scholarly journals Estimating measurement error in child language assessments administered by daycare educators in large scale intervention studies

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0255414
Author(s):  
E. F. Haghish ◽  
Werner Vach ◽  
Anders Højen ◽  
Dorthe Bleses
Keyword(s):  
Measurement Error ◽  
Large Scale ◽  
Intervention Studies ◽  
Science Studies ◽  
Linear Models ◽  
Screening Program ◽  
Child Language ◽  
Additional Measurement ◽  
Language Assessments ◽  
Using Data

Measurement error is a ubiquitous element of social science studies. In large-scale effectiveness intervention studies on child language, administration of the assessment of language and preliteracy outcomes by speech and language pathologists is costly in money and human resources. Alternatively, daycare educators can administer the assessment, which preserves considerable resources but may increase the measurement error. Using data from two nationwide child language intervention studies in Denmark, this article evaluates daycare educators’ measurement error when administering a test of language and preliteracy skills of 3 to 5 year old children that in part is used in a national screening program. Since children were randomly assigned to educators, hierarchical linear models can estimate the amount of additional measurement error caused by educators’ language assessments. The result shows that the amount of additional measurement error varied between different language subscales, ranging from 4% to 19%, which can be compensated for by increasing the sample size by the latter percentage. The benefits and risks of having daycare educators administer language assessments are discussed.

SOVIET SCIENTIFIC PERSONAL REPRESSIVE STATE APPARATUS AS AN OBJECT OF HISTORICAL SCIENCE OF SCIENCE STUDIES

2020 ◽  
pp. 27-35
Author(s):  
Alexander Allakhverdyan
Keyword(s):  
Science Policy ◽  
Large Scale ◽  
Science Studies ◽  
Soviet Science ◽  
State Repression ◽  
Developed Country ◽  
Systemic Analysis ◽  
State Apparatus ◽  
Dramatic Practice ◽  
Main Components

Numerous studies by Russian scientists and historians of science are devoted to the state science policy in the USSR and its well-known achievements, but not enough attention is paid to the negative, socially repressed aspects of the Soviet science policy. Repressions became one of the main components of the state's scientific and personnel policy in the Stalinist era. The systemic analysis of the development of Soviet science declared in the scientific literature, limited only by its indisputably outstanding achievements, without under-standing the origins, causes and mechanisms of the repressed state apparatus that operated in the same period, sharply reduces the overall picture of the reliability of the study of Soviet science. The purpose of the study is to comprehend the diverse and dramatic practice of state repression in the system of Soviet science, because in the world history of science no other developed country has experienced such large-scale and tragic events in the functioning of the scientific society.

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

Integration of statistical and administrative agricultural data from Namibia

2021 ◽  
pp. 1-22
Author(s):  
Emily Berg ◽  
Johgho Im ◽  
Zhengyuan Zhu ◽  
Colin Lewis-Beck ◽  
Jie Li
Keyword(s):  
Measurement Error ◽  
Data Collection ◽  
Administrative Data ◽  
Statistical Data ◽  
Data Sources ◽  
Extension Programs ◽  
Multiple Data ◽  
Administrative Agencies ◽  
Crop Area ◽  
Using Data

Statistical and administrative agencies often collect information on related parameters. Discrepancies between estimates from distinct data sources can arise due to differences in definitions, reference periods, and data collection protocols. Integrating statistical data with administrative data is appealing for saving data collection costs, reducing respondent burden, and improving the coherence of estimates produced by statistical and administrative agencies. Model based techniques, such as small area estimation and measurement error models, for combining multiple data sources have benefits of transparency, reproducibility, and the ability to provide an estimated uncertainty. Issues associated with integrating statistical data with administrative data are discussed in the context of data from Namibia. The national statistical agency in Namibia produces estimates of crop area using data from probability samples. Simultaneously, the Namibia Ministry of Agriculture, Water, and Forestry obtains crop area estimates through extension programs. We illustrate the use of a structural measurement error model for the purpose of synthesizing the administrative and survey data to form a unified estimate of crop area. Limitations on the available data preclude us from conducting a genuine, thorough application. Nonetheless, our illustration of methodology holds potential use for a general practitioner.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Use of Antiplatelet Agents Decreases the Positive Predictive Value of Fecal Immunochemical Tests for Colorectal Cancer but Does Not Affect Their Sensitivity

2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Yoonsuk Jung ◽  
Eui Im ◽  
Jinhee Lee ◽  
Hyeah Lee ◽  
Changmo Moon
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Screening Program ◽  
Antiplatelet Agents ◽  
Population Based ◽  
Small Sample ◽  
Antithrombotic Agents ◽  
Predictive Values ◽  
Prescription Rates ◽  
Small Sample Sizes

