Dingjing Shi
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View article: Comparing Likert and Slider Response Formats in Clinical Assessment: Evidence From Measuring Depression Symptoms Using CES-D 8
Comparing Likert and Slider Response Formats in Clinical Assessment: Evidence From Measuring Depression Symptoms Using CES-D 8 Open
This study compared various response formats in fitting confirmatory factor analysis models. Participants responded to the eight-item center for epidemiologic studies depression scale across five different response formats in a within-subj…
View article: A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R
A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R Open
With the increasing availability of large datasets in the behavioral and health sciences, the need for efficient and effective variable selection techniques has grown. While traditional methods like stepwise regression remain prevalent, nu…
View article: Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling
Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling Open
To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network…
View article: Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment
Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment Open
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 ga…
View article: A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R
A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R Open
With recent increases in the size of datasets currently available in the behavioral and health sciences, the need for efficient and effective variable selection techniques has increased. A plethora of techniques exist, yet only a few are u…
View article: Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis–Mass Spectrometry
Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis–Mass Spectrometry Open
Proteomics analysis of mass-limited samples has become increasingly important for understanding biological systems in physiologically relevant contexts such as patient samples, multicellular organoids, spheroids, and single cells. However,…
View article: Investigating variable selection techniques under missing data: A simulation study
Investigating variable selection techniques under missing data: A simulation study Open
Variable selection is one of the most pervasive problems researchers face, especially with the increased ease in data collection arising from online data collection strategies. Machine learning methods such as LASSO and elastic net regress…
View article: A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R
A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R Open
With recent increases in the size of datasets currently available in the psychological sciences, the need for efficient and effective variable selection techniques has increased. A plethora of techniques exist, yet only a few are used with…
View article: A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R
A Tutorial on Supervised Machine Learning Variable Selection Methods for the Social and Health Sciences in R Open
With recent increases in the size of datasets currently available in the psychological sciences, the need for efficient and effective variable selection techniques has increased. A plethora of techniques exist, yet only a few are used with…
View article: Top-down Proteomics Analysis of Picogram-level Complex Samples using Spray-Capillary-Based Capillary Electrophoresis Mass Spectrometry
Top-down Proteomics Analysis of Picogram-level Complex Samples using Spray-Capillary-Based Capillary Electrophoresis Mass Spectrometry Open
Proteomics analysis, including post-translational modifications (PTMs) of mass-limited samples has become an important method for understanding biological systems in physiologically relevant contexts, such as patient samples and in multice…
View article: A longitudinal network model to assess affect structures in ecological momentary assessment: Likert and slider response formats may not be equivalent
A longitudinal network model to assess affect structures in ecological momentary assessment: Likert and slider response formats may not be equivalent Open
Examining the factor structures of affective states has been a central topic in psychology (Eisele et al., 2021). Ecological Momentary Assessment (EMA) may be used to repeatedly collect information about behaviors and experiences in real-t…
View article: A Simulation Study Comparing the Use of Supervised Machine Learning Variable Selection Methods in the Psychological Sciences
A Simulation Study Comparing the Use of Supervised Machine Learning Variable Selection Methods in the Psychological Sciences Open
When specifying a predictive model for classification, variable selection (or subset selection) is one of the most important steps for researchers to consider. Reducing the necessary number of variables in a prediction model is vital for m…
View article: Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment (Preprint)
Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment (Preprint) Open
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 g…
View article: Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling
Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling Open
To understand psychological data, it is crucial to examine the structure and dimensions ofvariables. In this study, we examined alternative estimation algorithms to the conventionalGLASSO-based exploratory graph analysis (EGA; Golino and E…
View article: Variable Selection for Mediators under a Bayesian Mediation Model
Variable Selection for Mediators under a Bayesian Mediation Model Open
This study proposes a Bayesian variable selection approach to select mediators and quantify their respective posterior probabilities in exploratory mediation analysis. Monte Carlo simulation studies demonstrate that the proposed method has…
View article: Variable Selection for Mediators under a Bayesian Mediation Model
Variable Selection for Mediators under a Bayesian Mediation Model Open
This study proposes a Bayesian variable selection approach to select mediators and quantify their respective posterior probabilities in exploratory mediation analysis. Monte Carlo simulation studies demonstrate that the proposed method has…
View article: Longitudinal association between subjective and objective memory in older adults: a study with the Virginia Cognitive Aging Project sample
Longitudinal association between subjective and objective memory in older adults: a study with the Virginia Cognitive Aging Project sample Open
The association between subjective memory complaints (SMCs) and objective memory performance (OMP) has been consistently reported as small, but how the dynamics of this association changes as a function of depressive symptoms and the indiv…
View article: Corrigendum: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R
Corrigendum: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R Open
One practical challenge in observational studies and quasi-experimental designs isselection bias. The issue of selection bias becomes more concerning when dataare non-normal and contain missing values. Recently, a Bayesian robust two-stage…
View article: Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.
Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Open
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-…
View article: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R
A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R Open
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-sta…
View article: Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.
Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables. Open
The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on netw…
View article: Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial
Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial Open
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–…
View article: Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data
Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data Open
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 e…
View article: Bayesian Two-Stage Robust Causal Modeling with Instrumental Variables using Student's t Distributions
Bayesian Two-Stage Robust Causal Modeling with Instrumental Variables using Student's t Distributions Open
In causal inference research, the issue of the treatment endogeneity is commonly addressed using the two-stage least squares (2SLS) modeling with instrumental variables (IVs), where the local average treatment effect (LATE) is the causal e…
View article: The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation
The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation Open
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 da…