Latent class analysis of reading, decoding, and writing performance using the Academic Performance Test: concurrent and discriminating validity

Abstract

Aim: To explore and validate the best returned latent class solution for reading and writing subtests from the Academic Performance Test (TDE). Sample: A total of 1,945 children (6-14 years of age), who answered the TDE, the Development and Well-Being Assessment (DAWBA), and had an estimated intelligence quotient (IQ) higher than 70, came from public schools in Sao Paulo (35 schools) and Porto Alegre (22 schools) that participated in the 'High Risk Cohort Study for Childhood Psychiatric Disorders' project. They were on average 9.52 years old (standard deviation = 1.856), from the 1st to 9th grades, and 53.3% male. The mean estimated IQ was 102.70 (standard deviation = 16.44). Methods: Via Item Response Theory (IRT), the highest discriminating items ('a'>1.7) were selected from the TDE subtests of reading and writing. A latent class analysis was run based on these subtests. The statistically and empirically best latent class solutions were validated through concurrent (IQ and combined attention deficit hyperactivity disorder [ADHD] diagnoses) and discriminant (major depression diagnoses) measures. Results: A three-class solution was found to be the best model solution, revealing classes of children with good, not-so-good, or poor performance on TDE reading and writing tasks. The three-class solution has been shown to be correlated with estimated IQ and to ADHD diagnosis. No association was observed between the latent class and major depression. Conclusion: The three-class solution showed both concurrent and discriminant validity. This work provides initial evidence of validity for an empirically derived categorical classification of reading, decoding, and writing performance using the TDE. A valid classification encourages further research investing correlates of reading and writing performance using the TDE.