General SEM Discussions
http://openmx.psyc.virginia.edu/taxonomy/term/17/0
enLongitudinal SEM Person-specific Extrapolation
http://openmx.psyc.virginia.edu/thread/4133
<p>I have a question, which I closely related to an earlier question of me (<a href="http://openmx.psyc.virginia.edu/thread/3991" title="http://openmx.psyc.virginia.edu/thread/3991">http://openmx.psyc.virginia.edu/thread/3991</a>). Given a longitudinal SEM model, which has been fitted using many persons, is there a recommended approach to obtained person-specific predictions for unobserved time-points, as there is for obtaining factor scores? (see e.g <a href="http://www.tandfonline.com/doi/abs/10.1080/00273171.2012.730072" title="http://www.tandfonline.com/doi/abs/10.1080/00273171.2012.730072">http://www.tandfonline.com/doi/abs/10.1080/00273171.2012.730072</a> by Ryne Estabrooka & Michael Neale). </p>
http://openmx.psyc.virginia.edu/thread/4133#commentsGeneral SEM DiscussionsThu, 21 Apr 2016 10:59:50 +0000jkarch4133 at http://openmx.psyc.virginia.eduProfile Likelihood CI for mediated effect
http://openmx.psyc.virginia.edu/thread/4112
<p>Hello,</p>
<p>I am relatively new to OpenMx. I am wondering if it's possible to compute a profile likelihood CI using OpenMx. I saw the documentation for mxCI(), but I would appreciate if one can provide OpenMx code to estimate a profile Likelihood CI for mediated effect in single mediator model with all observed variable. </p>
<p>I also saw Pek, J. & Wu, H. (2015) article, but it appears that mxCI() can implement their approach, if I am correct.</p>
<p>Thanks for your time in advance,</p>
<p>Davood</p>
http://openmx.psyc.virginia.edu/thread/4112#commentsGeneral SEM DiscussionsWed, 09 Mar 2016 17:17:31 +0000dtofighi4112 at http://openmx.psyc.virginia.edulatent growth modeling
http://openmx.psyc.virginia.edu/thread/4110
<p>Hi all,</p>
<p>I'm not sure if my data can be analyzed using a latent growth curve model as discussed by Barbara Byrne in her EQS book. I have yearly economic data on various countries for the 2006-2014 period. Most items are on a 1-7 Likert scale and I have the average score on each item from the surveys of firms conducted in each country every year. The yearly samples do not necessarily include the same firms from previous years. However, approximately a third of the 2014 Survey respondents are “repeat” respondents—that is to say, they are executives who have previously taken part in the Survey. This improves the comparability of data across years.</p>
<p>The data setup would look like this:</p>
<p>Country Year Property rights Patents etc.<br />
USA 2006 5.1 4.8<br />
... ....<br />
USA 2014 4.9 3.5<br />
Canada 2006 5.3 4.5<br />
....<br />
Canada 2014 4.6 5.6<br />
Austria 2006.....<br />
.....</p>
<p>Does this look right? Can I treat each country-aggregate as a single observation and use a latent growth curve model? Thanks for your help.</p>
http://openmx.psyc.virginia.edu/thread/4110#commentsGeneral SEM DiscussionsMon, 07 Mar 2016 18:45:22 +0000ismail744110 at http://openmx.psyc.virginia.eduLikelihood Ratio Test Extended SEM with Definition Variables
http://openmx.psyc.virginia.edu/thread/4107
<p>Does anybody know a reference in which it is shown that the likelihood ratio test statistic converges to a Chi squared distribution for extended SEMs with definition variables?</p>
<p>With extended SEM I mean RAM models where the entries of A and S may be arbitrary algebras. In contrast to classical SEM where the entries of A,S are either fixed or a single parameter. With definition variables I mean that any fixed value in an SEM may be replaced by a person specific value such that it is different for every person.</p>
