scholarly journals Implementation of the forced answering option within online surveys: Do higher item response rates come at the expense of participation and answer quality?

Psihologija ◽  
2015 ◽  
Vol 48 (4) ◽  
pp. 311-326 ◽  
Author(s):  
Jean Décieux ◽  
Alexandra Mergener ◽  
Kristina Neufang ◽  
Philipp Sischka
Keyword(s):  
Data Quality ◽  
Data Gathering ◽  
Dropout Rates ◽  
Online Surveys ◽  
Online Data ◽  
Data Set ◽  
Unit Nonresponse ◽  
Frequent Use ◽  
Answer Quality

Online surveys have become a popular method for data gathering for many reasons, including low costs and the ability to collect data rapidly. However, online data collection is often conducted without adequate attention to implementation details. One example is the frequent use of the forced answering option, which forces the respondent to answer each question in order to proceed through the questionnaire. The avoidance of missing data is often the idea behind the use of the forced answering option. However, we suggest that the costs of a reactance effect in terms of quality reduction and unit nonresponse may be high because respondents typically have plausible reasons for not answering questions. The objective of the study reported in this paper was to test the influence of forced answering on dropout rates and data quality. The results show that requiring participants answer every question increases dropout rates and decreases quality of answers. Our findings suggest that the desire for a complete data set has to be balanced against the consequences of reduced data quality.

Data Quality Associated with Big Data Processing: A Survey

2021 ◽  
Vol 23 (06) ◽  
pp. 1011-1018
Author(s):  
Aishrith P Rao ◽  
◽  
Raghavendra J C ◽  
Dr. Sowmyarani C N ◽  
Dr. Padmashree T ◽  
...  
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Data Gathering ◽  
Cost Effective ◽  
Data Repository ◽  
Critical Approach ◽  
Critical Aspect ◽  
Quality Of Data ◽  
Data Group

With the advancement of technology and the large volume of data produced, processed, and stored, it is becoming increasingly important to maintain the quality of data in a cost-effective and productive manner. The most important aspects of Big Data (BD) are storage, processing, privacy, and analytics. The Big Data group has identified quality as a critical aspect of its maturity. Nonetheless, it is a critical approach that should be adopted early in the lifecycle and gradually extended to other primary processes. Companies are very reliant and drive profits from the huge amounts of data they collect. When its consistency deteriorates, the ramifications are uncertain and may result in completely undesirable conclusions. In the sense of BD, determining data quality is difficult, but it is essential that we uphold the data quality before we can proceed with any analytics. We investigate data quality during the stages of data gathering, preprocessing, data repository, and evaluation/analysis of BD processing in this paper. The related solutions are also suggested based on the elaboration and review of the proposed problems.

What Prevents You to Plug in to Online Surveys?

2022 ◽  
pp. 131-148
Author(s):  
Burcu Karabulut Coşkun ◽  
Ezgi Mor Dirlik
Keyword(s):  
Artificial Intelligence ◽  
Online Survey ◽  
Response Rate ◽  
Data Gathering ◽  
Theoretical Framework ◽  
Research Topic ◽  
Online Surveys ◽  
Online Data ◽  
Factors Affecting ◽  
Response Behaviors

In today's world, which has been administered by computers and artificial intelligence in many areas, online data gathering has become an inevitable way of collecting data. Many researchers have preferred online surveying, considering the advantages of this method over the classical ones. Hence, the factors that may affect the response rate of online surveying have become a prominent research topic. In line with the popularity of this issue, the purpose of this chapter was to clarify the concept of online surveys; give information about their types, advantages, and usage; and investigate the factors that affect the participants' response behaviors. Besides the discussions on the theoretical framework of online surveying, an online survey aiming to determine the factors affecting the participation in online surveying was administered to a group of people to investigate the response behaviors thoroughly. The findings revealed that rs might affect ants' response behaviors to online surveys in various ways radically.

scholarly journals Effect of Closed Loop Medication Administration on Drug Returns in Inpatient Facilities

2020 ◽  
Vol 8 (12) ◽  
Author(s):  
Eceberil Ozturk ◽  
Ilker Kose ◽  
Beytiye Elmas
Keyword(s):  
Patient Safety ◽  
Data Quality ◽  
Closed Loop ◽  
Medication Management ◽  
Medication Administration ◽  
Data Set ◽  
Return Rates ◽  
Conventional Drug ◽  
Turkish State

