Cited by 430Psychometric Methodologies and TestingBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

Henson, James M., Reise, Steven P., Kim, Kevin H.·2007·Structural Equation Modeling: A Multidisciplinary Journal

DOI 10.1080/10705510709336744

Open this paper in CoChat — free account

Abstract

The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) × 3 (exogenous latent mean difference) × 3 (endogenous latent mean difference) × 3 (correlation between factors) × 3 (mixture proportions) factorial design. In addition, the efficacy of several likelihood-based statistics (Akaike's Information Criterion [AIC], Bayesian Information Ctriterion [BIC], the sample-size adjusted BIC [ssBIC], the consistent AIC [CAIC], the Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test [aVLMR]), classification-based statistics (CLC [classification likelihood information criterion], ICL-BIC [integrated classification likelihood], normalized entropy criterion [NEC], entropy), and distributional statistics (multivariate skew and kurtosis test) were examined to determine which statistics best recover the correct number of components. Results indicate that the structural parameters were recovered, but the model fit statistics were not exceedingly accurate. The ssBIC statistic was the most accurate statistic, and the CLC, ICL-BIC, and aVLMR showed limited utility. However, none of these statistics were accurate for small samples (n = 500).

Integrity signals

Registry

Crossref · 10.1080/10705510709336744

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Finite Mixture Models
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669
Modeling by shortest data description
Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
Vuong, Q. H. (1989) · Econometrica · doi:10.2307/1912557 · Cited by 5,929
Measures of Multivariate Skewness and Kurtosis with Applications
Mardia, K. V. (1970) · Biometrika · doi:10.1093/biomet/57.3.519 · Cited by 5,125
Testing the number of components in a normal mixture
Lo, Y. (2001) · Biometrika · doi:10.1093/biomet/88.3.767 · Cited by 4,640

Show all 45 references

Research works printed but not linked: 5

Cutler A., 1993, Proceedings of the first U. S.-Japan conference on the Frontiers of Statistical Modeling, 149
Hays W. L., 1994, Statistics.
Leroux B. G., 1992, AnnalsofStatistics, 20, 1350
Mardia K. V., 1974, Sankhya, 36, 115
Muthén L. K., 2001, Mplus user's guide,