<p>In textbooks I can only find the proof for classical SEMs without definition variables. I am pretty sure that the switch from classical to extended SEM does not violate any of the assumptions used in standard proofs that the likelihood ratio test is Chi squared distribution. I am not so sure about the definition variables.</p>
http://openmx.psyc.virginia.edu/thread/4107#commentsGeneral SEM DiscussionsTue, 01 Mar 2016 16:50:48 +0000jkarch4107 at http://openmx.psyc.virginia.edu2-step SEM model respecification
http://openmx.psyc.virginia.edu/thread/4094
<p>Hi,</p>
<p>I am making a 2-steps approach to estimate a SEM model in the field of economics as depicted in the attached figure. In order to obtain the estimated values of the latent variables observation-by-observation for N=142 countries in the 1st step I am using the predict function of lavaan.</p>
<p>Whilst fit results for the individual measurement models of the 1st step are pretty good, at the moment I have not got acceptable RMSEA and TLI in the 2nd step (RMSEA: 0.245, TLI: 0.897) as shown in the following chart: </p>
<p> RMSEA CFI TLI SRMR<br />
1st step<br />
Governance 0.077 0.996 0.991 0.014<br />
Wealth 0.028 0.999 0.998 0.016<br />
Conflict 0.071 0.996 0.988 0.014<br />
Mutuality 0.067 0.991 0.983 0.029</p>
<p>2nd step<br />
Disentropy 0.245 0.966 0.897 0.023</p>
<p>Optimality cut < 0.08 > 0.95 > 0.95 < 0.08</p>
<p>I have a set of measurable variables for each of the four latent variables and am trying to improve the overall model following a trial-and-error method.</p>
<p>1. Could you give me some guidance on how to proceed in order to respecify the 2nd step model? </p>
<p>2. Will the 2nd step model improve if the individual 1st step models improve? </p>
<p>3. Could I perform any sort of statistical tests / analyses on all the available measurable variables in order to set the 1st step individual models determining an acceptable fit of the 2nd step model?</p>
<p>Thanks</p>
<p>(Note: this thread follows a post I submitted to this fórum - General SEM Discussions - on 04/25/2015)</p>
<p>[img_assist|nid=4093|title=2-step SEM Model|desc=|link=none|align=left|width=90|height=100]</p>
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http://openmx.psyc.virginia.edu/thread/4094#commentsGeneral SEM DiscussionsThu, 21 Jan 2016 19:27:10 +0000jmatosv4094 at http://openmx.psyc.virginia.eduModeration models: Moderators as interaction terms, and model drawing
http://openmx.psyc.virginia.edu/thread/4084
<p>This <a href="http://offbeat.group.shef.ac.uk/FIO/model1a.htm">link</a> depicts a simple manifest-variable moderation as shown in the images below (along with Mplus code). The path diagrams don't inclde residuals, or, for instance moderator <-> predictor covariance, nor how covariance is implement (if at all) between XW and the main effects X and W.</p>
<p>A couple of questions.</p>
<ol>
<li> Does anyone have a pointer to path diagrams that are complete (i.e with residuals and covariances?)
</li><li> Using RAM, can the moderation be implemented as an algebra on the X->Y path?
</li></ol>
<p><img src="http://openmx.psyc.virginia.edu/sites/default/files/moderation.png" alt="moderation" /></p>
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http://openmx.psyc.virginia.edu/thread/4084#commentsGeneral SEM DiscussionsSat, 12 Dec 2015 11:37:33 +0000tbates4084 at http://openmx.psyc.virginia.eduLogistic regression correcting for twin data
http://openmx.psyc.virginia.edu/thread/4074
<p>Hi OpenMX community!</p>
<p>I would like to run a simple logistic regression model, but with a classical twin dataset, correcting for the fact that they are twins and their zygosity MZ or DZ. Is there a way for such analysis using OpenMx?</p>
<p>Thanks!</p>
http://openmx.psyc.virginia.edu/thread/4074#commentsGeneral SEM DiscussionsMon, 09 Nov 2015 15:23:56 +0000Cindy.s4074 at http://openmx.psyc.virginia.eduSEM vs MANOVA: What shall I use?
