Latent class analysis
Find groups of people who share a response pattern.
When to use it
Use latent class analysis when you think the sample is a mixture of a few types, and you only see their answers. Each person gets a probability of belonging to each class. The class profiles say what a typical member answered.
Assumptions
Indicators are numeric. On 0/1 items Tensr fits a binary measurement model. On other numbers it treats the indicators as continuous. Rows with a missing indicator are dropped. You need at least five rows per class. The fit uses a fixed random seed, so the same data and the same class count give the same solution. If a class ends up empty, the run stops instead of printing a zero-size class.
Running it in Tensr
Options
Prop
Type
Reading the output
Four yes/no items scored 1 and 0, and two latent classes. One class was built to endorse the items more often. Membership is probabilistic, not a clean split.
| Class | n | % |
|---|---|---|
| 1 | 97 | 60.6% |
| 2 | 63 | 39.4% |
| Indicator | Class 1 | Class 2 |
|---|---|---|
| q1 | 0.2371 | 0.8254 |
| q2 | 0.2990 | 0.7778 |
| q3 | 0.0928 | 1.0000 |
| q4 | 0.4330 | 0.5873 |
Latent class analysis with 2 classes on 4 indicators. Latent class analysis with 2 classes on 4 indicators. Metrics: Classes = 2; Observations = 160; AIC = 842.435; BIC = 870.111; Entropy = 19.841; Log-likelihood = -412.217.
Reporting (APA 7)
Latent class analysis with 2 classes on 4 indicators. Report the estimate in the table. This procedure is not summarised by one p-value.
Coming from SPSS
The product menu is Multivariate → Latent Class Analysis. SPSS does not ship this under Analyze. The profile chart is one bar group per class.