This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues ...simultaneously. Bayesian methods are used for model estimation. Robust methods with Student’s t distributions are used to account for nonnormal data. Ignorable missing data are handled by multiple imputation techniques, while nonignorable missing data are handled by an added-on selection model structure. In addition to categorical treatment data, this study extends the work to continuous treatment variables. Monte Carlo simulation studies are conducted showing that the proposed Bayesian approach can well address common issues in existing methods. We provide a real data example on the early childhood relative age effect study to illustrate the application of the proposed method. The proposed method can be easily implemented using the R software package "ALMOND" (Analysis of Local Average Treatment Effect for missing or/and Nonnormal Data).
Applications of latent transition analysis (LTA) have emerged since the early 1990s, with numerous scientific findings being published in many areas, including social and behavioral sciences, ...education, and public health. Although LTA is effective as a statistical analytic tool for a person-centered model using longitudinal data, model building in LTA has often been subjective and confusing for applied researchers. To fill this gap in the literature, we review the components of LTA, recommend a framework of fitting LTA, and summarize what acceptable model evaluation tools should be used in practice. The proposed framework of fitting LTA consists of six steps depicted in Figure 1 from step 0 (exploring data) to step 5 (fitting distal variables). We also illustrate the framework of fitting LTA with data on concerns about school bullying from a sample of 1,180 students ranging from 5th to 9th grade (mean age = 12.2 years, SD = 1.29 years at Time 1) over three semesters. We identified four groups of students with distinct patterns of bullying concerns, and found that their concerns about bullying decreased and narrowed to specific concerns about rumors, gossip, and social exclusion over time. The data and command (syntax) files needed for reproducing the results using SAS PROC LCA and PROC LTA (Version 1.3.2) (2015) and Mplus 7.4 (Muthén and Muthén, 1998-2015) are provided as online supplementary materials.
Background Ecological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants’ behavior and experience in their natural ...environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no “gold standard” for EMA study design to promote compliance. Objective The purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs. Methods Participants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1—number of questions per EMA survey (15 vs 25); factor 2—number of EMAs per day (2 vs 4); factor 3—EMA prompting schedule (random vs fixed times); factor 4—payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5—EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined. Results Participants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance. Conclusions Study results have broad implications for developing best practices guidelines for future studies that use EMA methodologies. Trial Registration ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228
One practical challenge in observational studies and quasi-experimental designs is selection bias. The issue of selection bias becomes more concerning when data are non-normal and contain missing ...values. Recently, a Bayesian robust two-stage causal modeling with instrumental variables was developed and has the advantages of addressing selection bias and handle non-normal data and missing data simultaneously in one model. The method provides reliable parameter and standard error estimates when missing data and outliers exist. The modeling technique can be widely applied to empirical studies particularly in social, psychological and behavioral areas where any of the three issues (e.g., selection bias, data with outliers and missing data) is commonly seen. To implement this method, we developed an R package named ALMOND (
nalysis of
ATE (Local Average Treatment Effect) for
issing
r/and
onnormal
ata). Package users have the flexibility to directly apply the Bayesian robust two-stage causal models or write their own Bayesian models from scratch within the package. To facilitate the application of the Bayesian robust two-stage causal modeling technique, we provide a tutorial for the ALMOND package in this article, and illustrate the application with two examples from empirical research.
Latent basis growth modeling is a flexible version of the growth curve modeling, in which it allows the basis coefficients of the model to be freely estimated, and thus the optimal growth ...trajectories can be determined from the observed data. In this article, Bayesian estimation methods are applied for latent basis growth modeling. Because the latent basis coefficients are important parameters that determine the growth pattern in latent basis growth models, we evaluate the impact of different priors for the basis coefficients on parameter recovery and model estimation. Noninformative priors, informative priors with varying levels of accuracy and precision, and data-dependent priors are considered. In addition, the issue of model specification is treated as a prior selection procedure. The impact of model misspecification and priors for model parameters are investigated simultaneously. A Monte Carlo simulation study is conducted and suggests that misspecified models adversely affect the parameter estimation much more than inaccurate priors. Recommendations on prior selection in latent basis growth models are given based on the simulation results. A real data example on the development of schoolchildren’s reading ability is also provided to illustrate the comparison among different sets of priors.