Medication management in inpatient facilities is a crucial issue for patient safety. In inpatient conventional drug management, a common problem relates to drugs prescribed and delivered to patients being returned to the pharmacy without reason for the return. When reasons are given, they are not often regularly and correctly recorded. Closed Loop Medication Administration (CLMA) protects patient safety by managing all processes, including intake of the drug to the hospital's stock, administering the drug to the patient, and disposal of unused drugs using technology. CLMA is known to contribute positively to patient safety. However, there is no study on the effect of CLMA on the return of non-administered drugs. This study aims to analyze the effect of CLMA on drug return rates and investigate the data quality of reasons for drug returns. The research was carried out in three inpatient clinics of a Turkish state hospital (Bolu İzzet Baysal Public Hospital) where the CLMA was implemented in May of 2017. The data set obtained from the hospital information system (HIS) is anonymized. The study showed a significant increase in drug return rates after CLMA, and the data quality of drug return reasons is also significantly improved. These results show that CLMA contributes positively to drug return rates and the data quality of drug return reason records.

scholarly journals Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weijin Jiang ◽  
Junpeng Chen ◽  
Xiaoliang Liu ◽  
Yuehua Liu ◽  
Sijian Lv
Keyword(s):  
Data Quality ◽  
Distance Constraint ◽  
Mobile Crowd Sensing ◽  
Constraint Factor ◽  
Data Set ◽  
Selection Scheme ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Mobile Crowd

With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.

scholarly journals Importance of Continued Data Quality Assessment of Syndromic Production Data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Sophia Crossen
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Production Data ◽  
Quality Of Data ◽  
Data Set ◽  
Quality Degradation ◽  
Periodic Data ◽  
Data Quality Assessment

ObjectiveTo explore the quality of data submitted once a facility is movedinto an ongoing submission status and address the importance ofcontinuing data quality assessments.IntroductionOnce a facility meets data quality standards and is approved forproduction, an assumption is made that the quality of data receivedremains at the same level. When looking at production data qualityreports from various states generated using a SAS data qualityprogram, a need for production data quality assessment was identified.By implementing a periodic data quality update on all productionfacilities, data quality has improved for production data as a whole andfor individual facility data. Through this activity several root causesof data quality degradation have been identified, allowing processesto be implemented in order to mitigate impact on data quality.MethodsMany jurisdictions work with facilities during the onboardingprocess to improve data quality. Once a certain level of data qualityis achieved, the facility is moved into production. At this point thejurisdiction generally assumes that the quality of the data beingsubmitted will remain fairly constant. To check this assumption inKansas, a SAS Production Report program was developed specificallyto look at production data quality.A legacy data set is downloaded from BioSense production serversby Earliest Date in order to capture all records for visits which occurredwithin a specified time frame. This data set is then run through a SASdata quality program which checks specific fields for completenessand validity and prints a report on counts and percentages of null andinvalid values, outdated records, and timeliness of record submission,as well as examples of records from visits containing these errors.A report is created for the state as a whole, each facility, EHR vendor,and HIE sending data to the production servers, with examplesprovided only by facility. The facility, vendor, and HIE reportsinclude state percentages of errors for comparison.The Production Report was initially run on Kansas data for thefirst quarter of 2016 followed by consultations with facilities on thefindings. Monthly checks were made of data quality before and afterfacilities implemented changes. An examination of Kansas’ resultsshowed a marked decrease in data quality for many facilities. Everyfacility had at least one area in need of improvement.The data quality reports and examples were sent to every facilitysending production data during the first quarter attached to an emailrequesting a 30-60 minute call with each to go over the report. Thiscall was deemed crucial to the process since it had been over a year,and in a few cases over two years, since some of the facilities hadlooked at data quality and would need a review of the findings andall requirements, new and old. Ultimately, over half of all productionfacilities scheduled a follow-up call.While some facilities expressed some degree of trepidation, mostfacilities were open to revisiting data quality and to making requestedimprovements. Reasons for data quality degradation included updatesto EHR products, change of EHR product, work flow issues, engineupdates, new requirements, and personnel turnover.A request was made of other jurisdictions (including Arizona,Nevada, and Illinois) to look at their production data using the sameprogram and compare quality. Data was pulled for at least one weekof July 2016 by Earliest Date.ResultsMonthly reports have been run on Kansas Production data bothbefore and after the consultation meetings which indicate a markedimprovement in both completeness of required fields and validityof values in those fields. Data for these monthly reports was againselected by Earliest Date.ConclusionsIn order to ensure production data continues to be of value forsyndromic surveillance purposes, periodic data quality assessmentsshould continue after a facility reaches ongoing submission status.Alterations in process include a review of production data at leasttwice per year with a follow up data review one month later to confirmadjustments have been correctly implemented.

