A statistical exploration of the psychological and demographic variables influencing internet addiction.
This project investigates how factors like depression, age, education, and marital status affect the level of internet addiction. The analysis combines statistical tools and questionnaires to measure and validate hypotheses, offering insight into the growing psychological concern of internet overuse.
Internet addiction has increasingly become a modern behavioral concern. This project explores how psychological (e.g., depression) and demographic (e.g., age, education, marital status) variables correlate with or predict levels of addiction to the internet. The main goal is to detect statistically significant relationships and interpret them through descriptive and inferential statistics.
We collected responses from 80 participants, aged 14 to 48. The following standardized instruments were used:
- Young’s Internet Addiction Test (IAT) – Measures internet addiction (score: 20–100).
- Beck Depression Inventory II (BDI-II) – Assesses depression levels (score: 21–84).
- Eysenck Personality Questionnaire (EPQ) – Evaluates traits like extraversion and psychoticism.
Demographic Variables:
- Age
- Gender (coded: 0 = female, 1 = male)
- Marital status (0 = single, 1 = married, 2 = in relationship, 3 = prefer not to say)
- Education level (0 = <diploma to 6 = PhD)
- Employment status
The project uses descriptive statistics, ANOVA, ANCOVA, regression analysis, and normality/homogeneity tests with tools like: SPSS, Python, R and Amos
Statistical Techniques Applied:
- Shapiro–Wilk & Kolmogorov–Smirnov tests (normality)
- Levene’s test (variance homogeneity)
- One-way ANOVA
- Tukey’s Post Hoc
- ANCOVA (to adjust for covariates like age)
- Linear regression
| Test | Result | p-value | Interpretation |
|---|---|---|---|
| ANOVA (BDI-II → IAT) | F = 9.02 | 0.0004 | Depression significantly affects addiction |
| ANCOVA (BDI + Age → IAT) | Age = significant covariate | 0.0118 | Age impacts relationship strength |
| ANOVA (Education → IAT) | F = 3.53 | 0.0039 | Educational level affects addiction |
| ANOVA (Marital Status → IAT) | F = 2.81 | 0.0453 | Marital status affects addiction |
| Linear Regression (BDI) | Coef = 0.8031 | < 0.05 | Depression predicts addiction linearly |
| Gender | – | > 0.05 | No significant effect |
- Self-reported data
- Non-random sampling
- Small sample size (n = 80)
- Cross-sectional nature (no causality inference)
This project confirms that depression, age, education level, and marital status all significantly relate to internet addiction, with depression having the strongest influence. Findings align with psychological theory linking mental health with addictive behaviors in digital spaces.