Use Of Latent Profile Analysis Research Articles

Use of Latent Profile Analysis to Model the Translation of University Research into Health Practice and Policy: Exploration of Proposed Metrics

Use of Latent Profile Analysis to Model the Translation of University Research into Health Practice and Policy: Exploration of Proposed Metrics

Use of latent profile analysis to characterise patterns of participation in crowdsourcing

ABSTRACT Crowdsourced applications are plagued by participation inequality. Only a small number of individuals are responsible for the majority of contributions, while most are engaged at the periphery. Early efforts to understand participation inequality used a percentile approach and a single measure of engagement to describe participants, usually the number of activities completed. With the advent of increasingly sophisticated web analytics, methods for characterising patterns of participation have also become more sophisticated. More recent research uses multiple metrics and clustering algorithms to understand both intensity and duration of participation. We extend research on this topic by applying a model-based approach to analyse multiple metrics of participation. Specifically, we demonstrate the use of latent profile analysis to understand the underlying structure of crowdsourcer participation data from a citizen science project. We make comparisons between latent profile analysis and more traditional methods of characterising participation. We find that considering multiple metrics of engagement provides a more nuanced view of participation inequality, useful in generating both theoretical and applied research questions. We also find that a model-based approach offers several advantages over clustering algorithms including the ease with which results can be integrated in downstream analyses and the ability to report on model fit.

Use of latent profile analysis and k-means clustering to identify student anxiety profiles

BackgroundAnxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions.MethodsA cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering.ResultsLPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities.ConclusionsThe findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.

Ecological momentary assessment in eating disorders research: recent findings and promising new directions.

Ecological momentary assessment (EMA) is an important tool for clarifying common precipitants and consequences of eating disorder symptoms that might be meaningfully targeted in treatments for these pernicious disorders. This article reviews recent advances in EMA work conducted within clinical eating disorder samples. Published studies from the past 2.5 years can broadly be categorized as involving functional analysis of eating disorder behaviors, examining hypothesized predictors of eating disorder symptoms, or applying novel approaches to EMA data. Examples of the latter category include the use of latent profile analysis with EMA data, integration of neurocognitive (e.g., ambulatory inhibitory control task) or biological indicators (e.g., fMRI, plasma leptin), and examining changes in associations between momentary variables over time through multiwave EMA data collection. EMA studies in eating disorders have advanced significantly in recent years, with findings demonstrating strong support for the emotion regulation function of eating disorder behaviors and momentary predictors of distinct eating disorder symptoms. The use of novel statistical and data collection approaches represent exciting areas of growth, with likely implications for intervention approaches, including those that utilize ambulatory technology to deliver treatment.

Understanding Bullying, Victimization, and Bystander Behaviors Through Resource Control Theory

Resource control theory posits that individuals may utilize prosocial and/or coercive strategies to access social resources. Resource control theory has utility for understanding adolescents’ engagement in bullying role behaviors. The current study examined direct associations between bullying role behaviors (i.e., bullying, victimization, assisting, defending, and outsider behavior) and resource control strategies (i.e., prosocial and coercive resource control), as well as differences in bullying behaviors among groups of resource controllers identified through latent profile analysis (LPA). In a sample of 6th–8th grade students (N = 680; 44% female) via self-report, bullying role behaviors were assessed using the Bullying Participant Behaviors Questionnaire and resource control strategies were assessed using the Resource Control Strategies Inventory. Prosocial resource control was positively associated with defending behavior, and coercive resource control was positively associated with bullying and defending. The current study validated the use of LPA for grouping adolescents based on their use of resource control strategies, although the emergent groups differed slightly from the traditional approach. Findings suggest an absence of a purely coercive resource control group as posited by resource control theory. Advanced understanding of social behaviors, including the use of resource control strategies, leads to a more comprehensive approach to preventing bullying and victimization in schools.

Composition of motivation profiles at work using latent analysis: theory and evidence.

