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1.
Growth mixture modeling (GMM) has become a more popular statistical method for modeling population heterogeneity in longitudinal data, but the performance characteristics of GMM enumeration indexes in correctly identifying heterogeneous growth trajectories are largely unknown. Few empirical studies have addressed this issue. This study considered both homogeneous (a k = 1 growth trajectory) and heterogeneous (k = 3 different but unobserved growth trajectories) situations, and examined the performance of GMM in correctly identifying the latent trajectories in sample data. Four design conditions were manipulated: (a) sample size, (b) latent trajectory class proportions, (c) shapes of latent growth trajectories, and (d) degree of separation among latent growth trajectories. The findings suggest that, for k = 1 condition (1 homogenous growth trajectory), GMM's performance is reasonable in correctly identifying 1 latent growth trajectory (cf. Type I error control). However, for the k = 3 conditions (3 heterogeneous latent growth trajectories), GMM's general performance is very questionable (cf. Type II error). Different enumeration indexes varied considerably in their respective performances. Comparing the current results with previous GMM studies, the limitations of this study and future GMM enumeration research avenues are all discussed. 相似文献
2.
Relatively few empirical studies have examined the performance of enumeration indices in correctly identifying longitudinal population heterogeneity when present, and these studies have produced mixed results. In light of these findings, Muthén (2003) suggested that the inclusion of time-invariant (antecedent) and time-varying (concurrent) covariates, as well as allowing the extracted growth mixture classes to predict a distal outcome (consequent covariate), could improve the performance of enumeration indices. This Monte Carlo simulation study examined the performance of a large set of enumeration indices within these generalized growth mixture model (GGMM) conditions, as suggested by Muthén, by manipulating 4 design conditions: (a) sample size, (b) separation distance between adjacent latent growth trajectories, (c) growth trajectory membership proportions, and (d) the amount of mixture model variance explained by the covariates. The findings suggest: (a) at smaller (N = 500) sample sizes, enumeration indices were much more likely to select an incorrect model than the correct one, (b) at larger (N = 3,000) sample sizes, correct model identification occurred only in the largest separation distance and trajectory membership equality conditions, and (c) the inclusion of covariates had a negligible effect at smaller sample sizes, but covariate inclusion had a more favorable effect on data generation model identification in the largest sample size, largest trajectory separation distance, and equal trajectory membership proportions conditions. Suggestions for researchers and future empirical study possibilities are discussed. 相似文献
3.
Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to interclass distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo–Mendell–Rubin test (LMR), adjusted LMR, bootstrap likelihood ratio test, Bayesian Information Criterion (BIC), and sample-size-adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. Akaike's Information Criterion and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size. 相似文献
4.
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered. 相似文献
5.
Zachary K. Collier 《Structural equation modeling》2017,24(6):819-830
In this article, 3-step methods to include predictors and distal outcomes in commonly used mixture models are evaluated. Two Monte Carlo simulation studies were conducted to compare the pseudo class (PC), Vermunt’s (2010), and the Lanza, Tan, and Bray (LTB) 3-step approaches with respect to bias of parameter estimates in latent class analysis (LCA) and latent profile analysis (LPA) models with auxiliary variables. For coefficients of predictors of class membership, results indicated that Vermunt’s method yielded more accurate estimates for LCA and LPA compared to the PC method. With distal outcomes of latent classes and latent profiles, the LTB method produced the lowest relative bias of coefficient estimates and Type I error rates close to nominal levels. 相似文献
6.
For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates. 相似文献
7.
This paper investigates early childhood education (ECE) teachers’ self-reported and observed teacher-child interaction quality (TIQ) and the associated teachers’ professional competence features using a latent profile analysis (LPA) approach to identify the variations in the quality of classroom experiences in Chinese preschools. A total of 164 female preschool teachers aged 20–58 participated in this study. The analysis and discussions focus on four identified profiles of teachers: low-quality teachers (n = 9) characterised by lowest scores in both observational and self-reported TIQ; self-overestimated teachers (n = 59) with relatively low observed CLASS scores but high self-reported TIQ; self-underestimated teachers (n = 76) scoring relatively high in observed TIQ but low in self-assessed TIQ and low-level self-efficacy; and genuine high-quality teachers (n = 20) rated highest in both observational and self-reported TIQ. Findings on specific educational and psychological characteristics of the four profiles of teachers that we have identified through an LPA provide insights into teacher training in China. 相似文献
8.
The study of measurement invariance in latent profile analysis (LPA) indicates whether the latent profiles differ across known subgroups (e.g., gender). The purpose of the present study was to examine the impact of noninvariance on the relative bias of LPA parameter estimates and on the ability of the likelihood ratio test (LRT) and information criteria statistics to reject the hypothesis of invariance. A Monte Carlo simulation study was conducted in which noninvariance was defined as known group differences in the indicator means in each profile. Results indicated that parameter estimates were biased in conditions with medium and large noninvariance. The LRT and AIC detected noninvariance in most conditions with small sample sizes, while the BIC and adjusted BIC needed larger sample sizes to detect noninvariance. Implications of the results are discussed along with recommendations for future research. 相似文献
9.
