What Longitudinal Growth & Latent Curve Modeling got wrong, from 49 theses

Tried and failed

latent class analysis with fixed effects applied to multivariate survey exposure data. Outcome: did not converge. Reason: Model fitting failures and boundary estimation issues during optimization across multiple phases.

Health Impacts of Paid Domestic Work: A Focus on Social, Occupational, and Employment-related Health Inequities in the United States · Harvard · PhD

Tried and failed

latent profile analysis with freely estimated variances applied to multi-indicator observational data. Outcome: did not converge. Reason: overparameterization from estimating class-specific variances across many continuous indicators

An Ecological Approach to Emotion Socialization in the Early Childhood Classroom: Examining Contextual Influences on Children’s Early Social and Emotional Development · Harvard · PhD

Tried and failed

latent class discrete choice modeling applied to longitudinal discrete choice panels. Outcome: did not converge. Reason: simultaneous estimation of latent classes and random effects failed to achieve convergence and valid parameter estimates

Unobserved heterogeneity in discrete choice models of car ownership: a dynamic framework for medium to long term choice behaviour · Imperial · PhD

Tried and failed

growth mixture modeling applied to longitudinal substance use survey trajectories. Outcome: did not converge. Reason: unresolvable estimation errors when fitting multi-class trajectory models

Alcohol use among Latina/o adolescents : the role of immigration, family, and peer stressors · UT Austin · PhD

Tried and failed

joint estimation of measurement and latent class models applied to multivariate discrete outcome modeling. Outcome: did not converge. Reason: numerical non-convergence and intractable conditional independence violations across multiple latent constructs

Transportation in an Era of Disruption: How Generational Differences And New Transportation Technologies Are Influencing Travel Behavior · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected using 5-class latent class model because the estimation failed to converge.

Consumer Preference for Dairy Products in China · University College Dublin · PhD

Considered and rejected

Considered and rejected: Rejected a latent profile analysis model with freely estimated variances across profiles for all 10 indicators due to model convergence failures.

An Ecological Approach to Emotion Socialization in the Early Childhood Classroom: Examining Contextual Influences on Children’s Early Social and Emotional Development · Harvard · PhD

Tried and failed

growth mixture modeling for trajectory clustering applied to longitudinal student attendance rates. Outcome: no signal. Reason: Lack of class separation and small subpopulation sizes caused single-class models to fit best.

Contextualizing school mobility : investigating the influence of racially/ethnically-relevant factors on students’ mobility experiences · UT Austin · PhD

Tried and failed

high-order latent factor mixture modeling applied to cognitive ability test score data. Outcome: unstable. Reason: over-extraction led to degenerate class solutions, near-empty classes, or collapse into a single class

Are the effects of g on achievement smaller at higher ability levels? · UT Austin · PhD

Tried and failed

latent class choice modeling with many classes applied to discrete choice experiment survey data. Outcome: did not converge. Reason: model complexity with higher numbers of classes exceeded what the data could support

To plant or not to plant? Drivers of native woodland creation in the United Kingdom · Imperial · PhD

Tried and failed

increasing latent class count in mixture modeling applied to dichotomous behavioral survey indicators. Outcome: worse than baseline. Reason: four-class model showed non-significant likelihood ratio tests and worse information criteria than three-class model

Understanding patterns of electronic nicotine delivery systems use among young adults · UT Austin · PhD

Tried and failed

latent profile analysis with many classes applied to longitudinal survey data. Outcome: unstable. Reason: non-positive definite first-order derivative product matrix caused untrustworthy parameter standard errors

Investigating Ethnic-Racial Identity Development in White U.S. American Adolescents: Identifying Common Constellations and Exploring Measurement · Harvard · PhD

Tried and failed

latent profile analysis on standardized sum scores applied to psychological subscale scores. Outcome: no signal. Reason: hypothesized theoretical subgroup profile failed to emerge from the latent mixture model

Substance use disorder, shame, and self-forgiveness: A latent profile analysis · Iowa State · PhD

Considered and rejected

Considered and rejected: Rejected one-step simultaneous mixture modeling for substantive analyses because estimating growth and LCA simultaneously fundamentally changes latent class definitions beyond the observed indicator patterns.

