Technical Note: Using Latent Class Analysis versus K‐means or Hierarchical Clustering to Understand Museum Visitors

Schreiber, James B.; Pekarik, Andrew J.

doi:10.1111/cura.12050

Cited by 50 publications

(39 citation statements)

References 10 publications

(5 reference statements)

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“…LCA offers several advantages over other clustering methods, allowing the comparison to be statistically tested, so that the decision to adopt a particular model is less subjective. [36] Our results obtained from LCA highlight that awareness, attitude, practice and satisfaction of the management of SDB are heterogeneous among Italian paediatricians. Specifically, we identified two LCs related to trained paediatricians with a high level of satisfaction and high rate of multidisciplinary approach, compared to untrained ones who were more interested in attending training courses and reported a low level of parent awareness about potential serious complications of SDB.…”

Section: Discussionmentioning

confidence: 72%

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Nosetti

Paglietti

Brunetti³

et al. 2020

PLoS ONE

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AimTo identify subgroups regarding paediatricians' awareness, attitude, practice and satisfaction about management of Sleep-Disordered Breathing (SDB) in Italy using Latent Class Analysis (LCA). MethodsA cross-sectional study was conducted on a large sample of Italian paediatricians. Using a self-administered questionnaire, the study collected information on 420 Paediatric Hospital Paediatricians (PHPs) and 594 Family Care Paediatricians (FCPs). LCA was used to discover underlying response patterns, thus allowing identification of respondent groups with similar awareness, attitude, practice and satisfaction. A logistic regression model was used to investigate which independent variables influenced latent class membership. Analyses were performed using R 3.5.2 software. A p-value<0.05 was considered statistically significant. ResultsTwo classes were identified: Class 1 (n = 368, 36.29%) "Untrained and poorly satisfied" and Class 2 (n = 646, 63.71%) "Trained and satisfied." Involving paediatric pneumologists or otorhinolaryngologists in clinical practice was associated with an increased probability of Class 2 membership (OR = 5.88,]; OR = 15.95, 95%

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Section: Discussionmentioning

confidence: 72%

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Nosetti

Paglietti

Brunetti³

et al. 2020

PLoS ONE

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“…However, the K-means cluster approach risks misclassification (Magidson & Vermunt, 2002). Latent class analysis (LCA) is a person-centered approach that is more suitable and sophisticated for determining turnover intent among nurses because it generally reduces misclassification rates and facilitates obtaining precise classification results (Muth en & Muth en, 2000;Schreiber & Pekarik, 2014). Applying LCA can facilitate assigning individual nurses to statistically and clinically dissimilar subgroups (Zaslavsky et al, 2014), supporting the recommendation of McGilton, Boscart, Brown, and Bowers (2014) to develop interventions that are more tailored and individualized with the aim of reducing LTC nurse turnover.…”

Section: Identifying Subtypes Of Turnover Intentionmentioning

confidence: 99%

Job Demand and Job Satisfaction in Latent Groups of Turnover Intention Among Licensed Nurses in Taiwan Nursing Homes

Chen

Brown

Bowers

et al. 2015

Research in Nursing & Health

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Nurses' turnover intention is not dichotomous; it may reflect intent to leave the profession, intent to leave a type of facility, or intent to leave a specific workplace. In a latent class analysis (LCA) of data from 186 licensed nurses (RNs and LPNs) recruited from 25 nursing homes (NHs) in Taiwan, we classified nurses into turnover intention subgroups based on seven questionnaire items and used a multilevel contrast analysis to characterize the subgroups according to demographic and facility factors, job demand, and job satisfaction. A multilevel probit model was used to examine how job demand and job satisfaction influenced subgroup membership. Three turnover subgroups were identified: high turnover intention (12%), middle turnover intention (57%), and low turnover intention (31%). The high turnover intention subgroup comprised the youngest nurses and had the lowest percentage of registered nurses (RNs); nurses in this subgroup had worked the longest at the current NH and had the greatest likelihood of working at a for-profit facility. Nurses in the middle turnover intention subgroup had the lowest likelihood of working at a for-profit facility. Nurses in the low turnover intention subgroup were primarily RNs and had the shortest work experience in the current facility. Nurses in the high and middle turnover intention subgroups reported lower intrinsic job satisfaction than those with low turnover intention. Extrinsic job satisfaction mediated the relationship between job demand and turnover intention subgroup assignment. The results of this LCA can help target interventions to address heterogeneity of turnover intention and ultimately lessen turnover.

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“…To approach this research in a more systematic and scientific way, Pekarik began a collaboration in 2011 with Schreiber, a Duquesne University specialist in education research and mathematical methods. An article in this issue, Technical Note: Using Latent Class Analysis versus K‐means or Hierarchical Clustering to Understand Museum Visitors , by Schreiber and Pekarik (), examines mathematical methods of identifying patterns among visitors, using data directly related to the IPOP theory.…”

Section: Origin Of the Theorymentioning

confidence: 99%

IPOP: A Theory of Experience Preference

Pekarik¹,

Schreiber²,

Hanemann³

et al. 2014

Curator The Museum Journal

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The theory and practice of IPOP emerged from structured observations and interviews with visitors to the Smithsonian Institution museums in Washington, D.C. from the 1990s to the present-a dataset useful in constructing a long view. This research has had one overarching intention: to serve museum visitors better, that is, to provide visitors with experiences that are above average, special, significant, and memorable. In numerous studies and interviews during the last 16 years, visitors have repeatedly spoken about their reactions to Smithsonian museum exhibitions in four typologies distilling their primary interests: I = ideas, P = people, O = objects, and-as we were obliged to add at a later stage -a second P for "physical." The evidence suggests that exhibitions that strongly appeal to all four visitor typologies will be highly successful with visitors.

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Technical Note: Using Latent Class Analysis versus K‐means or Hierarchical Clustering to Understand Museum Visitors

Abstract: This paper discusses the

Cited by 50 publications

References 10 publications

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Job Demand and Job Satisfaction in Latent Groups of Turnover Intention Among Licensed Nurses in Taiwan Nursing Homes

IPOP: A Theory of Experience Preference

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