal of Information Systems Engineering & Management

View full text Add to dashboard Cite

This research aimed to group users into subgroups according to their levels of knowledge about technology. Statistical hierarchical and non-hierarchical clustering methods were studied, compared and used in the creations of the subgroups from the similarities of the skill levels with these users' technology. The research sample consisted of teachers who answered online questionnaires about their skills with the use of software and hardware with educational bias. The statistical methods of grouping were performed and showed the possibilities of groupings of the users. The analyses of these groups allowed to identify the common characteristics among the individuals of each subgroup. Therefore, it was possible to define two subgroups of users, one with skill in technology and another with skill with technology, so that the partial results of the research showed two main algorithms for grouping with 92% similarity in the formation of groups of users with skill with technology and the other with little skill, confirming the accuracy of the techniques of discrimination against individuals.

show abstract

“…Then y 2 is chosen to account for as much as possible of the remaining variation, subject to being uncorrelated with y 1 , and so on. The new variables defined by this process, y 1 , …, y q , are the principal components [11]. The first few components take into account for a substantial proportion of the variation in the original variables and they be used to provide a lower-dimensional summary of these variables.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Predicting TV programme audience by using twitter based metrics

Crisci

Grasso

Nesi

et al. 2017

Multimed Tools Appl

View full text Add to dashboard Cite

The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.Multimed Tools Appl

show abstract

An Introduction to Applied Multivariate Analysis with R

Cited by 353 publications

References 0 publications

Individual Differences Predict Patterns in Spontaneous Involuntary Musical Imagery

Individual Differences Predict Patterns in Spontaneous Involuntary Musical Imagery

Identifying User Profiles from Statistical Grouping Methods

Predicting TV programme audience by using twitter based metrics

Contact Info

Product

Resources

About