Comparing Grids With Vertical and Horizontal Item-by-Item Formats for PCs and Smartphones

2017 ◽  
Vol 36 (3) ◽  
pp. 349-368 ◽  
Author(s):  
Melanie Revilla ◽  
Mick P. Couper

Much research has been done comparing grids and item-by-item formats. However, the results are mixed, and more research is needed especially when a significant proportion of respondents answer using smartphones. In this study, we implemented an experiment with seven groups ( n = 1,476), varying the device used (PC or smartphone), the presentation of the questions (grids, item-by-item vertical, item-by-item horizontal), and, in the case of smartphones only, the visibility of the “next” button (always visible or only visible at the end of the page, after scrolling down). The survey was conducted by the Netquest online fieldwork company in Spain in 2016. We examined several outcomes for three sets of questions, which are related to respondent behavior (completion time, lost focus, answer changes, and screen orientation) and data quality (item missing data, nonsubstantive responses, instructional manipulation check failure, and nondifferentiation). The most striking difference found is for the placement of the next button in the smartphone item-by-item conditions: When the button is always visible, item missing data are substantially higher.

2021 ◽  
Vol 63 (4) ◽  
pp. 408-415
Author(s):  
Maria Rubio Juan ◽  
Melanie Revilla

The presence of satisficers among survey respondents threatens survey data quality. To identify such respondents, Oppenheimer et al. developed the Instructional Manipulation Check (IMC), which has been used as a tool to exclude observations from the analyses. However, this practice has raised concerns regarding its effects on the external validity and the substantive conclusions of studies excluding respondents who fail an IMC. Thus, more research on the differences between respondents who pass versus fail an IMC regarding sociodemographic and attitudinal variables is needed. This study compares respondents who passed versus failed an IMC both for descriptive and causal analyses based on structural equation modeling (SEM) using data from an online survey implemented in Spain in 2019. These data were analyzed by Rubio Juan and Revilla without taking into account the results of the IMC. We find that those who passed the IMC do differ significantly from those who failed for two sociodemographic and five attitudinal variables, out of 18 variables compared. Moreover, in terms of substantive conclusions, differences between those who passed and failed the IMC vary depending on the specific variables under study.


10.2196/15588 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e15588 ◽  
Author(s):  
Jill Meirte ◽  
Nick Hellemans ◽  
Mieke Anthonissen ◽  
Lenie Denteneer ◽  
Koen Maertens ◽  
...  

Background Patient-reported outcome measures (PROMs) are important in clinical practice and research. The growth of electronic health technologies provides unprecedented opportunities to systematically collect information via PROMs. Objective The aim of this study was to provide an objective and comprehensive overview of the benefits, barriers, and disadvantages of the digital collection of qualitative electronic patient-reported outcome measures (ePROMs). Methods We performed a systematic review of articles retrieved from PubMED and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed during all stages. The search strategy yielded a total of 2333 records, from which 32 met the predefined inclusion and exclusion criteria. The relevant ePROM-related information was extracted from each study. Results Results were clustered as benefits and disadvantages. Reported benefits of ePROMs were greater patient preference and acceptability, lower costs, similar or faster completion time, higher data quality and response rates, and facilitated symptom management and patient-clinician communication. Tablets were the most used ePROM modality (14/32, 44%), and, as a platform, Web-based systems were used the most (26/32, 81%). Potential disadvantages of ePROMs include privacy protection, a possible large initial financial investment, and exclusion of certain populations or the “digital divide.” Conclusions In conclusion, ePROMs offer many advantages over paper-based collection of patient-reported outcomes. Overall, ePROMs are preferred over paper-based methods, improve data quality, result in similar or faster completion time, decrease costs, and facilitate clinical decision making and symptom management. Disadvantages regarding ePROMs have been outlined, and suggestions are provided to overcome the barriers. We provide a path forward for researchers and clinicians interested in implementing ePROMs. Trial Registration PROSPERO CRD42018094795; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=94795


2018 ◽  
Vol 7 (4) ◽  
pp. 572-588
Author(s):  
Hanyu Sun ◽  
Roger Tourangeau ◽  
Stanley Presser

Abstract It is well established that taking part in earlier rounds of a panel survey can affect how respondents answer questions in later rounds. It is less clear, however, whether panel participation affects the quality of the data that respondents provide. We examined two panels to investigate how participation affects several indicators of data quality—including straightlining, item missing data, scale reliabilities, and differences in item functioning over time—and to test the hypotheses that it is less educated and older respondents who mainly account for any panel effects. The two panels were the GfK Knowledge Panel, in which some respondents completed up to four rounds measuring their attitudes toward terrorism and ways to counter terrorism, and the General Social Survey (GSS), in which respondents completed up to three rounds with an omnibus set of questions. The two panels differ sharply in terms of response rates and the level of prior survey experience of the respondents. Most of our comparisons are within-respondent, comparing the answers panel members gave in earlier rounds with those they gave in later rounds, but we also confirm the main results using between-subject comparisons. We find little evidence that respondents gave either better or worse data over time in either panel and little support for either the education or age hypotheses.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Tian ◽  
Bing Yu ◽  
Dan Yu ◽  
Shilong Ma

A large number of scientific researches and industrial applications commonly suffer from missing data. Some inappropriate techniques of missing value treatment compromise data quality, which detrimentally influences the knowledge discovery. In this paper, we propose a missing data completion method named CBGMI. Firstly, it separates the nonmissing data instances into several clusters by excluding the missing-valued entries. Then, it utilizes the entropy of the proximal category for each incomplete instance in terms of the similarity metric based on gray relational analysis. Experiments on UCI datasets and aerospace datasets demonstrate that the superiority of our algorithm to other approaches on validity.