scholarly journals Validity as a Measure of Data Quality in Internet of Things Systems

2021 ◽  
Author(s):  
Rishabh Deo Pandey ◽  
Itu Snigdh
Keyword(s):  
Data Quality ◽  
Data Sets ◽  
Major Focus ◽  
Quality Of Data ◽  
Data Set ◽  
Environment Analysis ◽  
Aggregated Data ◽  
Pervasive Environment ◽  
Validation Parameters

Abstract Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis.

Global indicators of X-ray data quality

2001 ◽  
Vol 34 (2) ◽  
pp. 130-135 ◽  
Author(s):  
Manfred S. Weiss
Keyword(s):  
Data Quality ◽  
Diffraction Data ◽  
Optical Resolution ◽  
Data Set ◽  
X Ray ◽  
Global Indicators ◽  
Nominal Resolution

Global indicators of the quality of diffraction data are presented and discussed, and are evaluated in terms of their performance with respect to various tasks. Based on the results obtained, it is suggested that some of the conventional indicators still in use in the crystallographic community should be abandoned, such as the nominal resolutiondminor the mergingRfactorRmerge, and replaced by more objective and more meaningful numbers, such as the effective optical resolutiondeff,optand the redundancy-independent mergingRfactorRr.i.m.. Furthermore, it is recommended that the precision-indicating mergingRfactorRp.i.m.should be reported with every diffraction data set published, because it describes the precision of the averaged measurements, which are the quantities normally used in crystallography as observables.

scholarly journals An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Ali S Afshar ◽  
Yijun Li ◽  
Zixu Chen ◽  
Yuxuan Chen ◽  
Jae Hun Lee ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intensive Care Unit ◽  
Heart Rate ◽  
Time Series ◽  
Intensive Care ◽  
Data Quality ◽  
Vital Signs ◽  
Data Set ◽  
Mimic Iii

Abstract Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.

scholarly journals NUCOME: A comprehensive database of nucleosome organization referenced landscapes in mammalian genomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaolan Chen ◽  
Hui Yang ◽  
Guifen Liu ◽  
Yong Zhang
Keyword(s):  
Data Quality ◽  
Cell Types ◽  
Data Sets ◽  
Data Quality Control ◽  
Data Set ◽  
Redundant Data ◽  
Nucleosome Organization ◽  
Public Data ◽  
Mammalian Genomes

Abstract Background Nucleosome organization is involved in many regulatory activities in various organisms. However, studies integrating nucleosome organization in mammalian genomes are very limited mainly due to the lack of comprehensive data quality control (QC) assessment and uneven data quality of public data sets. Results The NUCOME is a database focused on filtering qualified nucleosome organization referenced landscapes covering various cell types in human and mouse based on QC metrics. The filtering strategy guarantees the quality of nucleosome organization referenced landscapes and exempts users from redundant data set selection and processing. The NUCOME database provides standardized, qualified data source and informative nucleosome organization features at a whole-genome scale and on the level of individual loci. Conclusions The NUCOME provides valuable data resources for integrative analyses focus on nucleosome organization. The NUCOME is freely available at http://compbio-zhanglab.org/NUCOME.

scholarly journals distance based measure of data quality

2014 ◽  
Vol 11 (2) ◽  
Author(s):  
Pavol Král’ ◽  
Lukáš Sobíšek ◽  
Mária Stachová
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Weighting Function ◽  
Training Data ◽  
Data Sets ◽  
Belief Functions ◽  
Data Set ◽  
Additional Information ◽  
The Ideal

Data quality can be seen as a very important factor for the validity of information extracted from data sets using statistical or data mining procedures. In the paper we propose a description of data quality allowing us to characterize data quality of the whole data set, as well as data quality of particular variables and individual cases. On the basis of the proposed description, we define a distance based measure of data quality for individual cases as a distance of the cases from the ideal one. Such a measure can be used as additional information for preparation of a training data set, fitting models, decision making based on results of analyses etc. It can be utilized in different ways ranging from a simple weighting function to belief functions.

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