Publish or perish: Scientists are not given incentives to replicate others’ results, and even when they do so on their own steam, negative findings often go unpublished… this is not science nor research! this is a spectacle! Pusblish or perish culture generates PUBLISH AND PERISH results
Over the past few years, there has been a growing awareness that many experimentally established “facts” don’t seem to hold up to repeated investigation.
This was highlighted in a 2010 article in the New Yorker entitled The Truth Wears Off and since then, there have been many popular press accounts of different aspects of science’s current reproducibility crisis.
These include an exposé of the increasing number of retractions by scientific journals and damning demonstrations of failures to replicate high profile studies.
Articles in recent days have discussed how the majority of scientists might be more interested in funding and fame than “truth” and…..
Comment influencer les foules ? À travers la figure d’Edward Bernays (1891-1995), l’un des inventeurs du marketing et l’auteur de « Propaganda », un passionnant décryptage des méthodes de la « fabrique du consentement ». Si les techniques de persuasion des masses apparaissent en Europe à la fin du XIXe siècle pour lutter contre les révoltes ouvrières, elles sont développées aux États-Unis pour convaincre les Américains de s’engager dans la Première Guerre mondiale.
Here are some recommended readings for this very important and central topic, this system is not sustainable, it’s not a question of if BUT when? and it seems to be very close :
Every two years, the Marketing Science Institute asks every MSI member company Trustee to provide input to help set priorities for the research that will guide activities for the next few years.
These priorities enable the Marketing Science Institute to engage in its most critical mission: aligning marketing science and practice in order to create materially better outcomes for marketers and the customers they serve.
Statisticians often try to come up with alternatives to p-values. Here’s a recent attempt called ‘second generation p-values’.
Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value—a second-generation p-value (pδ)–that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priorispecifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.
The scientific paper—the actual form of it—was one of the enabling inventions of modernity. Before it was developed in the 1600s, results were communicated privately in letters, ephemerally in lectures, or all at once in books. There was no public forum for incremental advances. By making room for reports of single experiments or minor technical advances, journals made the chaos of science accretive. Scientists from that point forward became like the social insects: They made their progress steadily, as a buzzing mass.
The earliest papers were in some ways more readable than papers are today. They were less specialized, more direct, shorter, and far less formal. Calculus had only just been invented. Entire data sets could fit in a table on a single page. What little “computation” contributed to the results was done by hand and could be verified in the same way.
Scientists can only rely on an empirical finding if it is credible. In science, a credible finding is one that has (at minimum) repeatedly survived good faith attempts at proving it wrong along 3 dimensions: (1) method/data transparency, (2) analytic reproducibility/robustness, and (3) effect replicability. Curate Science is a platform to crowdsource the credibility of empirical research by curating its transparency, reproducibility/robustness, and replicability for cognitive and social psychology published literature.
The website curate science offers a searchable table of N=1,058 replications of 168 effects, this allows researchers to build reliable theoritical frameworks and test interesting theories. Can this idea inspire our friends in AMA, ACR, EMAC and in marketing research community? This will be a great idea …
The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.
Article:
Ashlee Humphreys, Rebecca Jen-Hui Wang; Automated Text Analysis for Consumer Research, Journal of Consumer Research, Volume 44, Issue 6, 1 April 2018, Pages 1274–1306, https://doi.org/10.1093/jcr/ucx104
Grinçant et sans concession : le dernier clip de Maxime Ginolin, alias MagiC JacK, a le mérite de mettre les pieds dans le plat en caricaturant l’hypocrisie d’une part de l’industrie de la mode qui aurait encore recours à la fourrure pour confectionner leurs lignes de vêtements. La question est pourtant légitime : à l’heure où la production de fourrure repose sur une industrialisation des méthodes dont les conséquences catastrophiques pour les animaux sont désormais bien connues à force d’enquêtes et de caméras cachées, comment justifier la souffrance et la violence que subissent ces êtres vivants dans les élevages ?
TrendHunter.com is the world’s largest, most popular trend community. A great ressources if you want to better understand the key trends emerging and have new ideas. I use it as a ressource in my courses to help students choose innovations they can work on for their projects and marketing approach, and to spot new consumer behaviours. This also can inspire academics for some research topics in consumption phenomena. The marketing professionals can also find ideas for their business and monitor the market evolution. A great ressource, and above all it’s for free 😉
Introduction to Mediation, Moderation, and Conditional Process Analysis describes the foundation of mediation and moderation analysis as well as their analytical integration in the form of « conditional process analysis », with a focus onPROCESS version 3 for SPSS and SAS (#processmacro) as the tool for implementing the methods discussed. Available as both an e-book and in print form, it is published by TheGuilford Press in their Methodology in the Social Sciences series.
