Multivariate Behavioral Research最新文献

The use of the lambda-mu-sigma (LMS) method for estimating centiles and producing reference ranges has received much interest in clinical practice, especially for assessing growth in childhood. However, this method may not be directly applicable where measures are based on a score calculated from question response categories that is bounded within finite intervals, for example, in psychometrics. In such cases, the main assumption of normality of the conditional distribution of the transformed response measurement is violated due to the presence of ceiling (and floor) effects, leading to biased fitted centiles when derived using the common LMS method. This paper describes the methodology for constructing reference intervals when the response variable is bounded and explores different distribution families for the centile estimation, using a score derived from a parent-completed assessment of cognitive and language development in 24 month-old children. Results indicated that the z-scores, and thus the extracted centiles, improved when kurtosis was also modeled and that the ceiling effect was addressed with the use of the inflated binomial distribution. Therefore, the selection of the appropriate distribution when constructing centile curves is crucial.

Redundancy analysis (RA) is a multivariate method that maximizes the mean variance of a set of criterion variables explained by a small number of redundancy variates (i.e., linear combinations of a set of predictor variables). However, two challenges exist in RA. First, inferential information for the RA estimates might not be readily available. Second, the existing methods addressing the dimensionality problem in RA are limited for various reasons. To aid the applications of RA, we propose a direct covariance structure modeling approach to RA. The proposed approach (1) provides inferential information for the RA estimates, and (2) allows the researcher to use a simple yet practical criterion to address the dimensionality problem in RA. We illustrate our approach with an artificial example, validate some standard error estimates by simulations, and demonstrate our new criterion in a real example. Finally, we conclude with future research topics.

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.

Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting approach that simultaneously models the within-unit changes with differential equation models and accounts for between-unit differences with mixed effects. This approach combines a variant of the Kalman filter, the continuous-discrete extended Kalman filter (CDEKF), and the Markov chain Monte Carlo method often employed in the Bayesian framework through the platform Stan. At the same time, it utilizes Stan's functionality of numerical solvers for the implementation of CDEKF. For an empirical illustration, we applied this method in the context of differential equation models to an empirical dataset to explore the physiological dynamics and co-regulation between couples.

A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study presents a series of Monte Carlo computer simulations that investigates Bayesian and multiple imputation strategies based on factored regressions. When the model's distributional assumptions are satisfied, these methods generally produce nearly unbiased estimates and good coverage, with few exceptions. Severe misspecifications that arise from substantially non-normal distributions can introduce biased estimates and poor coverage. Follow-up simulations suggest that a Yeo-Johnson transformation can mitigate these biases. A real data example illustrates the methodology, and the paper suggests several avenues for future research.

The impact of missing data on statistical inference varies depending on several factors such as the proportion of missingness, missing-data mechanism, and method employed to handle missing values. While these topics have been extensively studied, most recommendations have been made assuming that all missing values are from the same missing-data mechanism. In reality, it is very likely that a mixture of missing-data mechanisms is responsible for missing values in a dataset and even within the same pattern of missingness. Although a mixture of missing-data mechanisms and causes within a dataset is a likely scenario, the performance of popular missing-data methods under these circumstances is unknown. This study provides a realistic evaluation of methods for handling missing data in this setting using Monte Carlo simulation in the context of regression. This study also seeks to identify acceptable proportions of missing values that violate the missing-data mechanism assumed by the method used to handle missing values. Results indicate that multiple imputation (MI) performs better than other principled or ad-hoc methods. Different missing-data methods are also compared via the analysis of a real dataset in which mixtures of missingness mechanisms are created. Recommendations are provided for the use of different methods in practice.

The results in a structural equation modeling (SEM) analysis can be influenced by just a few observations, called influential cases. Tools have been developed for users of R to identify them. However, similar tools are not available for AMOS, which is also a popular SEM software package. We introduce the FINDOUT toolset, a group of SPSS extension commands, and an AMOS plugin, to identify influential cases and examine how these cases influence the results. The SPSS commands can be used either as syntax commands or as custom dialogs from pull-down menus, and the AMOS plugin can be run from AMOS pull-down menu. We believe these tools can help researchers to examine the robustness of their findings to influential cases.

Diffusion-based item response theory models are measurement models that link parameters of the diffusion model (drift rate, boundary separation) to latent traits of test takers. Similar to standard latent trait models, they assume the invariance of the test takers' latent traits during a test. Previous research, however, suggests that traits change as test takers learn or decrease their effort. In this paper, we combine the diffusion-based item response theory model with a latent growth curve model. In the model, the latent traits of each test taker are allowed to change during the test until a stable level is reached. As different change processes are assumed for different traits, different aspects of change can be separated. We discuss different versions of the model that make different assumptions about the form (linear versus quadratic) and rate (fixed versus individual-specific) of change. In order to fit the model to data, we propose a Bayes estimator. Parameter recovery is investigated in a simulation study. The study suggests that parameter recovery is good under certain conditions. We illustrate the application of the model to data measuring visuo-spatial perspective-taking.

With the advance of computer-based assessments, many process data, such as response times (RTs), action sequences, Eye-tracking data, the log data for collaborative problem-solving (CPS) and mouse click/drag becomes readily available. Findings from previous studies (e.g., Peng et al., Multivariate Behavioral Research, 1-20, 2021; Xu, The British Journal of Mathematical and Statistical Psychology, 73(3), 474-505, 2020; He & von Davier, Handbook of research on technology tools for real-world skill development (pp. 750-777). IGI Global, 2016; Man & Harring, Educational and Psychological Measurement, 81(3), 441-465, 2021) suggest a substantial relationship between this human-computer interactive process information and proficiency, which means these process data were potentially useful variables for psychological and educational measurement. To make full use of the process data, this paper aims to combine two useful and easily available types of process data, including the mouse click/drag traces and the response times, to the conventional cognitive diagnostic model (CDM) to better understand individual's response behavior and improve the classification accuracy of existing CDM. Then the full Bayesian analysis using Markov chain Monte Carlo (MCMC) was employed to estimate the proposed model parameters. The viability of the proposed model was investigated by an empirical data and two simulation studies. Results indicated the proposed model combing both types of process data could not only improve the attribute classification reliability in real data analysis, but also provide an improvement on item parameters recovery and person classification accuracy.