scholarly journals Replicator degrees of freedom allow publication of misleading failures to replicate

2019 ◽  
Vol 116 (51) ◽  
pp. 25535-25545 ◽  
Author(s):  
Christopher J. Bryan ◽  
David S. Yeager ◽  
Joseph M. O’Brien
Keyword(s):  
Data Analysis ◽  
Experimental Design ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Current Practice ◽  
False Negative ◽  
Ongoing Debate ◽  
Key Dimensions ◽  
Using Data

In recent years, the field of psychology has begun to conduct replication tests on a large scale. Here, we show that “replicator degrees of freedom” make it far too easy to obtain and publish false-negative replication results, even while appearing to adhere to strict methodological standards. Specifically, using data from an ongoing debate, we show that commonly exercised flexibility at the experimental design and data analysis stages of replication testing can make it appear that a finding was not replicated when, in fact, it was. The debate that we focus on is representative, on key dimensions, of a large number of other replication tests in psychology that have been published in recent years, suggesting that the lessons of this analysis may be far reaching. The problems with current practice in replication science that we uncover here are particularly worrisome because they are not adequately addressed by the field’s standard remedies, including preregistration. Implications for how the field could develop more effective methodological standards for replication are discussed.

scholarly journals Exploring Single-Cell Data with Deep Multitasking Neural Networks

2017 ◽  
Author(s):  
Matthew Amodio ◽  
David van Dijk ◽  
Krishnan Srinivasan ◽  
William S Chen ◽  
Hussein Mohsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Experimental Design ◽  
Single Cell ◽  
Large Scale ◽  
Dengue Infection ◽  
Data Representation ◽  
Data Generation ◽  
Cell Data

AbstractBiomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.A well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

scholarly journals On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures

2020 ◽  
pp. 107699862095905
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Current Practice ◽  
Simulation Studies ◽  
Population Parameters ◽  
Indicator Method ◽  
Fully Conditional Specification ◽  
Unbiased Estimates ◽  
Large Scale Assessments ◽  
Using Data

Large-scale assessments (LSAs) use Mislevy’s “plausible value” (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the properties of methods used in current practice for dealing with missing data in background variables in educational LSAs, which rely on the missing indicator method (MIM), with other methods based on multiple imputation. In this context, we present a fully conditional specification (FCS) approach that allows for a joint treatment of PVs and missing data. Using theoretical arguments and two simulation studies, we illustrate under what conditions the MIM provides biased or unbiased estimates of population parameters and provide evidence that methods such as FCS can provide an effective alternative to the MIM. We discuss the strengths and weaknesses of the approaches and outline potential consequences for operational practice in educational LSAs. An illustration is provided using data from the PISA 2015 study.

AKTUALISASI PEDIDIKAN KARAKTER DALAM PENGEMBANGAN REVOLUSI MENTAL BERBASIS ADIWIYATA DAN MULTIKULTURAL

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Suhardi Suhardi
Keyword(s):  
Data Analysis ◽  
Character Education ◽  
Achievement Motivation ◽  
Case Study Research ◽  
Individual Development ◽  
Human Beings ◽  
Soft Skill ◽  
Design Data ◽  
Strategic Role ◽  
Using Data

Mental revolution of education requires efforts to print educated human beings by having the motivation to meet the standards of achievement excellence, such as ethos of progress, ethics, achievement motivation, discipline, optimistic, productive, innovative and active views. This can be implemented with character education. Character education is one of the soft skill tools that can be integrated in learning in each subject. Learning activities using an active learning approach have a strategic role in instilling national character values so that students are able to behave and act on values that have become their personality. The purpose of this study was to find and analyze about: 1) Implementation of Character Education to Build Adiwiyata-Based Mental Revolution and Multiculturalism; 2) Implementation of Character Education to Build Mental Revolution in Organizational Culture. This study uses a qualitative approach with phenomenological naturatistics (phenomenology approach), with a descriptive type of case study research design. Data were analyzed using data analysis techniques: data reduction, data analysis and conclusions. The results of the study are: The application of character education to develop a mental revolution can be started from the character of building the environment. Environmental character is very important for individual development. The implementation of character education in building a mental revolution can emphasize the internalization of multicultural values and Adiwiyata which in the end will form a loving environmental awareness and foster a spirit of tolerance.

Peranan Ketua Pengurus dan Manajer Dalam Penyusunan dan Penerapan Laporan Keuangan Berbasis SAK ETAP Pada Koperasi di Kota Palembang

2019 ◽  
Vol 14 (2) ◽  
pp. 119
Author(s):  
Riza Syahputera ◽  
Martha Rianty
Keyword(s):  
Data Analysis ◽  
Sampling Technique ◽  
Financial Statements ◽  
Significant Positive Effect ◽  
Analysis Technique ◽  
Regression Test ◽  
Using Data ◽  
The City ◽  
Positive Effect

AbstractThis study aims to determine the effect of the role of the Chairperson and Cooperative Manager in the preparation and application of Financial Statements based on SAK ETAP in cooperatives in the city of Palembang. This research is a quantitative study using data obtained from questionnaires and measured using a Likert scale. The sampling technique used is purposive sampling. The sample used in this study was the Chairperson of the cooperative and the manager of the cooperative in the city of Palembang. The cooperatives studied were 203 cooperatives. The data analysis technique used is multiple linear regression test. The results showed that the role of cooperative leaders and managers had a significant positive effect on the preparation and application of SAK ETAP-based financial statements.Keywords : chairman, manager, SAK ETAP, cooperative

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Critique of a Pupillary Response Experiment

1967 ◽  
Vol 25 (2) ◽  
pp. 603-604 ◽  
Author(s):  
Roger P. Dooley ◽  
Donald J. Lehr
Keyword(s):  
Data Analysis ◽  
Experimental Design ◽  
Pupillary Response

This critique questions the experimental design, controls and data analysis of a recent pupillary response experiment by Hess and Polt (1966).

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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