Childhood Adversities and Adult Mental Health: Conceptualizing and Measuring Heterogeneity in Adversity Experience · Duke · Doctorate

Considered and rejected

Considered and rejected: Rejected a 4-class latent class growth curve model for depression because solutions converged on local maxima, had higher BIC (294141.890), lower entropy (0.79), and low class prevalence (3%).

Longitudinal Analyses Of Epigenetic Correlates Of Externalizing And Internalizing Disorders · Penn · PhD

Considered and rejected

Considered and rejected: Rejected a 5-class latent class model due to convergence difficulties and lack of theoretical interpretability/distinctness over the 4-class solution

CHARACTERIZING THE LIFESTYLE ENGAGEMENT OF OLDER ADULTS: IMPLICATIONS FOR COGNITIVE FUNCTIONING, PHYSICAL FRAILTY, AND FUTURE INTERVENTIONS · Johns Hopkins · PhD

Tried and failed

latent growth curve modeling applied to longitudinal proportional compliance data. Reason: poor linear model fit across time waves with high chi-square and RMSEA and low CFI

Examining the variations in and relations between nonresidential fathers’ financial contributions to and involvement with their children following divorce · UT Austin · PhD

Tried and failed

random intercept outcome model with predictor growth curve applied to longitudinal data with trending outcomes. Outcome: did not converge. Reason: model misspecification by assuming constant outcome mean over time when outcome actually exhibits a linear trend

Disaggregating Within-Person and Between-Person Effects in the Presence of Linear Time Trends in Time-Varying Predictors: Structural Equation Modeling Approach · Virginia Tech · Doctorate

Tried and failed

Linear mixed-effects modeling applied to longitudinal plant growth count data. Outcome: did not converge. Reason: Singular fit errors caused by non-linearity in the response variable

Ecology and evolution of the African ant acacia, Vachellia drepanolobium, and its multiple symbionts · Harvard · PhD

Considered and rejected

Considered and rejected: Rejected quadratic latent growth models in Chapter 4 due to model non-convergence under the multi-step ML approach.

Childhood Adversities and Adult Mental Health: Conceptualizing and Measuring Heterogeneity in Adversity Experience · Duke · Doctorate

Considered and rejected

Considered and rejected: Rejected quadratic growth models in piecewise latent growth curve models due to insufficient fixed parameters and unresolvable estimation errors in Mplus.

Contextualizing school mobility : investigating the influence of racially/ethnically-relevant factors on students’ mobility experiences · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected non-linear latent basis growth models for victimization due to linear nested models providing superior fit (T = .053, df = 1, p = .82)

Polygenic Resilience on the Association between Childhood Maltreatment, Delinquency, and Victimization · Sam Houston State University · PhD

Considered and rejected

Considered and rejected: Linear latent growth curve model was rejected (χ2(7) = 16.33, p = .02, CFI = 0.92, RMSEA = 0.06, SRMR = 0.10) because it failed established cut-offs for excellent fit compared to the quadratic model.

A Longitudinal Study of Gaming Patterns during the First Nine Months of the COVID-19 Pandemic · York University · Master's

Tried and failed

propensity-weighted latent growth modeling applied to longitudinal treatment effect estimation. Outcome: unstable. Reason: growth trajectory functional form misspecification caused severe parameter bias and frequent non-positive definite covariance matrices

Treatment effect estimation using latent growth models and propensity score weights with heterogeneous growth trajectory shapes · UT Austin · PhD

Considered and rejected

Considered and rejected: Latent growth curves were considered but decided against because linear change across distinct phases was not expected and maximum likelihood mixed-effects handle missing data better

Effectiveness of the Trauma Practice Approach for Adults in Trauma Treatment · York University · Master's

Considered and rejected

Considered and rejected: Cubic growth curve model rejected because it could not be fit adequately to the longitudinal data despite multiple modifications

Differential Effects of Multisystemic Factors on the Developmental Trajectories of Emotion Regulation · York University · PhD

Tried and failed

Piecewise linear trajectory modeling applied to Longitudinal growth estimation. Outcome: worse than baseline. Reason: Added model complexity without improving fit or interpretive benefit over a continuous specification

THE REAL WORK OF ASSESSMENT: EQUITY-CENTERED SENSEMAKING IN DUAL LANGUAGE LEADERSHIP · Penn · Doctorate

Tried and failed

quadratic multilevel growth curve modeling applied to longitudinal psychological symptom trajectories. Outcome: no signal. Reason: quadratic rate of change parameter was not statistically significant compared to linear trajectory

