The JASP Data Library Version 2
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Anomalous Perception in a Ganzfeld Condition - a Meta-Analysis of More Than 40 Years Investigation [Version 1; Peer Review: Awaiting Peer Review]
F1000Research 2021, 10:234 Last updated: 08 SEP 2021 RESEARCH ARTICLE Stage 2 Registered Report: Anomalous perception in a Ganzfeld condition - A meta-analysis of more than 40 years investigation [version 1; peer review: awaiting peer review] Patrizio E. Tressoldi 1, Lance Storm2 1Studium Patavinum, University of Padua, Padova, Italy 2School of Psychology, University of Adelaide, Adelaide, Australia v1 First published: 24 Mar 2021, 10:234 Open Peer Review https://doi.org/10.12688/f1000research.51746.1 Latest published: 24 Mar 2021, 10:234 https://doi.org/10.12688/f1000research.51746.1 Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the Abstract article can be found at the end of the article. This meta-analysis is an investigation into anomalous perception (i.e., conscious identification of information without any conventional sensorial means). The technique used for eliciting an effect is the ganzfeld condition (a form of sensory homogenization that eliminates distracting peripheral noise). The database consists of studies published between January 1974 and December 2020 inclusive. The overall effect size estimated both with a frequentist and a Bayesian random-effect model, were in close agreement yielding an effect size of .088 (.04-.13). This result passed four publication bias tests and seems not contaminated by questionable research practices. Trend analysis carried out with a cumulative meta-analysis and a meta-regression model with Year of publication as covariate, did not indicate sign of decline of this effect size. The moderators analyses show that selected participants outcomes were almost three-times those obtained by non-selected participants and that tasks that simulate telepathic communication show a two- fold effect size with respect to tasks requiring the participants to guess a target. -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Dentist's Knowledge of Evidence-Based Dentistry and Digital Applications Resources in Saudi Arabia
PTB Reports Research Article Dentist’s Knowledge of Evidence-based Dentistry and Digital Applications Resources in Saudi Arabia Yousef Ahmed Alomi*, BSc. Pharm, MSc. Clin Pharm, BCPS, BCNSP, DiBA, CDE ABSTRACT Critical Care Clinical Pharmacists, TPN Objectives: Drug information resources provide clinicians with safer use of medications and play a Clinical Pharmacist, Freelancer Business vital role in improving drug safety. Evidence-based medicine (EBM) has become essential to medical Planner, Content Editor, and Data Analyst, Riyadh, SAUDI ARABIA. practice; however, EBM is still an emerging dentistry concept. Therefore, in this study, we aimed to Anwar Mouslim Alshammari, B.D.S explore dentists’ knowledge about evidence-based dentistry resources in Saudi Arabia. Methods: College of Destiney, Hail University, SAUDI This is a 4-month cross-sectional study conducted to analyze dentists’ knowledge about evidence- ARABIA. based dentistry resources in Saudi Arabia. We included dentists from interns to consultants and Hanin Sumaydan Saleam Aljohani, those across all dentistry specialties and located in Saudi Arabia. The survey collected demographic Ministry of Health, Riyadh, SAUDI ARABIA. information and knowledge of resources on dental drugs. The knowledge of evidence-based dental care and knowledge of dental drug information applications. The survey was validated through the Correspondence: revision of expert reviewers and pilot testing. Moreover, various reliability tests had been done with Dr. Yousef Ahmed Alomi, BSc. Pharm, the study. The data were collected through the Survey Monkey system and analyzed using Statistical MSc. Clin Pharm, BCPS, BCNSP, DiBA, CDE Critical Care Clinical Pharmacists, TPN Package of Social Sciences (SPSS) and Jeffery’s Amazing Statistics Program (JASP). -
A Data Management Life-Cycle
