Markovian-Based Clustering of Internet Addiction Trajectories

Taushanov, Zhivko; Berchtold, André

doi:10.1007/978-3-319-95420-2_12

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Five papers address alternatives to the classical 'compute dissimilarities-partition the set of sequences' approach. These alternatives include feature-based and fuzzy clustering (Studer 2018) and non-dissimilarity-based methods such as those based on network representations of sequences (Cornwell 2018;Hamberger 2018), and Markov-based models (Helske et al 2018;Taushanov and Berchtold 2018). Alongside the two general papers (Courgeau 2018;Eerola 2018) in Part I, five papers demonstrate the benefit of combining SA with other methods to grasp the dynamics that drive the trajectories.…”

Section: Resultsmentioning

confidence: 99%

“…Part V includes three chapters that present advances in the original task of SA, namely the clustering of sequences. The chapter by Taushanov and Berchtold (2018) proposes clustering sequences of continuous data by means of a Markov-based mixture model-the hidden mixture transition distribution (HMTD) model-and applying their method to Swiss data obtained from the internet addiction test (IAT). The clustering is achieved by setting the transition matrix of the hidden states as the identity matrix, which makes their model a mixture of Gaussian distributions.…”

Section: Directions For the Future: The Chapters Of This Bookmentioning

confidence: 99%

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Sequence Analysis: Where Are We, Where Are We Going?

Ritschard

Studer

2018

Life Course Research and Social Policies

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Section: Resultsmentioning

confidence: 99%

Section: Directions For the Future: The Chapters Of This Bookmentioning

confidence: 99%

Sequence Analysis: Where Are We, Where Are We Going?

Ritschard

Studer

2018

Life Course Research and Social Policies

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“…In a longitudinal study conducted by Taushanov and Berchtold (2018), Swiss youth (N = 185) were clustered along their level of emotional well-being, body mass index, gender, and education track. The Markovian-based hidden mixture transition distribution (HMTD) model yielded four groups: 'group 1′ with average volatility and PIU level, 'group 2′ with relatively low scores and variability, 'group 3′ with very low variability and a constantly diminishing PIU score, and 'group 4′ with more complex trajectories and variability.…”

Section: Youth Clusters By Problematic Usementioning

confidence: 99%

The digital divide: Risk and protective factors and the differences in problematic use of digital devices among Hungarian youth

Kiss

Fitzpatrick

Pikó

2020

Children and Youth Services Review

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A number of psychological factors have been found to be relevant in terms of problematic use of digital devices. Some of them may serve as risk factors, while others mean protection. The main goal of present study was to determine user profiles and to examine differences among them based on several psychological variables using cluster analysis. Data were collected from high school and university students via an online questionnaire from November 2017 to January 2018 (N = 249; females: 62.2%, Mean age = 22.5 years, SD = 3.5). Based on bivariate correlations, sensation seeking, flow and boredom proneness acted as risk factors for developing problematic technological use. However, self-esteem, self-regulation and resilience were significant protective factors against problematic technological use. In an effort to develop a set of profiles that would capture this student population and their usage relative to risk, we identified four distinct user profiles through the use of cluster analysis. The first cluster was labeled as strongly protected sensation-seekers who were more prone to problematic use (18.5%) with moderately high levels of protection, but with the highest scores on the boredom proneness and sensation seeking scales. The second cluster consisted of more balanced and non-vulnerable users (26.1%), who achieved average scores on both risk and protective factors scales. The third cluster was labeled protected, conscious users who were slightly prone to problematic use (39.8%) with lower levels of problematic use and risk factors, and with higher levels of protective factors. The fourth cluster was labeled as strongly problematic, unprotected users (15.7%), who achieved the highest scores of problematic use, and were the less protected having the lowest levels of protective factors scores. We conclude that cluster analysis was suitable for detecting differences in vulnerability to problematic Internet and smartphone use. Psychological factors, such as sensation seeking, flow and boredom, self-esteem, self-regulation and resilience serve as useful tools for providing information to better understanding the role of risk and protective factors in the prevention of youth's problematic use of digital devices. Problematic Internet and smartphone useStudies apply the terms 'addiction' and 'problematic use'

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“…The Growth Mixture Model (GMM) model has become a reference in the continuous longitudinal data modeling, with various applications in criminology [13], health and medicine [14,15], psychology and social sciences [16][17][18], among others (see [19]). The GMM [14,20] is a model designed to discover and describe the unknown groups of sequences that share a similar pattern.…”

Section: Growth Models and The Growth Mixture Model (Gmm)mentioning

confidence: 99%

The Use of a Hidden Mixture Transition Distribution Model in Clustering Few but Long Continuous Sequences: An Illustration with Cognitive Skills Data

Taushanov

Ghisletta

2020

Symmetry

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In accordance with the theme of this special issue, we present a model that indirectly discovers symmetries and asymmetries between past and present assessments within continuous sequences. More specifically, we present an alternative use of a latent variable version of the Mixture Transition Distribution (MTD) model, which allows for clustering of continuous longitudinal data, called the Hidden MTD (HMTD) model. We compare the HMTD and its clustering performance to the popular Growth Mixture Model (GMM), as well as to the recently introduced GMM based on individual case residuals (ICR-GMM). The GMM and the ICR-GMM contrast with HMTD, because they are based on an explicit change function describing the individual sequences on the dependent variable (here, we implement a non-linear exponential change function). This paper has three objectives. First, it introduces the HMTD. Second, we present the GMM and the ICR-GMM and compare them to the HMTD. Finally, we apply the three models and comment on how the conclusions differ depending on the clustering model, when using a specific dataset in psychology, which is characterized by a small number of sequences (n = 102), but that are relatively long (for the domains of psychology and social sciences: t = 20). We use data from a learning experiment, in which healthy adults (19–80 years old) were asked to perform a perceptual–motor skills over 20 trials.

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Markovian-Based Clustering of Internet Addiction Trajectories

Cited by 5 publications

References 27 publications

Sequence Analysis: Where Are We, Where Are We Going?

Sequence Analysis: Where Are We, Where Are We Going?

The digital divide: Risk and protective factors and the differences in problematic use of digital devices among Hungarian youth

The Use of a Hidden Mixture Transition Distribution Model in Clustering Few but Long Continuous Sequences: An Illustration with Cognitive Skills Data

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