Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

Abstract

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.

Positive psychological profiles based on perceived health clustering in patients with cardiovascular disease: a longitudinal study

“…36 Although a similar design to that used by Bayrhuber et al for clustering perceived health variables was followed in this study, the two-step cluster analysis used in the current study is considered to be a more advantageous procedure compared with hierarchical approaches. 48 In these previous studies, clusters representing better self-reported health status were positively associated with a better psychosocial outcome 37 and other relevant variables such as work ability and health literacy, 36 while the negative psychological profile identified in patients with CVD was associated with mortality. 26 In the present study, the cluster representing higher selfreported mental and physical health was also associated Open access with better SES and psychological outcomes, reinforcing the relevance of considering clusters derived from selfreported health status in patients with a chronic condition.…”

“…14 The stability of this cluster solution was first tested by repeating the cluster analysis this time using the Akaike information criterion (AIC) as the index of fit to determine the optimal number of clusters. 48 The result showed a cluster solution of three profiles of perceived mental and physical health. The silhouette coefficient was 0.05 as in the previous analysis.…”

How Does Digital Competence Preserve University Students’ Psychological Well-Being During the Pandemic? An Investigation From Self-Determined Theory

“…To examine how the students were distributed based on their SES and demographic variables, as well as their digital competence and mental and emotional responses to online learning, a two-step cluster analysis was performed. Compared with conventional clustering techniques, the two-step cluster analysis has advantages in handling both categorical and continuous variables simultaneously, automatically determining the optimal number of clusters by comparing the values of clustering criteria across different model clustering solutions rather than arbitrary choices, and dealing with large data files (Kent et al, 2014;Benassi et al, 2020). It has been considered one of the most reliable analysis approaches for classifying individual cases into subgroups (Gelbard et al, 2007;Kent et al, 2014).…”

Sustainable Consumption Patterns in Different Settings

“…Step clustering is a hybrid approach that first uses a distance measure to separate groups and then employs a probabilistic approach to choose the optimal subgroup model (Benassi et al, 2020). Recent studies regarded Two-Step cluster analysis as one of the most reliable in terms of the number of subgroups detected and profiling efficiency (Rundle-Thiele et al, 2015;Coskun & Ozbuk, 2019;Benassi et al, 2020).…”