ggstatsplot` is an extension of the `ggplot2` package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich plots, which can either be used for quick data exploration or for publications/reports/notebooks/etc: https://cran.r-project.org/…/packages/ggstatsplot/index.html
Currently, it supports only the most common types of statistical tests (parametric, nonparametric, and robust versions of t-tets/ANOVA, correlation, and contingency tables analyses):
– violin plots (for comparisons *between* groups or conditions),
– pie charts (for categorical data),
A simple example, to start with! Each time when in your bibliography or scientific work you cite 90% of the time authors from the north despite the fact that similar works are conducted in the south, you are committing an epistemicid! You are making other productions invisible and marginal by giving an exclusive legitimacy to only some circles of knowledge production.
Another example, is the Islamic tradition of the « halaqa » in producing knowledge and as a research and academic methodology. A form or method that existed in the Islamic Spanish era, and that no longer exists today in the academic world.
A definition according to Quora: « Its a systematic destruction of any indigenous knowledge base. Any knowledge which doesn’t converge with the perpetrator’s knowledge system. It doesn’t believe in fusion or exchange of knowledge but complete disregard of the other’s knowledge. »
Marketing science has developed a large array of research methods to tackle important questions for marketing management. These were recently summarized and illustrated in the Handbook of Marketing Analytics. But which method should be used for which question? Natalie Mizik and Dominique Hanssens will select three of the 2018-2020 MSI Research Priorities—to be announced at the Spring 2018 Board of Trustees Meeting—and review successful applications of various research methods to address these questions and invite audience comments.
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
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.
Bayesian Statistics continues to remain incomprehensible in the ignited minds of many analysts. Being amazed by the incredible power of machine learning, a lot of us have become unfaithful to statistics. Our focus has narrowed down to exploring machine learning. Isn’t it true?
We fail to understand that machine learning is only one way to solve real world problems. In several situations, it does not help us solve business problems, even though there is data involved in these problems. To say the least, knowledge of statistics will allow you to work on complex analytical problems, irrespective of the size of data.
In 1770s, Thomas Bayes introduced ‘Bayes Theorem’. Even after centuries later, the importance of ‘Bayesian Statistics’ hasn’t faded away. In fact, today this topic is being taught in great depths in some of the world’s leading universities.
With this idea, I’ve created this beginner’s guide on Bayesian Statistics. I’ve tried to explain the concepts in a simplistic manner with examples. Prior knowledge of basic probability & statistics is desirable. By the end of this article, you will have a concrete understanding of Bayesian Statistics and its associated concepts.
