Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences

By Linda M. Collins, Stephanie T. Lanza. 2009

3,327 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7a5f71d400eae652cc0575fb0937a17a0971be50

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Citations in the corpus

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “Although there might be alternative assumptions that can also resolve the nonidentifiability issue, we prefer this conditional independence assumption for its similarity to the local independence assumption underlying most LCA models (Collins & Lanza, 2010).”
Stability, change, and implications of students’ motivation profiles: A latent transition analysis (2017, methodology): “The most similar model from the previous sequence is then converted to a longitudinal LTA Motivation Profiles 17 model (Collins & Lanza, 2010; Nylund et al., 2007), in order to more systematically investigate within-person stability and transitions in profile membership (Morin & Litalien, 2017).”
Latent Class Analysis (2021, methodology): “It is also referred to as latent transition model (Collins & Lanza, 2010), hidden Markov model (MacDonald & Zucchini, 1997; Visser, 2011), Markov switching or regime-switching model.”
Understanding the Risk of Attrition in Undergraduate Engineering: Results from the Project to Assess Climate in Engineering (2012, positive): “In contrast to factor analysis (another latent variable model) where the underlying construct is assumed to be continuous, latent class analysis views the latent variable as categorical, being made up of several latent classes (Collins & Lanza 2010).”
Optimizing team conflict dynamics for high performance teamwork (2017, methodology): “Conceptually, LTA identifies the occurrence and nature of transitions in latent profile membership over time (Collins & Lanza, 2010).”
Performance of the entropy as an index of classification accuracy in latent profile analysis: A Monte Carlo simulation study (2017, positive): “Entropy 作为分类精确性的标准化衡量指数 , 凡是影响潜类别分类精确性的因素均会对其产生影响, 其中最重要的影响因素是潜类别间距(Latent Class Separation)和类别内方差(e.g., Lubke & Muthén, 2007; Muthén, 2004)。潜类别间距是指潜类别间差异的大小, 反映在项目反应概率或均值上, 表现为不同类别个体间在所有观测指标上存在显著的差别(Collins & Lanza, 2009)。潜类别间距越大, 对于来自任一潜类别的个体来说, 将其划分到所属类别的精确性越高。如果两个类别间的差异不明显, 即潜类别间距小, 将个体精确”
Mixture Modeling for Lifespan Developmental Research (2019, background): “In addition to the estimation of LPA solutions at multiple time points, LTA also take into account the connections (i.e., individual transitions in profile membership) between the time specific LPA solutions (Collins & Lanza, 2009).”