This document discusses challenges with data collection across different research domains. It identifies several categories of data collection barriers: researcher barriers like fatigue; participant barriers such as inaccurate self-reporting; environmental barriers pertaining to the data collection setting; and issues with survey design like unclear questions. Specific examples are provided from public administration, public health, distance education, environmental, and market research that illustrate challenges related to inconsistent or poor quality data collection due to a lack of standardized processes, inadequate training, political and legal constraints, and issues incentivizing participation or protecting privacy.

1. DATA COLLECTION
CHALLENGES
Dr. SUPARNA DUTTA
METHODOLOGICAL REASONING & QUANTITATIVE
ANALYSIS
PUAD 715

2. Categories of Data Collection Barriers
■ RESEARCHER
– Researcher fatigue
■ PARTICIPANT
– Self-reporting inaccuracy
o Socially desirable responses
o Inaccurate recollection or recollection error
o Respondent demographics, e.g. age and education
o Participants are unwilling to disclose sensitive information
o Participants resist participation unless they see benefits
■ DATA COLLECTION ENVIRONMENT
– Hard-to-reach location, loud background
■ SURVEY INSTRUMENT DESIGN
– Lack of clarity of survey questions resulting in low responses

3. Example 1:
Public Administration Research
■ State of Ohio used to ask local governments to report on the quality of their roads
■ Their responses determined their funding and therefore perversely incentivized over-
reporting of damaged roads
■ Local officials did not have adequate data or clarity about data reporting methods
■ The data collected was inconsistent, incomplete, resulting from rough estimates, and
of poor quality
Hu,W., McCartt,A.T., &Teoh, E. R. (2011). Effects of red light camera enforcement on
fatal crashes in large US cities. Journal of safety research, 42(4), 277-282.

4. Example 2:
Public Health Research
■ Patient-reported outcomes (PROs), such as health-related quality of life (HRQL) are
used to evaluate treatment effectiveness in clinical trials
■ PROs are valued by patients and may inform important decisions in the clinical setting
■ Evidence from the UK’s Medical Research Council points to inconsistent standards in
data collection in trials mostly due to a lack of appropriate training of the collecting
staff (nurses and trialists)
Kyte, D., Ives, J., Draper, H., Keeley,T., & Calvert, M. (2013). Inconsistencies in quality of
life data collection in clinical trials: a potential source of bias? Interviews with research
nurses and trialists. PloS one, 8(10), e76625. doi:10.1371/journal.pone.0076625

5. Example 3:
Distance Education Research
■ Lack of standardization in the research process for distance education research
■ Incentivizing participation
■ Reliance on self-reported data
■ Accessing individual level data
– With sensitive and confidential information, Individual level data, meaning
information and data at the level of the student, is more difficult to access, yet
necessary when examining certain populations and student outcomes.
National Research Center for Distance Education andTechnologicalAdvancements
(DETA), University of Milwaukee. Retrieved from https://uwm.edu/deta/top-5-challenges-
in-conducting-deta-research/

6. Example 4:
Environmental Research
■ The monitoring of ambient environmental conditions is essential to environmental
management and regulation
■ However, effective monitoring is subject to a range of institutional, political, and legal
constraints
■ “Political pressure or myopia, conflicting agency goals, the need for institutional
autonomy, or a reluctance of agency scientists to pursue monitoring” are some of
these data collection constraints
Biber, E. (2013).The challenge of collecting and using environmental monitoring
data. Ecology and Society, 18(4).

7. Example 5:
Market Research in Consumer Goods
■ Poor survey design resulting in lower response rates
■ Data privacy issues preventing data sharing
Consumers are sensitive about sharing data with market research companies
■ Survey bias (the design favors one segment of the market, e.g. younger age-group)
■ Observational research may be intrusive and uncomfortable for the consumers
-A fast-food chain's researchers explored if there was a need for a new location of its store
-They survey people going through the drive-through line
-Although the survey was brief, they annoyed customers by slowing down the line
-The customers left the business site
https://www.iresearchservices.com/3-biggest-challenges-faced-by-market-research-companies/
https://smallbusiness.chron.com/examples-marketing-research-problems-23051.html

8. Questions?
Email to suparna.dutta@franklin.edu