Sorted by citations

Items 101–200 of 430

Mean and Covariance Structure Mixture Models
Pastor, D. A., & Gagné, P. (2013) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-62396-246-320251012 · Cited by 40
Market segmentation with mixture regression models: Understanding measures that guide model selection
Sarstedt, M. (2008) · Journal of Targeting Measurement and Analysis for Marketing · doi:10.1057/jt.2008.9 · Cited by 39
Use of factor mixture modeling to capture Spearman's law of diminishing returns☆
Reynolds, M. R., Keith, T. Z., & Beretvas, S. N. (2010) · Intelligence · doi:10.1016/j.intell.2010.01.002 · Cited by 33
Core self‐evaluations, perceptions of group potency, and job performance: The moderating role of individualism and collectivism cultural profiles
O’Neill, T., McLarnon, M. J. W., Xiu, L., & Law, S. J. (2015) · Journal of Occupational and Organizational Psychology · doi:10.1111/joop.12135 · Cited by 39
Heterogeneity of posttraumatic stress symptoms in a highly traumatized low income, urban, African American sample
Nugent, N. R., Koenen, K. C., & Bradley, B. (2012) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2012.07.012 · Cited by 41
Disaster exposure and patterns of disaster preparedness: A multilevel social vulnerability and engagement perspective
Cong, Z., Feng, G., & Chen, Z. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117798 · Cited by 38
A person-oriented approach to understanding dimensions of parenting in low-income mothers
Cook, G. A., Roggman, L. A., & D’zatko, K. W. (2012) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2012.06.001 · Cited by 41
Viewing exercise goal content through a person-oriented lens: A self-determination perspective
Lindwall, M., Josefsson, K. W., Sebire, S. J., & Standage, M. (2016) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2016.06.011 · Cited by 38
A longitudinal examination of nurses’ need satisfaction profiles: A latent transition analysis
Huyghebaert‐Zouaghi, T., Morin, A. J. S., Forest, J., Fouquereau, E., & Gillet, N. (2020) · Current Psychology · doi:10.1007/s12144-020-00972-1 · Cited by 43
Student engagement with school and personality: a biopsychosocial and person‐centred approach
Moreira, P., Inman, R. A., Cloninger, K. M., & Cloninger, C. R. (2020) · British Journal of Educational Psychology · doi:10.1111/bjep.12388 · Cited by 38
Developmental Latent Patterns of Identification as Mostly Heterosexual Versus Lesbian, Gay, or Bisexual
Calzo, J. P., Masyn, K. E., Austin, S. B., Jun, H., & Corliss, H. L. (2016) · Journal of Research on Adolescence · doi:10.1111/jora.12266 · Cited by 41
Unrestricted Mixture Models for Class Identification in Growth Mixture Modeling
Liu, M., & Hancock, G. R. (2014) · Educational and Psychological Measurement · doi:10.1177/0013164413519798 · Cited by 40
Mixture Modeling Methods for the Assessment of Normal and Abnormal Personality, Part II: Longitudinal Models
Wright, A. G. C., & Hallquist, M. N. (2013) · Journal of Personality Assessment · doi:10.1080/00223891.2013.830262 · Cited by 38
Patterns of Tobacco Product Use and Correlates Among Adults in the Population Assessment of Tobacco and Health (PATH) Study: A Latent Class Analysis
Kypriotakis, G., Robinson, J. D., Green, C. E., & Cinciripini, P. M. (2018) · Nicotine & Tobacco Research · doi:10.1093/ntr/nty025 · Cited by 36
A latent transition analysis investigating the nature, stability, antecedents, and outcomes of occupational commitment profiles for school principals
Houle, S. A., Morin, A. J. S., Fernet, C., Vandenberghe, C., & Tóth‐Király, I. (2020) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2020.103460 · Cited by 37
A Longitudinal Person-Centered Perspective on Positive and Negative Affect at Work
Sandrin, É., Morin, A. J. S., Fernet, C., & Gillet, N. (2020) · The Journal of Psychology · doi:10.1080/00223980.2020.1781033 · Cited by 38
Typology of religiosity/spirituality in relation to perceived health, depression, and life satisfaction among older Korean immigrants
Roh, S., Lee, Y.-S., Lee, J. H., & Martin, J. I. (2013) · Aging & Mental Health · doi:10.1080/13607863.2013.848837 · Cited by 35
