LatentGOLD 6.0 Academic
An Academic license can be purchased by users associated with a University or College. Individuals in a commercial or government setting can purchase a Commercial license.
Discounts for multiple users are automatically applied to your order (2-4 licenses – 15%, 5-9 licenses – 20%, 10+ licenses – 25%).
Latent GOLD 6.0® is designed to operate on XP/Vista, Windows 7/8, Windows 10 or Windows 11.
System Requirements: 128MB Drive Space, 512MB of RAM.
Input files: SPSS system files, delimited text files.
Before purchasing the program, you can try out the free demo version of the program, which allows access to all program features with sample data files.
Tutorials take you step-by-step through several analyses of these sample files. These tutorials along with various publications are available on our website. Upon purchase of the program users can download a 200 page User's Guide or other Manuals that cover a wide range of topics on Latent Class Analysis and Latent GOLD® .
Latent GOLD® can handle ASCII Text data formats as well as SPSS files.
There is NO limit concerning the number of records. The time will depend on several factors including the # of variables and records, speed of your machine, and the requested output. For many models, Latent GOLD® runs 20 or more times faster than other Latent Class programs and version 5.0 is much faster than earlier versions. We suggest trying the demo program to see how fast Latent GOLD® works on your machine.
Latent GOLD® implements the 3 most important types of latent class (LC) models. It was designed to be extremely easy to use and to make it possible for people without a strong statistical background to apply LC analysis to their own data in a safe and easy way. LEM is a command language research tool that Prof. Jeroen Vermunt developed for applied researchers with a strong statistical background who want to apply nonstandard log-linear and latent class models to their categorical data. With LEM you can specify more probability structures with many more kinds of restrictions (if you know how to do it), but is not designed to be Windows friendly, requires strict data and input formats and does not provide error checks.
With Latent GOLD, continuous and count variables can be included in the model, and special LC output not available in LEM is provided, such as various graphs, classification statistics, and bivariate residuals. Latent GOLD® also has faster (full Newton-Raphson) and safer (sets of starting values, Bayes constants) estimation methods for LC models than LEM. Both programs give information on nonidentifiability and boundary solutions, but Latent GOLD® , unlike LEM, can prevent boundary solutions through the use of Bayes constants.
The set of example data files on our website contains various event history analysis examples. The setup for several Event History models can be opened in Latent GOLD using the HELP GUI Example Regression menu. Full tutorials are not yet available for these. However, to get you started, you might look at the data file land.sav, the full reference for which is " Land, K.C., Nagin, D.S., and McCall (2001). Discrete-time hazard regression models with hidden heterogeneity: the semi-parametric mixed Poisson approach. Sociological Methods and Research, 29, 342-373." Another good example is jobchange.dat.
Land.sav contains information on 411 males from working-class area of London who were followed from ages 10 through 31. The dependent variable is "first serious delinquency". As can be seen, there is one record for each time point, which is called a person-period data format. The dependent "first" is zero for all records of a person, expect for the last if a person experienced the event of interest at that age. The variables age and age_sq are the duration variables. These can also be seen as time-varying predictors. The variable "tot" is a time-constant covariate/predictor (a composite risk factor). Of course the ID should be used as Case ID to indicate which records belong to the same case.
The dependent "first" can be treated as a Poisson count or as a binomial count. The former option yields a piece-wise constant log-linear hazard model, the latter a discrete-time logit. If treated as Poisson count, it is best to set the exposure to one half (exp_half: event occurs in the middle of the interval) for the time point at which the event occurs. With a binomial count the exposure should be one all the time (=default). Age and age_sq should be used as class-dependent predictors. You identify two groups with clearly different age pattern in the rate of first delinquency. The variable "tot" can be used as class-independent predictor, but more interesting is to use it as covariate: does the risk factor determine the type of delinquency rajectory?
This example can be modified.extended in many ways. - you can include other time-varying predictors than the time variables. These can be assumed to have the same or different effects across classes. - you can include information on another event. In that case your classes describe the pattern in multiple events - you can include as many covariates as you want (this will usually be demographics, but can also be a treatment) - you can model the time dependence as nominal, yielding a Cox-like model.
A general reference on event history combined with LC analysis is Vermunt (1997), Log-linear event history analysis. Sage Publications.
Latent GOLD® is a Windows program but we have had anecdotal reports that the emulation software Wine and other virtual machine software have allowed users to run Latent GOLD on Macintosh machines.
Please note that we only support the installation process of Latent GOLD® on a Windows machine.