scholarly journals Multilevel Latent Profile Analysis With Covariates

2018 ◽  
Vol 21 (4) ◽  
pp. 931-954 ◽  
Author(s):  
Anne Mäkikangas ◽  
Asko Tolvanen ◽  
Kaisa Aunola ◽  
Taru Feldt ◽  
Saija Mauno ◽  
...  
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Data Sets ◽  
Organizational Research ◽  
University Employees ◽  
Level 1 ◽  
Using Data ◽  
Demand Control ◽  
Level 2 ◽  
Latent Profile

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the data affect the probability of a membership of a particular profile. We demonstrate the use of MLPA by investigating job characteristics profiles based on the job-demand-control-support (JDCS) model using data from 1,958 university employees clustered in 78 work departments. The implications of the results for organizational research are discussed, together with several issues related to the potential of MLPA for wider application.

scholarly journals Identifying Student Subgroups as a Function of School Level Attributes: A Multilevel Latent Class Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Georgios D. Sideridis ◽  
Ioannis Tsaousis ◽  
Khaleel Al-Harbi
Keyword(s):  
High School Students ◽  
Latent Profile Analysis ◽  
Bayes Factor ◽  
Latent Class ◽  
Information Criteria ◽  
School Level ◽  
School Students ◽  
Level 1 ◽  
Level 2 ◽  
Latent Profile

The purpose of the present study was to profile high school students’ achievement as a function of their demographic characteristics, parent attributes (e.g., education), and school behaviors (e.g., number of absences). Students were nested within schools in the Saudi Arabia Kingdom. Out of a large sample of 500k, participants involved 3 random samples of 2,000 students measured during the years 2016, 2017, and 2018. Randomization was conducted at the student level to ensure that all school units will be represented and at their respective frequency. Students were nested within 50 high schools. We adopted the multilevel latent profile analysis protocol put forth by Schmiege et al. (2018) and Mäkikangas et al. (2018) that account for nested data and tested latent class structure invariance over time. Results pointed to the presence of a 4-profile solution based on BIC, the Bayes factor, and several information criteria put forth by Masyn (2013). Latent profile separation was mostly guided by parents’ education and the number of student absences (being positive and negative predictors of high achievement classes, respectively). Two models tested whether the proportions of level 1 profiles to level 2 units are variable and whether level 2 profiles vary as a function of level 1 profiles. Results pointed to the presence of significant variability due to schools.

scholarly journals A latent profile analysis of Pacific health values

2021 ◽  
Vol 21 (8) ◽  
pp. 531-544
Author(s):  
Sarah Ann Kapeli
Keyword(s):  
Psychological Distress ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Health And Wellbeing ◽  
Pacific Population ◽  
Proxy Indicators ◽  
Pacific Health ◽  
Using Data ◽  
Pacific Community ◽  
Latent Profile

Introduction: Pacific health models that centre Pacific values, can serve as a tool to address Pacific disparities in healthcare. In this study, we broadly draw upon the health concepts of these models to determine how Pacific values are translate across Pacific health and wellbeing. Methods: Using data from the New Zealand Attitudes and Values Study, we identified proxy indicators of common Pacific values. With these proxy indicators we developed a LP Latent Profile Analysis A to uncover subgroups of Pacific peoples based on their orientation towards each proxy indicator and their association with psychological distress. Findings: We identified four subgroups of Pacific peoples: (1) 65% of Pacific peoples identified strongly with Pacific values with low associated psychological distress (2) 18% of Pacific peoples identified moderately with Pacific values with medium associated psychological distress (3) 5% of Pacific peoples identified less with Pacific values with low associated psychological distress (4) 12% of Pacific peoples identified ambivalent with Pacific values with high associated psychological distress. Conclusions: These results suggest that Pacific values and the utility of Pacific health models are an appropriate way of framing health and wellbeing for a vast majority of our Pacific population. However, we also need to recognise the incredible diversity among our Pacific community and be understanding and accommodating of the diverse ways that Pacific peoples can express what they consider valuable.

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