Bedtime Smartphone Use, Perceived Stress and Sleep Quality among Bengaluru UG Students (Jamovi)

Bedtime Smartphone Use, Perceived Stress and Sleep Quality is a quantitative, correlational research project for a BA Psychology Semester 6 Project Work paper. It asks a question most undergraduates can answer from their own nights: does the phone we scroll after lights-off travel together with how stressed we feel and how badly we sleep?

The study surveys 200 undergraduate students (18–22 years) from two degree colleges in Bengaluru using three standardised, freely usable instruments — the Perceived Stress Scale (PSS-10), the Pittsburgh Sleep Quality Index (PSQI) and the Smartphone Addiction Scale – Short Version (SAS-SV) — plus a short self-developed schedule on bedtime phone habits (minutes of use after lights-off, phone kept on the bed, notifications on or off).

Data are scored in Excel and analysed in Jamovi (SPSS works identically): descriptive statistics, reliability (Cronbach's alpha) for each scale in this sample, normality checks, Pearson/Spearman correlations, gender comparison with an independent-samples t-test and a small multiple regression predicting the PSQI global score.

The bundle gives you the complete research design, a ready questionnaire structure, scoring keys, an analysis plan with the exact Jamovi menus, ethics and consent wording, a report chapter plan in APA 7 style, a 12-slide presentation outline and a viva bank. It is deliberately a beginner-level study — one design, one sample, three scales — which is exactly what an 80 + 20 Semester 6 project needs to be defensible.

Abstract

Undergraduate students in Indian cities combine long commutes, internal assessments, part-time work and near-constant smartphone access. Night-time phone use has been linked in international research to later sleep onset and poorer sleep quality, and perceived stress is a well-known correlate of disturbed sleep. This project examines, in a sample of 200 Bengaluru undergraduates, the relationships among problematic smartphone use (SAS-SV), bedtime phone use (minutes after lights-off), perceived stress (PSS-10) and sleep quality (PSQI). A cross-sectional correlational design is used; data are analysed in Jamovi using correlation, t-test and multiple regression. The findings are expected to inform sleep-hygiene awareness sessions by the college counselling cell.

Introduction

Sleep is not a luxury for students — it consolidates memory, regulates mood and protects attention. The PSQI treats a global score above 5 as "poor sleep", and Indian campus surveys routinely find a large share of students above that cut-off. At the same time, the smartphone has moved from the desk to the pillow: alarms, reels, chats and late-night assignment groups all live on one device. Stress sits in the middle of this picture, because a stressed student may use the phone to escape, and a phone that keeps buzzing may itself keep stress high.

Review gap

Most published work on smartphone use and sleep comes from East Asian, European and North American samples, or from Indian medical and engineering colleges. Arts, science and commerce undergraduates in affiliated city colleges — the largest group of students in Karnataka — are studied far less. Many existing studies also measure "screen time" in general rather than use after lights-off, which is the behaviour most directly tied to sleep. This project addresses both gaps on a small, local scale.

Hypotheses

• H1: SAS-SV scores will correlate positively with PSQI global scores.
• H2: PSS-10 scores will correlate positively with PSQI global scores.
• H3: Bedtime phone-use minutes will correlate positively with sleep latency (PSQI component 2).
• H4: Stress and smartphone addiction together will significantly predict sleep quality.
• H5: There will be no significant gender difference in PSQI global scores (exploratory, two-tailed).

Feasibility

• Technical: all three scales are short (10 + 19 + 10 items), take about 12 minutes together, and are free for academic research with citation. Jamovi is free and open-source.
• Operational: data collection needs permission from two principals and two classroom slots or a Google Form link shared through class representatives.
• Time: 12 weeks is sufficient — 3 for review and tools, 3 for data collection, 3 for analysis, 3 for writing and viva preparation.
• Economic: printing 220 questionnaires costs under ₹900; everything else is free.

Faculty and counsellors in Bengaluru degree colleges increasingly report students who are sleepy in first-hour classes, miss morning internals and describe themselves as "always tired but not able to sleep". The usual explanation offered is "too much phone", but there is little local data showing whether bedtime smartphone use is actually related to poor sleep in these students, how large that relationship is, and whether perceived stress is part of the same picture.

Without such evidence, awareness sessions rely on general advice rather than findings about the students in front of them. This project therefore studies the relationship between problematic smartphone use, bedtime phone use, perceived stress and sleep quality among undergraduate students of two Bengaluru colleges, using standardised scales and correlational statistics, and translates the results into specific, practical recommendations for the college counselling cell and for students themselves.

In scope

• Undergraduate students (BA, B.Com, B.Sc, BBA, BCA) aged 18–22 in two degree colleges affiliated to Bengaluru City University.
• A cross-sectional survey using three standardised self-report scales plus a short bedtime-habits schedule.
• Quantitative analysis in Jamovi: descriptives, reliability, correlation, t-test and multiple regression.
• Recommendations for sleep hygiene and phone habits.

Out of scope

• Clinical diagnosis of insomnia, depression or any disorder — this is a screening-level academic study.
• Objective sleep measurement (actigraphy, polysomnography) or phone-logged screen time.
• Causal claims: a correlational design cannot say that phones cause poor sleep.
• Students under 18 or outside the two selected colleges.

Research design

A cross-sectional, correlational survey design. Variables are measured once, as they naturally occur, and relationships are examined statistically; no variable is manipulated.

Variables

Sample and sampling

• Population: UG students of two BCU-affiliated colleges in Bengaluru (one in the south, one in the east of the city).
• Sample size: a G*Power calculation for detecting a correlation of r = .25 with 95% power (two-tailed, α = .05) gives N ≈ 202; 220 questionnaires are distributed to allow for incomplete forms.
• Technique: stratified convenience sampling — equal numbers from I, II and III year, and from arts/science/commerce streams where possible.
• Inclusion: 18–22 years, owns a smartphone, consents. Exclusion: self-reported diagnosed sleep disorder or current psychiatric treatment; night-shift workers.

Procedure

1. Permission letters to both principals; guide approves the questionnaire.
2. Pilot with 15 students (not included in the final sample) to check timing and wording.
3. Administration in class slots (paper) or through a Google Form shared by class representatives; the investigator is present or reachable for doubts.
4. Consent page → demographics → SAS-SV → PSS-10 → PSQI → bedtime-habits schedule → debrief text with helpline numbers.

Analysis plan (Jamovi)

1. Scoring in Excel using the published keys; paste into Jamovi.
2. Exploration → Descriptives (mean, SD, skewness, kurtosis, Shapiro–Wilk).
3. Factor → Reliability Analysis — Cronbach's α for each scale.
4. Regression → Correlation Matrix — Pearson (or Spearman if non-normal) for H1–H3.
5. T-Tests → Independent Samples (Welch's) for H5, with Cohen's d.
6. Regression → Linear Regression — PSQI on PSS-10 and SAS-SV (H4), reporting R², β, VIF.

Timeline (12 weeks)

• Experimental follow-up: a two-week "phone out of the bedroom" intervention with pre–post PSQI, using a waitlist control group.
• Objective measures: phone-logged screen time (Digital Wellbeing / Screen Time reports) instead of self-reported minutes.
• Mediation analysis: test whether stress mediates the link between smartphone addiction and sleep (Jamovi jAMM module or PROCESS in SPSS).
• Broader sample: compare hostel residents with day scholars, or colleges in Bengaluru with colleges in a tier-2 city like Tumakuru.
• Add academic outcomes: attendance in first-hour classes or internal-assessment scores.