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Nonrespondents

Nonrespondents refer to individuals who do not respond to research studies, surveys, or other data collection efforts.
This group can introduce bias and limit the generalizability of research findings.
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Most cited protocols related to «Nonrespondents»

The NESARC-III target population was the US noninstitutionalized civilian population 18 years or older, including residents of selected group quarters (eg, group homes, workers’ dormitories). As detailed elsewhere,19 we used probability sampling to select respondents randomly. Primary sampling units were individual counties or groups of contiguous counties; secondary sampling units, groups of US Census–defined blocks; and tertiary sampling units, households within the secondary sampling units. Finally, eligible adults within sampled households were randomly selected. We oversampled Hispanic, black, and Asian respondents, and in households with at least 4 eligible individuals who were ethnic or racial minorities, 2 respondents were selected (n = 1661). The total sample size consisted of 36 309 respondents. The screener- and person-level response rates were 72.0% and 84.0%, respectively, yielding a total NESARC-III response rate of 60.1%, comparable to those of most current US national surveys.16 ,20 Data were collected from April 2012 through June 2013 and analyzed in October 2014.
Data were adjusted for oversampling (including selection of 2 persons in selected households) and screener- and person-level nonresponse, then weighted through poststratification analyses to represent the US civilian population based on the American Community Survey 2012.21 Table 1 shows the weighted distribution of the NESARC-III population characteristics. These weighting adjustments were found to compensate adequately for nonresponse. When participants were compared with the total eligible sample, including nonrespondents, no significant differences were found in the percentages of Hispanic, black, or Asian respondents, population density, vacancy rate, proportion of the population in group quarters, or proportion of renters at the segment level. At the individual level, we found no differences in Hispanic ethnicity between respondents and the total eligible sample. Respondents included a slightly higher percentage of men (48.1% vs 46.2%), a greater percentage of those aged 60 to 69 years (13.7% vs 12.6%), and smaller percentages of those aged 40 to 49 (18.1% vs 18.3%) and 30 to 39 (16.7% vs 17.4%) years than in the eligible sample.
Interviewer field methods, detailed elsewhere,19 involved initial structured home study, in-person training, ongoing supervision, and random respondent callbacks to verify data. Oral informed consent was electronically recorded, and respondents received $90.00 for survey participation. Protocols were approved by the institutional review boards of the National Institutes of Health and Westat (the contractor for the NESARC-III).
Publication 2015
Adult Asian Americans Ethics Committees, Research Ethnicity Hispanics Households Interviewers Nonrespondents Racial Minorities Supervision Target Population Workers
Association between IBS and nongenetic risk factors, including risk factors assayed by recall from the DHQ, was tested using logistic regression conditioning on age and sex (Supplementary Note, ‘Nongenetic associations’).
Standard genetic quality control was carried out to remove samples with poor genotype quality and variants with poor genotyping or imputation performance. Only participants of European ancestry were included in the discovery dataset due to the limited number of non-European ancestry participants. GWAS were conducted using a linear mixed model (BOLT-LMM v.2.3.2)53 (link) to control for population stratification and relatedness. Meta-analysis of GWAS summary statistics was carried out using METAL (March 2011 release)54 (link). The UKB GWAS was stratified into DHQ respondents and nonrespondents, with results meta-analyzed to avoid genetic confounding with questionnaire response (Supplementary Fig. 14).
We assigned loci to candidate genes using annotations from FUMA v.1.3.455 (link), as well as from a colocalization analysis using Coloc v.3.2-156 (link) on multi-tissue expression data from the Genotype-Tissue Expression (GTEx) consortium56 (link),57 (link). We calculated SNP heritability and coheritability (rg, genetic correlation) using univariate and bivariate LDSC48 (link) against a range of traits via the LD Hub website33 (link). Other statistical analyses were carried out using R v.3.6.1; any P values were obtained from two-sided tests unless otherwise specified.
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Publication 2021
Europeans Gene Expression Regulation Genetic Loci Genome-Wide Association Study Genotype Mental Recall Metals Nonrespondents Reproduction Tissues
