Questions tagged [structural-equation-modeling]
Structural Equation Modeling is a multivariate technique. It is based on formulating a set of linear relations between variables, some of which may be latent, and estimating the whole system, typically by analyzing the covariance matrix of the observed variables.
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How are factor scores estimated for categorical data (SEM)?
On the Mplus discussion page, Muthén said the following (2000):
The WLSMV estimator is used with categorical outcomes. Unlike
continuous outcomes, with categorical outcomes a factor score
coefficient ...
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Mediation analysis with interaction effect
I am currently trying to amend a paper that I am writing in order to include the interaction effects (a4 and b4 in the spreadsheet) This results in the following sum-product $$a_1b_1 + a_2b_2 + a_3b_3 ...
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Do scale applying approaches need to be the same for longitudinal invariance testing and the subsequent growth curve model?
I have a question concerning scaling approaches in longitudinal invariance testing and
a subsequent latent growth curve.
That is, for example, if the invariance test was conducted with a marker ...
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analysis of critical success factor of lean implementation to identify the influenced dimention
Clustering vs EFA which is a good tool for structural equation modeling to identify the influenced variables also to support the hypothesis?
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Structural (causal) interpretation of the completeness condition
Consider two random variables $X,Y$. We say the joint distribution of $P(X,Y)$ is complete w.r.t. $X$ if the following condition holds:
For all $y$, $E\{g(x)|y\}=0$ almost everywhere if and only $g(x)...
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Multi-group path analysis - Equality-constrained versus free model in lavaan [closed]
I am working on a multi-group path analysis using lavaan (I only have observed variables); however, it has been a challenge to find literature on this. Most of the literature is intended for SEM; ...
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Can we use a likert scale of 0 to 5 for SEM analysis?
I am learning to do SEM analysis and have finished the data collection. In my questionnaire I have scales measured from $0$ to $5$ which measure emotions ($0$ = not feeling it at all to $5$ feeling it ...
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power analysis for cross-lagged panel model with random intercept using powRICLPM
I want to compute power analysis for cross-lagged panel model with random intercept using powRICLPM package (Related publication)
Authors of the package did a great job and the power analysis seems to ...
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MLE of multivariate normal distribution when the VCV matrix is full of equations
Short Version: Given a variance covariance matrix for my multivariate normal distribution where the entries are equations of other parameters, how do I find which of those parameter values maximizes ...
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When does SEM have little to no benefit over multiple regression, and there is a distinction without a difference between two approaches?
I recently saw a case where someone fit a SEM with 20 latent variables (with many indicators each) predicting a single latent variable (of several indicators), and suggested it was evidence for some ...
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The use of p-values in tests of d-separation
In his very helpful online book on structural equation modeling, Jon Lefcheck writes the following concerning d-separation tests for SEMs:
Once the model is fit, statistical independence is assessed ...
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SEM/regression: Identify better predictor
I have data from a longitudinal study, specifically MRI data and performance on a neuropsychological test at two time points. I would like to test whether the change in gray matter in Brain Region A ...
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Do you need to correlate factor residuals of factors that are measured at the same time point
I am running a second-order (multiple indicator) latent growth curve.
The model has three latent factors (excluding the growth intercept and slope factors) that each have 4-5 indicators. Two of them ...
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Is there a proof of the equivalence between Cronbach's alpha and reliability calculation from a CFA?
Given a uni-dimensional CFA, reliability can be calculated as:
$$
\omega = {\left(\sum_{i=1}^p \lambda_i\right)^2\over \left(\sum_{i=1}^p \lambda_i\right)^2 + \sum_{i=1}^p\sigma^2_i}
$$
where $\...
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How do different sets of restrictions in a SEM with moderation affect results?
I'm trying to run a latent moderated mediation analysis SEM with R lavaan. It is essentially "First Stage Moderation", per Edwards & Lambert (2007), with a few extra factors. I'm using ...
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Can a wrongly specified lavaan SEM lead to huge effects?
I am trying to replicate a study that created a structural equation model (SEM) to explain effects on the intention to reduce meat consumption. The interactions of the latent variables are as follows:
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How to interpret standardized simple slopes and indirect effects in R with semTools and lavaan?
I am attempting to obtain a standardized solution for simple slopes and indirect effects in a SEM in R using lavaan and semTools::probe2WayMC, following the methods of Schoemann & Jorgensen (2021) ...
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Centering variables in Lavaan - Path analysis
I hope you're doing well. I'm currently knee-deep in path analysis using the lavaan package for my research. My analysis involves moderation, and centering the variables may simplify coefficient ...
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Test of Group Invariance with 3 Groups
Say, we have three groups and we want to run a test of group invariance with the following data.
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Should I interpret the scaled or the standard model in SEM (lavaan)?
I'm attempting to fit a structural equation model in lavaan. My model looks like this:
...
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Estimating error variance for simulated path analysis
I want to run a simulation using lavaan and simsem to determine the sample size to use in a study using path analysis. The ...
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Psychometrics - transferability of CFA and factor loadings to observed scores/sum scores in scale development
I have come across a situation in a dataset using a not very used scale, evaluated partly using confirmatory factor analysis (CFA). The CFA has provided support for a number of subscales. In some of ...
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How to plot relationship between each predictor and response in GLM with multiple predictor
I have a structural equation like this:
So I contruct two model which follow the formula of:
"B ~ A" and "C ~ A + B"
The parameter for "B ~ A" will be βB0, βB1, σB
The ...
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What to set "intercept factor" and "slope factor" as in model?
I was struggling a bit with understanding what exactly the intercept factor and slope factor is in a model. I need to estimate these in an a priori power analysis I am trying to do.
My understanding ...
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How can I calculate the required sample size for Latent Growth Curve Modelling?