Brief Adjustment Scale - 6 for Measurement-Based Care: Further Evaluation of Psychometric Properties, Ethnic Samples, and Clinical Utility · Virginia Tech · Master's

Tried and failed

latent growth curve modeling with structured residuals applied to longitudinal measurement invariance testing. Outcome: did not converge. Reason: Non-positive definite residual covariance matrices under configural, weak, or strict invariance constraints

Longitudinal reciprocal relations among reading, executive function, and social emotional skills · UT Austin · PhD

Tried and failed

latent growth curve modeling applied to high-frequency longitudinal self-report data. Outcome: did not converge. Reason: severe multicollinearity across dense measurement waves and insufficient sample size causing singularity errors

Trait and state individual differences’ impact on rapid immersed symptoms of cybersickness (RISC) trajectories · Iowa State · PhD

Tried and failed

multilevel modeling with aggregate contextual predictors applied to longitudinal adolescent mental health data. Outcome: did not converge. Reason: insufficient convergence to compute higher-level standard errors

A life course exploration of social and temporal variability in the association between parental divorce and adolescent mental health trajectories · UT Austin · PhD

Tried and failed

Latent growth curve modeling with time-varying covariates applied to longitudinal substance use survey data. Outcome: did not converge. Reason: Model complexity with time-varying covariates exceeded estimation limits even after 100,000 iterations

Alcohol use among Latina/o adolescents : the role of immigration, family, and peer stressors · UT Austin · PhD

Tried and failed

fully latent product-indicator structural equation modeling applied to moderated mediation analysis. Outcome: did not converge. Reason: numerical convergence issues with complex latent interaction estimation

Career Calling in Older Adults: A Socioemotional Selectivity Perspective · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected a latent variable SEM approach for SRM-dependent Response Surface Analysis because the structural equation models failed to converge.

Stereotypes and Social Decisions: The Interpersonal Consequences of Socioeconomic Status · University of Oregon · PhD

Considered and rejected

Considered and rejected: Rejected treating cognitive impairment as a latent variable across waves due to non-convergence, using an exogenous manifest baseline variable instead.

Living alone in later life: Understanding independence, adaptation, and well-being in the face of impairment using the National Health and Aging Trends Study · Iowa State · PhD

Tried and failed

single-factor confirmatory factor analysis applied to heterogeneous multidimensional latent vulnerability constructs. Outcome: did not converge. Reason: indicators lacked unidimensionality and exhibited complex error covariance structures preventing convergence and fit

Social determinants of health and buprenorphine adherence among individuals with opioid use disorder · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected Latent Transition Analysis (LTA) because measures changed between Grade 1 and Grade 2 (lacked measurement invariance).

Cognitive Dimensions of Early Numeracy: Exploring Profiles of Early Math Achievement in Canadian Students · Carleton University · Master's

Considered and rejected

Considered and rejected: Rejected Latent Class Growth Analysis (LCGA) because significant intraclass variability violated LCGA zero-variance assumptions

Positive parenting practices, child competence, and adolescent desistance from delinquency: Attachment and transactional effects · Iowa State · PhD

Tried and failed

collapsing distinct latent factors into single construct applied to structural equation modeling of cognitive rates. Reason: forced unidimensionality caused significant model misfit because underlying components represent dissociable processes

Explaining Individual Differences in the Rate of Focusing Attention: Dissociating Attention Control from Drift Rates · Georgia Tech · PhD

Tried and failed

single latent factor model applied to heterogeneous phenotypic genetic correlation matrices. Outcome: worse than baseline. Reason: suboptimal fit compared to a multidimensional correlated factor model across distinct psychiatric traits

An examination of the genetic etiology of substance use and serious mental illness · UT Austin · PhD

Tried and failed

Confirmatory factor analysis applied to Crowdsourced psychological survey data. Outcome: unstable. Reason: Produced Heywood cases and out-of-bounds latent correlation estimates exceeding 1.0 due to poor data quality

Concerns and Recommendations for Pain and Masculinity Research using Amazon Mechanical Turk: A Cautionary Tale · Texas Tech · PhD

Considered and rejected

Considered and rejected: Discarded the Ebbinghaus latent factor from SEM Model D due to unsolvable Heywood cases (negative error variance / explained variance exceeding 100%).