Three authors (L.S.R., M.T., and J.B.S.) independently extracted the following data from each article using a standardized form: study design; geographic location; years of survey; year in school; sample size; average age of participants; number and percentage of male participants; diagnostic or screening method used; outcome definition (ie, specific diagnostic criteria or screening instrument cutoff); and reported prevalence estimates of depression, depressive symptoms, or suicidal ideation. Whether students who screened positive for depression sought psychiatric or other mental health treatment also was extracted. When there were studies involving the same population of students, only the most comprehensive or recent publication was included.
The same 3 authors independently assessed the risk of bias of these nonrandomized studies using a modified version of the Newcastle-Ottawa scale, which assesses sample representativeness and size, comparability between respondents and nonrespondents, ascertainment of depressive or suicidal symptoms, and thoroughness of descriptive statistics reporting (complete details regarding scoring appear in eMethods 2 in the Supplement).17 (link) Studies were judged to be at low risk of bias (≥3 points) or high risk of bias (<3 points). A fourth author (D.A.M.) resolved discrepancies through discussion and adjudication.
Publication 2016
Depressive Symptoms Diagnosis Dietary Supplements Males Mental Health Nonrespondents Student
The following information was independently extracted from each article by 2 trained investigators (D.A.M. and M.A.R.) using a standardized form: study design, geographic location, years of survey, specialty, postgraduate level, sample size, average age of participants, number and percentage of male participants, diagnostic or screening method used, outcome definition (ie, specific diagnostic criteria or screening instrument cutoff), and reported prevalence of depression or depressive symptoms. The most comprehensive publication was used when there were several involving the same population of residents. A modified version of the Newcastle-Ottawa Scale was used to assess the quality of nonrandomized studies included in systematic reviews and meta-analyses.17 (link) This scale assesses quality in several domains: sample representativeness and size, comparability between respondents and nonrespondents, ascertainment of depressive symptoms, and statistical quality (full details regarding scoring are provided in eMethods 2 in the Supplement). Studies were judged to be at low risk of bias (≥3 points) or high risk of bias (<3 points). All discrepancies were resolved by discussion and adjudication of a third reviewer (S.S.).
Publication 2015
Depressive Symptoms Diagnosis Dietary Supplements Males Nonrespondents
For the Multi-State Nursing Care and Patient Safety survey, we used a two-stage sampling design to collect information from registered nurses in California, Pennsylvania, New Jersey, and Florida. Our sampling frame was the state licensure lists for 2006–07. We surveyed nurses by mail at their homes.
Nurses who worked in hospitals were asked to provide the name of their employer, which allowed us to aggregate responses by hospital for the analysis of nurses’ reports and patient satisfaction. The response rate was 36 percent. To test for sample bias, we conducted a random-sample survey of nonrespondents from Pennsylvania and California, received a response rate of 91 percent, and found no response bias pertinent to this report.12 Further details on the sampling approach have been described elsewhere,9 (link) and are available in the Appendix.13 The survey included questions about the nurses’ employment status and, for working nurses, their setting, role, work environment, experience of burnout, and job satisfaction. As in other work,14 (link) we assessed burnout in terms of emotional exhaustion, which is the depletion of one’s emotional and physical resources due to work stress as measured on the nine-item emotional exhaustion subscale of the Maslach Burnout Inventory.6 (link) Burnout is common in human service occupations such as nursing, and it results in nurses’ distancing themselves emotionally and cognitively from their work.6 (link) Nurses were classified as burned out if their score was higher than the published average (27 or higher) for workers in health professions.15 ,16 Overall we measured job satisfaction and nurses’ satisfaction with specific aspects of their jobs—including salaries, benefits, opportunities for advancement, work schedules, independence, and professional status—on a four-point scale from “very satisfied” to “very dissatisfied.” Satisfaction measures were dichotomized so that nurses who reported being either “very dissatisfied” or “a little dissatisfied” were characterized as “dissatisfied.”
Publication 2011
Burnout, Psychological Emotions Health Personnel Homo sapiens Job Satisfaction Nonrespondents Nurses Nursing Care Patient Safety Physical Examination Reading Frames Registered Nurse Satisfaction Workers

