scholarly journals Causality on longitudinal data: Stable specification search in constrained structural equation modeling

2017 ◽  
Vol 27 (12) ◽  
pp. 3814-3834 ◽  
Author(s):  
Ridho Rahmadi ◽  
Perry Groot ◽  
Marieke HC van Rijn ◽  
Jan AJG van den Brand ◽  
Marianne Heins ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Chronic Fatigue ◽  
Causal Modeling ◽  
Model Complexity ◽  
Equation Modeling ◽  
Data Set ◽  
Finite Data

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

scholarly journals Variable Selection in Structural Equation Models with Regularized MIMIC Models

2018 ◽  
Author(s):  
Ross Jacobucci ◽  
Rogier Kievit ◽  
Andreas Markus Brandmaier
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Short Term Memory ◽  
Model Complexity ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Linear Regression Models ◽  
Visual Short Term Memory ◽  
Mimic Models ◽  
Small N

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioural data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters (also known as the “small n, large p” problem). We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p settings, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors, or when sample size is small. We illustrate the power of this approach in a N=627 example from the CAM-CAN study, modeling the neural determinants of visual short term memory. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.

scholarly journals A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models

2019 ◽  
Vol 2 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Ross Jacobucci ◽  
Andreas M. Brandmaier ◽  
Rogier A. Kievit
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Short Term Memory ◽  
Small Sample Size ◽  
Small Sample ◽  
Model Complexity ◽  
Equation Modeling ◽  
Linear Regression Models ◽  
Demographic Correlates

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real-world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This difficult modeling scenario entails large models with a limited number of observations given the number of parameters. Here, we describe a particular strategy to overcome this challenge: regularization, a method of penalizing model complexity during estimation. Regularization has proven to be a viable option for estimating parameters in this small-sample, many-predictors setting, but so far it has been used mostly in linear regression models. We show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts and how it can be extended to regularized structural equation modeling. We then evaluate our approach using a simulation study, showing that regularized structural equation modeling outperforms traditional structural equation modeling in situations with a large number of predictors and a small sample size. Next, we illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short-term memory and identifying demographic correlates of stress, anxiety, and depression.

scholarly journals How to Estimate Absolute-Error Components in Structural Equation Models of Generalizability Theory

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 113-133
Author(s):  
Terrence D. Jorgensen
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Absolute Error ◽  
Data Matrix ◽  
Equation Modeling ◽  
Error Components ◽  
Planned Missing Data ◽  
Mean Structure

Structural equation modeling (SEM) has been proposed to estimate generalizability theory (GT) variance components, primarily focusing on estimating relative error to calculate generalizability coefficients. Proposals for estimating absolute-error components have given the impression that a separate SEM must be fitted to a transposed data matrix. This paper uses real and simulated data to demonstrate how a single SEM can be specified to estimate absolute error (and thus dependability) by placing appropriate constraints on the mean structure, as well as thresholds (when used for ordinal measures). Using the R packages lavaan and gtheory, different estimators are compared for normal and discrete measurements. Limitations of SEM for GT are demonstrated using multirater data from a planned missing-data design, and an important remaining area for future development is discussed.

scholarly journals Using Structural Equation Modeling to Study Traits and States in Intensive Longitudinal Data

2020 ◽  
Author(s):  
Sebastian Castro-Alvarez ◽  
Jorge Tendeiro ◽  
Rob Meijer ◽  
Laura Francina Bringmann
Keyword(s):  
Longitudinal Data ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Longitudinal Research ◽  
Equation Modeling ◽  
Intensive Longitudinal Data ◽  
The Common ◽  
The Stability

Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the Latent State-Trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to non-convergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks.

scholarly journals Gaussian Process Panel Modeling – Kernel-Based Longitudinal Modeling

2017 ◽  
Author(s):  
Julian Karch ◽  
Andreas Markus Brandmaier ◽  
Manuel Voelkle
Keyword(s):  
Longitudinal Data ◽  
Gaussian Process ◽  
Hierarchical Linear Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Modular System ◽  
Equation Modeling ◽  
Multiple Time ◽  
Modeling Approaches ◽  
Special Cases

