scholarly journals The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

2018 ◽  
Vol 1 (4) ◽  
pp. 501-515 ◽  
Author(s):  
Hannah Moshontz ◽  
Lorne Campbell ◽  
Charles R. Ebersole ◽  
Hans IJzerman ◽  
Heather L. Urry ◽  
...  
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Psychological Research ◽  
Distributed Network ◽  
Research Projects ◽  
Mental Processes ◽  
Background Structure ◽  
Psychological Science ◽  
Rigorous Research ◽  
Research Questions

Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.

scholarly journals The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network

2018 ◽  
Author(s):  
Hannah Moshontz ◽  
Lorne Campbell ◽  
Charles R. Ebersole ◽  
Hans IJzerman ◽  
Heather L. Urry ◽  
...  
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Psychological Research ◽  
Distributed Network ◽  
Research Projects ◽  
Mental Processes ◽  
Background Structure ◽  
Psychological Science ◽  
Rigorous Research ◽  
Research Questions

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

scholarly journals The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network

2018 ◽  
Author(s):  
Gerit Pfuhl
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Collaborative Network ◽  
Distributed Network ◽  
Research Projects ◽  
Mental Processes ◽  
Background Structure ◽  
Psychological Science ◽  
Rigorous Research ◽  
Representative Samples

Concerns have been growing about the veracity of psychological findings. Many findings in psychological science are based on studies with insufficient statistical power and non-representative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Large-scale collaboration, in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

scholarly journals The importance of heterogeneity in large-scale replications

2020 ◽  
Vol 8 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Matthew H. Goldberg ◽  
Sander van der Linden
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Large Scale ◽  
Theory Development ◽  
Scientific Research ◽  
Psychological Science ◽  
Study Results ◽  
Research Questions ◽  
Size Heterogeneity

In a large-scale replication effort, Klein et al. (2018, https://doi.org/10.1177/2515245918810225) investigate the variation in replicability and effect size across many different samples and settings. The authors concluded that, for any given effect being studied, heterogeneity across samples and settings does not explain failures to replicate. In the current commentary, we argue that the heterogeneity observed indeed has implications for replication failures, as well as for statistical power and theory development. We argue that psychological scientific research questions should be contextualized—considering how historical, political, or cultural circumstances might affect study results. We discuss how a perspectivist approach to psychological science is a fruitful way for designing research that aims to explain effect size heterogeneity.

scholarly journals The Importance of Heterogeneity in Large-Scale Replications

2019 ◽  
Author(s):  
Matthew Goldberg ◽  
Sander van der Linden
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Large Scale ◽  
Theory Development ◽  
Scientific Research ◽  
Psychological Science ◽  
Study Results ◽  
Research Questions ◽  
Size Heterogeneity

In a large-scale replication effort, Klein et al., (2018) investigate the variation in replicability and effect size across many different samples and settings. The authors concluded that, for any given effect being studied, heterogeneity across samples and settings does not explain failures to replicate. In the current commentary, we argue that the heterogeneity observed indeed has implications for replication failures, as well as for statistical power and theory development. We argue that psychological scientific research questions should be contextualized—considering how historical, political, or cultural circumstances might affect study results. We discuss how a perspectivist approach to psychological science is a fruitful way for designing research that aims to explain effect size heterogeneity.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2020 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Ryan Drabble ◽  
Kristen Lindquist
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Human Cognition ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind

Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

Digital Traces: New Data, Resources, and Tools for Psychological-Science Research

2019 ◽  
Vol 28 (6) ◽  
pp. 560-566 ◽  
Author(s):  
Anat Rafaeli ◽  
Shelly Ashtar ◽  
Daniel Altman
Keyword(s):  
Research Methods ◽  
Large Scale ◽  
New Technologies ◽  
Science Research ◽  
Psychological Research ◽  
Data Sources ◽  
Human Psychology ◽  
Psychological Science ◽  
Large Scale Data ◽  
Scale Data

New technologies create and archive digital traces—records of people’s behavior—that can supplement and enrich psychological research. Digital traces offer psychological-science researchers novel, large-scale data (which reflect people’s actual behaviors), rapidly collected and analyzed by new tools. We promote the integration of digital-traces data into psychological science, suggesting that it can enrich and overcome limitations of current research. In this article, we review helpful data sources, tools, and resources and discuss challenges associated with using digital traces in psychological research. Our review positions digital-traces research as complementary to traditional psychological-research methods and as offering the potential to enrich insights on human psychology.

Psychological Science Accelerator: A Promising Resource for Clinical Psychological Science

2020 ◽  
Author(s):  
Julie Beshears ◽  
Biljana Gjoneska ◽  
Kathleen Schmidt ◽  
Gerit Pfuhl ◽  
Toni Saari ◽  
...  
Keyword(s):  
Clinical Psychology ◽  
Large Scale ◽  
Clinical Psychologists ◽  
Collaborative Network ◽  
Psychological Science ◽  
Rigorous Research ◽  
International Collaborative

Recent methodological reforms have succeeded in improving the rigor, accessibility, and transparency of psychological science, but these advances have not successfully proliferated certain subfields, including clinical psychology. Large-scale, crowdsourced collaborations offer clinical psychological scientists a way to conduct rigorous research on a scale not otherwise accessible to most researchers. The Psychological Science Accelerator (PSA) is an international collaborative network of psychological scientists that facilitates rigorous and generalizable research. In this chapter, we describe how the PSA can help clinical psychologists and clinical psychological science more broadly.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2020 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Curtis Puryear ◽  
Ryan Drabble ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Human Cognition ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind

Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

scholarly journals Microaggressions as part of the historical context of stigma and prejudice

2017 ◽  
Author(s):  
Michael W. Kraus ◽  
Jun Won Park
Keyword(s):  
Historical Context ◽  
Psychological Science ◽  
Marginalized Groups ◽  
Rigorous Research ◽  
Research Questions ◽  
Research Studies

In this comment we articulate one central weakness in Lilienfeld’s (2017) perspective on microaggression research in psychological science: Namely, that any analysis of modern forms of expressed prejudice, be they subtle or overt, that does not acknowledge the historical context in which these forms of prejudice are expressed is likely to be fraught with challenges and potential for misunderstanding. Here we articulate how this ahistorical context of prejudice has a prominent history in psychological science, and has frequently led otherwise well-meaning and rigorous research studies to incomplete conclusions about the prejudice experienced by historically marginalized groups. We then discuss how this ahistorical perspective on expressed prejudice leads to misconceptions about microaggression research. Ultimately, the Lilienfeld (2017) piece is a compelling case for considering, whenever possible, the perspectives of those for whom the surrounding historical context of prejudice is most salient. This need is particularly great with respect to research questions that examine the experience of prejudice and thereby directly rely on the wisdom of individuals who come from traditionally marginalized groups and thus are personally steeped in the history, traditions, and thought perspectives that arise from those conditions.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2021 ◽  
pp. 174569162110048
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Curtis Puryear ◽  
Ryan Drabble ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind ◽  
Psychological Paradigms

Humans have been using language for millennia but have only just begun to scratch the surface of what natural language can reveal about the mind. Here we propose that language offers a unique window into psychology. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—natural-language processing and comparative linguistics—are contributing to how we understand topics as diverse as emotion, creativity, and religion and overcoming obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods and highlight the best way to combine language analysis with more traditional psychological paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

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