This Monte Carlo simulation adds to the growing body of enumeration index performance research in continuous response variable mixture models by addressing the issue of the performance of these indexes in discrete-time survival mixture analysis (DTSMA) models. Results showed that although all enumeration indexes performed very well in identifying a homogeneous DTSMA model (i.e., k = 1 hazard function in the sample data), the findings also showed that the enumeration indexes performed poorly in identifying the correct number of unobserved hazard functions present in a heterogeneous (i.e., k = 3) DTSMA model. More important, the performance of the enumeration indexes for the heterogeneous DTSMA models did not improve as the sample size, the effect of time-invariant covariates, or adjacent hazard function separation distance increased, which is inconsistent with some previous Monte Carlo simulation results. The limitations of this Monte Carlo simulation study and future empirical investigation possibilities are both discussed. 相似文献
10.
W. Holmes Finch 《Educational and psychological measurement》2021,81(3):523
Social scientists are frequently interested in identifying latent subgroups within the population, based on a set of observed variables. One of the more common tools for this purpose is latent class analysis (LCA), which models a scenario involving k finite and mutually exclusive classes within the population. An alternative approach to this problem is presented by the grade of membership (GoM) model, in which individuals are assumed to have partial membership in multiple population subgroups. In this respect, it differs from the hard groupings associated with LCA. The current Monte Carlo simulation study extended on prior work on the GoM by investigating its ability to recover underlying subgroups in the population for a variety of sample sizes, latent group size ratios, and differing group response profiles. In addition, this study compared the performance of GoM with that of LCA. Results demonstrated that when the underlying process conforms to the GoM model form, the GoM approach yielded more accurate classification results than did LCA. In addition, it was found that the GoM modeling paradigm yielded accurate results for samples as small as 200, even when latent subgroups were very unequal in size. Implications for practice were discussed. 相似文献
11.
Martha A. O’Daniel Rinsland 《Journal of Experimental Education》2013,81(3):307-317
Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture modeling, as well as the application to complex multilevel sampling designs. The goal of this study was to investigate—through a Monte Carlo simulation study—the performance of several methods for parameterizing multilevel latent class analysis. Of particular interest was the comparison of several such models to adequately fit Level 1 (individual) data, given a correct specification of the number of latent classes at both levels (Level 1 and Level 2). Results include the parameter estimation accuracy as well as the quality of classification at Level 1. 相似文献
12.
Barbara R. Foorman Yaacov Petscher Christopher Stanley Adrea Truckenmiller 《Journal of research on educational effectiveness》2017,10(3):619-645
The objective of this study was to determine the latent profiles of reading and language skills that characterized 7,752 students in kindergarten through tenth grade and to relate the profiles to norm-referenced reading outcomes. Reading and language skills were assessed with a computer-adaptive assessment administered in the middle of the year and reading outcome measures were administered at the end of the year. Three measures of reading comprehension were administered in third through tenth grades to create a latent variable. Latent profile analysis (LPA) was conducted on the reading and language measures and related to reading outcomes in multiple regression analyses. Within-grade multiple regressions were subjected to a linear step-up correction to guard against false-discovery rate. LPA results revealed five to six profiles in the elementary grades and three in the secondary grades that were strongly related to standardized reading outcomes, with average absolute between-profile effect sizes ranging from 1.10 to 2.53. The profiles in the secondary grades followed a high, medium, and low pattern. Profiles in the elementary grades revealed more heterogeneity, suggestive of strategies for differentiating instruction. 相似文献
13.
A latent variable modeling procedure for examining whether a studied population could be a mixture of 2 or more latent classes is discussed. The approach can be used to evaluate a single-class model vis-à-vis competing models of increasing complexity for a given set of observed variables without making any assumptions about their within-class interrelationships. The method is helpful in the initial stages of finite mixture analyses to assess whether models with 2 or more classes should be subsequently considered as opposed to a single-class model. The discussed procedure is illustrated with a numerical example. 相似文献
14.
Confirmatory latent profile analysis (CLPA) was used with the normative sample from the Kaufman Test of Educational Achievement, 3rd ed. (KTEA‐3) to determine whether it was possible to identify a latent class of individuals whose scores were consistent with the academic strengths and weaknesses related to dyslexia. The CLPA identified a class of individuals consistent with dyslexia across four‐grade level groups (first–second, third–fifth, sixth–eighth, and ninth–twelfth). The results of the CLPA were applied to the KTEA‐3 clinical samples of those with known clinical diagnoses. Individuals with Specific Learning Disorder in Reading and/or Written Expression had a higher probability of being in the dyslexia latent class. The use of CLPA as a tool for learning disability diagnosis appears plausible, though much more research is needed. The strengths, limitations, and future directions for the use of CLPA in diagnosis are discussed. 相似文献
15.
