Stata gsem latent class

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Stata gsem latent class

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We believe that there are groups in our population and that individuals in these groups behave differently. But we don't have a variable that identifies the groups.

The groups may be consumers with different buying preferences, adolescents with different patterns of behavior, or health status classifications. LCA lets us identify and understand these unobserved groups. It lets us know who is likely to be in a group and how that group's characteristics differ from other groups.

In latent class models, we use a latent variable that is categorical to represent the groups, and we refer to the groups as classes. Latent class models contain two parts. One fits the probabilities of who belongs to which class. The other describes the relationship between the classes and the observed variables. The LCA models that Stata can fit include the classic models: probability of class membership binary items And extensions: covariates determining the probability of class membership items that are binary, ordinal, continous, or even any of the other types that Stata's gsem can fit SEM path models that vary across latent classes.

Let's work with a classic model using an example of teen behavior but on fictional data. We will use these items to fit a latent class model with three unobserved behavior classes.

We type. We will not show the output of this command. If we had included predictors of the class probabilities or fit a latent profile model with continuous outcomes or fit a path model, the results would be more interesting. In this classic model, however, the reported coefficients are not very informative. Instead, we will use the estat lcprob and estat lcmean commands to estimate statistics that we can interpret easily.

The results are the probabilities of alcoholtruantetc. Our items are binary events. Had alcohol been the amount of alcohol consumed per day, estat lcmean would have reported average alcohol consumption for each class. We can use margins and marginsplot to visually compare the probabilities of participating in these activities across classes.

If we believe class membership depends on parents' income, we can include it in the model for C by typing.

We moved logit inside the parentheses for the five behavior items.

stata gsem latent class

This means it applies only to those equations. We don't need to say that the model for C is multinomial logit; that is automatic. We are not limited to logit models for our items. If the behavior items are instead continuous, we can type. Still, this just scratches the surface of what we can do with gsem 's latent class features. For instance, gsem fits path models such as.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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It only takes a minute to sign up. Class being binaries, and profile being continuous, not sure what to call this. My lone continuous variable is really important, and making it dichotomous does not make sense theoretically.

Note inclusion of a covariate. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Ask Question. Asked 2 years ago.

stata gsem latent class

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Latent class analysis

I'm using Stata 15's new gsem commands. ShannonC ShannonC 3 3 silver badges 10 10 bronze badges. Active Oldest Votes. Bryan Bryan 1 1 silver badge 5 5 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.StataCorp LP.

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If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. Please note that corrections may take a couple of weeks to filter through the various RePEc services. Economic literature: papersarticlessoftwarechaptersbooks. FRED data. Latent class analysis LCA allows us to identify and understand unobserved groups in our data.

These groups may be consumers with different buying preferences, adolescents with different patterns of behaviour, or different health status classifications.

Stata 15 introduced new features for performing LCA. In this presentation, I will demonstrate how to use gsem with categorical latent variables to fit standard latent class models — models that identify unobserved groups based on a set of categorical outcomes.

I will also show how we can extend the standard model to include additional equations and to identify groups using continuous, count, ordinal, and even survival times outcomes. Kristin MacDonald, Handle: RePEc:boc:usug as. More about this item Statistics Access and download statistics. Corrections All material on this site has been provided by the respective publishers and authors.

Louis Fed.Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Andrea Baldin. Latent class - gsem command 06 Sep Dear Stata users, I am using the Latent Class Analysis feature available in Stata 15 and I would have some questions for the expert users: 1 For the membership functions covariates I have a dummy variable for some classes which doesn't report the standard errors and confidence interval.

What is the reason and how should I interpret that variable? In particular, for the command -estat lcprob I obtain r and for the command -estat lcmean I obtain r What do they mean? Thank you very much for your attention. Tags: None. Weiwen Ng. Andrea, To your first question: in my experience in latent class analysis, when I use the -nonrtolerance- option and I get a model with missing SEs for some parameters, then the model won't converge at all if I try to fit it using the normal convergence criteria i.

Basically, you have convergence trouble. Can you show your command and your output in code delimiters? Omit the iteration log or use the -nolog- option. To your second point, you can usually click on the error in the command window, and it will explain the error.Login or Register Log in with.

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Page of 1. Filtered by:. Paul Spin. I am attempting to describe the relationship between asthma and school attendance using eight binary asthma variables for 1, students. My approach involves three steps that I would like to implement with gsem. They are: Characterize three classes of respondents based on the observed asthma responses: 1 low likelihood of asthma; 2 unmet asthma care needs; 3 managed asthma.

These groups have been validated in a separate exploratory latent class analysis. Asthma 1 constrained However, when I include Step 1I get an error:. I've spent time reading through the users forum and Stata manual but it's possible that I missed something.

Any advice is appreciated. Thank you, Paul. Tags: None. Red Owl.

