Latent Class Analysis for Developmental Research

Lanza, Stephanie T.; Cooper, Brittany Rhoades

doi:10.1111/cdep.12163

Cited by 376 publications

(272 citation statements)

References 59 publications

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“…Identifying such heterogeneity of behavioral response patterns within emotion conditions is essential for future research on this topic. Use of latent class analysis or cluster analysis may be a candidate analytic technique to more objectively achieve this goal (see Lanza & Cooper, 2016). …”

Section: Discussionmentioning

confidence: 99%

Infant differential behavioral responding to discrete emotions.

Walle¹,

Reschke²,

Camras³

et al. 2017

Emotion

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Emotional communication regulates the behaviors of social partners. Research on individuals' responding to others' emotions typically compares responses to a single negative emotion compared with responses to a neutral or positive emotion. Furthermore, coding of such responses routinely measure surface level features of the behavior (e.g., approach vs. avoidance) rather than its underlying function (e.g., the goal of the approach or avoidant behavior). This investigation examined infants' responding to others' emotional displays across 5 discrete emotions: joy, sadness, fear, anger, and disgust. Specifically, 16-, 19-, and 24-month-old infants observed an adult communicate a discrete emotion toward a stimulus during a naturalistic interaction. Infants' responses were coded to capture the function of their behaviors (e.g., exploration; prosocial behavior; security seeking). The results revealed a number of instances indicating that infants use different functional behaviors in response to discrete emotions. Differences in behaviors across emotions were clearest in the 24-month-old infants, though younger infants also demonstrated some differential use of behaviors in response to discrete emotions. This is the first comprehensive study to identify differences in how infants respond with goal-directed behaviors to discrete emotions. Additionally, the inclusion of a function-based coding scheme and interpersonal paradigms may be informative for future emotion research with children and adults. Possible developmental accounts for the observed behaviors and the benefits of coding techniques emphasizing the function of social behavior over their form are discussed.

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Section: Discussionmentioning

confidence: 99%

Infant differential behavioral responding to discrete emotions.

Walle¹,

Reschke²,

Camras³

et al. 2017

Emotion

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show abstract

“…Latent classes probabilistically determine participant membership (i.e., each participant has a unique probability for each of the latent classes, for a sum total of 1 across all latent classes; Lanza & Cooper, 2016;Muthén & Asparouhov, 2003).…”

Section: Ratio Approachmentioning

confidence: 99%

Strategy selection versus flexibility: Using eye-trackers to investigate strategy use during mental rotation.

Nazareth¹,

Killick²,

Dick³

et al. 2019

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Spatial researchers have been arguing over the optimum cognitive strategy for spatial problem-solving for several decades. The current article aims to shift this debate from strategy dichotomies to strategy flexibility—a cognitive process, which although alluded to in spatial research, presents practical methodological challenges to empirical testing. In the current study, participants’ eye movements were tracked during a mental rotation task (MRT) using the Tobii ×60 eye-tracker. Results of a latent profile analysis, combining different eye movement parameters, indicated two distinct eye-patterns—fixating and switching patterns. The switching eye-pattern was associated with high mental rotation performance. There were no sex differences in eye-patterns. To investigate strategy flexibility, we used a novel application of the changepoint detection algorithm on eye movement data. Strategy flexibility significantly predicted mental rotation performance. Male participants demonstrated higher strategy flexibility than did female participants. Our findings highlight the importance of strategy flexibility in spatial thinking and have implications for designing spatial training techniques. The novel approaches to analyzing eye movement data in the current paper can be extended to research beyond the spatial domain.

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“…Note that there are other introductions available, tailored to clinical research (Collins & Lanza, 2010), pediatrics (Berlin, Williams, & Parra, 2014), and developmental research (Kaplan, 2008;Lanza & Cooper, 2016). In the following, we will first elaborate on the usefulness of these models for learning research in comparison to the limits of more common analytic techniques.…”

Section: Highlightsmentioning

confidence: 99%

“…They assume that the relation between variables can be applied to all learners in the same way: in other words, that there is homogeneity in the nature of the individual differences (Bergman & Magnusson, 1997;Collins & Lanza, 2010). These linear techniques are thus restricted to quantitative individual differences, assuming that learners differ quantitatively in the amount of something, but not qualitatively (Lanza & Cooper, 2016;Sterba & Bauer, 2010). For example, in learning research it is common to perform statistical analyses on sum scores from learning measures.…”

Section: Learners and Learning: A Person-centered Approachmentioning

confidence: 99%

“…The classes are formed such that there is as much similarity within a class while at the same time as much differences between the classes as possible (Lanza & Cooper, 2016). The identification of these latent classes can be useful for characterizing qualitative differences between learners, which may be missed with traditional analytic approaches.…”

Section: What Is a Latent Class Or Latent Profile Model?mentioning

confidence: 99%

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Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Hickendorff

Edelsbrunner

McMullen

et al. 2018

Learning and Individual Differences

186

118

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Highlights• learning is often multidimensional, heterogeneous, and discontinuous• traditional statistical analyses are limited in capturing this complexity• latent class and latent profile models identify subgroups of learners• latent transition models characterize discontinuous, non-linear, learning paths• these models contribute to our understanding of learning and individual differences 3 Informative Tools for Characterizing Individual Differences in Learning: Latent Class, Latent Profile, and Latent Transition AnalysisThis article gives an introduction to latent class, latent profile, and latent transition models for researchers interested in investigating individual differences in learning and development. The models allow analyzing how the observed heterogeneity in a group (e.g., individual differences in conceptual knowledge) can be traced back to underlying homogeneous subgroups (e.g., learners differing systematically in their developmental phases). The estimated parameters include a characteristic response pattern for each subgroup, and, in the case of longitudinal data, the probabilities of transitioning from one subgroup to another over time. This article describes the steps involved in using the models, gives practical examples, and discusses limitations and extensions. Overall, the models help to characterize heterogeneous learner populations, multidimensional learning outcomes, nonlinear learning pathways, and changing relations between learning processes. The application of these models can therefore make a substantial contribution to our understanding of learning and individual differences.

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Latent Class Analysis for Developmental Research

Cited by 376 publications

References 59 publications

Infant differential behavioral responding to discrete emotions.

Infant differential behavioral responding to discrete emotions.

Strategy selection versus flexibility: Using eye-trackers to investigate strategy use during mental rotation.

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

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