AI-POWERED DATA QUALITY ANALYSIS
Unlocking insights: assessing the quality of conventional and image-based responses on books at home in an online mobile survey
This paper addresses the quality of visual data collected via online surveys, comparing it with conventional question formats. It focuses on collecting information about books at home from parents in Spain using photos and traditional questions. The study identifies 18 indicators for data quality across both formats. Findings reveal significant measurement errors in conventional questions (high DK, rounding, discrepancies) but also challenges with image classification (low completeness for detailed info, interrater reliability issues). While photos offer unique insights (e.g., book titles) and a more conservative baseline for counts, they have lower completeness for detailed items compared to conventional methods. The study concludes that combining both formats is optimal, leveraging images for visual data and conventional questions for specific details, while emphasizing the need to consider respondent preferences and technological advancements.
Executive Impact
This research provides a framework for integrating visual data into enterprise-level surveys, improving data accuracy and enriching insights. For market research, it allows for more precise product inventory or lifestyle assessments. In social sciences, it enables more accurate measures of cultural capital and household assets, moving beyond self-reported biases. By understanding the quality trade-offs, organizations can design hybrid surveys that maximize data utility while minimizing respondent burden.
Key Benefits:
- Reduce social desirability bias in self-reported data
- Capture nuanced visual information (e.g., product details, condition)
- Improve data accuracy for physical asset inventories
- Enhance engagement through novel data collection methods
- Identify and mitigate measurement errors in hybrid surveys
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Conventional Data Completeness Across 11 Items
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Image-Based Data Completeness Across 11 Items
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Photos With Enough Visual Quality For Analysis
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Respondents Reporting Problems Submitting Photos
Deep Analysis & Enterprise Applications
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This paper develops a comprehensive methodology for assessing data quality in online surveys, specifically comparing conventional and image-based formats for collecting information on books at home. It leverages existing Total Survey Error (TSE) frameworks and introduces new indicators tailored for visual data.
18
Data Quality Indicators Proposed
Survey Data Quality Assessment Process
Identify Relevant Indicators
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Design Survey with Hybrid Formats
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Collect Conventional & Image Data
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Classify Visual Data
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Calculate Quality Metrics
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Compare & Analyze Discrepancies
Analysis of conventional survey responses revealed significant measurement errors. High rates of 'Don't Know' (20-42%) and extensive rounding (43-77%) for book counts indicate that respondents often provide estimates rather than exact figures. Discrepancies between reported totals and sums of categories further highlight these issues.
77%
Max % of Rounded Answers (Book Counts)
| Error Type | Impact on Data Quality |
|---|---|
| Non-substantive answers (DK) |
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| Rounding (e.g., to multiples of 5 or 10) |
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| Out-of-range values |
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| Discrepancies in sums |
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Image-based data, while promising, presented its own set of challenges. While 99% of photos had sufficient visual quality and were 'in line' with requests, achieving full classification for detailed items like book categorization (64% partial) and languages (59% partial) proved difficult. Interrater reliability was low to moderate (Cohen's Kappa/ICC 0.4) for complex classifications, indicating challenges for human coders. Additionally, 27% of respondents reported problems (contextual, technical, understanding issues) when submitting photos.
99%
Photos with Enough Visual Quality
27%
Respondents Reporting Photo Submission Problems
Challenge: Extracting Granular Data from Images
While photos are excellent for capturing presence and approximate counts of books, extracting granular data such as specific titles, authors, or precise languages from every book in an image proved challenging. Factors like lighting, book spine visibility, and potential for overlapping items limited the completeness of detailed classifications. Future AI-driven image analysis could significantly improve this.
Comparing both formats, conventional questions yielded information for 76% of items, while image-based only provided 4% completeness for all 11 items (though higher for core book counts). Discrepancies in book counts were common between methods, with conventional methods tending to overreport (mean difference up to 63 books), suggesting a social desirability bias. The study advocates for a hybrid approach, leveraging photos for visual evidence and conventional questions for specific, hard-to-extract details. This balance ensures broader participation while enriching data quality.
76%
Conventional Data Completeness (11 items)
4%
Image Data Completeness (11 items)
| Feature | Conventional Questions | Image-Based Responses |
|---|---|---|
| Completeness (11 items) |
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| Social Desirability Bias |
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| Granular Detail (e.g., titles) |
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| Measurement Errors |
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| Unique Insights |
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