PurposeDrawing from self-determination theory, the present study aims to identify the structures and functions of work motivation profiles by using a person-centered approach and to explore the relationships between work motivations and different work performances in the workplace.Materials and methodsThe participants in this research are from different provinces in China, such as Beijing, Tianjin, Shandong, and Hebei. The participants work in state-owned enterprises, private enterprises, foreign-funded enterprises, and public institutions. We contacted the head of the human resources department of the company where the employee was located, and then the human resources supervisor sent the questionnaire to the relevant employees for data collection. In Study 1, we obtained a total of 842 valid questionnaires. In Study 2, employee work performance was assessed by immediate supervisors, and we obtained a total of 630 valid matched data. This study identified the structure and function of work motivation profiles using a person-centered approach, ie, latent profile analysis. The analysis of variance approach was used to explore the relationship between work motivations and different work performances in the workplace.ResultsUsing latent profile analysis, we found 5 work motivation profiles: dominant, high-midrange, low-midrange, intrinsic motivation-minor and intrinsic motivation-dominant. The five different profiles varied in the level (quantitative differences: dominant, high-midrange, and low-midrange) and shape (qualitative differences: intrinsic motivation-minor and intrinsic motivation-dominant) of the profile indicators. We found that these profiles differentially predicted employee performance. Our results reveal new insights into work motivations and how different work motivation profiles affect work performance.ConclusionWe employed a new perspective to better understand the relations between motivations and work performance under the framework of self-determination theory. We were able to demonstrate that (1) different motivation strategies at work do consistently exist and (2) latent profile membership differentiates employee work performance. Our results show that high performance can be exhibited when an employee’s motivation is the dominant type. High-dominant employees with high intrinsic motivation and low extrinsic motivation exhibit worse task performance. The results of this research show that a person-centered approach can better clarify the complexity of the process regarding how work motivations interact within an employee. The major theoretical contribution of this research is the use of latent profile analysis (LPA) to demonstrate five different subpopulations that can exhibit different combinations of work motivations in the emerging market of China. Second, our results show that identified regulation is important in predicting work performance. Third, we advanced the self-determination theory (SDT) research by exploring the relations between different motivation profiles and work performance.

Finding the Nuance in Eldercare Measurement: Latent Profiles of Eldercare Characteristics

There has historically been little consensus around the measurement of the eldercare construct. While authors most frequently employ a dichotomous operationalization of eldercare, there is likely greater nuance to the caregiver experience that goes uncaptured and can serve to explain inconsistencies in eldercare findings. The current study takes a person-centered approach to eldercare in order to assess how different caregiving characteristics manifest within individuals. Using an archival data source, we conduct a latent profile analysis of 840 employed caregivers to detect patterns within eldercare experiences and relate these contexts to various work-related and psychological outcomes. Overall, we find that the latent profiles are primarily driven by three eldercare variables: (1) the amount of time spent attending to eldercare responsibilities, (2) the living situation of the care recipient, and (3) the impairment of the care recipient. We also discovered that one of the profiles (characterized by long-term, in-home eldercare for a care recipient with a physical or mental disability) exhibited significantly higher family-to-work conflict relative to other profiles. Our comparisons of eldercare profiles serve to illustrate the scenarios in which eldercare is most influential. Additionally, the person-centered approach revealed various caregiving contexts that were otherwise not captured by the variable-centered approach, supporting the use of latent profile analysis in the work-family domain. The findings have implications for the future operationalization of the eldercare construct, as well as for organizational eldercare policies and interventions.

Latent profile analysis: another approach to look at parent–teacher agreement on preschoolers’ behavior problems

ABSTRACTInformant agreement is a common research issue in the context of children social emotional assessment. The present study analyzed parent–teacher agreement in ratings of behavior problems (BPs) using latent profile analysis (LPA). Analyses were performed for 1000 preschoolers rated, for the Preschool and Kindergarten Behavior Scales – Second Edition (PKBS-2), independently by parents and teachers. Eight clusters were obtained based on the five supplemental subscales of the Problem Behavior scale of the PKBS-2. These clusters isolated groups of children across the entire range of BPs that varied in level of rater agreement. Results highlighted that parents tend to rate their children with more BPs than teachers do. Agreement is better for children with extreme levels of BPs (low or high). Findings reinforce the usefulness of the PKBS-2 and support the use of LPA as an alternative to traditional approaches.

Fuzzy Profiles

Person-centered approaches to organizational scholarship can provide critical insights into how sets of related constructs uniquely combine to predict outcomes. Within micro topics, scholars have begun to embrace the use of latent profile analysis (LPA), identifying constellations of constructs related to organizational commitment, turnover intentions, emotional labor, recovery, and well-being, to name a few. Conversely, macro scholars have utilized fuzzy set qualitative comparative analysis (fsQCA) to examine numerous phenomena, such as acquisitions and business strategies, as configurations of explanatory conditions associated with firm-level outcomes. What remains unclear, however, is the extent to which these two approaches deliver similar versus unique insights when applied to the same topic. In this paper, we offer an overview of the ways these two methods converge and diverge, and provide an empirical demonstration by applying both LPA and fsQCA to examine a multidimensional personality construct—core self-evaluations (CSE)—in relation to job satisfaction. In so doing, we offer guidance for scholars who are either choosing between these two methods, or are seeking to use the two methods in a complementary, theory-building manner.