Latent classes of PTSD symptoms in Iraq and Afghanistan female veterans
Hebenstreit, C. L., Madden, E., & Maguen, S. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.04.061 · Cited by 35
Beyond the Diagnosis: Using Patient Characteristics and Domains of Tendon Health to Identify Latent Subgroups of Achilles Tendinopathy
Hanlon, S. L., Pohlig, R. T., & Silbernagel, K. G. (2021) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2021.10271 · Cited by 36
A test of work motivation profile similarity across four distinct samples of employees
Gillet, N., Morin, A. J. S., Ndiaye, A., Colombat, P., & Fouquereau, E. (2020) · Journal of Occupational and Organizational Psychology · doi:10.1111/joop.12322 · Cited by 34
An Empirical Typology of the Latent Programmatic Structure of Community College Student Success Programs
Hatch, D. K., & Bohlig, E. M. (2015) · Research in Higher Education · doi:10.1007/s11162-015-9379-6 · Cited by 34
Polydrug use trajectories and differences in impulsivity among adolescents
Loredo, V. M., Hermida, J. R. F., de la Torre‐Luque, A., & Fernández‐Artamendi, S. (2018) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2018.07.003 · Cited by 39
Spurious Latent Classes in the Mixture Rasch Model
Alexeev, N., Templin, J. L., & Cohen, A. S. (2011) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.2011.00146.x · Cited by 34
Foster parenting together: Foster parent couples
Orme, J. G., & Combs-Orme, T. D. (2013) · Children and Youth Services Review · doi:10.1016/j.childyouth.2013.11.017 · Cited by 33
Treatment-seeking behaviours for depression in the general population: Results from the National Epidemiologic Survey on Alcohol and Related Conditions
Carragher, N., Adamson, G., Bunting, B., & McCann, S. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2009.05.009 · Cited by 32
Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models
Yu, H., & Park, J. (2014) · Multivariate Behavioral Research · doi:10.1080/00273171.2014.900431 · Cited by 32
Measurement Invariance Across Groups in Latent Growth Modeling
Kim, E., & Willson, V. L. (2014) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2014.915374 · Cited by 32
Dispositional Mindfulness Profiles in Adolescents and their Associations with Psychological Functioning and Hypothalamic–Pituitary–Adrenal Axis Hormones
Calvete, E., Fernández‐González, L., Echezarraga, A., & Orue, I. (2019) · Journal of Youth and Adolescence · doi:10.1007/s10964-019-01128-6 · Cited by 32
Predictors and Outcomes of Growth Mixture Modeled Trajectories Across an Exposure‐Based PTSD Intervention With Veterans
Allan, N. P., Grös, D. F., Myers, U. S., Korte, K. J., & Acierno, R. E. (2016) · Journal of Clinical Psychology · doi:10.1002/jclp.22408 · Cited by 32
On the merits of coherent leadership empowerment behaviors: A mixture regression approach
Chénard‐Poirier, L. A., Morin, A. J. S., & Boudrias, J. (2017) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2017.08.003 · Cited by 32
Longitudinal investigation of anxiety sensitivity growth trajectories and relations with anxiety and depression symptoms in adolescence
Allan, N. P., Felton, J. W., Lejuez, C. W., MacPherson, L., & Schmidt, N. B. (2015) · Development and Psychopathology · doi:10.1017/s0954579415000590 · Cited by 34
A Characterization of Power Method Transformations through L ‐Moments
Headrick, T. C. (2011) · Journal of Probability and Statistics · doi:10.1155/2011/497463 · Cited by 30
Is the Dark Triad Better Studied Using a Variable- or a Person-Centered Approach? An Exploratory Investigation
Kam, C. C. S., & Zhou, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161628 · Cited by 30
Local Optima in Mixture Modeling
Shireman, E. M., Steinley, D., & Brusco, M. J. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1160359 · Cited by 30
Profiles of global and target-specific work commitments: Why compatibility is better and how to achieve it
Meyer, J. P., Morin, A. J. S., Rousseau, V., Boudrias, J., & Brunelle, É. (2021) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2021.103588 · Cited by 35
Organizational Readiness for Change (ORC) test used in the implementation of assessment instruments and treatment methods in a Swedish National study
