Latent Profile Analysis (LPA) is a person-centered statistical technique used to identify unobserved (latent) subgroups within a population based on continuous variables (Collins & Lanza, 2010). Unlike traditional clustering methods such as k-means clustering, LPA employs probabilistic modeling to classify individuals into distinct profiles based on their shared characteristics (Muthén & Muthén, 2000).
Latent Profile Analysis (LPA) identifies subgroups within a dataset based on observed variables. Unlike variable-centered approaches (e.g., regression, factor analysis), LPA reveals hidden heterogeneity in a population, making it particularly useful in psychological, educational, and organizational research (Morin et al., 2016). Below are key strengths of LPA in research:
1. Identifies Hidden Subgroups (Person-Centered Approach)
Traditional methods assume that one model fits all individuals, but LPA identifies meaningful subgroups based on unique response patterns. For example, in Psychological Capital (PsyCap) research, LPA can reveal high, moderate, and low PsyCap profiles, helping to understand how different individuals experience resilience, hope, efficacy, and optimism (Chen et al., 2021).
2. More Flexible Than Traditional Clustering Methods
Unlike K-means clustering, LPA assigns individuals to latent groups based on probabilistic estimation, reducing classification errors (Spurk et al., 2020). LPA allows for model comparison using fit indices (e.g., BIC, AIC, entropy), improving accuracy in determining the optimal number of profiles (Nylund et al., 2007).
3. Captures Complex Psychological Constructs
Psychological variables like motivation, emotional regulation, or personality often do not form clear-cut categories but exist on a spectrum. LPA allows for continuous latent variables, making it suitable for research on behavioral, psychological, and educational phenomena (Morin et al., 2011).
4. Can Be Combined With Other Methods (e.g., SEM, Longitudinal Analysis)
LPA can be integrated with Structural Equation Modeling (SEM) to test predictors and outcomes of different profiles (Wang & Hanges, 2011) or in longitudinal LPA (LLPA) allows researchers to track how PsyCap or personality profiles change over time, improving developmental psychology and organizational research (Spurk et al., 2020).
How Latent Profile Analysis (LPA) Uncovers Subgroups in Educational Research and Its Research Implications
Latent Profile Analysis (LPA) is a powerful statistical technique in educational research that helps uncover hidden subgroups (profiles) within a population based on continuous variables (Spurk et al., 2020). Instead of assuming that all students are the same, LPA identifies distinct student profiles that may have different learning behaviors, psychological characteristics, or academic outcomes. LPA is good for educational research implication for many reasons such as;
1. Personalized Education Strategies: Identifying subgroups helps tailor interventions to meet students’ specific needs (Wang & Hanges, 2011). for example: If LPA reveals low-efficacy but high-resilience students, educators can design targeted self-efficacy training to boost their confidence.
2. Improving Student Support Systems: LPA helps institutions detect at-risk students early. If LPA identifies a low PsyCap, low-engagement group, universities can provide extra mentoring, counseling, or tutoring.
3. Enhancing Educational Policy and Curriculum Design: LPA findings can guide curriculum improvements by addressing the needs of specific student subgroups (Morin et al., 2016). For example If LPA finds that students with low self-regulated learning struggle in online courses, institutions can add more structured guidance.
4. Advancing Research on Psychological and Behavioral Differences: LPA provides a nuanced, data-driven approach to studying psychological diversity in education. Rather than treating all students as one group, LPA can show how different PsyCap profiles relate to academic resilience and career success.
5. Supporting Longitudinal and Cross-Cultural Comparisons: LPA allows for tracking changes in student profiles over time or comparing educational systems across countries. for example: A longitudinal study might show how PsyCap profiles evolve as students progress through college.
Latent Profile Analysis (LPA) is a valuable tool in educational research because it uncovers hidden subgroups among students, allowing for personalized interventions, improved policies, and deeper psychological insights. By using well-chosen input variables, LPA provides meaningful, actionable findings that can enhance education at both the individual and institutional levels.