Previous studies have evaluated the effects of antithrombotic agents on the performance of fecal immunochemical tests (FITs) for the detection of colorectal cancer (CRC), but the results were inconsistent and based on small sample sizes. We studied this topic using a large-scale population-based database. Using the Korean National Cancer Screening Program Database, we compared the performance of FITs for CRC detection between users and non-users of antiplatelet agents and warfarin. Non-users were matched according to age and sex. Among 5,426,469 eligible participants, 768,733 used antiplatelet agents (mono/dual/triple therapy, n = 701,683/63,211/3839), and 19,569 used warfarin, while 4,638,167 were non-users. Among antiplatelet agents, aspirin, clopidogrel, and cilostazol ranked first, second, and third, respectively, in terms of prescription rates. Users of antiplatelet agents (3.62% vs. 4.45%; relative risk (RR): 0.83; 95% confidence interval (CI): 0.78–0.88), aspirin (3.66% vs. 4.13%; RR: 0.90; 95% CI: 0.83–0.97), and clopidogrel (3.48% vs. 4.88%; RR: 0.72; 95% CI: 0.61–0.86) had lower positive predictive values (PPVs) for CRC detection than non-users. However, there were no significant differences in PPV between cilostazol vs. non-users and warfarin users vs. non-users. For PPV, the RR (users vs. non-users) for antiplatelet monotherapy was 0.86, while the RRs for dual and triple antiplatelet therapies (excluding cilostazol) were 0.67 and 0.22, respectively. For all antithrombotic agents, the sensitivity for CRC detection was not different between users and non-users. Use of antiplatelet agents, except cilostazol, may increase the false positives without improving the sensitivity of FITs for CRC detection.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals The Relationships Between Serum DHEA-S and AMH Levels in Infertile Women: A Retrospective Cross-Sectional Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1211
Author(s):  
Li-Te Lin ◽  
Kuan-Hao Tsui
Keyword(s):  
Generalized Linear Models ◽  
Large Scale ◽  
Linear Models ◽  
Positive Association ◽  
Cross Sectional Study ◽  
Dehydroepiandrosterone Sulphate ◽  
Sectional Study ◽  
Cross Sectional ◽  
Infertile Women ◽  
The Relationship

The relationship between serum dehydroepiandrosterone sulphate (DHEA-S) and anti-Mullerian hormone (AMH) levels has not been fully established. Therefore, we performed a large-scale cross-sectional study to investigate the association between serum DHEA-S and AMH levels. The study included a total of 2155 infertile women aged 20 to 46 years who were divided into four quartile groups (Q1 to Q4) based on serum DHEA-S levels. We found that there was a weak positive association between serum DHEA-S and AMH levels in infertile women (r = 0.190, p < 0.001). After adjusting for potential confounders, serum DHEA-S levels positively correlated with serum AMH levels in infertile women (β = 0.103, p < 0.001). Infertile women in the highest DHEA-S quartile category (Q4) showed significantly higher serum AMH levels (p < 0.001) compared with women in the lowest DHEA-S quartile category (Q1). The serum AMH levels significantly increased across increasing DHEA-S quartile categories in infertile women (p = 0.014) using generalized linear models after adjustment for potential confounders. Our data show that serum DHEA-S levels are positively associated with serum AMH levels.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

Developing individualized feedback for listening assessment: Combining standard setting and cognitive diagnostic assessment approaches

2021 ◽  
pp. 026553222199547
Author(s):  
Shangchao Min ◽  
Lianzhen He
Keyword(s):  
Large Scale ◽  
Standard Setting ◽  
Diagnostic Assessment ◽  
Efl Learners ◽  
Cognitive Diagnostic Assessment ◽  
Item Level ◽  
Language Assessments ◽  
Individualized Feedback ◽  
Proficiency Classifications ◽  
Acceptable Reliability

In this study, we present the development of individualized feedback for a large-scale listening assessment by combining standard setting and cognitive diagnostic assessment (CDA) approaches. We used the performance data from 3358 students’ item-level responses to a field test of a national EFL test primarily intended for tertiary-level EFL learners. The results showed that proficiency classifications and subskill mastery classifications were generally of acceptable reliability, and the two kinds of classifications were in alignment with each other at individual and group levels. The outcome of the study is a set of descriptors that describe each test taker’s ability to understand certain level of oral texts and his or her cognitive performance. The current study, by illustrating the feasibility of combining standard setting and CDA approaches to produce individualized feedback, contributes to the enhancement of score reporting and addresses the long-standing criticism that large-scale language assessments fail to provide individualized feedback to link assessment with instruction.

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