Daily social media use, sleep quality, and anxiety levels among university students: a cross-sectional survey

What this example demonstrates

• Reports descriptive statistics, Cronbach-style reliability checks, Pearson correlations, simple and multiple regression with standardized coefficients (β*), and one-way ANOVAs for gender and study year.
• Tests a bootstrap mediation model (400 resamples) for sleep quality as a mediator between daily social media hours and anxiety; reports indirect effect 0.092, 95% CI [0.053, 0.133], ~20% of total effect.
• Presents results in APA-style sentences with test statistics, p values, and effect sizes (e.g., β* = 0.704, p < .001, R² = .495 for social media → anxiety).
• Includes correlation and regression tables plus figures for the mediation path and scatter relationships.
• Discussion and limitations explicitly note single-item measures and cross-sectional causality limits.

First pages preview

How this paper was generated

1. 1

   Upload simulated survey CSV

   A 280-row simulated university student dataset (social media hours, sleep quality, anxiety, demographics) was uploaded to Generate Paper.

2. 2

   Automated analysis and drafting

   Data2Paper cleaned the file, ran the planned inferential tests (regression, mediation bootstrap, ANOVAs), built tables and figures, and drafted all IMRaD sections with references (~6 minutes).

3. 3

   Export full PDF

   The complete 16-page PDF you can download here is the unedited AI output—use it to see formatting, reporting style, and section structure.