Considerations for Including Variables in Latent Profile Analysis (LPA)
Latent Profile Analysis (LPA) relies heavily on input variables (also called indicator variables) because these are the foundation for identifying latent subgroups. The selection, quality, and conceptual relevance of input variables directly influence the accuracy, interpretability, and theoretical contribution of the profiles uncovered (Spurk et al., 2020). Including an excessive number of variables in Latent Profile Analysis (LPA) can lead to several methodological and interpretative challenges. Below are key issues that researchers should consider:
1. Overfitting and Model Complexity
An excessive number of variables can cause overfitting, where the model captures random noise rather than meaningful patterns. This may result in the creation of too many latent profiles that lack theoretical or practical relevance (Collins & Lanza, 2010).
2. Increased Computational Burden and Convergence Issues
LPA relies on maximum likelihood estimation (MLE), which becomes computationally intensive as the number of variables increases. As a result, the model may fail to converge or require extended processing time (Masyn, 2013).
3. Identifiability Issues
With more variables, the number of estimated parameters increases, which can lead to model instability. The model may produce different results with each run due to difficulties in finding a unique solution (Nylund-Gibson & Choi, 2018).
4. Difficulty in Interpretation
A higher number of variables can lead to complex and ambiguous profiles that are difficult to interpret. This may result in artificially separated profiles that do not reflect meaningful subgroups (Tein, Coxe, & Cham, 2013) which are Spurious Profiles.
5. Small Class Sizes and Poor Classification Accuracy
Adding more variables increases the risk of generating small latent classes, which compromises the reliability of posterior probability estimates and reduces classification accuracy (Nylund-Gibson & Choi, 2018).
6. Decline in Model Fit and Entropy
Excessive variables can lead to lower entropy values, indicating poorer classification certainty. Additionally, fit indices such as AIC and BIC may suggest a worse-fitting model due to unnecessary complexity (Tein et al., 2013).
Spurious Profiles in LPA
Spurious profiles refer to latent classes that appear in LPA results but do not represent real or meaningful subgroups in the population. These profiles often arise due to overfitting, poor variable selection, or inadequate sample size (Masyn, 2013). Awareness of spurious profiles is crucial before selecting variables and interpreting results.
Causes of Spurious Profiles
1. Too Many Variables: An excessive number of variables may create artificial distinctions between groups that do not exist in reality.
2. Small Sample Size: With limited data, LPA may generate classes based on random noise, leading to unstable solutions.
3. Overfitting: Selecting an excessive number of classes (e.g., forcing five or six when two or three are optimal) can result in meaningless subgroups.
4. Highly Correlated Variables: If variables are highly collinear, the model may generate unnecessary class separations that lack theoretical justification.
5. Inappropriate Model Selection: Relying solely on statistical fit indices (e.g., AIC, BIC, Entropy) without considering interpretability may lead to the selection of models with spurious profiles.
Practical Limits for the Number of Variables in LPA
Guidelines from prior research suggest that the number of variables included in an LPA model should remain manageable to ensure interpretability and model stability. A typical LPA model should not exceed 12 variables unless supported by a large sample size and advanced modeling techniques (Collins & Lanza, 2010; Masyn, 2013; Nylund-Gibson & Choi, 2018).
Recommended Variable Limits Based on Sample Size:
1. Small sample (<200): ≤5 variables
2. Medium sample (200-500): 5-8 variables
3. Large sample (500-1000): Up to 10-12 variables
4. Very large sample (>5000): 12- Variables
References
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Wiley. https://doi.org/10.1002/9780470567333
Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541-572.
Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. D. Little (Ed.), The Oxford handbook of quantitative methods: Vol. 2. Statistical analysis (pp. 551–611). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199934898.013.0025
Morin, A. J. S., Morizot, J., Boudrias, J.-S., & Madore, I. (2016). A person-centered evaluation of PsyCap. Journal of Positive Psychology, 11(6), 675–687.
Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440–461. https://doi.org/10.1037/tps0000176
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis in vocational behavior research. Journal of Vocational Behavior, 120, 103426.
Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. https://doi.org/10.1080/10705511.2013.824781
Wang, M., & Hanges, P. J. (2011). Latent profile analysis and structural equation modeling. Journal of Applied Psychology, 96(2), 305–319.