Eun Sook Kim Seang-Hwane Joo Philseok Lee Yan Wang Stephen Stark 《Structural equation modeling》2016,23(6):870-887
This simulation study examines the efficacy of multilevel factor mixture modeling (ML FMM) for measurement invariance testing across unobserved groups when the groups are at the between level of multilevel data. To this end, latent classes are generated with class-specific item parameters (i.e., factor loading and intercept) across the between-level classes. The efficacy of ML FMM is evaluated in terms of class enumeration, class assignment, and the detection of noninvariance. Various classification criteria such as Akaike’s information criterion, Bayesian information criterion, and bootstrap likelihood ratio tests are examined for the correct enumeration of between-level latent classes. For the detection of measurement noninvariance, free and constrained baseline approaches are compared with respect to true positive and false positive rates. This study evidences the adequacy of ML FMM. However, its performance heavily depends on the simulation factors such as the classification criteria, sample size, and the magnitude of noninvariance. Practical guidelines for applied researchers are provided. 相似文献
16.
Latent class analysis (LCA) is an increasingly popular tool that researchers can use to identify latent groups in the population underlying a sample of responses to categorical observed variables. LCA is most commonly used in an exploratory fashion whereby no parameters are specified a priori. Although this exploratory approach is reasonable when very little prior research has been conducted in the area under study, it can be very limiting when much is already known about the variables and population. Confirmatory latent class analysis (CLCA) provides researchers with a tool for modeling and testing specific hypotheses about response patterns in the observed variables. CLCA is based on placing specific constraints on the parameters to reflect these hypotheses. The popular and easy-to-use latent variable modeling software package Mplus can be used to conduct a variety of CLCA types using these parameter constraints. This article focuses on the basic principles underlying the use of CLCA, and the Mplus programming code necessary for carrying it out. 相似文献
17.
Freund Micha-Josia Gnambs Timo Lockl Kathrin Wolter Ilka 《European Journal of Psychology of Education - EJPE》2022,37(4):1015-1034
This article examines the development of reading and mathematical competence in early secondary education and aims at identifying distinct profiles of competence development. Since reading and mathematical competences are highly correlated both cross-sectionally and longitudinally, we expected to find a generalized profile of competence development with students developing parallel in reading and mathematical competences. Moreover, previous research confirmed individuals’ specific focus on one of the two domains, for example, in their interest, self-concept, or motivation. Also, differences in competence levels between both domains were found in cross-sectional studies. Therefore, we hypothesized that additional to the generalized profile, there are specialized profiles of competence development with students developing distinctively faster in one of the two domains. To identify both types of profiles, latent growth mixture modeling was used on a sample of 5,301 students entering secondary education from the German National Educational Panel Study. To demonstrate the robustness of the results, these analyses were repeated using different model specifications and subgroups with higher homogeneity (with students belonging to the highest track, i.e., “Gymnasium”). The results indicate only small to non-existent specialized profiles of competence development in all conditions. This finding of roughly parallel development of reading and mathematical competences throughout early secondary education indicates that potential specializations are less important at this point in students’ educational careers.
相似文献18.
Insincere respondents can have an adverse impact on the validity of substantive inferences arising from self-administered questionnaires (SAQs). The current study introduces a new method for identifying potentially invalid respondents from their atypical response patterns. The two-step procedure involves generating a response inconsistency (RI) score for each participant and scale on the SAQ and subjecting the resulting scores to latent profile analysis to identify classes of atypical RI respondent profiles. The procedure can be implemented post–data collection and is illustrated through a survey of school climate that was administered to N = 52,102 high school students. Results of this screening procedure revealed high levels of specificity and expected levels of concordance when contrasted with the results of traditionally used methods of screening items and response time. Contrasts between valid and invalid respondents revealed similar patterns across the three screening procedures when compared across external measures of academics and risk behaviors. 相似文献
19.
Expectancy-value motivation profiles were identified in a sample of US ninth-grade students in 2009 (n = 19,259) using latent profile analysis. Of four distinct profiles, two were high, one typical, and one low in math and in science. In each area, the two high profiles were distinguished by (1) high self-efficacy with lower utility value and (2) high utility value with lower self-efficacy. High-ability was identified by a math score at least one standard deviation above the mean within the race/ethnicity group. Forty-one percent of high-ability students had high math motivation, while only 27% had high science motivation. Evidence of disidentification was observed. Some high-ability students had low motivation in math (15%) and science (28%). Implications for talent development and gifted education are discussed. 相似文献
20.
Elin Borg 《British Journal of Sociology of Education》2015,36(8):1127-1148
Latent profile analysis was used to identify different categories of students having different ‘profiles’ using self-reported classroom behaviour. Four categories of students with unique classroom behaviour profiles were identified among secondary school students in Oslo, Norway (n = 1570). Analyses examined how classroom behaviour categories are related to gender and school performance and whether a dual understanding of gender in school is helpful when trying to explain achievement differences as supposed to classroom behaviour categories. Analyses showed that gender was a better predictor of school achievement than classroom behaviour categories, even though the behaviour categories did contribute to the explanation of variance in students’ academic marks above and beyond gender. 相似文献