science for a changing world Central Energy Resources Team Data Management A Data Management Life-Cycle Introduction Documented, reliable, and accessible data and information are essential building blocks supporting scientific research and applications that enhance society's knowledge base (fig. 1). The U.S. Geological Survey (USGS), a leading provider of science data, information, and knowledge, is uniquely positioned to integrate science and natural resource information to address societal needs. The USGS Central Energy Resources Team (USGS-CERT) provides critical information and knowledge on tnu iuu the quantity, quality, and distribution of the Nation's and the 10101010110 world's oil, gas, and coal resources. 1C001111001 By using a life-cycle model, the USGS-CERT Data Manage 1001001"*: ment Project is developing an integrated data management system 10^19*1- to (1) promote access to energy data and information, (2) increase data documentation, and (3) streamline product delivery to the public, scientists, and decision makers. The project incorporates web-based technology, data cataloging systems, data processing Data Information routines, and metadata documentation tools to improve data Figure 1. Access to organized and documented data and access, enhance data consistency, and increase office efficiency. information increases knowledge. Implementation Summary and Future Directions In a life-cycle model, data and information are transformed into tangible products and knowledge by a continuous flow in which The Data Management Project addresses USGS science the output of one process becomes the input of others (fig. 2). \ goals and affects how the Central Energy Resources Team manages data and carries out its programs. Our task-oriented "Find, Get, Use, Deliver, and Maintain" The tools and processes developed are based on strategy incorporates life-cycle concepts and directs USGS-CERT the life-cycle approach and our "Find, Get, Use, data management tasks and implementation. -
11. Correlation and Linear Regression
11. Correlation and linear regression The goal in this chapter is to introduce correlation and linear regression. These are the standard tools that statisticians rely on when analysing the relationship between continuous predictors and continuous outcomes. 11.1 Correlations In this section we’ll talk about how to describe the relationships between variables in the data. To do that, we want to talk mostly about the correlation between variables. But first, we need some data. 11.1.1 The data Table 11.1: Descriptive statistics for the parenthood data. variable min max mean median std. dev IQR Dan’s grumpiness 41 91 63.71 62 10.05 14 Dan’s hours slept 4.84 9.00 6.97 7.03 1.02 1.45 Dan’s son’s hours slept 3.25 12.07 8.05 7.95 2.07 3.21 ............................................................................................ Let’s turn to a topic close to every parent’s heart: sleep. The data set we’ll use is fictitious, but based on real events. Suppose I’m curious to find out how much my infant son’s sleeping habits affect my mood. Let’s say that I can rate my grumpiness very precisely, on a scale from 0 (not at all grumpy) to 100 (grumpy as a very, very grumpy old man or woman). And lets also assume that I’ve been measuring my grumpiness, my sleeping patterns and my son’s sleeping patterns for - 251 - quite some time now. Let’s say, for 100 days. And, being a nerd, I’ve saved the data as a file called parenthood.csv. -
Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library
7 Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library Open-File Report 2015–1141 U.S. Department of the Interior U.S. Geological Survey Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library By Kelleen M. List, Brian J. Buczkowski, Linda P. McCarthy, and Alice M. Orton Open-File Report 2015–1141 U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior SALLY JEWELL, Secretary U.S. Geological Survey Suzette M. Kimball, Acting Director U.S. Geological Survey, Reston, Virginia: 2015 For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment—visit http://www.usgs.gov/ or call 1–888–ASK–USGS (1–888–275–8747). For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprod/. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. Suggested citation: List, K.M., Buczkowski, B.J., McCarthy, L.P., and Orton, A.M., 2015, Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library: U.S. Geological Survey Open- File Report 2015–1141, 16 p., http://dx.doi.org/10.3133/ofr20151141. -
Fatigue and Psychological Distress in the Working Population Psychometrics, Prevalence, and Correlates
