SayPro Generate Topic List for Data Quality Assessments Using GPT: Provide 100 questions for assessing

Here’s a list of 100 questions to assess data quality in survey data collection, focused on accuracy, reliability, completeness, consistency, and validity:

1. Is the survey response accurate according to the source data?
2. Are the survey questions clear and unambiguous?
3. How do you ensure that respondents understood each question?
4. Was the data entry process standardized and consistent?
5. Were the survey data collectors trained adequately?
6. How often do you encounter missing responses in the survey data?
7. Are there any patterns in missing responses?
8. Are respondents’ answers consistently aligned with the question wording?
9. Is the response rate acceptable for the sample size?
10. How does the sample size compare to the intended population size?
11. Did any respondents skip any sections of the survey?
12. Are there any duplicated responses in the dataset?
13. Were responses checked for logical consistency?
14. Were there any outliers in the data?
15. Do the survey responses match the expected distribution of answers?
16. How is nonresponse bias being addressed?
17. Were there any discrepancies between the pilot survey and the final survey data?
18. Did any respondents provide contradictory answers to related questions?
19. Was the survey administered using a uniform method across all respondents?
20. Are the sampling methods representative of the target population?
21. Was random sampling used appropriately?
22. Were any over-sampled or under-sampled groups identified?
23. Are there biases in the way questions are asked (leading questions)?
24. How was the survey population selected?
25. Is there any evidence of survey fatigue among respondents?
26. Are there duplicate records in the dataset?
27. Was the survey properly pre-tested or piloted?
28. How were data quality checks incorporated into the survey process?
29. How were skipped questions handled by the survey platform?
30. Were any participants excluded due to unreliable responses?
31. Did respondents’ demographic information match their answers?
32. Were any inconsistencies identified between survey answers and external data sources?
33. How frequently are reliability checks run on the survey data?
34. How often are data entry errors identified and corrected?
35. Are responses properly coded in categorical questions?
36. Are open-ended responses correctly classified or coded?
37. Did respondents encounter any technical issues while completing the survey?
38. Are survey questions designed to minimize response bias?
39. Are respondents encouraged to answer all questions honestly?
40. Was there a significant drop-off in responses midway through the survey?
41. Are there any indications that the survey was filled out too quickly or without careful thought?
42. Were survey instructions and terms clearly defined for respondents?
43. Were there sufficient response categories for each question?
44. How frequently is the survey methodology reviewed for improvements?
45. Does the dataset have any unusual or unexpected patterns?
46. Were demographic characteristics balanced in the survey sample?
47. Was survey data anonymized and confidential to ensure honest responses?
48. How is the survey data validated after collection?
49. Were the results cross-checked with other independent surveys?
50. How often is data consistency reviewed during the collection process?
51. Were controls in place to avoid fraudulent survey submissions?
52. How were outlier data points handled in the analysis?
53. Are respondent qualifications verified before survey participation?
54. Did you encounter difficulty obtaining representative responses?
55. Are survey questions phrased to avoid leading answers?
56. How does the data address the objectives of the survey?
57. Were respondents’ responses coded consistently across the dataset?
58. Was there any evidence of respondents misinterpreting questions?
59. Were there changes to the survey format after the initial rollout?
60. Was a balance between quantitative and qualitative questions maintained?
61. Were response scales clearly defined and consistent throughout the survey?
62. Did the survey allow for the capture of all necessary variables?
63. Were incomplete or invalid responses flagged for follow-up?
64. Was the survey tested across different devices or platforms?
65. Was there a mechanism in place for validating respondent eligibility?
66. Were response trends analyzed for any signs of bias?
67. How was the timeliness of data collection ensured?
68. Was the survey able to measure the intended indicators effectively?
69. How did the survey responses correlate with previous survey findings?
70. How often are survey data entries cross-checked for completeness?
71. Was the data sampling weighted to reflect the population accurately?
72. How was the accuracy of responses verified during data collection?
73. Was response time tracked to evaluate the quality of answers?
74. Was there any difficulty in gathering sufficient responses for analysis?
75. Was the survey design periodically updated to reflect any feedback from respondents?
76. Were validation checks conducted during data entry or after collection?
77. Was respondent bias monitored or corrected throughout the process?
78. Did respondents exhibit signs of social desirability bias in responses?
79. Was the data subjected to any quality control audits?
80. Were the survey questions structured to minimize respondent confusion?
81. Did any respondents provide irrelevant or incoherent answers?
82. Were responses analyzed to check for possible data contamination?
83. How was the quality of open-ended responses verified?
84. Were there any obvious contradictions between survey responses and the target population’s characteristics?
85. Did any inconsistencies arise from data entry or transcription errors?
86. Was there a system in place to cross-check responses for completeness?
87. Was the survey conducted in a way that encouraged honest and accurate reporting?
88. How did you handle any discrepancies discovered between different data sources?
89. Were results cross-checked by multiple researchers or analysts?
90. Was the data collection tool user-friendly for all participants?
91. How often were data collection standards reviewed and updated?
92. Was sufficient information provided for respondents to make informed answers?
93. Was data anonymity and privacy properly ensured during collection?
94. Were there any signs of intentional misrepresentation in responses?
95. Were there any known data entry errors in the dataset?
96. Was the sample group representative of the larger population in terms of key characteristics?
97. How was the reliability of the survey process measured over time?
98. Was a proper audit trail maintained for all data entry procedures?
99. Were the collected data points thoroughly reviewed for consistency before analysis?
100. Was a data quality framework used to assess every stage of the survey process?

These questions can be used to thoroughly assess the data quality of survey-based data collection and ensure its integrity for analysis and decision-making.