Billsten, J., Fridell, M., Holmberg, R. G., & Ivarsson, A. (2017) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2017.10.004 · Cited by 30
The Effect of Model Misspecification on Growth Mixture Model Class Enumeration
McNeish, D., & Harring, J. R. (2017) · Journal of Classification · doi:10.1007/s00357-017-9233-y · Cited by 29
Trajectories and predictors of response in youth anxiety CBT: Integrative data analysis.
Skriner, L. C., Chu, B. C., Kaplan, M., Bodden, D. H. M., Bögels, S. M., Kendall, P. C., Nauta, M. H., Silverman, W. K., Wood, J. J., Barker, D., et al. (2018) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000367 · Cited by 32
Profiles of Mindfulness across Adulthood
Ford, C. G., Wilson, J. M., Altman, N., Strough, J., & Shook, N. J. (2020) · Mindfulness · doi:10.1007/s12671-020-01372-z · Cited by 30
Benefits of Psychological Detachment From Work: Does Autonomous Work Motivation Play a Role?
Olafsen, A. H., & Bentzen, M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00824 · Cited by 44
Students’ multidimensional profiles of math engagement: Predictors and outcomes from a self‐system motivational perspective
Miller, C. J., Perera, H. N., & Maghsoudlou, A. (2020) · British Journal of Educational Psychology · doi:10.1111/bjep.12358 · Cited by 38
Polygenic risks for joint developmental trajectories of internalizing and externalizing problems: findings from the ALSPAC cohort
Speyer, L. G., Neaves, S., Hall, H. A., Hemani, G., Lombardo, M. V., Murray, A. L., Bonnie Auyeung, B., & Luciano, M. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13549 · Cited by 35
The Instrumental Role of Personality Traits: Using Mixture Structural Equation Modeling to Investigate Individual Differences in the Relationships Between the Big Five Traits and Life Satisfaction
Heidemeier, H., & Göritz, A. (2016) · Journal of Happiness Studies · doi:10.1007/s10902-015-9708-7 · Cited by 25
Clustering of health risk behaviors among adolescents in Kilifi, Kenya, a rural Sub-Saharan African setting
Ssewanyana, D., Abubakar, A. A., Newton, C. R. J. C., Otiende, M., Mochamah, G., Nyundo, C., Walumbe, D., Nyutu, G., Amadi, D., Doyle, A. M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242186 · Cited by 28
Shifts in fish community composition and structure linked to seasonality in a tropical river
Duarte, C., Antão, L. H., Magurran, A. E., & de Deus, C. P. (2022) · Freshwater Biology · doi:10.1111/fwb.13975 · Cited by 28
Diversified resources and academic influence: patterns of university–industry collaboration in Chinese research-oriented universities
Xia, F., Yang, X., & Chen, L. (2015) · Scientometrics · doi:10.1007/s11192-015-1618-2 · Cited by 30
Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration
Depaoli, S. A., Winter, S. D., Lai, K., & Guerra-Peña, K. (2019) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1593813 · Cited by 28
Nonconvergence, covariance constraints, and class enumeration in growth mixture models.
McNeish, D., Harring, J. R., & Bauer, D. J. (2023) · Psychological Methods · doi:10.1037/met0000456 · Cited by 27
Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
Guerra-Peña, K., & Steinley, D. (2016) · Educational and Psychological Measurement · doi:10.1177/0013164416633735 · Cited by 27
Protective and Risk Factors at the Intersection of Chronic Pain, Depression, Anxiety, and Somatic Amplification: A Latent Profile Approach
Kim, S., Lee, J., & Boone, D. (2022) · Journal of Pain Research · doi:10.2147/jpr.s340382 · Cited by 26
Use of Latent Profile Analysis in Studies of Gifted Students
Mammadov, S., Ward, T. J., Cross, J. R., & Cross, T. L. (2016) · Roeper Review · doi:10.1080/02783193.2016.1183739 · Cited by 26
Treatment response trajectories in a three-week CPT-Based intensive treatment for veterans with PTSD
Held, P., Smith, D. L., Bagley, J. M., Kovacevic, M., Steigerwald, V. L., Horn, R. V., & Karnik, N. S. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.07.004 · Cited by 26
The impact of personal motivation on perceived effort and performance of pro-environmental behaviors
