scholarly journals THE PERSON-ORIENTED APPROACH IN THE FIELD OF EDUCATIONAL PSYCHOLOGY

2013 ◽  
Vol 5 (1) ◽  
pp. 79-88
Author(s):  
Diana Raufelder ◽  
Danilo Jagenow ◽  
Frances Hoferichter ◽  
Kate Mills Drury
Keyword(s):  
Individual Differences ◽  
Latent Class Analysis ◽  
Frequency Analysis ◽  
Educational Psychology ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Class Analysis ◽  
Configural Frequency Analysis ◽  
Oriented Approach

Individual differences are a fundamental component of psychology, but these differences are often treated as “noise” or “errors” in variable-oriented statistical analyses. Currently, there is a small but emerging body of research using the person-oriented approach. In this paper a brief theoretical and methodological overview of the person-oriented approach is given. A person-oriented approach is often preferable where the main theoretical and analytical unit is a pattern of operating factors, rather than individual variables. In order to illustrate the relevance of this approach to research in educational psychology several representative statistical methods are outlined, two of which employ a person-oriented approach (latent class analysis/ latent profile analysis, configural frequency analysis/ prediction configural frequency analysis) and one that combines person and variable-oriented approaches. Examples of data analyses are used to demonstrate that variable and person-oriented approaches provide the researcher with different information that can be complementary. Key words: configural frequency analysis, educational psychology, individual differences, latent class analysis, person-oriented approach.

scholarly journals A primer to latent profile and latent class analysis

2021 ◽  
Author(s):  
Johannes Bauer
Keyword(s):  
Individual Differences ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
External Variables ◽  
Response Profiles ◽  
Person Centered Approach ◽  
Conceptual Overview ◽  
Latent Profile

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data, and to estimate the proportional size and response profiles of these classes in the population. Moreover, latent class membership can serve as predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centered approach that is useful for analyzing individual differences in prerequisites, processes, or outcomes of learning. The chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using freely available data and the R statistical environment.

scholarly journals Highly Engaged but Burned Out: Intra-Individual Profiles in the US Workforce

2018 ◽  
Author(s):  
Julia Moeller ◽  
Zorana Ivcevic ◽  
Arielle E. White ◽  
Jochen Menges ◽  
Marc A. Brackett
Keyword(s):  
Information Processing ◽  
Frequency Analysis ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Configural Frequency Analysis ◽  
The Us ◽  
Processing Demands ◽  
Individual Profiles ◽  
High Engagement ◽  
The U.S

Purpose: This study used the job demands-resources model to investigate intra-individual engagement–burnout profiles, and demands–resources profiles. Methodology: A representative sample of the U.S. workforce was surveyed online. Latent profile analysis (LPA) and configural frequency analysis examined intra-individual profiles and their inter-relations.Findings: A negative inter-individual correlation between engagement and burnout suggested that burnout tends to be lower when engagement is high, but intra-individual analyses identified both aligned engagement–burnout profiles (high, moderate, and low on both variables), and discrepant profiles (high engagement–low burnout; high burnout–low engagement). High engagement and burnout co-occurred in 18.8% of workers. These workers reported strong mixed (positive and negative) emotions and intended to leave their organization.Another LPA identified three demands–resources profiles: (1) low demands–low resources, but moderate self-efficacy, (2) low workload and bureaucracy demands but moderate information processing demands–high resources, and (3) high demands–high resources. Workers with high engagement–high burnout profiles often reported high demands–high resources profiles. In contrast, workers with high engagement–low burnout profiles often reported profiles of high resources, moderate information processing demands, and low other demands.Originality/value: This study examined the intersection of intra-individual engagement–burnout profiles and demands–resources profiles. Previous studies examined only one of these sides or relied on inter-individual analyses. Interestingly, many employees appear to be optimally engaged while they are burned-out and considering to leave their jobs. Demands and resources facets were distinguished in the LPA, revealing that some demands were associated with resources and engagement.

scholarly journals Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis

2021 ◽  
Vol 18 (16) ◽  
pp. 8590
Author(s):  
Paweł A. Atroszko ◽  
Bartosz Atroszko ◽  
Edyta Charzyńska
Keyword(s):  
Latent Class Analysis ◽  
Undergraduate Students ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Well Being ◽  
Addictive Behaviors ◽  
Class Analysis ◽  
Full Sample ◽  
Theoretical Assumptions ◽  
Latent Profile

Background: Relatively strong theoretical assumptions and previous studies concerning co-occurring addictive behaviors suggest a subpopulation representing general proclivity to behavioral addictions (BAs), and there are gender-specific subpopulations. This study aimed to compare latent profile analysis (LPA) and latent class analysis (LCA) as the methods of investigating different clusters of BAs in the general student population and among students positively screened for at least one BA. Participants and procedure: Analyses of six BAs (study, shopping, gaming, Facebook, pornography, and food) and their potential antecedents (personality) and consequences (well-being) were conducted on a full sample of Polish undergraduate students (N = 1182) and a subsample (n = 327) of students including individuals fulfilling cutoff for at least one BA. Results: LPA on the subsample mostly replicated the previous four profiles found in the full sample. However, LCA on a full sample did not replicate previous findings using LPA and showed only two classes: those with relatively high probabilities on all BAs and low probabilities. LCA on the subsample conflated profiles identified with LPA and classes found with LCA in the full sample. Conclusions: LCA on dichotomized scores (screened positively vs. negatively) were less effective in identifying clear patterns of interrelationships between BAs based on relatively strong theoretical assumptions and found in previous research. BAs can be investigated on the whole spectrum of behavior, and person-centered analyses might be more useful when they are based on continuous scores. This paper provides more detailed analyses of the four basic clusters of BAs, prevalence, and co-occurrence of particular BAs within and between them, their gender and personality risk factors, relationships to well-being, and their interrelationships as emerging from the results of this and previous studies.

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