Personality profiles: Using latent profile analysis to model personality typologies

Latent profile analysis (LPA) has been presented as an effective tool to model interactions between the dimensions of personality. LPA models personality typologies to reflect the hierarchical nature of the personality construct, which can then be used to make further inferences in applied research. This approach may also address consistent modeling concerns in personality research related to lack of model fit in structural equation modeling analyses. However, further research is needed to support the use of LPA in personality research, including a need for consensus on the nature of function of the resulting personality profiles. The present study uses data from a sample of high school students (n=374) on an IPIP five-factor inventory to exemplify the use of LPA in personality research and continue the discussion around the benefits and challenges associated with this approach. The resulting three-profile solution in the present study agrees with a prior study, but does not align with some studies that identified a five-profile solution. Suggestions are offered as to why these two different personality typology models may have been identified.

Use of Latent Profile Analysis in Studies of Gifted Students

To date, in gifted education and related fields various conventional factor analytic and clustering techniques have been used extensively for investigation of the underlying structure of data. Latent profile analysis is a relatively new method in the field. In this article, we provide an introduction to latent profile analysis for gifted education researchers. We briefly trace the history of this method, focusing particularly on advancements of latent class models and their advantages over traditional clustering approaches. This is followed by the overview of statistical indicators that can be used to choose an optimal model. We illustrate use of latent profile analysis in the field through a sample study on the Big Five personality types of gifted students.

Are salespeople born or made? Biology, personality, and the career satisfaction of salespeople

Purpose – The purpose of this paper is to investigate the relationship between personality traits and both job and career satisfaction among salespeople. The authors also wished to examine the extent to which biologically based personality traits provided insights into job and career satisfaction. Design/methodology/approach – The authors used latent profile analysis (LPA) to assess a sample of 299 salespeople along the dimensions of emotional stability, extraversion, work drive, teamwork orientation, customer service orientation, optimism and both job and career satisfaction. Findings – The findings revealed two distinct groups, and these groups were markedly different along the biologically based traits of optimism, extraversion, emotional stability and along both job and career satisfaction. The differences across these groups were especially pronounced for career satisfaction. Practical implications – Our findings suggest that firms might wish to devote limited resources to improving the skill sets of salespeople with the “right” disposition, rather than attempting to train candidates to cope with the emotional and visceral aspects of sales. Originality/value – The relationship between biologically based traits and job/career satisfaction has not yet been examined. Moreover, the use of LPA provides interesting insights that regression or structural equation modeling-based approaches would not.

Latent Profile Analyses of Test Anxiety

In an era of test-based accountability, there has been a renewed interest in understanding the relationship between test anxiety and test performance. The development and validation of test anxiety scales have grown with the rise of test anxiety research. Research is needed to critically examine the psychometric properties of these scales prior to widespread use. The purpose of this brief report is to demonstrate the use of latent profile analysis (LPA) to develop test anxiety profiles on the FRIEDBEN Test Anxiety Scale. LPA was performed using a sample of 1,133 students from five high schools in a Midwestern state. Results indicate three distinct test anxiety profiles (i.e., high, mid, and low anxiety profiles).

Use of Latent Profile Analysis to Assess the Validity of a Peer-Rejected Group of Children

The goal of this article was to validate the existence and qualities of a peer-rejected group of children using latent profile analysis (LPA). Two separate racially/ethnically diverse samples (Study 1: N = 2,052 second graders; Study 2: N = 594 fourth and fifth graders) completed peer nominations of liking and disliking, from which we calculated Social Preference and Social Impact scores. These scores served as indicators in the LPAs to form LPA groups. In addition, we collected self-, teacher-, and peer-report report data on aggression, depressive symptoms, peer victimization, and social competence. In each sample, an LPA group emerged in which most children were classified as rejected using the Coie, Dodge, and Coppotelli (1982; CDC) approach (Study 1: 95%; Study 2: 86%). However, in both samples, only a minority of children classified as rejected using the CDC approach fell into this LPA group (Study 1: 46%; Study 2: 36%). The LPA group that mirrored the CDC rejected group received more maladjusted scores than all other LPA groups on aggression, depressive symptoms, peer victimization, and social competence. Furthermore, when compared to children classified as rejected using only the CDC approach, children classified as rejected under both the LPA and CDC approaches were more maladjusted in terms of all sociometric and socioemotional variables. LPA analyses across two developmental levels validated the existence of an empirically derived group of children who overlapped closely with the CDC rejected group. However, this group was considerably smaller and more maladjusted than the CDC rejected group.