Longitudinal panel data obtained from multiple individuals measured at multiple time points are crucial for psychological research. To analyze such data, a variety of modeling approaches such as hierarchical linear modeling or linear structural equation modeling are available. Such traditional parametric approaches are based on a relatively strong set of assumptions, which are often not met in practice. We present a flexible modeling approach for longitudinal data that is based on the Bayesian statistical learning method Gaussian Process Regression. We term this novel approach Gaussian Process Panel Modeling (GPPM). We show that GPPM subsumes most common modeling approaches for longitudinal data such as linear structural equation models and state-space models as special cases but also extends the space of expressible models beyond them. GPPM offers great flexibility in model specification, facilitates both parametric and nonparametric modeling in a single framework, enables continuous-time modeling as well as person-specific predictions, and offers a modular system that allows the user to piece together hypotheses about change by selecting from and combining predefined types of trajectories or dynamics. We demonstrate the utility of GPPM based on a selection of models and data sets.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

scholarly journals Genetic and Environmental Influences on Disability Pension Due To Mental Diagnoses: Limited Importance of Major Depression, Generalized Anxiety, and Chronic Fatigue

2015 ◽  
Vol 19 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Jurgita Narusyte ◽  
Annina Ropponen ◽  
Kristina Alexanderson ◽  
Pia Svedberg
Keyword(s):  
Major Depression ◽  
Disability Pension ◽  
Structural Equation ◽  
Environmental Influences ◽  
Chronic Fatigue ◽  
Equation Modeling ◽  
Computer Assisted ◽  
Generalized Anxiety ◽  
Environmental Variance ◽  
Genetic Contributions

Background:Previous research indicates that liability to disability pension (DP) due to mental diagnoses is moderately influenced by genetic factors. This study investigates whether genetic contributions to the liability to DP due to mood and neurotic diagnoses overlap with the genetic influences on major depression (MD), generalized anxiety disorder (GAD), or chronic fatigue (CF).Method:A prospective cohort study including 9,985 female twins born in Sweden 1933–1958. The presence of MD, GAD, and CF was assessed by computer-assisted telephone interviews conducted in 1998–2002. Data on DP due to mood and neurotic diagnoses were obtained from nationwide registers for the years 1998–2010. Common genetic and environmental influences on the phenotypes were estimated by applying structural equation modeling.Results:The prevalence of MD/GAD was 30%, CF 8%, and DP due to mood and neurotic diagnoses 3% in 2010. Genetic effects on MD/GAD explained 31% of the total genetic variation in DP, whereas genetic contributions in common with CF were small and not significant. The majority of the total non-shared environmental variance in DP (85%) was explained by the factors that were unique to DP.Conclusions:Large proportions of genetic and non-shared environmental influences in DP due to mood and neurotic diagnoses were not explained by the contributions from MD/GAD or CF. The results suggest that the process leading to DP is complex and influenced by factors other than those related to the disorder underlying DP.

From managing customers to joint venturing with customers: co-creating service value in the digital age

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christine Falkenreck ◽  
Ralf Wagner
Keyword(s):  
Perceived Risk ◽  
Structural Equation ◽  
Business Models ◽  
Joint Venture ◽  
Digital Age ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Data Set ◽  
Content Type

Purpose Until today, scholars claim that the phenomenon of “co-creation” of value in an “interacted” economy and in the context of positive actor-to-actor relationships has not been adequately explored. This study aims to first to identify and separate the accessible values of internet of things (IoT)-based business models for business-to-business (B2B) and business-to-government (B2G) customer groups. It quantifies the drivers to successfully implement disruptive business models. Design/methodology/approach Data were gathered from 292 customers in Western Europe. The conceptual framework was tested using partial least square structural equation modeling. Findings Managing disruptions in the digital age is closely related to the fact that the existing trust in buyer-seller relationships is not enough to accept IoT projects. A company’s digitalization capabilities, satisfaction with the existing relationship and trust in the IoT credibility of the manufacturer drives the perceived value of IoT-based business models in B2B settings. Contrastingly, in B2G settings, money is less important. Research limitations/implications Research refers to one business field, the data set is of European origin only. Findings indicate that the drivers to engage in IoT-related projects differ significantly between the customer groups and therefore require different marketing management strategies. Saving time today is more important to B2G buyers than saving money. Practical implications The disparate nature of B2B and B2G buyers indicates that market segmentation and targeted marketing must be considered before joint-venturing in IoT business models. To joint venture supply chain partners co-creating value in the context of IoT-related business models, relationship management should be focused with buyers on the same footing, as active players and co-developers of a personalized experience in digital service projects. Originality/value Diverging from established studies focusing on the relationship within a network of actors, this study defines disruptive business models and identifies its drivers in B2B and B2G relationships. This study proposes joint venturing with B2B and B2G customers to overcome the perceived risk of these IoT-related business models. Including customers in platforms and networks may lead to the co-creation of value in joint IoT projects.

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