Tutorial 1: Using Latent GOLD® to Estimate LC Cluster Models
download PDF
Watch Video Tutorial
+ 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® to Estimate DFactor Models
download PDF
Watch Video Tutorial
+ overview
 overview
In this tutorial, we reexamine the results obtained from tutorial #1 using discrete factor (DFactor) models
instead of LC Cluster models. We show how a 2DFactor model consisting of 2 dichotomous factors can be
viewed as a restricted form of the 4cluster model and use the L^{2} difference statistic to test whether the
unrestricted 4class 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 Biplot
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 SICHAID addon 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 Highdimensional 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 (1class) 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.
 Highdimensional data – With only 6 juices being rated, use of the 16 correlated attributes as predictors yields a highdimensional 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 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 latentclass 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.

New Latent GOLD 5.0 Tutorials

StepThree Tutorial #1
download PDF
+ overview
 overview
In many latent class analysis applications the interest lies not only in identifying the latent classes, but also in relating the class membership to external variables of interest. This can be done using a onestep or a threestep approach. In many instances researchers prefer the threestep approach, because it is more intuitive to first build a latent class model, and then relate it to covariates or distal outcomes. Until recently the problem with the three step approach was that the parameter estimates of the third step model were underestimated. Based on the work of Bolck, Croon and Hagenaars (2004), Vermunt (2010) developed two methods for correcting for the bias in the third step. The methods were further extended by Bakk, Tekle and Vermunt (2013). The new Step3 submodule in Latent GOLD implements these biasadjusted threestep methods. Also the Latent GOLD Syntax has options for stepthree modeling.
In this tutorial, you will:

StepThree Tutorial #2
download PDF
+ overview
 overview
In Tutorial 1, we developed a 3class latent class model using PURPOSE, ACCURACY, UNDERSTA, and COOPERAT as indicators. In this tutorial, we will show how to use the Step3 module in Latent GOLD 5.0 to obtain an algorithm (equations) and related SPSS syntax for scoring new cases as a function of those same indicators. That is, the indicators will now be used as predictors in the regression equation.

StepThree Tutorial #3
download PDF
+ overview
 overview
We begin by opening a saved 3class latent class model using GLUCOSE, Insulin, and SSPG as indicators, where the variances each of these indicators was specified to be classdependent, and a direct effect between INSULIN and GLUCOSE was included in the model (‘model 5’). In this tutorial, we will show how to use the Step3 module in Latent GOLD 5.0 to obtain an algorithm (equations) and related SPSS syntax for scoring new cases based on this model. That is, in these equations the 3 indicators will be used as predictors.

Markov Tutorial #1: Latent GOLD Longitudinal Analysis of Brand Loyalty
download PDF
+ overview
 overview
Goals – To show how Latent GOLD’s new latent Markov module can be useful in measuring brand loyalty and more generally in handling autocorrelation in longitudinal data.
 Distinguish between ‘wide’ and ‘long’ formatted data
 Use Latent GOLD 5.0 to introduce Latent Class (LC) Growth and Latent Markov (LM) Models
 Describe the use of Model Fit Statistics including new Longitudinal Bivariate Residuals (LBVRs)
 Show how to generate brand purchase forecasts for future time points

Markov Tutorial #2: Latent GOLD Longitudinal Analysis of Life Satisfaction
download PDF
+ overview
 overview
The goal of this tutorial is to show how Latent GOLD 5.0 can be used to estimate time heterogeneous and mixture latent Markov models, especially useful in explaining unusual time trends.

Markov Tutorial #3: Latent GOLD Longitudinal Analysis of Sparse Data
download PDF
+ overview
 overview
The goal of this tutorial is to show how Latent GOLD 5.0 can be used to estimate latent Markov models with
 sparse longitudinal (panel) data
 unequal time intervals
 time constant and time varying predictors

We would like to thank our colleagues at the Statistical Consulting Group at UCLA Academic Technology Services for creating video seminars for our tutorials. Tutorials for which video is available are marked with the icon.
