Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

Ferguson, Sarah L.; Moore, E. Whitney G.; Hull, Darrell M.

doi:10.1177/0165025419881721

“…A relatively new data collection platform, mTurk allows for rapid, inexpensive data collection mirroring quality that has been observed through traditional data collection methods, [46][47] including generally high reliability and validity. 44 Though not a mechanism for probability sampling, 48 mTurk samples appear to mirror the US population in terms of intellectual ability 49 and most, but not all, sociodemographic characteristics. 50 To ensure data quality, minimum quali cations were speci ed to initiate the survey (task approval rating >97%, successful completion of more than 100, but fewer than 10,000 tasks, US-based IP address).…”

Section: Data Collectionmentioning

confidence: 95%

“…To identify belief pro les, we used Latent Pro le Analysis (LPA), a speci c case of a nite mixture model that enables identi cation of subgroups of people according to patterns of relationships among selected continuous variables (i.e., "indicators," in mixture modelling terminology). 44 The goal of LPA is to identify the fewest number of latent classes (i.e., homogenous groups of individuals) that adequately explains the unobserved heterogeneity of the relationships between indicators within a population.…”

Section: Addressing Misinformationmentioning

confidence: 99%

Misinformation about COVID-19: Evidence for Differential Latent Profiles and a Strong Association with Trust in Science

Agley

¹

,

Xiao

²

2020

Preprint

3

1

0

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BackgroundThe global spread of coronavirus disease 2019 (COVID-19) has been mirrored by diffusion of misinformation and conspiracy theories about its origins (such as 5G cellular networks) and the motivations of preventive measures like vaccination, social distancing, and face masks (for example, as a political ploy). These beliefs have resulted in substantive, negative real-world outcomes but remain largely unstudied.MethodsThis was a cross-sectional, online survey (n=660). Participants were asked about the believability of five selected COVID-19 narratives, their political orientation, their religious commitment, and their trust in science (a 21-item scale), along with sociodemographic items. Data were assessed descriptively, then latent profile analysis was used to identify subgroups with similar believability profiles. Bivariate (ANOVA) analyses were run, then multivariable, multivariate logistic regression was used to identify factors associated with membership in specific COVID-19 narrative believability profiles.ResultsFor the full sample, believability of the narratives varied, from a low of 1.94 (SD=1.72) for the 5G narrative to a high of 4.56 (SD=1.64) for the zoonotic (scientific consensus) narrative. Four distinct belief profiles emerged, with the preponderance (70%) of the sample falling into Profile 1, which believed the scientifically accepted narrative (zoonotic origin) but not the misinformed or conspiratorial narratives. Other profiles did not disbelieve the zoonotic explanation, but rather believed additional misinformation to varying degrees. Controlling for sociodemographics, political orientation and religious commitment were marginally, and typically non-significantly, associated with COVID-19 belief profile membership. However, trust in science was a strong, significant predictor of profile membership, with lower trust being substantively associated with belonging to Profiles 2 through 4.ConclusionsBelief in misinformation or conspiratorial narratives may not be mutually exclusive from belief in the narrative reflecting scientific consensus; that is, profiles were distinguished not by belief in the zoonotic narrative, but rather by concomitant belief or disbelief in additional narratives. Additional, renewed dissemination of scientifically accepted narratives may not attenuate belief in misinformation. However, prophylaxis of COVID-19 misinformation might be achieved by taking concrete steps to improve trust in science and scientists, such as building understanding of the scientific process and supporting open science initiatives.

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“…First, descriptive statistics were computed and reported for believability of COVID-19 narratives, religious commitment, political orientation, trust in science, and sociodemographic characteristics (e.g., race/ethnicity, sex, sexual orientation, education level 1). 40 We used maximum likelihood and a robust estimator (Huber-White, MLR estimator in Mplus) to handle the non-normal distribution of the indicators (absolute value of skew ranged from 0.30 to 1.67, and of kurtosis ranged from 1.70 to 4.39). LPA is an unsupervised machine learning technique to identify unobserved groups or patterns from the observed data.…”

Section: Discussionmentioning

confidence: 99%

“…LPA is an unsupervised machine learning technique to identify unobserved groups or patterns from the observed data. 40,49 Compared to traditional cluster analysis, LPA adapts a person-centered approach to identify the classes of participants who may follow different patterns of beliefs in COVID-19 narratives with unique estimates of variances and covariate in uences.…”

Section: Discussionmentioning

confidence: 99%

“…To identify belief pro les, we used Latent Pro le Analysis (LPA), a speci c case of a nite mixture model that enables identi cation of subgroups of people according to patterns of relationships among selected continuous variables (i.e., "indicators," in mixture modelling terminology). 40 The goal of LPA is to identify the fewest number of latent classes (i.e., homogenous groups of individuals) that adequately explains the unobserved heterogeneity of the relationships between indicators within a population.…”