http://openmx.psyc.virginia.edu/thread/4032
<p>Hi guys, </p>
<p>I need help... My PhD thesis is about consumer behaviour analysis. In detail I look at what actions by companies trigger what customer reactions. In my quantitative study, I would like to look at the following dependent variables: </p>
<p>Behavior (with respective items)<br />
Attitude (...)<br />
Emotion (...)</p>
<p>And the following independent variables:</p>
<p>Opportunity for explanation (given/not given)<br />
Alternative presented (yes/no)</p>
<p>My question is... Do I really need SEM? My professor wants me to do so, but I would rather like to use an experimental MANOVA design. </p>
<p>I would appreciate any help!</p>
http://openmx.psyc.virginia.edu/thread/4032#commentsGeneral SEM DiscussionsFri, 28 Aug 2015 16:35:11 +0000ChristophC14032 at http://openmx.psyc.virginia.eduCreating a latent variable for composite variable.
http://openmx.psyc.virginia.edu/thread/4016
<p>Is it possible to take a latent variable (for example Marketing) as for a composite variable which can be measured through its components ( for example promotional expenditure, price discounts etc). We can always argue that Marketing is itself a measured variable but if we take it as a composite function of all the activities that comprises of marketing then is it possible to use it as a latent variable? Will it violate any principles of SEM or factor analysis ?<br />
A quick response will be highly helpful as I'm reaching the deadline of my project submission and I'm stuck due to this problem.<br />
Thank you in advance.</p>
http://openmx.psyc.virginia.edu/thread/4016#commentsGeneral SEM DiscussionsTue, 14 Jul 2015 06:31:44 +0000Sahil1044016 at http://openmx.psyc.virginia.eduObserved variable in structural component of SEM
http://openmx.psyc.virginia.edu/thread/4015
<p>Hi, I'm new to SEM and I'm trying to build a model of marketing mix using sem.<br />
My question is that is it possible to incorporate an observed variable in the structural component of SEM which has an effect on a latent variable or is being affected by a latent variable?<br />
I have attached a pictoral representation of my question.<br />
Please help.<br />
Thank you in advance,<br />
Sahil.</p>
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http://openmx.psyc.virginia.edu/thread/4015#commentsGeneral SEM DiscussionsTue, 14 Jul 2015 04:54:48 +0000Sahil1044015 at http://openmx.psyc.virginia.eduImportance of a path v/s factor
http://openmx.psyc.virginia.edu/thread/4009
<p>Hello!</p>
<p>I have a very general question that I would like to seek some help with. I know that the direct\indirect impacts for each variable in the model can be computed using a multiplication rule. However, this gives us the importance of the variable and not the path. How can I understand the importance of the path, should I simply add the co-efficients?</p>
<p>I would really appreciate a quick revert!</p>
<p>Thanks,<br />
Vaibhav</p>
http://openmx.psyc.virginia.edu/thread/4009#commentsGeneral SEM DiscussionsThu, 18 Jun 2015 07:01:26 +0000LearnSEM84009 at http://openmx.psyc.virginia.eduModel with latent variables
http://openmx.psyc.virginia.edu/thread/4008
<p>Hello!</p>
<p>I have a very general question regarding Covariance based SEMs. I have a full SEM model (with latent variables) and my client is wondering how a change in one attribute for one of the factors could affect the end dependent variable. Are standard SEM methodologies capable of answering this question? If so, how should I approach it?</p>
<p>A quick revert will be really helpful! Thanks!</p>
http://openmx.psyc.virginia.edu/thread/4008#commentsGeneral SEM DiscussionsWed, 17 Jun 2015 16:53:56 +0000LearnSEM84008 at http://openmx.psyc.virginia.eduPosterior predictive distribution and SEM
http://openmx.psyc.virginia.edu/thread/3991
<p>In short my question is: Are posterior predictive distributions used in the SEM world? If yes, under which name und for what purpose?</p>