Individual differences in visual (mis)perception: a multivariate statistical approach · EPFL · PhD

Tried and failed

second-order latent growth modeling applied to small-sample longitudinal data with high residual variance. Outcome: unstable. Reason: small sample size combined with large time-specific residual variance produced biased estimates and underestimated standard errors

How Well Can Two-Wave Models Recover the Three-Wave Second Order Latent Model Parameters? · Virginia Tech · Doctorate

Tried and failed

ordinary least squares linear regression applied to biomarker trajectory modeling. Reason: residuals violated homoscedasticity and normality assumptions

NEW DISCOVERIES IN THE ASSOCIATIONS OF STATIN MEDICATION EXPOSURE AND LIVER CIRRHOSIS-RELATED SEQUELAE · Harvard · Master's

Tried and failed

random intercept model without predictor detrending applied to longitudinal data with trending predictors. Outcome: unstable. Reason: linear time trends in predictors were unmodeled, causing severe parameter bias and standard error inflation

Disaggregating Within-Person and Between-Person Effects in the Presence of Linear Time Trends in Time-Varying Predictors: Structural Equation Modeling Approach · Virginia Tech · Doctorate

Tried and failed

two-wave latent growth modeling applied to longitudinal trajectory estimation with heteroscedasticity. Outcome: worse than baseline. Reason: heteroscedastic residual errors and large residual variance inflated standard errors and reduced statistical power

How Well Can Two-Wave Models Recover the Three-Wave Second Order Latent Model Parameters? · Virginia Tech · Doctorate

Tried and failed

omitting measurement error heteroskedasticity in moderated models applied to latent growth curve modeling. Reason: unmodeled residual variance moderation severely biases latent variance moderation estimates and coverage rates

Measurement and structural moderation effects in growth models : a Bayesian moderated nonlinear factor analysis approach · UT Austin · PhD

Tried and failed

Gaussian assumptions in latent growth models applied to non-normal longitudinal data. Outcome: worse than baseline. Reason: distributional misspecification under moderate-to-severe non-normality caused massive positive bias in trajectory parameters and covariances

Factored regression approach for structural equation models with non-normal continuous data · UT Austin · PhD

Tried and failed

linear mixed models with factor analytic covariance applied to longitudinal multi-environment trial data. Reason: models produced systematic bias in trend estimation across multi-decade unbalanced historical series

Strategies to untangle genetic and non-genetic sources of variation in cultivar development programs · Iowa State · PhD

Tried and failed

replacing autoregressive term with static seasonal indicator applied to hierarchical longitudinal survey response modeling. Outcome: did not converge. Reason: removing autoregressive dependence failed to adequately capture temporal dynamics necessary for successful model fit

SIFting Through Surveys: Leveraging Satellite Data for Improved Understanding of Crop Yields and Food Insecurity in Sub-Saharan Africa and Beyond · Harvard · PhD

Tried and failed

autoregressive latent trajectory with structured residuals applied to longitudinal behavioral symptom trajectories. Outcome: no signal. Reason: cross-lagged residual paths were statistically non-significant and did not improve overall model fit

The Development of Internalizing and Externalizing Symptoms in Middle Childhood and Substance Use Prior to Middle School Entry · Harvard · PhD

Tried and failed

Penalized regression on raw levels instead of differences applied to longitudinal high-dimensional omics data. Outcome: worse than baseline. Reason: Fails to remove time-invariant effects and handles non-linear dynamic predictor trajectories poorly

Variable Selection for High-Dimensional Longitudinal Omics Data with a Continuous or Misclassified Binary Outcome · Cornell · PhD

Considered and rejected

Considered and rejected: Rejected autoregressive correlation structures for longitudinal binary substance use modeling after BIC comparisons indicated non-autoregressive models were optimal.