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It also positions Latent Profile Analysis, Latent Transition Analysis, and Finite Mixture Modeling, but they produce a categorical latent whose levels represent the classes. I don't think those fit your problem. Comment Post Cancel. Thank you for your feedback and the table. Row 2 applies as our manifest variables are binary. I think the appropriate column choice might be arbitrary.

We have eight manifest variables on asthma, which vary in their capacity to detect actual asthma diagnoses classification error and whether asthma symptoms are under control.

We suspect that it is uncontrolled asthma, rather than asthma per se, that influences attendance. One might think of the two latent variables as continuous scores. I hope this clarifies what I'm attempting to accomplish. My understanding is that IRT applies when you have a single underlying trait math ability and several manifest variables math test questions.

I am not sure how IRT carries over to scenarios with potentially overlapping latent variables math ability; mathematical interest with a common set of manifest variables.Why Stata?

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stata gsem latent class

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Model class membership. Multiple-group models. Goodness of fit. Stata: Data Analysis and Statistical Software. Go Stata. Purchase Products Training Support Company. ORDER STATA Latent class analysis LCA Discover and understand unobserved groups latent classes in your data—whether the groups are consumers with different buying preferences, healthy and unhealthy individuals, or teens with high, medium, and low risk of high school drop out.

You can use LCA as a model-based method of classification. Or you can fit SEM path models and test for differences across the unobserved groups.

Estimate the proportion of the population in each group, estimate group means, and more. Model types Latent class models Latent profile models Finite mixture models Path models with categorical latent variables Multiple-group models with known groups Categorical latent variables measured by Binary items Ordinal items Continuous items Count items Categorical items Fractional items Survival times Model class membership Predictors of class membership Multinomial logistic model Starting values EM algorithm Fixed or random starting values Select number of random draws Constraints Easily specify equality constraints across classes Constrain one parameter Cross-class equality constraints—just type lcinvariant cons to constrain intercepts Multiple-group models Allow for differences in LCA across known groups Group estimation is as easy as group agegroup Some parameters constrained and others estimated freely across groups Goodness of fit Likelihood-ratio test vs saturated model G 2 statistic AIC BIC.

Inferences Expected means, probabilities, or counts in each class Expected proportion of population in each class AIC and BIC information criteria Wald tests of linear and nonlinear constraints Likelihood-ratio tests Contrasts Pairwise comparisons Linear and nonlinear combinations of coefficients with SEs and CIs Predictions Class membership Posterior class membership Predicted means, probabilities, counts For each latent class Marginal with respect to latent classes Marginal with respect to posterior latent classes Survivor function Density function Distribution function Postestimation Selector View and run all postestimation features for your command Automatically updated as estimation commands are run Watch Postestimation Selector.

Stata New in Stata Why Stata? Order Stata. Company Contact us Customer service Announcements Search.The new command gsem allows us to fit a wide variety of models; among the many possibilities, we can account for endogeneity on different models. As an example, I will fit an ordinal model with endogenous covariates.

The ordinal probit model is used to model ordinal dependent variables. Let me show you an example; I will first fit a standard ordinal probit model, both with oprobit and with gsem. Then, I will use gsem to fit an ordinal probit model where the residual term for the underlying linear regression has a standard deviation equal to 2.

We will see that as a result, the coefficients, as well as the cut-points, will be multiplied by 2. This model is defined analogously to the model fitted by -ivprobit- for probit models with endogenous covariates; we assume an underlying model with two equations. I will re-scale the first equation, preserving the correlation. That is, I will consider the following system:.

By introducing a latent variable in both equations, I am modeling a correlation between the error terms. Now, after estimating the system 2we can recover the parameters in 1 as follows:. Otherwise, you will see a coefficient for L that is virtually zero. In Stata However, this time we will use the older parameterization, which will allow you to visualize the different components more easily.

These are the results we obtain when we transform the values reported by gsem to the original parameterization:. The estimates are quite close to the values used for the simulation. If you try to perform the estimation with the wrong sign for the coefficient for L, you will get a number that is virtually zero if you get convergence at all.

In this case, the evaluator is telling us that the best value it can find, provided the restrictions we have imposed, is zero. If you see such results, you may want to try the opposite sign. If both give a zero coefficient, it means that this is the solution, and there is not endogeneity at all.

If one of them is not zero, it means that the non-zero value is the solution. As stated before, in Stata Home About. RSS Twitter Facebook. Subscribe to the Stata Blog Receive email notifications of new blog posts. Tags StataProgramming ado ado-command ado-file Bayes Bayesian bayesmh binary biostatistics conference coronavirus COVID do-file econometrics endogeneity estimation Excel format gmm graphics import marginal effects margins Mata meeting mlexp nonlinear model numerical analysis OLS power precision probit programming putexcel random numbers runiform sample size SEM simulation Stata matrix command Stata matrix function statistics time series treatment effects users group.

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