Latent Variable Mixture Modeling

Latent variable mixture modeling is becoming increasingly popular in nursing research, in part due to the sophistication of the method in identifying relationships, patterns, and clusters in the data. The aim of this study was to provide an overview of mixture modeling techniques, specifically as applied to nursing research, and to present examples from two studies to illustrate how these techniques may be used cross-sectionally and longitudinally. The first data example demonstrates the use of latent profile analysis as applied to the St. George respiratory symptoms questionnaire in 2,232 smokers from the Lovelace Smokers Cohort. The second data example demonstrates growth mixture modeling as applied to condom use trajectories among 728 at-risk adolescents on probation. Three classes of symptoms emerged among the smokers cohort: those who were high on all symptoms, those who were low on all symptoms, and those who were high on cough and phlegm only. These classes were then distinguishable by participant gender and wood smoke exposure. In the second data example, four classes of condom use emerged. Only 59% of the sample indicated the previously reported decline in condom use over time; condom use remained stable or significantly increased for the remaining 41%. Both sets of results provide additional substantive information about patterns in the data that were not apparent from previously applied traditional methodological techniques. Considerations for the use of latent variable mixture modeling in nursing research are discussed.

A latent profile analysis of sedentary and physical activity patterns

Aim: The aim of the current study was to examine the physical activity and sedentary behavioral patterns of children and adolescents using the model-based latent profile analysis (LPA) approach. Subject and methods: A sample of 847 school children aged 10 to 16 years from 28 Singapore schools took part in the study. Physical activity and sedentary behavior were assessed using a 7-day physical activity recall questionnaire. Results: The findings of the present study showed that there were at least five groups of students with unique sedentary and physical activity patterns. A few clusters were genderspecific. For example, groups 1 and 4 were femaledominated clusters and group 3 was male-dominated. The younger children in the primary school had different sedentary and physical activity behaviors. Conclusions: This study demonstrates the use of LPA to group individuals into homogenous groupings in order to help researchers make informed decisions about the number of underlying groupings. This can have benefits for targeting specific groups in interventions. © Springer-Verlag 2011.

Comparison of 3 Different Analytic Approaches for Determining Risk-Related Active and Sedentary Behavioral Patterns in Adolescents

Much of the research conducted to date implies overweight youth exhibit uniform active and sedentary behavioral patterns. This approach negates the possibility that multiple co-occurring, and seemingly contrasting, behaviors may manifest within the same individual. We present a substantive dialogue on alternative analytical approaches to identifying risk-related active/sedentary behavioral patterns associated with overweight in adolescents. Comparisons were made among latent profile analysis (LPA), cluster analysis (CA), and multinomial logistic regression (MLR). A cross sectional sample of youth (N = 6603; 12-18 yrs) completed a questionnaire assessing: physical activity (PA); competing activities (COMP); and sedentary activities (SED). Demographics associated with PA (age, sex, BMI) were used as covariates/predictors. Comparisons among methods revealed that LPA and CA detected subgroupings of behavioral patterns associated with overweight, each unique in regards to behaviors and demographic characteristics, whereas MLR results followed established associations of low PA and high SED without subgroup separation. Use of LPA and CA provides a rich understanding of behavioral patterns and the related demographic characteristics. Decisions guiding the selection of analytical techniques are discussed.

Use of Latent Profile Analysis to Identify Eating Disorder Phenotypes in an Adult Australian Twin Cohort

The relationships among the different eating disorders that exist in the community are poorly understood, especially for residual disorders in which bingeing or purging occurs in the absence of other behaviors. To examine a community sample for the number of mutually exclusive weight and eating profiles. Data regarding lifetime eating disorder symptoms and weight range were submitted to a latent profile analysis. Profiles were compared regarding personality, current eating and weight, retrospectively reported life events, and lifetime depressive psychopathology. Longitudinal study among female twins from the Australian Twin Registry in whom eating was assessed by a telephone interview. A community sample of 1002 twins (individuals) who had participated in earlier waves of data collection. Number and clinical character of latent profiles. The best fit was a 5-profile solution with women who were (1) of normal weight with few lifetime eating disorders (4.3%), (2) overweight (10.6% had a lifetime eating disorder), (3) underweight and generally had no eating disorders except for 5.3% who had restricting anorexia nervosa, (4) of low to normal weight (89.0% had a lifetime eating disorder), and (5) obese (37.0% had a lifetime eating disorder). Each profile contained more than 1 type of lifetime eating disorder except for the third profile. Women in the first and third profiles had the best functioning, with women in the fourth and fifth profiles having similarly poorer functioning. The women in the fourth group had a symptom profile distinctive from the other 4 groups in terms of severity; they were also more likely to have had lifetime major depression and suicidality. Lifetime weight ranges and the severity of eating disorder symptoms affected clustering more than the type of eating disorder symptom.