The Role of Coding Time in Estimating and Interpreting Growth Curve Models.

Biesanz, Jeremy C.; Deeb-Sossa, Natalia; Papadakis, Alison A.; Bollen, Kenneth A.; Curran, Patrick J.

doi:10.1037/1082-989x.9.1.30

Cited by 337 publications

(321 citation statements)

References 51 publications

Supporting

Mentioning

312

Contrasting

Order By: Relevance

“…Finally, time (values = 1, 2 and 3) was centred around 1, since we were interested in predicting student initial accounting competence at the end of grade 9 (Biesanz et al 2004). All variables were z-standardised in order to obtain comparable estimates.…”

Section: Estimating Effects Of Level 3 Variablesmentioning

confidence: 99%

Determinants of competence development in accounting in upper secondary education

Helm

2015

Empirical Res Voc Ed Train

View full text Add to dashboard Cite

Section: Estimating Effects Of Level 3 Variablesmentioning

confidence: 99%

Determinants of competence development in accounting in upper secondary education

Helm

2015

Empirical Res Voc Ed Train

View full text Add to dashboard Cite

“…In the first example, we applied SEEDMC to a linear growth curve model described in Biesanz, Deeb-Sossa, Papadakis, Bollen, & Curran (2004). This model had five measurement occasions (i.e., MNM = 5), resulting in seven possible CD designs and 48 PM designs that satisfy the restrictions described above.…”

Section: Example 1: Linear Gcmmentioning

confidence: 99%

Search for efficient complete and planned missing data designs for analysis of change

Fan

Rhemtulla

et al. 2015

Behav Res

View full text Add to dashboard Cite

The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. This article proposes a systematic and flexible procedure named SEEDMC (SEarch for Efficient Designs using Monte Carlo Simulation) to search for efficient CD and PM designs for growth-curve modeling under budget constraints. This procedure allows researchers to identify efficient designs for multiple effects separately and simultaneously, and designs that are robust to MCAR attrition. SEEDMC is applied to identify efficient designs for key change parameters in linear and quadratic growth models. The identified efficient designs are summarized and the strengths and possible extensions of SEEDMC are discussed.Keywords Planned missing data designs . Growth curve modeling . Efficiency . Longitudinal data collectionThe design of longitudinal data collection is an essential component of any study of change. Factors that researchers must consider when planning such designs include but are not limited to the number and allocation of repeated measures, sample size, and minimizing unwanted factors such as carry-over effects and attrition. In addition to these considerations, researchers are typically constrained by a finite budget and limited resources which should also be taken into account at the design stage. A well-designed study will lead to statistical tests that are maximally efficient, that is, precise and powerful (Berger & Wong, 2009), given available resources.Two families of designs can be used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. Each of these designs represents a unique combination of design factors (e.g., number of repeated measures per participant and allocation of the repeated measures to time points), leading to the differential efficiency of target statistical procedures. Given two designs, the more efficient one produces parameter estimates that have smaller sampling variability, leading to smaller standard errors, tighter confidence intervals, and thus higher power to detect non-zero effects. Thus, more efficient designs are preferred.Although past research has studied CD designs and specific PM designs, no study so far has searched for the most efficient design among all possible CD and PM designs. Furthermore, the effect of unplanned missingness (missingness that is not due to design, e.g., attrition) has been studied for CD designs but not for PM designs. Given that unplanned missingness is ubiquitous in longitudinal studies, this is an important issue to address. Identifying efficient designs is not a trivial matter. As shown in the examples below, using an efficient design may save up to 75 % of the research budget while achieving the same efficiency to detect target effects when compared to commonly ...

show abstract

“…Simulation data were generated through the following procedure using actual parameter estimates from child weight data of T = 5 in the National Longitudinal Survey of Youth (Biesanz et al 2004;cf Bollen and Curran 2006, pp. 94-96).…”

Section: Data Generationmentioning

confidence: 99%

“…In the main simulation, parameter estimates reported in Biesanz et al (2004) were used to generate data. To confirm the generalizability of the results, we specified different factor means (i.e., μ I , μ S1 and μ S2 ) and (class-invariant)variance-covariance matrix , and performed a similar supplemental simulation.…”

Section: Supplemental Simulationsmentioning

confidence: 99%

The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

2018

View full text Add to dashboard Cite

Behavioral researchers have shown growing interest in structural equation model trees (SEM Trees), a new recursive partitioning-based technique for detecting population heterogeneity. In the present research, we conducted a large-scale simulation to investigate the performance of latent growth curve model (LGCM)-based SEM Trees for uncovering between-individual differences in patterns of within-individual change. Simulation results showed that the correct estimation rates of the number of classes are most strongly related to the agreement rate of the covariate with its true latent profile, and the number of true classes also has a serious negative impact on correct estimation rates of the number of classes. SEM Trees is not always sensitive to the influence of model misspecification, and its impact differs according to a complex function of the types of misspecification as well as the statistical properties of the template model. On the whole, LGCM-based SEM Trees is a robust and stable approach under possible model misspecifications.

show abstract

The Role of Coding Time in Estimating and Interpreting Growth Curve Models.

Cited by 337 publications

References 51 publications

Determinants of competence development in accounting in upper secondary education

Determinants of competence development in accounting in upper secondary education

Search for efficient complete and planned missing data designs for analysis of change

The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

Contact Info

Product

Resources

About