scholarly journals Quality Data Essential for Modeling Water Cycles Effectively

2021 ◽  
Author(s):  
John Wu
Keyword(s):  
Quality Data ◽  
Water Cycles

Evidence-Based Practice & Practice-Based Evidence Applied to Adult, Medical Speech-Language Pathology

2013 ◽  
Vol 18 (1) ◽  
pp. 14-26 ◽  
Author(s):  
Rik Lemoncello ◽  
Bryan Ness
Keyword(s):  
Evidence Based Practice ◽  
Single Case ◽  
Quality Data ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Clinical Practices ◽  
High Quality ◽  
Randomized Controlled Clinical Trials ◽  
Relative Paucity ◽  
Language Pathology

In this paper, we review concepts of evidence-based practice (EBP), and provide a discussion of the current limitations of EBP in terms of a relative paucity of efficacy evidence and the limitations of applying findings from randomized controlled clinical trials to individual clinical decisions. We will offer a complementary model of practice-based evidence (PBE) to encourage clinical scientists to design, implement, and evaluate our own clinical practices with high-quality evidence. We will describe two models for conducting PBE: the multiple baseline single-case experimental design and a clinical case study enhanced with generalization and control data probes. Gathering, analyzing, and sharing high-quality data can offer additional support through PBE to support EBP in speech-language pathology. It is our hope that these EBP and PBE strategies will empower clinical scientists to persevere in the quest for best practices.

Reliability of MTurk Data From Masters and Workers

2020 ◽  
Vol 41 (1) ◽  
pp. 30-36
Author(s):  
Steven V. Rouse
Keyword(s):  
Cognitive Abilities ◽  
Online Survey ◽  
Quality Data ◽  
Standard Samples ◽  
Online Data ◽  
Approval Ratings ◽  
Cognitive Abilities Test ◽  
Elite Status ◽  
Master Status ◽  
Amazon's Mechanical Turk

Abstract. Previous research has supported the use of Amazon’s Mechanical Turk (MTurk) for online data collection in individual differences research. Although MTurk Masters have reached an elite status because of strong approval ratings on previous tasks (and therefore gain higher payment for their work) no research has empirically examined whether researchers actually obtain higher quality data when they require that their MTurk Workers have Master status. In two different online survey studies (one using a personality test and one using a cognitive abilities test), the psychometric reliability of MTurk data was compared between a sample that required a Master qualification type and a sample that placed no status-level qualification requirement. In both studies, the Master samples failed to outperform the standard samples.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

When belief and evidence conflict: Factors that mitigate consumers' selective use of price-quality data

2004 ◽  
Author(s):  
Tracy Meyer ◽  
James Kellaris ◽  
Frank Kardes ◽  
Maria L. Cronley ◽  
Steven S. Posavac
Keyword(s):  
Quality Data

PENGARUH KUALITAS PELAYANAN DAN WORD OF MOUTH TERHADAP KEPUTUSAN PEMBELIAN DI KAFE POTRET MEDAN

2019 ◽  
Vol 13 (2) ◽  
pp. 199
Author(s):  
Asrye Tutur Sinaga ◽  
Nurul Wardani
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Service Quality ◽  
Word Of Mouth ◽  
Hypothesis Test ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Quality Data ◽  
Purchasing Decisions ◽  
Study Hypothesis

AbstrakPenelitian ini bertujuan untuk mengetahui dan dapat menjelaskan pengaruh Kualitas Pelayanan dan Word Of Mouth terhadap Keputusan pembelian di Kafe Potret Medan. Populasi dalam penelitian ini adalah 700 orang ditentukan dari jumlah pengunjung Kafe Potret Medan dalam kurun waktu satu minggu, dan sampel yang digunakan berjumlah 88 pengunjung. Sedangkan tehnik pengumpulan data menggunakan angket (kuesioner) dan pengujiannya yaitu uji kualitas data dan uji asumsi klasik.Pengujian hipotesis menggunakan analisis regresi linier berganda, uji F, uji t, dan uji R2. Hipotesis penelitian dimensi Kualitas Pelayanan dan Word Of Mouth secara parsial terhadap Keputusan Pembelian diterima jika t hitung > t tabel dengan tingkat signifikan 0.05.Nilai t tabel dalam penelitian ini 1,662. Nilai t hitung variabel X1 sebesar 1,990 t hitung  > t tabel maka hipotesis diterima, nilai t hitung variabel X2 sebesar 2,628 t hitung > t tabel maka hipotesis diterima. Dari 2 variabel, variabel Word Of Mouth yang paling dominan mempengaruhi Keputusan Pembelian  sebesar 2,628. Kata Kunci : Kualitas Pelayanan, Word Of Mouth, Keputusan Pembelian AbstractThe purpose of this study is to identify and able to explain the influence of Service Quality and Word Of Mouth to Purchasing Decisions of Kafe Potret Medan. The population in this study were 700 people from visitors Kafe Potret Medan in one week, and the samples used were 88 visitors. While the techniques of data collection using the questionnaire and use the test of quality data and classical assumption. The hypothesis test uses multiple linear regression analysis, F test, R square and t test. The study hypothesis was partially of Service Quality and Word Of Mouth dimension to  Purchasing Decisions is acceptable if t hitung > t tabel with a significant level 0.05. The t tabel value in this study 1.662. The t hitung X1 is 1.990 that mean t hitung > t tabel then the hypothesis is accepted, t hitung X2 is 2.628 that mean t hitung > t tabel then the hypothesis is accepted. From 2 variables fascination that the most dominant variable for Purchasing Decisions is Word Of Mouth of 2.628. Keywords : Service Quality, Word Of Mouth, Purchasing Decisions

DESIGNING INTERACTIVE DIGITAL LEARNING MODULE IN BIOLOGY

TABULARASA ◽  
2015 ◽  
Vol 12 (2) ◽  
Author(s):  
Wenny Pintalitna ◽  
Herbet Sipahutar ◽  
Fauziyah Harahap
Keyword(s):  
Small Group ◽  
Group Testing ◽  
Quality Data ◽  
Digital Learning ◽  
Text Learning ◽  
Interactive Learning Environment ◽  
Learning Modules ◽  
Large Group ◽  
Learning Module ◽  
Medium Group

Interactive learning environment can substantially improve student learning and retention of key biology concepts. In this case report, we describe our approach for the design of interactive digital learning module to teach digestive system concepts in Grade 11 learners at SMAN 2 Balige with 180 subjects are selected according to total sampling method. The research method is the development with Dick and Carey model.  Subject of learning module assessment consists of two Biology matter experts, two learning module experts, one electronic media expert, three students for individual trials, ten students and teachers as small group testing, thirty students of SMAN 1 Berastagi for medium group testing, and 60 students of SMAN 2 Balige as large group testing. Quality data of product developed were collected using questionnaires. The results of developmental research showed that: (1) Module assessment by matter, learning modules and media experts were very decent criteria (88.30%, 93.98%, 88.25%); 2) Large group testing of interactive, electronic and text learning modules, respectively were 92.53%, 86.064%, 81.355% belong to very decent criteria; (3) Medium group testing respectively were 84.59%, 80.18%, 76.56% belong to decent criteria; (6) Small group testing respectively were 75.71%, 73.20%, 71.19% belong to decent criteria.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

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