Dreijerink, L., Handgraaf, M. J. J., & Antonides, G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.977471 · Cited by 26
Longitudinal pathways of numerical abilities in preschool: Cognitive and environmental correlates and relation to primary school mathematics achievement.
Bakker, M., Torbeyns, J., Verschaffel, L., & Smedt, B. D. (2022) · Developmental Psychology · doi:10.1037/dev0001491 · Cited by 36
Regime-Switching Bivariate Dual Change Score Model
Chow, S., Grimm, K. J., Filteau, G., Dolan, C. V., & McArdle, J. J. (2013) · Multivariate Behavioral Research · doi:10.1080/00273171.2013.787870 · Cited by 26
Change in basic need frustration in relation to perfectionism, anxiety, and performance in elite junior performers
Haraldsen, H. M., Solstad, B. E., Ivarsson, A., Halvari, H., & Abrahamsen, F. E. (2019) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13614 · Cited by 37
Developmental trajectories of empathic concern in infancy and their links to social competence in early childhood
Paz, Y., Davidov, M., Orlitsky, T., Roth‐Hanania, R., & Zahn‐Waxler, C. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13516 · Cited by 25
Coping strategies, stigmatizing attitude, and cyberbullying among Chinese college students during the COVID-19 lockdown
Yang, F., Sun, J., Li, J., & Lyu, S. (2022) · Current Psychology · doi:10.1007/s12144-022-02874-w · Cited by 28
Length of moderate-to-vigorous physical activity bouts and cardio-metabolic risk factors in elementary school children
Willis, E. A., Ptomey, L. T., Szabo‐Reed, A. N., Honas, J. J., Lee, J., Washburn, R. A., & Donnelly, J. E. (2015) · Preventive Medicine · doi:10.1016/j.ypmed.2015.01.022 · Cited by 24
Parenting within residential neighborhoods: A pluralistic approach with African American and Latino families at the center
Witherspoon, D. P., May, E. M., McDonald, A. R., Boggs, S., & Bámaca‐Colbert, M. Y. (2019) · Advances in child development and behavior · doi:10.1016/bs.acdb.2019.05.004 · Cited by 28
AAAPT: Assessment of the Acute Pain Trajectory
Bayman, E. O., Oleson, J., & Rabbitts, J. A. (2020) · Pain Medicine · doi:10.1093/pm/pnaa440 · Cited by 23
In search of the pseudo-transformational leader: A person-centered approach
Tian, A. W., Meyer, J. P., Ilic-Balas, T., Espinoza, J. A., & Pepper, S. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113675 · Cited by 34
Treatment engagement in adolescents with severe psychiatric problems: a latent class analysis
Roedelof, A. J. M., Bongers, I. L., & van Nieuwenhuizen, C. (2013) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-013-0385-y · Cited by 26
A Person-Centered Perspective on the Combined Effects of Global and Specific Levels of Job Engagement
Gillet, N., Morin, A. J. S., Jeoffrion, C., & Fouquereau, E. (2020) · Group & Organization Management · doi:10.1177/1059601119899182 · Cited by 28
Long-term outcomes of Sweden's Contact Family Program for children
Brännström, L., Vinnerljung, B., & Hjern, A. (2013) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2013.01.009 · Cited by 22
Symptom Distress Profiles in Hospitalized Patients in Sweden: A Cross‐Sectional Study
Henoch, I., Sawatzky, R. G., Falk, H., Fridh, I., Ung, E. J., Sarenmalm, E. K., Ozanne, A., Öhlén, J., & Falk, K. (2014) · Research in Nursing & Health · doi:10.1002/nur.21624 · Cited by 22
Youth Sport Coaches’ Well-Being Across the Season: The Psychological Costs and Benefits of Giving Empowering and Disempowering Sports Coaching to Athletes
Solstad, B. E., Ivarsson, A., Haug, E., & Ommundsen, Y. (2018) · International Sport Coaching Journal · doi:10.1123/iscj.2017-0026 · Cited by 22
Predictors and outcomes of core and peripheral sport motivation profiles: A person-centered study
Tóth‐Király, I., Amoura, C., Bőthe, B., Orosz, G., & Rigó, A. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1736765 · Cited by 29
Profiles of teacher self-efficacy and their relations to teacher demographics and affective well-being: A social cognitive perspective
Sun, Y., & Yin, H. (2024) · Teaching and Teacher Education · doi:10.1016/j.tate.2024.104855 · Cited by 24
RETRACTED: Part II: a step-by-step guide to latent class analysis