Most recents protocols related to «Nonrespondents»

A logistic regression was run comparing respondents and nonrespondents by gender, race, ethnicity, age, income, education, and preexisting mental health and medical conditions. Before running the logistic regression for age (the only continuous variable), the assumption of linearity between age and free-response response was tested. Participants’ ages were divided into quantiles, and logits were plotted by age category. The relationship was monotonic, therefore meeting the assumptions of logistic regression. R was used for all analyses, and ggplot2 within the package tidyverse was used for all figures, except where noted [67 ,68 (link)].
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Publication 2023
Ethnicity Gender Mental Health Nonrespondents
This research used data from the population-based Norwegian Survey of Health and Ageing (NORSE) (Strand et al. 2021 (link)), a study of health and living conditions conducted with a representative sample of the population 60 years old or above in the former Oppland County in Norway. The Norwegian Tax Administration gave permission to draw a random sample from the National Population Register. Three age strata were used: 60–69, 70–79 and 80+ years, with equal numbers drawn from each age group, achieving oversampling of the older age groups. Eligible participants were mailed by regular post a four-page leaflet and invitation letter with description of the study aims, testing procedures, and how data would be handled after the data collection. The leaflet contained ethical clearances and consent procedure, as well as how participants later could withdraw their consent at any time. Those willing to participate either sent a mobile text message or sign up using a pre-paid letter (Strand et al. 2021 (link)). Data were collected during 2017–2019. Out of 5981 invitations, a total of 957 participated. Descriptives of the sample were published in 2021 (Strand et al 2021 (link)). The 817 respondents with a valid response on the outcome variable assessing subjective age are included in the current analysis (14% response rate). Final-year nursing students at the Norwegian University of Science and Technology in Gjøvik, who were specially trained for the data collection, collected the data through standardized face to-face interviews, either at home or in local healthcare clinics or offices. Full population data from Oppland County for 2017 by age, sex, and level of education provided by Statistics Norway were used to create population weights to control for selection bias (Valliant and Dever 2018 ). This strategy provided us with information on the total population, including all nonrespondents, from administrative registries.
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Publication 2023
Age Groups Conditioning, Psychology Face Nonrespondents Students, Nursing
During July 2021‒March 2022, all 4,800 positive COVID-19 test results reported during August 2020‒February 2022 were followed up with confidential electronic surveys sent to each patient at least 28 days after their initial positive result that included questions about long COVID. Those data were merged with the COVID-19 case investigation data.
For the long COVID follow-up survey data collection, we determined that 143 persons had >2 COVID-19 diagnoses during August 2020‒February 2022; those persons were only included once in the long COVID follow-up data collection, resulting in a total of 4,657 persons who were COVID-19 positive during the study period. The follow-up survey had a response rate of 32% (1,482/4,657). We observed major differences in age, university affiliation, underlying conditions, and vaccination status at the time of test between follow-up survey respondents and nonrespondents (Appendix 1). A total of 11 respondents completed the follow-up survey twice but were only counted once for the response rate. Not all responses were usable in the final analysis: 141 did not have a complete initial case investigation, and 3 did not provide responses to the survey questions about long COVID, removing them from the final sample. Thus, the final analytic sample consisted of 1,338 respondents (Table 1).
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Publication 2023
COVID 19 Diagnosis Nonrespondents Patients Post-Acute COVID-19 Syndrome Vaccination

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Publication 2023
Antiviral Agents Disease, Chronic Ethics Committees, Research Gastrointestinal Diseases Hepatitis C, Chronic Liver Liver Diseases Nonrespondents Patients Physician Assistant Physicians Practitioner, Nurse Specialists
In addition to cross-sectional surveys, participants were surveyed on a daily (before August 2021) or weekly (after August 2021) basis regarding symptoms including the following: scratchy throat, painful sore throat, cough, runny nose, fever or chills, temperature >100.4°F or 38.0°C, muscle aches, nausea, vomiting, diarrhea, shortness of breath, unable to taste or smell, and red or painful eyes. We did not use these surveys to classify people as having Long COVID. We categorized the surveys for each individual by time period relative to date of SARS-CoV-2 positive test into 30–60 days preinfection, 0–30 days preinfection, 0–30 days postinfection, 30–90 days postinfection, 90–180 days postinfection, 180–365 days postinfection, and >365 days postinfection. For each period, we summed the proportion of respondents averaging ≥1 symptom and the average number of symptoms reported by Long COVID status among respondents to the cross-sectional survey and among nonrespondents infected with SARS-CoV-2, and individuals without infection (averaged over all time points since March 2020).
Publication 2023
Chills Cough Diarrhea Dyspnea Fever Infection Myalgia Nausea Nonrespondents Pain Pain, Eye Pharynx Post-Acute COVID-19 Syndrome Rhinorrhea SARS-CoV-2 Sense of Smell Sore Throat Taste

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More about "Nonrespondents"

Nonrespondents, also known as non-responders, refer to individuals who do not participate in research studies, surveys, or data collection efforts.
This group can introduce bias and limit the generalizability of research findings, which is a common challenge faced by researchers and statisticians.
To address this issue, researchers often utilize statistical software like SAS 9.4, Stata 14, SPSS 26, and Qualtrics to analyze and manage their data.
Nonrespondents can arise for various reasons, such as lack of interest, inconvenience, or inability to participate.
This phenomenon is particularly prevalent in online surveys, where response rates can be lower compared to traditional paper-based methods.
Identifying and mitigating the impact of nonrespondents is crucial to ensure the validity and reliability of research outcomes.
PubCompare.ai's AI-driven platform can help researchers address the challenges posed by nonrespondents.
By leveraging intelligent comparisons, the platform can assist in locating the best protocols from literature, preprints, and patents, enhancing the accuracy and reliability of the research process.
This workflow optimization can be further complemented by the use of statistical software like SPSS v25, Stata SE version 16, and SAS version 9.4.
Incorporating strategies to identify and handle nonrespondents, such as implementing effective data collection methods, using appropriate statistical techniques, and leveraging AI-powered tools like PubCompare.ai, can lead to improved research accuracy, enhanced generalizability, and more reliable findings.
This, in turn, can contribute to the advancement of scientific knowledge and the development of more effective interventions or policies.