I am looking to calculate what sample size I would need for my study by running an a priori power analysis.
Is there any way (e.g an R package) I can calculate the required sample size required for ...
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Interpertation of a conditional quadratic latent growth curve model (i.e., with predictors)
I have a conditional quadratic latent growth curve model and am wondering how to interpret the results.
My predictor of interest is significantly associated with the slope factor (B = -0.45, p = .001) ...
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Latent growth curve model: should you include predictors if the variance for slope and/or intercept factor is not significant?
Hi I am running a series of latent growth curve models.
I am wondering: can predictors of the slope and intercept factor(s) be included when the slope and/or intercept variance are not significant, or ...
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semPower: Accounting for Clustered Standard Errors in SEM Power Analysis with Fixed Effects Model
I am planning to analyse clustered data with a structural equation model and as I am planning on submitting my project as a registered report, I am first trying to do an in-depth power analysis (it is ...
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Latent growth curve model: The variance-covariance matrix of the estimated parameters (vcov) does not appear to be positive definite
I have run a latent growth curve model in R using lavaan and got the below warning. It would be good to hear suggestions on how to resolve this warning. The full output is below.
Note that I dummy-...
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Constructing a structural equation model/causal graph
I would like to understand some intuitions behind the following causal graph/SCM.
Where as $X_1, X_2$ are expenditure on promotional activities.
My main interest lies in understanding the fact that ...
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Comparing factor scores between groups
I have participants who have taken an intervention and are being measured at two different time-points. It is expected that the intervention will improve their knowledge, attitude, confidence etc. and ...
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How to study what moderates a correlation?
I'd like to illustrate my objective with an example:
Imagine we have collected data on the height, weight, and level of sports activity (represented as either a continuous or categorical variable) ...
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Structural Equation Model design
Is it necessary in structural equation modeling (SEM) to incorporate all potential independent variables that could affect the dependent variable? Or is it acceptable to examine the influence of only ...
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R lavaan: quadratic regression of latent variables
Is there a way of specifying a quadratic regression between latent variables in lavaan? (Or in another R package for structural equation modeling?) That is, latent variable y, ...
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Latent growth curve model with a continuous time-invariant covariate, multi-group or covariate model?
A Latent growth curve model with a time-invariant covariant can be specified with (at least) two ways, by using the multigroup model approach or the "regression approach".
By regression ...
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Effect of robust maximum likelihood estimator in structural equation modelling when data is normal?
I understand that when data are nonnormal robust maximum likelihood estimator can be used.
I'm wondering are there any disadvantages of using a robust maximum likelihood estimator when the data are ...
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Is it possible to have an Indicator-spesific Trait factor model within a multiple indicator second order growth model?
I am running a multiple-indicator growth curve model over 7 time points.
One of my items has a large residual variance and seems to covary very well among themselves.
Thus, I assumed that it has a ...
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Structural equation modeling (latent growth models): robust estimators to handle outliers?
Can I use robust estimators (e.g., "MLM" and "MLR"estimator lavaan options) to overcome outliers within my sample, or should I remove outliers?
For context, I am modelling the ...
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Structural equation modelling in lavaan [closed]
I used factor analysis and structural equation modelling using lavaan in RStudio. Before adding the residual covariances and regression into the SEM model, the ...
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Path analysis for longitudinal data
I want to conduct a path analysis for longitudinal data with t = 13 (the model is presented below). Is it possible to make a path model for each individual at each time point? I am quite familiar with ...
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Goodness of fit of structural equation modelling
I am currently working on a structural equation modeling project using the lavaan package in R. The model satisfied all the goodness of fit tests (GFI, AGFI, CFI, ...
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How to interpret PLS-MGA’s results based on the significance of the corresponding group specific path coefficients
As a more general follow-up question to Elisa’s post on “[h]ow to interpret regression coefficients” when carrying out a PLS-structural-equation-modeling multigroup analysis (PLS-MGA), when comparing ...
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Robustness check in Structural Equation Modelling (SEM) in R
I have conducted SEM analysis in R and used Maximum Likelihood Robust estimator as my data are categorical and deviate from multivariate normality. when I submitted my manuscript, one reviewer asked ...
Bahar
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Can you run a SEM on a correlation matrix only (without a covariance matrix)?
I have access to correlations between my variables of interest and the overall sample size. But I don't have standard deviations so I can't construct a covariance matrix. Can I still run a SEM on the ...
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Lavaan growth model: to treat endogenous variable as ordinal or continuous
I am modelling the trajectories of scores on two cognitive tests (i.e., PAL and SOS) measured at four time points. To do this I am creating separate latent growth curve models for each cognitive test, ...
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Structural Equation Modelling vs Mixed Effects Model
I have a question regarding what type of statistical analysis works best for my study design.
The research question is finding out what variables predict why a psychotherapy intervention is effective ...
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Modeling non-additive seasonal variable
Let's say I have some variable $x_{1}$ and some variable $x_{2}$ and I have the result of $y$ on a specific day/week/month/year. I believe $y$ is a random variable and its result is not very useful. ...
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Invariance coding for running a latent growth model running under partial (scalar or metric) invariance
I am running a second-order latent growth curve with 7 time points.
The model has a four indicator latent variables at the measurement level so I ran a longitudinal invariance test.
The model holds ...
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Linear Model, Identification of Structural Parameter
I'm reviewing the foundations of linear regression using Wooldridge's Econometric Analysis of Cross Section and Panel Data and Cameron and Trivedi's Microeconometrics : Methods and Applications. The ...
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Causal inference in DAGs and resulting structural equations
I am trying to understand the difference between the two modelling approaches described below that stems from the causal graphs.
Our goal is to causally measure the total treatment effect of our ...
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