Aflaki, K., Vigod, S. N., & Ray, J. G. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.05.009 · Cited by 21
Retracted
The reading profiles of late elementary English learners with and without risk for dyslexia
Miciak, J., Ahmed, Y., Capin, P., & Francis, D. J. (2022) · Annals of Dyslexia · doi:10.1007/s11881-022-00254-4 · Cited by 30
Fitting Structural Equation Model Trees and Latent Growth Curve Mixture Models in Longitudinal Designs: The Influence of Model Misspecification
Usami, S., Hayes, T., & McArdle, J. F. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2016.1266267 · Cited by 22
Model Selection for Multilevel Mixture Rasch Models
Şen, S., Cohen, A. S., & Kim, S. (2018) · Applied Psychological Measurement · doi:10.1177/0146621618779990 · Cited by 21
Heterogeneity in the dynamic change of cognitive function among older Chinese people: A growth mixture model
Qiu, P., Zeng, M., Kuang, W., Meng, S. S., Cai, Y., Wang, H., & Wan, Y. (2020) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5334 · Cited by 20
Dynamics of Language Learning Motivation and Emotions: A Parallel-Process Growth Mixture Modeling Approach
Yu, H., Peng, H., & Lowie, W. M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.899400 · Cited by 29
Longitudinal Weight Loss Patterns and their Behavioral and Demographic Associations
Szabo‐Reed, A. N., Lee, J., Ptomey, L. T., Willis, E. A., Schubert, M. M., Washburn, R. A., & Donnelly, J. E. (2015) · Annals of Behavioral Medicine · doi:10.1007/s12160-015-9740-1 · Cited by 21
The Support Needs of Children with Intellectual Disability and Autism: Implications for Supports Planning and Subgroup Classification
Shogren, K. A., Shaw, L. A., Wehmeyer, M. L., Thompson, J. R., Lang, K. M., Tassé, M. J., & Schalock, R. L. (2017) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-016-2995-y · Cited by 20
Health Care Utilization, Transition Readiness, and Quality of Life: A Latent Class Analysis
Traino, K. A., Sharkey, C. M., Perez, M. N., Bakula, D. M., Roberts, C. M., Chaney, J. M., & Mullins, L. L. (2020) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsaa099 · Cited by 20
Mood and Personality Characteristics are Associated with Metamemory Knowledge Accuracy in a Community-Based Cohort of Older Adults
Colvin, L. E., Malgaroli, M., Chapman, S., MacKay‐Brandt, A., & Cosentino, S. (2018) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617717001345 · Cited by 20
Internet-Risk Classes of Adolescents, Dispositional Mindfulness and Health-Related Quality of Life: A Mediational Model
Calvete, E., Fernández-González, L., González-Cabrera, J., Machimbarrena, J. M., & Orue, I. (2020) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2019.0705 · Cited by 19
Early Brain Development: African-American Mothers’ Cognitions about the First Three Years
Combs-Orme, T. D., Orme, J. G., & Lefmann, T. (2012) · Child and Adolescent Social Work Journal · doi:10.1007/s10560-012-0294-9 · Cited by 19
A Prospective Study of Knowledge Sharing at Work Based on Self-Determination Theory
Halvari, A. E. M., Ivarsson, A., Halvari, H., Olafsen, A. H., Solstad, B. E., Niemiec, C. P., Deci, E. L., & Williams, G. C. (2021) · Scandinavian Journal of Work and Organizational Psychology · doi:10.16993/sjwop.140 · Cited by 20
Females in the Juvenile Justice System
Bright, C. L., Kohl, P. L., & Jonson‐Reid, M. (2011) · Crime & Delinquency · doi:10.1177/0011128711421652 · Cited by 19
Heterogeneity in men's marijuana use in the 20s: Adolescent antecedents and consequences in the 30s
Washburn, I. J., & Capaldi, D. M. (2014) · Development and Psychopathology · doi:10.1017/s0954579414000686 · Cited by 19
Semantic radical knowledge and word recognition in Chinese for Chinese as foreign language learners
Su, X., & Kim, Y.-S. (2014) · Reading in a Foreign Language · doi:10.64152/10125/66690 · Cited by 19
Profiles of Risk for Suicidal Behavior in Past and Current United States Military Personnel: Latent Profile Analysis of Current Risk Factors
Allan, N. P., Holm-Denoma, J., Conner, K., Zuromski, K. L., Saulnier, K., & Stecker, T. (2018) · Archives of Suicide Research · doi:10.1080/13811118.2018.1506843 · Cited by 17
The vital few foster mothers