Using the bivariate dual change score approach, the present study investigated the directionality of the SMC-OMP association in a sample of healthy older adults (N = 2,057) from the Virginia ...Cognitive Aging Project. The sample was assessed throughout 10 years, five time points, and the impact of education, depressive symptoms, and low-memory functioning was tested. The Memory Functioning Questionnaire was used to assess SMC. There was a lack of longitudinal association with no significant coupling effects found between subjective and objective memory. After including depressive symptoms as a covariate, Frequency of Forgetting significantly predicted subsequent negative changes in OMP . A similar result was found for the low-memory functioning group after the inclusion of depression, with the frequency of memory complaints predicting subsequent memory decline . Our results do not support a predictive value of SMC without accounting for the influence of depressive symptoms and low-memory functioning.
•Longitudinal association between depressive symptoms and memory for elderly.•Bivariate dual change score models supported a unidirectional association.•Depressive symptoms predicted subsequent ...change in memory over ten years.•Somatic symptoms and depressed affect predicted negative change in memory.•Positive affect predicted positive change in memory.
The directionality of the longitudinal association between depressive symptoms and memory remains a topic of intense debate. A unidirectional association where depression impacts the change in memory (or vice-versa) and a bidirectional association where the trajectories of both dimensions affect each other lead to different clinical implications. Method: This study investigated the depression-memory longitudinal association in a sample of 2057 older adults aged between 60 and 99 years old from the Virginia Cognitive Aging Project. The bivariate dual change score model was used to investigate the directionality of the association between episodic memory and three dimensions of depression (somatic symptoms, depressed affect, and positive affect) throughout ten years (five measurement points), controlling for education and sex. Results: the bidirectional model showed the best fit between somatic symptoms and memory, with a significant coupling effect observed from initial somatic symptoms to subsequent changes in memory. For depressed and positive affect, the unidirectional model with initial levels of depression predicting following changes in memory showed the best fit to the data, with significant coupling effects observed. Higher initial levels of somatic symptoms and depressed affect predicted a subsequent decline in memory, and higher initial levels of positive affect predicted subsequent better memory performance. Statistical adjustments for covariates (education and sex) had no significant influence on these associations. Conclusions: Our findings support a unidirectional association with higher depressive symptoms preceding a steeper decline in memory in older adults. We discuss the clinical implications for depressive symptoms as a predictor of subsequent memory decline.
Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike ...other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented: one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale.
Translational Abstract
Understanding the structure and composition of data is an important undertaking for a wide range of scientific domains. An initial step in this endeavor is to determine how the data can be summarized into a smaller set of meaningful variables (i.e., dimensions). In this article, we extend a state-of-the-art network science approach, called exploratory graph analysis (EGA), used to identify the dimensions that exist in multivariate data. Using Monte Carlo methods, we compared EGA with several traditional eigenvalue-based approaches that are commonly used in the psychological literature including parallel analysis. Additionally, the simulation study evaluated the performance of new variants of the EGA method and considered a wider set of realistic conditions, such as unidimensional structures and variables of continuous and categorical levels of measurement. We found that EGA performed as well as or better than the most accurate traditional method (i.e., parallel analysis). Importantly, EGA offers a few advantages over traditional methods: (a) it provides an intuitive visual representation of the results, (b) this representation offers a more complex understanding of the data's structure, and (c) the algorithm is deterministic meaning there are fewer researcher degrees of freedom. In sum, our study demonstrates that EGA can accurately identify the underlying structure of multivariate data, while retaining the complexity of the data's structure. This implies that researchers can meaningfully summarize their data without sacrificing the finer details.
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