Cumulative Vulnerabilities: Substance Use in Adolescence and in Recovery · Virginia Tech · Doctorate

Tried and failed

adding intra-year seasonal fixed effects applied to multi-year longitudinal growth curve modeling. Outcome: no signal. Reason: did not substantively change trajectory interpretation or improve model fit

THE REAL WORK OF ASSESSMENT: EQUITY-CENTERED SENSEMAKING IN DUAL LANGUAGE LEADERSHIP · Penn · Doctorate

Tried and failed

principal component auxiliary variable imputation applied to planned missing longitudinal latent growth models. Outcome: worse than baseline. Reason: slope parameter estimates exhibited persistent high bias exceeding acceptable thresholds across all conditions

Evaluating Missing Data Recovery Techniques in Two Wave Planned Missing Data Designs for Estimating Latent Growth Parameters · Texas Tech · PhD

Tried and failed

regression on weighted cluster averages applied to latent factor prediction models. Outcome: worse than baseline. Reason: produces biased estimators compared to best linear predictor methods

STRUCTURED LATENT FACTOR MODELS: IDENTIFIABILITY, ESTIMATION, INFERENCE AND PREDICTION · Cornell · PhD

Tried and failed

principal component auxiliary variables for missing data applied to small sample longitudinal growth modeling. Outcome: unstable. Reason: small per-group sample sizes caused excessive non-admissible solutions in planned missing designs

Evaluating Missing Data Recovery Techniques in Two Wave Planned Missing Data Designs for Estimating Latent Growth Parameters · Texas Tech · PhD

Left open

Apply growth mixture modeling to longitudinal middle school engagement indicators to identify declining engagement sub-populations for dropout prediction. Blocker: Access to longitudinal student-level administrative education data.

Using Machine Learning to Advance High School Dropout Prediction and Prevention · Penn · PhD

Left open

Fit latent growth models to longitudinally evaluate the interplay between adolescent negative affect and functional outcomes using the study's multi-wave cohort data. Blocker: Requires the private longitudinal multi-wave adolescent clinical/behavioral dataset used in the thesis.

Characterizing Negative Affect Across Adolescence: Evidence from Cross-Sectional, Longitudinal, and Neuroimaging Approaches · Harvard · PhD

Left open

Model longitudinal reciprocal relations between reading and social-emotional learning skills during adolescence or high school transitions using latent growth modeling with structured residuals. Blocker: Requires longitudinal adolescent cohort data measuring reading and social-emotional learning skills across high school transitions.

Longitudinal reciprocal relations among reading, executive function, and social emotional skills · UT Austin · PhD

Left open

Investigate long-term effects and diminishing returns of repeated multi-session interactions with AI learning tools through longitudinal user studies. Blocker: Requires designing and conducting multi-session longitudinal human subject studies with students over extended periods

EMPIRICAL EVIDENCE THAT USING AI TOOLS CAN ENHANCE HUMAN COGNITION · Penn · PhD

Left open

Fit longitudinal growth models across multiple time points to predict summer reading outcomes using longitudinal student reading scores. Blocker: Requires private multi-time-point longitudinal reading assessment data from the specific student cohort studied

Self-reported summer reading of English learners with reading difficulties · UT Austin · PhD

Left open

Compare contextual economic inequality measures to resolve theoretical divergences between cross-sectional and longitudinal empirical patterns. Blocker: Lack of specific theoretical framework or methodological approach for resolving the cross-sectional versus longitudinal divergences.

Economic Inequality Through a Contextual Lens: Spatial Variation in Beliefs, Opinion, and Policy Support · Cornell · PhD

Left open

Evaluate early childhood absenteeism trajectories and contextual household risk factors during and post-COVID-19 pandemic using longitudinal latent growth modeling. Blocker: Requires restricted longitudinal student attendance and household-level demographic data spanning the COVID-19 pandemic period

An Ecological Approach to Understanding the Predictors and Outcomes of Absenteeism in the Early Years · Harvard · PhD

Left open

Evaluate long-term retention and downstream problem-solving transfer of learned inquiry conceptual models using post-test or delayed assessment data. Blocker: Requires longitudinal student learning experiments or downstream assessment data not provided in the dataset

Generalised Modeling of Inquiry Behaviour: From Learning to Understanding · EPFL · PhD

Left open

Develop theoretical frameworks and longitudinal studies to measure the effects of gamified learning on knowledge retention and critical learning skills. Blocker: Requires human subjects, longitudinal classroom testing, and lacks concrete operational methodology.

Learners, Players, Designers | Un approccio gamificato all'innovazione educativa e all'apprendimento permanente in Design · Politecnico di Torino · PhD