Author(s):  
Shawn Turner ◽  
Luke Albert ◽  
Byron Gajewski ◽  
William Eisele

Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.


Author(s):  
Hatice Uenal ◽  
David Hampel

Registries are indispensable in medical studies and provide the basis for reliable study results for research questions. Depending on the purpose of use, a high quality of data is a prerequisite. However, with increasing registry quality, costs also increase accordingly. Considering these time and cost factors, this work is an attempt to estimate the cost advantages of applying statistical tools to existing registry data, including quality evaluation. Results for quality analysis showed that there are unquestionable savings of millions in study costs by reducing the time horizon and saving on average € 523,126 for every reduced year. Replacing additionally the over 25 % missing data in some variables, data quality was immensely improved. To conclude, our findings showed dearly the importance of data quality and statistical input in avoiding biased conclusions due to incomplete data.


2018 ◽  
Author(s):  
Robert Goodspeed ◽  
Xiang Yan ◽  
Jean Hardy ◽  
V.G. Vinod Vydiswaran ◽  
Veronica J. Berrocal ◽  
...  

BACKGROUND Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or smartphones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. OBJECTIVE In this study, we compared the data quality of location information collected from two mobile devices which determine location in different ways — a GPS watch and a smartphone with Google’s Location History feature enabled. METHODS Twenty-one chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A smartphone-based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at four random times throughout each day. Participants also took also part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent phone and watch trace data to compare the devices’ trace data to their memory of their activities on those days. Trace data from the devices were compared on the basis of: (1) missing data days; (2) reasons for missing data; (3) distance between the route data collected for matching day and the associated EMA survey locations; and (4) activity space total area and density surfaces. RESULTS The watch resulted in a much higher proportion of missing data days, with missing data explained by technical differences between the devices, as well as participant behaviors. The phone was significantly more accurate in detecting home locations, and marginally significantly more accurate for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation. CONCLUSIONS The most suitable mobile device for location based health research depends on the particular study objectives. Further, data generated from mobile devices, such as GPS phone and smart watches, requires careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the phone’s demonstrated greater precision in recording indoor activities. CLINICALTRIAL N/A


Author(s):  
A. Vladimirova ◽  

With the emergence of new resources and methods, issues related to data quality are getting even more crucial. As for the recent rise of historical network research, no doubts, that missing data problems attract the special attention of scholars. In this paper mirror statistics on exports and imports of the Southeast Asian countries are used as a case to demonstrate the importance of properly conducted diagnostics.


Trials ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jessica E. Lockery ◽  
◽  
Taya A. Collyer ◽  
Christopher M. Reid ◽  
Michael E. Ernst ◽  
...  

Abstract Background Large-scale studies risk generating inaccurate and missing data due to the complexity of data collection. Technology has the potential to improve data quality by providing operational support to data collectors. However, this potential is under-explored in community-based trials. The Aspirin in reducing events in the elderly (ASPREE) trial developed a data suite that was specifically designed to support data collectors: the ASPREE Web Accessible Relational Database (AWARD). This paper describes AWARD and the impact of system design on data quality. Methods AWARD’s operational requirements, conceptual design, key challenges and design solutions for data quality are presented. Impact of design features is assessed through comparison of baseline data collected prior to implementation of key functionality (n = 1000) with data collected post implementation (n = 18,114). Overall data quality is assessed according to data category. Results At baseline, implementation of user-driven functionality reduced staff error (from 0.3% to 0.01%), out-of-range data entry (from 0.14% to 0.04%) and protocol deviations (from 0.4% to 0.08%). In the longitudinal data set, which contained more than 39 million data values collected within AWARD, 96.6% of data values were entered within specified query range or found to be accurate upon querying. The remaining data were missing (3.4%). Participant non-attendance at scheduled study activity was the most common cause of missing data. Costs associated with cleaning data in ASPREE were lower than expected compared with reports from other trials. Conclusions Clinical trials undertake complex operational activity in order to collect data, but technology rarely provides sufficient support. We find the AWARD suite provides proof of principle that designing technology to support data collectors can mitigate known causes of poor data quality and produce higher-quality data. Health information technology (IT) products that support the conduct of scheduled activity in addition to traditional data entry will enhance community-based clinical trials. A standardised framework for reporting data quality would aid comparisons across clinical trials. Trial registration International Standard Randomized Controlled Trial Number Register, ISRCTN83772183. Registered on 3 March 2005.


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