<p>Let <img src=http://openmx.psyc.virginia.edu/sites/default/files/1_1.png /> be an SEM. Lets say there are a lot of missing values in our observed sample y. We can still get a maximum likelihood estimate for \hat{theta} using FIML. Thus, we get a fitted SEM, or in other words a multivariate normal distribution <img src=http://openmx.psyc.virginia.edu/sites/default/files/2_1.png />. Lets now say that I partition my data set y into all values that are missing a and all values that are not missing b. Thus, y=[a b]. I can then get the conditional distribution of a given b using my fitted SEM. A bayesian would call this the posterior predictive distribution.</p>
<p>I understand that in this <a href="http://openmx.psyc.virginia.edu/thread/3977">thread</a> RobK proposes to use the posterior predictive distribution for prediction in this <a href="http://openmx.psyc.virginia.edu/thread/3977#comment-5986">post</a>. Michael gives a first hint that using the posterior predictive distribution might be equivalent to "EM imputation of missing data using the model-implied covariance and means as the imputation model" in this <a href="http://openmx.psyc.virginia.edu/thread/3977#comment-5987">post</a>. Can somebody elaborate on that? Do you know of other techniques that are commonly used by SEM researchers that employ the posterior predictive distribution?</p>
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http://openmx.psyc.virginia.edu/thread/3991#commentsGeneral SEM DiscussionsWed, 20 May 2015 17:59:16 +0000jkarch3991 at http://openmx.psyc.virginia.eduHow to conduct SEM on multiple datasets resulting from MI?
http://openmx.psyc.virginia.edu/thread/3983
<p>Hi, forgive me if you see duplicate messages. I previously posted the question on my last thread. But I think it's more proper to make it as a new topic. </p>
<p>My question is like this：Since I want the missing value of my dataset, whereas FIML doesn't provide that, I chose multiple imputation (MI) to deal with my missing data instead of FIML. The problem is after MI (say, I did 5 imputations), I get 5 imputed datasets. How can I use these datasets to build SEM? Shall I conduct SEM on each imputed dataset separately? If so, how can I combine the model parameters and model fitness indice (e.g. TLI, CFI, RMSEA)?</p>
<p>I find there is one previous thread in this forum talking about the same problem: <a href="http://openmx.psyc.virginia.edu/thread/118" title="http://openmx.psyc.virginia.edu/thread/118">http://openmx.psyc.virginia.edu/thread/118</a> . It tells that OpenMx cannot deal with the multiple imputed datasets directly, we need to do the combination manually. Can I know the details how to do this manually? Do we conduct SEM separately on each imputed dataset and average all the parameters and weights? Thanks in advance. </p>
http://openmx.psyc.virginia.edu/thread/3983#commentsGeneral SEM DiscussionsThu, 07 May 2015 13:38:30 +0000jasperrr3983 at http://openmx.psyc.virginia.eduFactor loadings are regression coefficients
http://openmx.psyc.virginia.edu/thread/3980
<p>I’m a PhD student in the field of economics very new using SEM and MX software. </p>
<p>I’d appreciate your feedback with regard to the following basic questions.</p>
<p>Let’s construct a SEM model where 3 measurable variables: X, Y and Z interact with a latent variable G and assume that the factor loadings of latent variable G over the measurable variables X, Y and Z are 0.3, 0.4 and 0.1 respectively. The model is constructed sourced on the correlation matrix (3x3) of the measurable variables.</p>
<p>Then, as far as factor loadings are considered as regression coefficients:</p>
<p>Question 1: could I estimate that G = 0.3*X + 0.4*Y + 0.1*Z?</p>
<p>Question 2: could you confirm me whether the obtained regression coefficients sourced on the correlation matrix are standardized or unstandardized coefficients?</p>
<p>Thanks</p>
http://openmx.psyc.virginia.edu/thread/3980#commentsGeneral SEM DiscussionsWed, 29 Apr 2015 15:27:16 +0000jmatosv3980 at http://openmx.psyc.virginia.edu