Journal of Psychosomatic Research 52 (2002) 445–452 Fatigue and psychological distress in the working population Psychometrics, prevalence, and correlates Ute Bu¨ltmanna,*, Ijmert Kanta, Stanislav V. Kaslb, Anna J.H.M. Beurskensa, Piet A. van den Brandta aDepartment of Epidemiology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands bDepartment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA Received 11 October 2000 Abstract Objective: The purposes of this study were: (1) to explore the distinct pattern of associations was found for fatigue vs. relationship between fatigue and psychological distress in the psychological distress with respect to demographic factors. The working population; (2) to examine associations with demographic prevalence was 22% for fatigue and 23% for psychological and health factors; and (3) to determine the prevalence of fatigue distress. Of the employees reporting fatigue, 43% had fatigue only, and psychological distress. Methods: Data were taken from whereas 57% had fatigue and psychological distress. Conclusions: 12,095 employees. Fatigue was measured with the Checklist The results indicate that fatigue and psychological distress are Individual Strength, and the General Health Questionnaire (GHQ) common in the working population. Although closely associated, was used to measure psychological distress. Results: Fatigue was there is some evidence suggesting that fatigue and psychological fairly well associated with psychological distress. A separation -
Relevance of Data Mining in Digital Library
International Journal of Future Computer and Communication, Vol. 2, No. 1, February 2013 Relevance of Data Mining in Digital Library R. N. Mishra and Aishwarya Mishra Abstract—Data mining involves significant process of Data Mining concept has been delimitated in multiple identifying the extraction of hidden predictive information ways by different organizations, scientists etc. Wikipedia, from vast array of databases and it is an authoritative new has visualized data mining a non-trivial extraction of technology with potentiality to facilitate the Libraries and implicit and potentially useful information from data and Information Centers to focus on the most important the science of extracting useful information from large data information in their data warehouses. It is a viable tool to predict future trends and behaviors in the field of library and sets or databases (http://en.wikipedia. org/wiki/Data information service for deducing proactive, knowledge-driven mining). Marketing Dictionary defines data mining as a decisions. Mechanized, prospective analyses of data mining process for extraction of customer information from a vast move beyond the analyses of past events provided by gamut of databases with the help of the feasible software to retrospective tools typical of decision support systems. Data isolate and identify previously unknown patterns or trends. Mining, Process of Data Mining, Knowledge Discovery in Multiple techniques with the help of technologies are Databases, DBMS, Data Mining Techniques etc. etc. have been discussed in this paper. employed in data mining for extraction of data from the heave of databases. Intelligence Encyclopedia has, however, Index Terms—Data mining, KDD, artificial neural Networks, defined data mining as a statistical analysis technique to sequential pattern, modeling, DMT. -
When Psychometrics Meets Epidemiology and the Studies V1.Pdf
When psychometrics meets epidemiology, and the studies which result! Tom Booth [email protected] Lecturer in Quantitative Research Methods, Department of Psychology, University of Edinburgh Who am I? • MSc and PhD in Organisational Psychology – ESRC AQM Scholarship • Manchester Business School, University of Manchester • Topic: Personality psychometrics • Post-doctoral Researcher • Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh. • Primary Research Topic: Cognitive ageing, brain imaging. • Lecturer Quantitative Research Methods • Department of Psychology, University of Edinburgh • Primary Research Topics: Individual differences and health; cognitive ability and brain imaging; Psychometric methods and assessment. Journey of a talk…. • Psychometrics: • Performance of likert-type response scales for personality data. • Murray, Booth & Molenaar (2015) • Epidemiology: • Allostatic load • Measurement: Booth, Starr & Deary (2013); (Unpublished) • Applications: Early life adversity (Unpublished) • Further applications Journey of a talk…. • Methodological interlude: • The issue of optimal time scales. • Individual differences and health: • Personality and Physical Health (review: Murray & Booth, 2015) • Personality, health behaviours and brain integrity (Booth, Mottus et al., 2014) • Looking forward Psychometrics My spiritual home… Middle response options Strongly Agree Agree Neither Agree nor Disagree Strong Disagree Disagree 1 2 3 4 5 Strongly Agree Agree Unsure Disagree Strong Disagree -
Statistical Analysis in JASP
Copyright © 2018 by Mark A Goss-Sampson. All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author except for the purposes of research, education or private study. CONTENTS PREFACE .................................................................................................................................................. 1 USING THE JASP INTERFACE .................................................................................................................... 2 DESCRIPTIVE STATISTICS ......................................................................................................................... 8 EXPLORING DATA INTEGRITY ................................................................................................................ 15 ONE SAMPLE T-TEST ............................................................................................................................. 22 BINOMIAL TEST ..................................................................................................................................... 25 MULTINOMIAL TEST .............................................................................................................................. 28 CHI-SQUARE ‘GOODNESS-OF-FIT’ TEST............................................................................................. 30 MULTINOMIAL AND Χ2 ‘GOODNESS-OF-FIT’ TEST. .......................................................................... -
Resources for Mini Meta-Analysis and Related Things Updated on June 25, 2018 Here Are Some Resources That I and Others Find
Resources for Mini Meta-Analysis and Related Things Updated on June 25, 2018 Here are some resources that I and others find useful. I know my mini meta SPPC article and Excel were not exhaustive and I didn’t cover many things, so I hope this resource page will give you some answers. I’ll update this page whenever I find something new or receive suggestions. Meta-Analysis Guides & Workshops • David Wilson, who wrote Practical Meta-Analysis, has a website containing PowerPoints and macros (for SPSS, Stata, and SAS) as well as some useful links on meta-analysis o I created an Excel template based on his slides (read the Excel page to see how this template differs from my previous SPPC template) • McShane and Böckenholt (2017) wrote an article in the Journal of Consumer Research similar to my mini meta paper. It comes with a very cool website that calculates meta- analysis for you: https://blakemcshane.shinyapps.io/spmetacase1/ • Courtney Soderberg from the Center for Open Science gave a mini meta-analysis workshop at SIPS 2017. All materials are publicly available: https://osf.io/nmdtq/ • Adam Pegler wrote a tutorial blog on using R to calculate mini meta based on my article • The founders of MetaLab at Stanford made video tutorials on meta-analysis in general • Geoff Cumming has a video tutorial on meta-analysis and other “new statistics” or read his Psychological Science article on the “new statistics” • Patrick Forscher’s meta-analysis syllabus and class materials are available online • A blog post containing 5 tips to understand and carefully -
Best Practices Using Base SAS Software
Best Practices Using June 27, 2012 BASE SAS Software Best Practices Using BASE SAS® Software Chapter 1: Best Practices 1.1 Introduction 1.2 Techniques for Conserving CPU and Memory 1.3 Techniques for Minimizing I/O Operations 1.4 Techniques for Conserving Disk Space 1.5 Creating and Using Indexes with SAS Data Sets 1.6 Techniques to Minimize Network Traffic (Self-Study) 2 1 Best Practices Using June 27, 2012 BASE SAS Software What Are Best Practices? Best practices can reduce usage of the following five critical system resources to improve performance: CPU I/O Disk Space Memory Data Storage Space Reducing one resource often increases another. 3 Deciding What Is Important for Efficiency Your Programs Your Site Your Data 4 2 Best Practices Using June 27, 2012 BASE SAS Software Understanding Efficiency at Your Site Hardware Operating Environment System Load SAS Environment 5 Knowing How Your Program Will Be Used The importance of efficiency increases with the following: the complexity of the program or the size of the files being processed the number of times that the program will be executed 6 3 Best Practices Using June 27, 2012 BASE SAS Software Knowing Your Data 7 Considering Trade-Offs In this seminar, many tasks are performed using one or more techniques. To decide which technique is most efficient for a given task, benchmark (measure and compare) the resource usage of each technique. You should benchmark with the actual data to determine which technique is the most efficient. The effectiveness of any efficiency technique depends greatly on the data with which you use the technique.