Section: Factors Associated With Beliefsmentioning

confidence: 99%

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Existence of Differential Belief Profiles of COVID-19 Narratives: The Role of Trust in Science

Agley

¹

,

Xiao

²

2020

Preprint

1

0

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BackgroundThe global spread of coronavirus disease 2019 (COVID-19) has been mirrored by diffusion of misinformation and conspiracy theories about its origins (such as 5G cellular networks) and the motivations of preventive measures like vaccination, social distancing, and face masks (for example, as a political ploy). These beliefs have resulted in substantive, negative real-world outcomes but remain largely unstudied.MethodsThis was a cross-sectional, online survey (n=660). Participants were asked about the believability of five selected COVID-19 narratives, their political orientation, their religious commitment, and their trust in science (a 21-item scale), along with sociodemographic items. Data were assessed descriptively, then latent profile analysis was used to identify subgroups with similar believability profiles. Bivariate (ANOVA) analyses were run, then multivariable, multivariate logistic regression was used to identify factors associated with membership in specific COVID-19 narrative believability profiles.ResultsFor the full sample, believability of the narratives varied, from a low of 1.94 (SD=1.72) for the 5G narrative to a high of 4.56 (SD=1.64) for the zoonotic (scientific consensus) narrative. Four distinct belief profiles emerged, with the preponderance (70%) of the sample falling into Profile 1, which believed the scientifically accepted narrative (zoonotic origin) but not the misinformed or conspiratorial narratives. Other profiles did not disbelieve the zoonotic explanation, but rather believed additional misinformation to varying degrees. Controlling for sociodemographics, political orientation and religious commitment were marginally, and typically non-significantly, associated with COVID-19 belief profile membership. However, trust in science was a strong, significant predictor of profile membership, with lower trust being substantively associated with belonging to Profiles 2 through 4.ConclusionsBelief in misinformation or conspiratorial narratives may not be mutually exclusive from belief in the narrative reflecting scientific consensus; that is, profiles were distinguished not by belief in the zoonotic narrative, but rather by concomitant belief or disbelief in additional narratives. Additional, renewed dissemination of scientifically accepted narratives may not attenuate belief in misinformation. However, prophylaxis of COVID-19 misinformation might be achieved by taking concrete steps to improve trust in science and scientists, such as building understanding of the scientific process and supporting open science initiatives.

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Exploring profiles of hormone exposure: Associations with cognition in a population‐based cohort of early adolescents

Chaku

¹

,

Barry

²

2023

Infant and Child Development

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During adolescence, increases in pubertal hormones lead to reproductive maturity as well as changes in cognitive development. Yet, little is known about how to best characterize interindividual differences in hormone concentrations. The goal of the current study was to examine the antecedents and consequences of membership in empirically derived hormone profiles. Data were drawn from 10,816 youth (48.1% female, Mage = 9.91, SDage = 0.63) in the Adolescent Brain Cognitive Development (ABCD) study. Latent profile analysis (LPA) stratified by sex was used to derive hormone profiles using basal testosterone, dehydroepiandrosterone and estradiol (girls only). Eight profiles (four female profiles and four male profiles) were identified. They primarily represented differences in hormone levels (e.g., low hormonal milieus vs. high hormonal milieus), but also reflected discordance (e.g., high estradiol versus. high testosterone). There were limited sociodemographic differences, particularly after correcting for multiple comparisons, but youth with higher hormone milieus exhibited better inhibitory control, visuospatial processing and decision‐making skills even after adjusting for age, pubertal status and demographic data. Thus, youth‐centered approaches that focus on patterns of individual characteristics suggest that hormonal markers may provide unique information about associations between biological and cognitive development.Highlights Latent profile analysis was used to derive constellations of testosterone, DHEA and estradiol (girls only) in a population sample of early adolescents. Hormonal milieus were indexed by four female and four male profiles, varying primarily in hormone levels (i.e., low, average or high hormonal milieus), but also exhibiting some discordance (i.e., high DHEA, high testosterone, or high estradiol). Higher pubertal status was typically associated with higher hormone levels, but pubertal status did not always differentiate between profiles that exhibited discordance. In general, youth in profiles with higher hormone levels had better inhibitory control, visuospatial processing and decision‐making skills 2 years later compared to those in profiles with lower hormone levels.

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Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

Cited by 371 publications

References 46 publications

Misinformation about COVID-19: Evidence for Differential Latent Profiles and a Strong Association with Trust in Science

Misinformation about COVID-19: Evidence for Differential Latent Profiles and a Strong Association with Trust in Science

Existence of Differential Belief Profiles of COVID-19 Narratives: The Role of Trust in Science

Exploring profiles of hormone exposure: Associations with cognition in a population‐based cohort of early adolescents

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