Multivariate Methods • Latent Class Analysis • The Latent Class Analysis Report • Latent Class Model for <k> Clusters Report
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Latent Class Model for <k> Clusters Report
The Latent Class Model Report for <k> Clusters contains the following results and outlines:
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Model Summary
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Parameter Estimates
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Transposed Parameter Estimates
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Effect Sizes
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MDS Plot
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Mixture Probabilities
Model Summary
A summary of the model for the specified number of clusters appears at the top of each Latent Class Model report. The model summary contains the -LogLikelihood, Number of Parameters, BIC, and AIC, all of which can be used to determine how well the model fits the data. Lower values of -LogLikelihood, BIC, and AIC indicate better fits. For more information, see Likelihood, AICc, and BIC in the Fitting Linear Models book. The Number of Parameters give the number of unique parameters in the latent class model. For more information, see Statistical Details for the Latent Class Analysis Platform.
Parameter Estimates
The Parameter Estimates report contains two tables. Each table contains rows corresponding to the model clusters. The first table gives the numerical results. The second table graphs the results with share charts.
The Overall column in both tables shows the probability of an observation belonging to each cluster. (These are the γ parameters. See Statistical Details for the Latent Class Analysis Platform.)
The remaining columns in the first table are grouped with vertical dividers according to the Y columns specified in the Latent Class Analysis launch window. Each group of columns has a column for each level of the corresponding Y column. In each group, the value in a given row and column is the conditional probability of the response indicated by the column, given that the observation belongs to the cluster identified by the row. (These are the ρ parameters.)
The graph in the second table shows the conditional probability values as share charts. For each cluster and each Y, the conditional probabilities given cluster membership are plotted as a horizontal stacked bar chart. The stacking of bars follows the order of appearance of the variables in the table of values.
Tip: You can select one or more rows in either table in the Parameter Estimates report to select the observations assigned to the corresponding clusters.
Transposed Parameter Estimates
The Transposed Parameter Estimates report contains a table that is the transpose of the first table shown in the Parameter Estimates report. Here the clusters are shown as columns. The conditional probabilities for each cluster are shown for each response category of each Y column in the analysis.
Note: The estimates from the Overall column are not included in the transposed table.
Effect Sizes
The Effect Sizes table compares the Y columns across clusters. The statistics in each row of this table are obtained from a contingency table analysis of expected counts for cluster membership by levels of a Y column. The expected counts are obtained by multiplying the number of observations in each cluster by the conditional probabilities for each level of the Y column.
For each response, the Pearson chi-square statistic, X2, is calculated for the contingency table of expected counts for levels by clusters. Let n represent the number of observations. The value in the Effect Size column is defined as follows:
Each value in the LR Logworth column shows -log10(pLR) where pLR is the likelihood ratio test p-value for the contingency table of expected counts. A Logworth value above 2 corresponds to significance at the 0.01 significance level.
Tip: You can select one or more rows in the Effect Sizes table to select the corresponding columns in the data table.
MDS Plot
The MDS Plot contains one point for each cluster. It is a two-dimensional representation of cluster proximity. Clusters that are closer together are more similar. The plot is created from a dissimilarity matrix of the ρ parameters. For more information about MDS plots, see Multidimensional Scaling in the Consumer Research book.
Mixture Probabilities
The Mixture Probabilities table displays probabilities of cluster membership for each row. The Most Likely Cluster column indicates the cluster with the highest probability of membership for each row.
Note: Rows that contain a missing value for one or more of the Y columns are excluded from the analysis and do not appear in the Mixture Probabilities table.

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Help created on 9/19/2017