LPA2: a Fortran V Computer Program for Green's Solution of Latent Class Analysis Applied to Latent Profile Analysis

1975 ◽  
Vol 35 (1) ◽  
pp. 163-166 ◽  
Author(s):  
Bertil Mardberg
Keyword(s):  
Computer Program ◽  
Latent Class Analysis ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Class Analysis ◽  
Latent Profile

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.

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 Phenotyping Chronic Pelvic Pain Based on Latent Class Modeling of Physical Examination

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
B. W. Fenton ◽  
S. F. Grey ◽  
M. Reichenbach ◽  
M. McCarroll ◽  
V. Von Gruenigen
Keyword(s):  
Physical Examination ◽  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Wall Region ◽  
Uterosacral Ligaments ◽  
Pain Pressure Threshold ◽  
Latent Profile

Introduction. Defining clinical phenotypes based on physical examination is required for clarifying heterogeneous disorders such as chronic pelvic pain (CPP). The objective of this study was to determine the number of classes within 4 examinable regions and then establish threshold and optimal exam criteria for the classes discovered. Methods. A total of 476 patients meeting the criteria for CPP were examined using pain pressure threshold (PPT) algometry and standardized numeric scale (NRS) pain ratings at 30 distinct sites over 4 pelvic regions. Exploratory factor analysis, latent profile analysis, and ROC curves were then used to identify classes, optimal examination points, and threshold scores. Results. Latent profile analysis produced two classes for each region: high and low pain groups. The optimal examination sites (and high pain minimum thresholds) were for the abdominal wall region: the pair at the midabdomen (PPT threshold depression of > 2); vulvar vestibule region: 10:00 position (NRS > 2); pelvic floor region: puborectalis (combined NRS > 6); vaginal apex region: uterosacral ligaments (combined NRS > 8). Conclusion. Physical examination scores of patients with CPP are best categorized into two classes: high pain and low pain. Standardization of the physical examination in CPP provides both researchers and general gynecologists with a validated technique.

scholarly journals Basic Reproduction Number of the 2019 Novel Coronavirus Disease in the Major Endemic Areas of China: A Latent Profile Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Honglv Xu ◽  
Yi Zhang ◽  
Min Yuan ◽  
Liya Ma ◽  
Meng Liu ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Reproduction Number ◽  
Profile Analysis ◽  
Hunan Province ◽  
Initial Value ◽  
Endemic Areas ◽  
Novel Coronavirus ◽  
Latent Profile

Objective: The aim of this study is to analyze the latent class of basic reproduction number (R0) trends of the 2019 novel coronavirus disease (COVID-19) in the major endemic areas of China.Methods: The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic areas. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19. The latent class of R0 was analyzed using the latent profile analysis (LPA) model.Results: The median R0 calculated from the SARS and COVID-19 parameters were 1.84–3.18 and 1.74–2.91, respectively. The R0 calculated from the SARS parameters was greater than that calculated from the COVID-19 parameters (Z = −4.782 to −4.623, p < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province, and Chongqing Municipality) was relatively low and decreased slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province, and Jiangsu Province) was relatively high and decreased rapidly. Moreover, the initial R0 value of class 3 (Hubei Province) was in the range between that of classes 1 and 2, but the higher R0 level lasted longer and decreased slowly.Conclusion: The results indicated that the overall R0 trend is decreased with the strengthening of comprehensive prevention and control measures of China for COVID-19, however, there are regional differences.

scholarly journals Latent Profile Analyses of Depressive Symptoms in Younger and Older Oncology Patients

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1383-1398 ◽  
Author(s):  
Rebecca M. Saracino ◽  
Heining Cham ◽  
Barry Rosenfeld ◽  
Christian J. Nelson
Keyword(s):  
Depressive Symptoms ◽  
Older Patients ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Class Differences ◽  
Patients With Cancer ◽  
Latent Profile

The aging of America will include a significant increase in the number of older patients with cancer, many of whom will experience significant depressive symptoms. Although geriatric depression is a well-studied construct, its symptom presentation in the context of cancer is less clear. Latent profile analysis was conducted on depressive symptoms in younger (40-64 years) and older (≥65 years) patients with cancer ( N = 636). The sample was clinically heterogeneous (i.e., included all stages, dominated by advanced stage disease). Participants completed questionnaires including the Center for Epidemiological Studies Depression Scale, which was used for the latent profile analysis. A four-class pattern was supported for each age group. However, the four-class pattern was significantly different between the younger and older groups in terms of the item means within each corresponding latent class; differences were primarily driven by severity such that across classes, older adults endorsed milder symptoms. An unexpected measurement issue was uncovered regarding reverse-coded items, suggesting that they may generate unreliable scores on the Center for Epidemiological Studies Depression Scale for a significant subset of patients. The results indicate that cancer clinicians can expect to see depressive symptoms along a continuum of severity for patients of any age, with less severe symptoms among older patients.

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 Heterogeneity of social anxiety in treatment-seeking adults who stutter: A latent profile analysis

2021 ◽  
Author(s):  
Tomosumi Haitani ◽  
Naomi Sakai ◽  
Koichi Mori ◽  
Tomohito Houjou
Keyword(s):  
Social Anxiety ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Profile Analysis ◽  
Demographic Data ◽  
Data Communication ◽  
Anxiety Scale ◽  
Situational Factors ◽  
Adults Who Stutter ◽  
Latent Profile

Purpose: Adults who stutter (AWS) often experience social anxiety. Social anxiety is explained by several situational factors, one of which is a factor for telephone, which is unique to AWS. This unique social anxiety, which has not been observed in individuals with social anxiety disorder (SAD), may lead to heterogeneity or distinct subtypes of AWS. The present study aimed to investigate the heterogeneity of social anxiety in AWS in terms of feared social situations.Methods: Social anxiety was measured using the fear/anxiety scale of the Liebowitz Social Anxiety Scale (LSAS). The scores of the five subscales in the LSAS in 562 AWS were analyzed using latent profile analysis. First, the number of latent classes (subtypes) was determined through statistical criteria and interpretability. Next, the profiles of social anxiety, demographic data, communication attitudes, and the overall severity of social anxiety of the subtypes were investigated.Results: Five latent class solutions led to good classifications. About one-quarter of AWS (156) were included in a subtype with sub-clinical levels of overall severity of social anxiety but severe social anxiety in telephone situations. Among them, 100 AWS showed severe social anxiety only in telephone situations. Psychosocial factors, including employment status and communication attitude, were related to extracted subtypes.Conclusions: Some AWS have severe social anxiety specific to telephone situations, which is not proportional to the overall severity of social anxiety. The telephone-specific subtype of social anxiety has not been empirically extracted in principal diagnosis of SAD and can be unique in AWS.

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