Non-experimental designs are common in nursing and allied health research wherein study participants often represent more than a single population or interest. Hence, methods used to identify subgroups and explore heterogeneity have become popular. Latent class mixture modeling is a versatile and person-centered analytic strategy that allows us to study questions about subgroups within samples. In this article, a worked example of latent class mixture modeling is presented to help expose researchers to the nuances of this analytic strategy.
StampKDPrasunMLeeCS, et al. Nursing research in heart failure care: A position statement of the American Association of Heart Failure Nurses (AAHFN). Heart Lung2018; 47: 169–175.
2.
JungTWickramaKA.An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol Compass2008; 2: 302–317.
LeeCSMuddJOAuldJ, et al. Patterns, relevance and predictors of heart failure dyadic symptom appraisal. Eur J Cardiovasc Nurs2017; 16: 595–604.
5.
LeeCSGelowJMMuddJO, et al. Profiles of self-care management versus consulting behaviors in adults with heart failure. Eur J Cardiovasc Nurs2015; 14: 63–72.
6.
LeeCSChienCVBidwellJT, et al. Comorbidity profiles and inpatient outcomes during hospitalization for heart failure: An analysis of the US nationwide inpatient sample. BMC Cardiovasc Disord2014; 14: 73.
7.
LeeCSMuddJOGelowJM, et al. Background and design of the profiling biobehavioral responses to mechanical support in advanced heart failure study. J Cardiovasc Nurs2014; 29: 405–415.
8.
HansenLLyonsKSDieckmannNF, et al. Background and design of the symptom burden in end-stage liver disease patient-caregiver dyad study. Res Nurs Health2017; 40: 398–413.
9.
LeeCSHiattSODenfeldQE, et al. Symptom-hemodynamic mismatch and heart failure event risk. J Cardiovasc Nurs2015; 30: 394–402.
10.
LanzaSTDziakJJHuangL, et al. Proc LCA & Proc LTA users’ guide (Version 1.3.2). University Park, Pennsylvania: The Methodology Center, Penn State, 2015.
11.
StataCorp. Stata structural equation modeling reference manual release 16. College Station, TX: StataCorp LLC., 2019.
RamNGrimmKJ. Methods and measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev2009; 33: 565–576.
14.
JurgensCYLeeCSRiegelB. Psychometric analysis of the heart failure somatic perception scale as a measure of patient symptom perception. J Cardiovasc Nurs2017; 32: 140–147.
15.
LeeCSMuddJOHiattSO, et al. Trajectories of heart failure self-care management and changes in quality of life. Eur J Cardiovasc Nurs2015; 14: 486–494.
16.
LenzERPughLCMilliganRA, et al. The middle-range theory of unpleasant symptoms: An update. ANS Adv Nurs Sci1997; 19: 14–27.
17.
LeeCSGelowJMChienCV, et al. Implant strategy-specific changes in symptoms in response to left ventricular assist devices. J Cardiovasc Nurs2018; 33: 144–151.
18.
LeeCSGelowJMDenfeldQE, et al. Physical and psychological symptom profiling and event-free survival in adults with moderate to advanced heart failure. J Cardiovasc Nurs2014; 29: 315.
19.
NylundKLAsparouhovTMuthenB. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Modeling2007; 14: 535–569.
20.
LoYMendellNRRubinDB. Testing the number of components in a normal mixture. Biometrika2001; 88: 767–778.
21.
ChenQLuoWPalardyJP, et al. The efficacy of common fit indices for enumerating classes in growth mixture models when nested data structure is ignored: A Monte Carlo study. SAGE Open2017; 7. DOI: 10.1177/2158244017700459.
22.
LubkeGNealeMC. Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood?Multivariate Behav Res2006; 41: 499–532.
23.
LeeCSVelloneELyonsKS, et al. Patterns and predictors of patient and caregiver engagement in heart failure care: A multi-level dyadic study. Int J Nurs Stud2015; 52: 588–597.
24.
BidwellJTHigginsMKReillyCM, et al. Shared heart failure knowledge and self-care outcomes in patient-caregiver dyads. Heart Lung2018; 47: 32–39.
25.
LeeCSBidwellJTPaturzoM, et al. Patterns of self-care and clinical events in a cohort of adults with heart failure: 1 Year follow-up. Heart Lung2018; 47: 40–46.
26.
AuldJPMuddJOGelowJM, et al. Patterns of heart failure symptoms are associated with self-care behaviors over 6 months. Eur J Cardiovasc Nurs2018; 17: 543–551.
27.
MacCallumRCWidamanKFZhangS, et al. Sample size in factor analysis. Psychol Methods1999; 4: 84–99.
28.
DziakJJLanzaSTTanX. Effect size, statistical power and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. Struct Equ Modeling2014; 21: 534–552.