Characteristics and Psychosocial Correlates of Problematic Internet Use Behaviors Among Racial/Ethnic Minority Youth in the United States: A Latent Class Analysis

Background:A rapidly growing body of literature has identified that problematic Internet use (PIU) is increasingly prevalent among youth worldwide. Youth with PIU evidence health issues, mental distress symptoms, and behavioral problems. However, very few studies have examined characteristics and psychosocial correlates of PIU among racial/ethnic minority youth. Moreover, psychometric properties of many measurements for PIU have only been tested with White and Asian youth. Thus, this study aims to: (1) examine the psychometric properties of the Problem Internet Use Questionnaire (PIUQ), a widely-used measure for PIU, in a sample of African American and Latino youth; and (2) investigate the differences in characteristics and psychosocial correlates of PIU between youth with PIU and their non-PIU peers using a latent class analysis (LCA).

Methods: Cross-sectional data was collected from 247 youth (M age = 16.0, SD= 1.2) in a face-to-face classroom setting using Qualtrics survey software to record responses. Most of the sample (69.2%) was African American, and 27.1% were Latino/a. More than half (63.6%) were female (35.6% male). Confirmatory factory analysis (CFA) was used to examine the validity of the 18-item PIUQ based on the factor structure suggested by empirical evidence. LCA was used to identify clusters of youth according to their response patterns. The item responses were treated as categorical variables (5-level). Bayesian information criteria, the Vuong-Lo-Mendell-Rubin test, and Bootstrap likelihood ratio test were used to identify the appropriate number of classes/clusters. Bivariate analyses were used to examine the levels of PIU and psychosocial correlates with the identified latent classes/clusters.

Results: The CFA supported a 3-factor structure of the PIUQ (i.e., obsession with Internet use, neglecting obligations due to Internet use, and diminished control over Internet use) in this sample [χ² (83) = 142.66, p < .001, RMSEA = .055, CFI = .96]. Three items were removed from the PIUQ due to low factor loadings (< .40). LCA was conducted using the remaining 15 items of the PIUQ, and identified a 3-classes solution as the optimal model. The classes/clusters were labelled as: normal use (n = 70, 29.5%), intense use (n = 125, 52.7%), and problematic use (n = 42, 17.8%). In addition, one-way ANOVA revealed significant differences in levels of PIU, parental monitoring, and sleep problems among youths who were classified as problematic, intense and normal users (p < .05). 

Conclusion and Implications: The CFA demonstrated that the PIUQ is a valid measure to assess PIU among racial/ethnic minority youth. LCA classified 3 levels of Internet use in this sample. Findings demonstrate the significance of using LCA to explore clusters of youth regarding their levels of Internet use given that no valid cut-off scores for measures of PIU have been established. Moreover, almost 20% of the sample were identified as having PIU. Youth who were classified as problematic users evidenced significantly higher levels of PIU, more severe sleep problems, and lower levels of parental monitoring compared to their peers who were classified as normal and intense users. Implications to research and clinical practice will be discussed.