Tutorials

All sample datasets and sample .lgf files used the tutorials below are downloaded to your computer when you install the demo version of Latent GOLD. To download individual datasets and .lgf files, please refer to our Sample Datasets Page.

Tutorial 1: Using Latent GOLD® 4.5 to Estimate LC Cluster Models   download PDF          + overview - overview In this tutorial, we use 4 categorical indicators to show how to estimate LC Cluster models and interpret the resulting output. In this tutorial, you will: Open a data file Setup and estimate traditional latent class (cluster) models Explore which models best fit the data Generate and interpret output and interactive graphs Save results

Tutorial 2: Using Latent GOLD® 4.5 to Estimate DFactor Models   download PDF          + overview - overview In this tutorial, we re-examine the results obtained from tutorial #1 using discrete factor (DFactor) models instead of LC Cluster models. We show how a 2-DFactor model consisting of 2 dichotomous factors can be viewed as a restricted form of the 4-cluster model and use the L2 difference statistic to test whether the unrestricted 4-class model provides an improvement. In addition, this tutorial illustrates: The use of the Ordinal scale type Estimating DFactor models Factor Loadings Output Restricting Factor Loadings to Zero Joint Profile output Classification Output The Bi-plot For these data the DFactor models provide additional insights into the different survey respondent types.

Tutorial 3: LC Regression with Repeated Measures download PDF          + overview - overview This tutorial shows how to develop Latent Class (LC) Regression models using the sample data file “conjoint.sav”. You will learn how to: Select the dependent variable and specify its scale type Distinguish predictors from covariates Impose restrictions on the predictor effects Specify covariates as active or inactive Determine the number of latent classes (i.e., segments) Examine R2 and various other information related to model prediction

Tutorial 4: Profiling LC Segments using the CHAID Option download PDF          + overview - overview In this tutorial, we obtain further insights into the latent class segments obtained from tutorials #1 and #2 using additional variables (covariates) to profile these segments in terms of respondent demographics – gender (SEX), education (EDUCR), marital status (MARITAL), and age (AGE). This tutorial illustrates: Use of ‘inactive’ covariates feature to describe LC segments Use of the SI-CHAID add-on program to obtain additional descriptive profiles and tests of significance In addition, it illustrates Use of the Grouping option to reduce the number of categories of a variable

Tutorial 6A: Comparing Segments obtained from LC Cluster and DFactor Models in a Consumer Preference Study download PDF          + overview - overview The goal of this research was to determine if consumers can be segmented in a meaningful way on the basis of their liking ratings of the crackers. In this tutorial 6A we will estimate and compare a number of LC Cluster and DFactor models and interpret the resulting segments. In Tutorial 6B we will use Regression Models to obtain segments. In this tutorial, you will: Estimate LC Cluster and DFactor Models Examine various output including the Loadings output from DFactor Models Use the DFactor module to obtain ‘clusters’ after ‘factoring out’ a nuisance factor Use the ‘Equal Effects’ option to obtain a general factor

Tutorial 7A: Latent Class Growth Model download PDF          + overview - overview What you will learn: To use Latent GOLD to identify distinct latent class growth trajectories in the data. To name the identified latent class subgroups based on their growth patterns classify 36 cases as unchanged (Class 1 above) classify 17 cases as improved (Class 2 above) classify the remaining 6 cases as Unstable (Class 3 above) How to estimate a latent class growth model (Poisson mixture model) to these data that shows those receiving the drug treatment were significantly more likely than the placebo group to improve and significantly less likely to show no change over their baseline seizure rate (p = .02).

Tutorial 7B: Latent Class Growth Model Using an Active Covariate download PDF          + overview - overview Continuation of Tutorial 7B

Tutorial 8: LC Regression with High-dimensional Data download PDF          + overview - overview The overall goal is to predict the liking ratings as a function of the 16 attributes. There are 2 methodological challenges that need to be addressed in accomplishing this goal: Observations are not independent -- Since these data consist of multiple records per case, traditional (1-class) regression methods generally suffer from violation of the independent observations assumption which yields suboptimal prediction, since residuals from records associated with the same judge may be correlated. In this tutorial we show how a latent class (LC) regression can be used to identify 2 LC segments having different OJ preferences, to account for correlated observations. High-dimensional data – With only 6 juices being rated, use of the 16 correlated attributes as predictors yields a high-dimensional data situation such that traditional regression is not possible due to multicollinearity. We use the Correlated Component Regression (CCR) methods implemented in CORExpress to address this problem. Two specific goals are: Goal 1 – to determine if the judges can be segmented on the basis of their juice liking ratings. Goal 2 – to determine if the juice attributes can predict the liking ratings, and if so which attributes are the most important predictors for each segment.

Advanced Tutorial: Latent GOLD 4.5 and IRT Modeling download PDF          + overview - overview This tutorial shows that various IRT/latent trait models can be estimated with the Cluster and/or Regression modules in Latent GOLD Advanced (LGA), by including a continuous factor (CFactor) in the model. While LGA uses a somewhat different parameterization of these models, they can be easily transformed to obtain the traditional parameters (item locations, difficulties, threshholds, etc). The .pdf shows how to do this, by illustrating the equivalences between the LGA and standard IRT parameterizations. We also show how latent-class based IRT models can be defined using the DFactor module, as well as how these relate to standard IRT models. Data files: Download all data files for this example The .lgf files show how to use LGA to estimate various IRT and IRT mixture models. Simply open the .lgf files from within the (demo or standard) LGA program and select 'Estimate All' from the Model Menu. Several IRT models, appropriately labeled will be estimated, so you can view the Parameters and other Output files. Note: These .lgf files are designed to show how to set up these types of models. Since the data sets are small, they do not make good examples for 'mixture' IRT models (i.e., IRT models containing 2 or more latent classes). As a result, when estimating mixture variants of the IRT models, you may well encounter local solutions, even with many random startsets and many start iterations.