AYP Survey Insights
How We Collected Our Survey Data
Understanding the unique perspectives of Adventist Young Professionals (AYPs) across the Philippines through a thoughtful, multi-channel approach.
Defining Our Scope
Understanding who we're studying and why our approach matters.
Understanding the unique perspectives of Adventist Young Professionals (AYPs) across the Philippines is no easy task—especially when there's no official database or complete list of who they are. That's why we developed a thoughtful, multi-channel approach to ensure the voices we heard were as diverse, authentic, and representative as possible.
Below is a detailed explanation of how we defined our target group, calculated our ideal sample size, and designed our data collection method to minimize bias.
📌 Defining the Target Population
We began with the 2023 reported figures of the Seventh-day Adventist (SDA) membership in the Philippines, which totaled approximately 1.4 million members across the four union conferences:
- • North Philippine Union Conference: 463,233
- • Southeastern Philippine Union Conference: 426,549
- • Southwestern Philippine Union Conference: 291,877
- • Central Philippine Union Conference: 219,955
While we didn't have an exact breakdown of how many of these members are "young professionals," we made a reasonable estimate based on demographic trends and local church observations. We assumed that 30% of total members are likely to fall into the Adventist Young Professionals category, giving us an estimated target population of 420,184 AYPs.
Sample Size Calculation
Ensuring our findings are statistically meaningful.
📊 Sample Size Calculation
To make sure our survey findings would be statistically meaningful, we calculated the ideal sample size using the following criteria:
- • Confidence Level: 95%
- • Margin of Error: ±5%
- • Estimated Population Proportion: 30%
- • Total Target Population: 1,500,000 (for broader SDA estimations)
Using a recognized sample size formula with finite population correction, this resulted in a recommended sample size of approximately 323 respondents. This means that if at least 323 AYPs participated in our survey, we could be 95% confident that the results reflect the views of the larger group, within a ±5% margin of error.
Addressing Potential Bias
Our multi-channel approach to ensure diverse and representative responses.
🔍 Addressing Potential Bias
We were very intentional in choosing a multi-channel distribution method to reduce potential sampling and selection bias. Here's how we did it:
1. Paid Facebook Ads
We used Facebook's detailed targeting tools to reach likely Adventist Young Professionals. Filters included:
- • Age group (typically 20–40 years old)
- • Interests such as "Seventh-day Adventist Church," "youth ministry," and "AYM"
- • Education level and employment status when available
This gave us access to AYPs across the country, regardless of whether they were connected to our personal networks.
Why this matters: It prevented over-reliance on a single church, region, or demographic, helping ensure a more diverse sample.
2. Offline Sharing and Peer Networks
We also invited friends, church leaders, and small group facilitators to share the survey offline. This included:
- • Announcements during church meetings or care groups
- • Printed QR codes shared at youth and professional gatherings
- • Word-of-mouth referrals across different Adventist circles
Why this matters: It balanced out the digital reach of Facebook, especially for individuals who may not be active online but still belong to the AYP community.
🔁 Blending Digital & Organic Reach
The combination of online and offline methods allowed us to reach a wider spectrum of AYPs—including those:
- • In urban and rural settings
- • In active church roles or peripheral church engagement
- • With various levels of access to technology and social media
This hybrid approach helps reduce bias compared to methods that rely solely on either personal contacts or social media algorithms.
💬 What About Voluntary Response Bias?
It's true that any open survey will attract responses primarily from those who are motivated to participate. However, we took several steps to minimize this:
- • Kept the survey short, clear, and easy to answer
- • Used inclusive language to ensure all AYPs felt invited
- • Avoided overly technical or religious jargon that might discourage participation
We also made sure the survey was accessible on mobile devices and shared during peak engagement times, especially evenings and weekends.
✅ Final Thoughts
While no sampling method is perfect—especially when dealing with a hard-to-reach population—we believe our approach strikes a practical balance between reach, diversity, and accuracy. By thoughtfully blending Facebook targeting with offline community networks, we created a pathway for a broad and credible range of voices to be heard.
We're confident that the insights drawn from this survey reflect the real experiences, opinions, and aspirations of Adventist Young Professionals across the country.
If you have questions about the methodology or would like to view the raw survey data, feel free to get in touch.
Data Analysis Approach
Our comprehensive approach to analyzing the survey data.
Our analysis methodology combines quantitative and qualitative approaches to provide comprehensive insights into the experiences of young Adventist professionals. We employ advanced analytics techniques to uncover patterns and trends in the survey responses.
Sentiment Analysis
Using natural language processing to analyze emotional tone and sentiment in responses, helping us understand participants' feelings and attitudes.
Topic Modeling
Employing LDA (Latent Dirichlet Allocation) to identify key themes and topics within the survey responses.
Statistical Analysis
Conducting statistical analysis to understand patterns, correlations, and significant relationships in the data.
Visualization
Creating interactive visualizations to present findings in an accessible and engaging format.
Collection
- Survey Responses
- Data Validation
- Quality Checks
Processing
- Data Cleaning
- Text Processing
- Feature Extraction
Analysis
- Pattern Recognition
- Insight Generation
- Visualization