• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

What Is Latent Class Analysis?

by Karen Grace-Martin 9 Comments

One of the most common—and one of the trickiest—challenges in data analysis is deciding how to include multiple predictors in a model, especially when they’re related to each other.

Here’s an example. Let’s say you are interested in studying the relationship between work spillover into personal time as a predictor of job burnout.

You have 5 categorical yes/no variables that indicate whether a particular symptom of work spillover is present (see below).

While you could use each individual variable, you’re not really interested if one in particular is related to the outcome. Perhaps it’s not really each symptom that’s important, but the idea that spillover is happening.

One possibility is to count up the number of items to which each respondent said yes. This variable will measure the degree to which spillover is happening. In many studies, this is just what you need.

But it doesn’t tell you something important—whether there are certain combinations that generally co-occur, and is it these combinations that affect burnout?

In other words, what if it’s not just the degree of spillover that’s important, but the type?

Enter Latent Class Analysis (LCA).

LCA is a measurement model in which individuals can be classified into mutually exclusive and exhaustive types, or latent classes, based on their pattern of answers on a set of categorical indicator variables. (Factor Analysis is also a measurement model, but with continuous indicator variables).

 

Probability of ‘Yes’ response for each Class

Item

Class 1

(20%)

Class 2

(61%)

Class 3

(12%)

Class 4

(7%)

Regularly brings home work to work on in the evenings

.30

.08

1.0

.66

Is asked to work weekends to meet deadlines

.10

.03

.47

1.0

Is expected to answer emails from the office within an hour outside of working hours

.93

.04

.15

.96

Checks work email from home

.84

.45

.91

.94

Is expected to be on call during vacations

.66

.15

.06

.88

 

True class membership is unknown for each individual. As categories of a latent variable, these classes can’t be directly measured other than through the patterns of responses on the indicator variables.

There are two sets of parameters in an LCA. The first is the set of inclusion probabilities that any random person will be in any latent class. You can see in the example above that there are 4 classes, and that 20% of respondents are in Class 1, 61% are in Class 2, etc.

The blue numbers in each column are the second type of parameters, equivalent to factor loadings in confirmatory factor analysis. Each is the conditional probability that someone in a particular class would respond ‘yes’ to a certain item. These parameters are used to interpret the classes.

For example, the largest class, Class 2, might be interpreted as the “Low Spillover” group. Their probability of answering ‘yes’ to any of the 5 questions is relatively low. The only one that is a little bit high is ‘Checks work email from home,’ but even so, this group does this at the lowest probability of any of the classes.

Likewise, Class 4, the smallest, has a pretty high probability of answering ‘yes’ to every single question. This class would be the “High Spillover” group.

So far, it’s not very interesting, right? It just seems a level of degree.

But Classes 1 and 3 are more interesting.

Class 1 has pretty high probabilities of answering ‘yes’ to three of the questions and very low probabilities of answering ‘yes’ to the other two. If you examine what they’re saying yes to, they’re all about being available to the company outside of work hours. So their personal lives are often interrupted, but they’re not regularly working long hours.

Compare this to class 3. Class 3 is quite different.  Members of Class 3 are highly likely to check work email from home, but they’re also regularly putting in extra work in the evenings and, to a lesser extent, on weekends. They’re not expected to be at the beck and call of work, however. (Maybe they’re the ones in the office working late).

These are two qualitatively different ways of having work spill into home life, and they could have different impacts on burnout. This is how Latent Class Analysis can be so useful.

In this example, we were able to use Latent Class Analysis to identify a latent typology that is used as a predictor variable, but there are many other uses within statistics, too.

So be sure to keep LCA on your radar—you never know when it might come in handy.

The Pathway: Steps for Staying Out of the Weeds in Any Data Analysis
Get the road map for your data analysis before you begin. Learn how to make any statistical modeling – ANOVA, Linear Regression, Poisson Regression, Multilevel Model – straightforward and more efficient.

Tagged With: categorical variable, conditional probability, Correlated Predictors, inclusion probability, latent class analysis, latent variable

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Latent Class Analysis
  • Member Training: Matrix Algebra for Data Analysts: A Primer

Reader Interactions

Comments

  1. Meghna Chakraborty says

    June 19, 2020 at 2:04 pm

    Hi, I would like to estimate the different factors influencing the child restraint use. I have two age groups of children, 0 to 3 years, and 4 to 7 years. My independent variables are driver age, race, gender, vehicle type etc. Can I run an LCM model using the children group as a latent class? Do you think, I am getting it conceptually right? Please suggest. Thank you very much!

    Reply
    • Karen Grace-Martin says

      June 25, 2020 at 4:51 pm

      Hi Meghna,

      I don’t think so. The latent classes are groupings you don’t actually know, but are inferring from patterns in the data. I assume you have observed data on the child’s age?

      Reply
  2. Larry says

    April 14, 2020 at 11:17 am

    Interesting. How do we determine the classes? Do we use Maximum Likelihood?

    Reply
    • Karen Grace-Martin says

      April 17, 2020 at 2:35 pm

      Hi Larry,

      Yes, that’s what the LCA is doing. Finding the classes. And yes, it uses Maximum Likelihood to do so.

      Reply
  3. Annette Ponnock says

    October 21, 2019 at 3:09 pm

    I have card sort data and I want to run an LCA to determine classes of people who grouped cards similarly. How would I go about doing that?

    Reply
  4. Ella Ganio says

    July 19, 2019 at 9:16 pm

    our survey is all about learning facilities and environment.these are the choices that will be used. is this a 4 point or 5 point likert scale?

    4-Very satisfied 3–satisfied 2–slightly satisfied 1–not satisfied 0-No Experience in the facility

    would i solve for 0 too?

    Reply
    • Karen Grace-Martin says

      August 22, 2019 at 1:18 pm

      Ella, It’s not really a 5-point scale b/c 0 isn’t part of the ordering. It’s a qualitatively different category. So this variable isn’t entirely ordinal, but it certainly is categorical, so you can definitely use LCA on it.

      Reply
  5. Noel McGinn says

    April 14, 2018 at 2:45 pm

    Can Latent Class Analysis be done using SPSS Statistics 23?

    Reply
    • Karen Grace-Martin says

      May 15, 2018 at 11:30 am

      Not version 23. I know Stata, R, MPlus, and SAS can all do it.

      Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Please note that, due to the large number of comments submitted, any questions on problems related to a personal study/project will not be answered. We suggest joining Statistically Speaking, where you have access to a private forum and more resources 24/7.

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Introduction to SPSS Software Tutorial

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT