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Building a collaborative Psychological Science: Lessons learned from ManyBabies 1
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CC BY-NC-ND
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The field of infancy research faces a difficult challenge: some questions require samples that are simply too large for any one lab to recruit and test. ManyBabies aims to address this problem by forming large-scale collaborations on key theoretical questions in developmental science, while promoting the uptake of Open Science practices. Here, we look back on the first project completed under the ManyBabies umbrella – ManyBabies 1 – which tested the development of infant-directed speech preference. Our goal is to share the lessons learned over the course of the project and to articulate our vision for the role of large-scale collaborations in the field. First, we consider the decisions made in scaling up experimental research for a collaboration involving 100+ researchers and 70+ labs. Next, we discuss successes and challenges over the course of the project, including: protocol design and implementation, data analysis, organizational structures and collaborative workflows, securing funding, and encouraging broad participation in the project. Finally, we discuss the benefits we see both in ongoing ManyBabies projects and in future large-scale collaborations in general, with a particular eye towards developing best practices and increasing growth and diversity in infancy research and psychological science in general. Throughout the paper, we include first-hand narrative experiences, in order to illustrate the perspectives of researchers playing different roles within the project. While this project focused on the unique challenges of infant research, many of the insights we gained can be applied to large-scale collaborations across the broader field of psychology.

Subject:
Social Science
Material Type:
Reading
Author:
Casey Lew-Williams
Catherine Davies
Christina Bergmann
Connor P. G. Waddell
J. Kiley Hamlin
Jessica E. Kosie
Jonathan F. Kominsky
Leher Singh
Liquan Liu
Martin Zettersten
Meghan Mastroberardino
Melanie Soderstrom
Melissa Kline
Michael C. Frank
Krista Byers-Heinlein
Date Added:
11/13/2020
Introduction to Comparative Government and Politics, 1st ed.
Conditional Remix & Share Permitted
CC BY-NC
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Introduction to Comparative Government and Politics is the first open educational resource (OER) on the topic of comparative politics, and the second OER textbook in political science funded by ASCCC OERI, in what we hope will become a complete library for the discipline. This textbook aligns with the C-ID Course Descriptor for Introduction to Comparative Government and Politics in content and objectives. It is organized thematically, with each chapter accompanied by a case study or a comparative study, one of the main methodological tools used in comparative politics. By contextualizing the concepts, we hope to help students learn the comparative method, which to this day remains one of the most important methodological tools for all researchers.

Subject:
Political Science
Social Science
Material Type:
Textbook
Provider:
Academic Senate of California Community Colleges
Author:
Byran Martin
Charlotte Lee
Dino Bozonelos
Jessica Scarffe
Josh Franco
Julia Wendt
Masahiro Omae
Stefan Veldhuis
Date Added:
12/08/2022
Missing Data and Multiple Imputation Decision Tree
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This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

Subject:
Mathematics
Statistics and Probability
Material Type:
Diagram/Illustration
Reading
Author:
Alex Uzdavines
Ben Van Dusen
Daria Gerasimova
David Moreau
Denver Brown
James M. Clay
Jayson Nissen
Jessica A. R. Logan
Kathleen Schmidt
Keven Joyal-Desmarais
Kevin M. King
Mahmoud M. Elsherif
Martin Vasilev
Max A. Halvorson
Menglin Xu
Pamela E. Davis-Kean
Rick A. Cruz
Sierra Bainter
Adrienne D. Woods
Date Added:
04/25/2022
Review of Communication in the Real World: An Introduction to Communication Studies
Unrestricted Use
CC BY
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Review of Communication in the Real World: An Introduction to Communication Studies: https://drive.google.com/open?id=1tgWb7Hvuwc5JMiUesMRk3Xnh5ev_kh6v7S4MjxdwMXM

Subject:
Business and Communication
Communication
Material Type:
Textbook
Author:
Jessica Martin
Date Added:
02/08/2021
Review of The Public Speaking Project: Public Speaking, The Virtual Text
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CC BY
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Review of The Public Speaking Project: Public Speaking, The Virtual Text
https://drive.google.com/open?id=1uuLLtoCW_Zg8w4bgztKpqdI-udvq0E_9WJZCQkea78I

Subject:
English Language Arts
Speaking and Listening
Material Type:
Textbook
Author:
Jessica Martin
Date Added:
06/17/2020
Using Linear Regression to Explore Environmental Factors Affecting Vector-borne Diseases [version 1.0]
Conditional Remix & Share Permitted
CC BY-SA
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In this activity students will use linear regression to analyze real data on vector-borne diseases and explore how environmental factors such as climate change or population density influence the transmission of these diseases.

Subject:
Ecology
Life Science
Mathematics
Statistics and Probability
Material Type:
Activity/Lab
Full Course
Lecture
Lesson Plan
Provider:
BioQUEST Curriculum Consortium
Provider Set:
Quantitative Biology at Community Colleges
Author:
Andy Adams
Jessica A Adams
John J Bray
Suzanne Lenhart
Tami Imbierowicz
Breonna Martin
Date Added:
09/02/2020