Response Times as an Indicator of Data Quality: Associations with Question, Interviewer, and Respondent Characteristics in a Health Survey of Diverse Respondents

2020 ◽  
pp. 253-266
Author(s):  
Dana Garbarski ◽  
Jennifer Dykema ◽  
Nora Cate Schaeffer ◽  
Dorothy Farrar Edwards
Keyword(s):  
Data Quality ◽  
Health Survey ◽  
Response Times ◽  
Respondent Characteristics

scholarly journals A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

2021 ◽  
Author(s):  
Victoria Leong ◽  
Kausar Raheel ◽  
Sim Jia Yi ◽  
Kriti Kacker ◽  
Vasilis M. Karlaftis ◽  
...  
Keyword(s):  
Data Quality ◽  
Response Times ◽  
Psychological Research ◽  
Cognitive Testing ◽  
Quality Data ◽  
High Quality ◽  
Online Data ◽  
Test Environment ◽  
Web Based ◽  
Cognitive Data

Background. The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods. A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. Results. The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions. These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data (Preprint)

2021 ◽  
Author(s):  
Victoria Leong ◽  
Kausar Raheel ◽  
Jia Yi Sim ◽  
Kriti Kacker ◽  
Vasilis M Karlaftis ◽  
...  
Keyword(s):  
Data Quality ◽  
Response Times ◽  
Psychological Research ◽  
Cognitive Testing ◽  
Quality Data ◽  
High Quality ◽  
Online Data ◽  
Test Environment ◽  
Web Based ◽  
Cognitive Data

BACKGROUND The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. OBJECTIVE Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). METHODS A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. RESULTS The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. CONCLUSIONS These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance. CLINICALTRIAL N.A.

scholarly journals The Danish National Health Survey 2010. Study design and respondent characteristics

2012 ◽  
Vol 40 (4) ◽  
pp. 391-397 ◽  
Author(s):  
Anne Illemann Christensen ◽  
Ola Ekholm ◽  
Charlotte Glümer ◽  
Anne Helms Andreasen ◽  
Michael Falk Hvidberg ◽  
...  
Keyword(s):  
Study Design ◽  
National Health ◽  
Health Survey ◽  
National Health Survey ◽  
Respondent Characteristics

scholarly journals Assessment of Anthropometric Data Following Investments to Ensure Quality: Kenya Demographic Health Surveys Case Study, 2008 to 2009 and 2014

2018 ◽  
Vol 39 (3) ◽  
pp. 406-419 ◽  
Author(s):  
Eva Leidman ◽  
Louise Masese Mwirigi ◽  
Lucy Maina-Gathigi ◽  
Anna Wamae ◽  
Andrew Amina Imbwaga ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Data Quality ◽  
Health Survey ◽  
Quality Standard ◽  
Missing Measurements ◽  
Anthropometric Data ◽  
Digit Preference ◽  
Training And Supervision ◽  
Z Scores ◽  
Height For Age

Background: Evidence-based nutrition programs depend on accurate estimates of malnutrition derived from data collected in population representative surveys. The feasibility of obtaining accurate anthropometric data as part of national, multisectoral surveys has been a debated issue. Objectives: The study aimed to evaluate changes in anthropometric data quality corresponding to investments by the Kenya Ministry of Health and nutrition sector partners for the 2014 Kenya Demographic Health Survey. Methods: Anthropometric data collected during the 2008 to 2009 and 2014 Kenya surveys were reanalyzed to assess standard parameters of quality: standard deviation, skewness, and kurtosis of z-score values for 3 anthropometric indicators (weight for height, height for age, and weight for age), percentage of children with missing measurements and outlier values, digit preference, and heaping of age. Results: A total of 9936 households were selected in 2008 to 2009, and 39 679 households were selected in 2014. Standard deviation of z-scores for all 3 indicators was smaller in 2014 than in 2008 to 2009. Applying original Demographic and Health Survey exclusion criteria, weight for height z-scores were 1.16 in 2014, 10.1% narrower than 2008 to 2009. The percentage of outlying values declined significantly from 2008 to 2009 to 2014 for both height for age and weight for height ( P < .001). Digit preference scores in 2014 improved for both weight ( P = .011) and height ( P < .001) suggesting less rounding of terminal digits. Conclusions: All tests of data quality suggest an improvement in 2014 relative to 2008 to 2009, despite the complexity implied by the larger sample. This improvement corresponds with efforts to enhance training and supervision of anthropometry, suggesting a positive effect of these enhancements.

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