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Journal ArticleDOI

Research Commentary---Too Big to Fail: Large Samples and the p-Value Problem

TLDR
This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay.
Abstract
The Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. There are many advantages to large samples, but researchers using statistical inference must be aware of the p-value problem associated with them. In very large samples, p-values go quickly to zero, and solely relying on p-values can lead the researcher to claim support for results of no practical significance. In a survey of large sample IS research, we found that a significant number of papers rely on a low p-value and the sign of a regression coefficient alone to support their hypotheses. This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay. We believe that addressing the p-value problem will increase the credibility of large sample IS research as well as provide more insights for readers.

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Conflating relevance with practical significance and other issues: Commentary on Sen, Smith, and Van Note’s “statistical significance versus practical importance in information systems research”

TL;DR: In this paper , the authors argue that relevance is about the real-world usefulness of research findings rather than their impressiveness for the researcher audience, an essential quality of research spanning beyond its findings and not merely limited to statistical studies, and determined by nonacademics rather than academics.
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Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

TL;DR: In this paper , the authors highlight methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health, including practices, publication norms, and education.
Journal ArticleDOI

Effects of self-stereotype on older adults' self-integrity through the intervening effects of sense of coherence and empathy

TL;DR: Wang et al. as mentioned in this paper examined the mediating effects of sense of coherence and empathy on the relationship between self-stereotype and self-integrity and found that selfstereotypes are negatively associated with sense of selfherence, empathy and selfintegrity.
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The wedding bells of war: The influence of armed conflict on child marriages in West Africa

TL;DR: In this paper , the authors argue that armed conflict plays an important role in the occurrence of child marriages; it influences the supply of and demand for young brides, and in cases where belligerents use war tactics specifically focused on harming young girls, such as sexual violence and girl recruitment, early marriage might increase as the result of armed conflict.
Journal ArticleDOI

How mentally healthy we are during the pandemic? Findings from changed travel behavior

TL;DR: In this article , the authors examined the relationship between anxiety and depression, and correlated various sociodemographic, income, job status, health-related factors, and travel behavior changes in six large U.S. cities.
References
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Journal ArticleDOI

Things I Have Learned (So Far).

TL;DR: The application of statistics to psychology and the other sociobiomedical sciences has been studied extensively as discussed by the authors, including the principles "less is more" (fewer variables, more highly targeted issues, sharp rounding off), "simple is better" (graphic representation, unit weighting for linear composites), and "some things you learn aren't so."
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To Explain or to Predict

TL;DR: The distinction between explanatory and predictive models is discussed in this paper, and the practical implications of the distinction to each step in the model- ing process are discussed as well as a discussion of the differences that arise in the process of modeling for an explanatory ver- sus a predictive goal.
Journal ArticleDOI

Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets

TL;DR: It is suggested that identity-relevant information about reviewers shapes community members' judgment of products and reviews and shows that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce.
Journal ArticleDOI

Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets

TL;DR: In this paper, the authors used a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, and provided evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information.
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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

TL;DR: McCoch as discussed by the authors provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear model for counts and other outcomes.
Trending Questions (1)
What are the positives of large samples in research?

Large samples in research provide researchers with more statistical power, increased generalizability of findings, and the ability to detect smaller effect sizes.