Cherry, D. J., & Orme, J. G. (2013) · Children and Youth Services Review · doi:10.1016/j.childyouth.2013.07.001 · Cited by 19
Contribution of personal and environmental factors on positive psychological functioning in adolescents
Fadda, D., Scalas, L. F., & Meleddu, M. (2015) · Journal of Adolescence · doi:10.1016/j.adolescence.2015.05.019 · Cited by 20
Profiles of Psychological Resilience in College Students With Disabilities
Ganguly, R., & Perera, H. N. (2018) · Journal of Psychoeducational Assessment · doi:10.1177/0734282918783604 · Cited by 23
A model of psychosis and its relationship with impairment
Jonas, K. G., & Markon, K. E. (2013) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-012-0642-2 · Cited by 18
An investigation of the joint longitudinal trajectories of low body weight, binge eating, and purging in women with anorexia nervosa and bulimia nervosa
Lavender, J. M., Young, K. P. D., Franko, D. L., Eddy, K. T., Kass, A. E., Sears, M. S., & Herzog, D. B. (2010) · International Journal of Eating Disorders · doi:10.1002/eat.20880 · Cited by 18
Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling: A Monte Carlo Simulation Study
Wang, Y., Hsu, H.-Y. M., & Kim, E. (2021) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2021.1910036 · Cited by 18
Sleep Patterns Related to Emotion Dysregulation Among Adolescents and Young Adults
Fisher, R. S., Dattilo, T. M., Sharkey, C. M., Traino, K. A., Espeleta, H. C., Krietsch, K. N., Chaney, J. M., & Mullins, L. L. (2021) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsab084 · Cited by 17
Three year outcomes in infants with a family history of autism and/or attention deficit hyperactivity disorder
Charman, T., Pasco, G., Hendry, A. J., Bazelmans, T., Narvekar, N., Goodwin, A., Halkola, H., Agyapong, M., Holman, R., Ali, J. B., et al. (2023) · JCPP Advances · doi:10.1002/jcv2.12189 · Cited by 16
Latent Class Analysis of Peer Conformity
Kosten, P. A., Scheier, L. M., & Grenard, J. L. (2012) · Youth & Society · doi:10.1177/0044118x12454307 · Cited by 17
Temporal attitudes profile transition among adolescents: A longitudinal examination using mover−stayer latent transition analysis.
Morgan, G. B., Wells, K. E., Andretta, J. R., & McKay, M. T. (2016) · Psychological Assessment · doi:10.1037/pas0000383 · Cited by 19
The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories
Usami, S., Jacobucci, R., & Hayes, T. (2018) · Computational Statistics · doi:10.1007/s00180-018-0815-x · Cited by 17
Improving convergence in growth mixture models without covariance structure constraints
McNeish, D., & Harring, J. R. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280220981747 · Cited by 18
Late-onset or chronic overweight/obesity predicts low self-esteem in early adolescence: a longitudinal cohort study
Gong, W., Fong, D., Wang, M., Lam, T.-H., Chung, T., & Ho, S. (2022) · BMC Public Health · doi:10.1186/s12889-021-12381-5 · Cited by 15
Prevalence of multimodal species abundance distributions is linked to spatial and taxonomic breadth
Antão, L. H., Connolly, S. R., Magurran, A. E., Soares, A. M. V. M., & Dornelas, M. (2016) · Global Ecology and Biogeography · doi:10.1111/geb.12532 · Cited by 17

Authors: Henson, J. M., Reise, S. P., & Kim, K. H.
Affiliations: Old Dominion University (US)
University of California, Los Angeles (US)
University of Pittsburgh (US)
ORCID: Henson, James M.
Reise, Steven P.
Kim, Kevin H.
Venue: Structural Equation Modeling: A Multidisciplinary Journal
Publisher: Informa UK Limited
Published: 2007-05
Volume: 14
Issue: 2
Pages: 202-226
ISSN: 1070-5511 (ISSN-L)
1532-8007
DOI: 10.1080/10705510709336744
OpenAlex: W1985321835
Cited by: 429
References: 45
Citations by year: 2026: 13 · 2025: 23 · 2024: 24 · 2023: 21 · 2022: 29 · 2021: 34 · 2020: 43 · 2019: 26 · 2018: 23 · 2017: 24 · 2016: 30 · 2015: 30 · 2014: 18 · 2013: 28 · 2012: 18
FWCI: 15.26
Type: journal article
Language: en
Open access: closed
Locations: Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: model fit statistics, number of components, sample size, Akaike information criterion, Bayesian information criterion, entropy (information theory)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks