Reaction time (RT) is one of the most common types of measure used in experimental psychology. Its distribution is not normal (Gaussian) but resembles a convolution of normal and exponential distributions (Ex-Gaussian). One of the major assumptions in parametric tests (such as ANOVAs) is that variables are normally distributed. Hence, it is acknowledged by many that the normality assumption is not met. This paper presents different procedures to normalise data sampled from an Ex-Gaussian distribution in such a way that they are suitable for parametric tests based on the normality assumption. Using simulation studies, various outlier elimination and transformation procedures were tested against the level of normality they provide. The results suggest that the transformation methods are better than elimination methods in normalising positively skewed data and the more skewed the distribution then the transformation methods are more effective in normalising such data. Specifically, transformation with parameter lambda -1 leads to the best results.

Financial inclusion is a fundamental and multidimensional matter that has acquired importance on the global agenda in recent years. In addition, it is still a source of great interest and concern for lawmakers, international organizations, scholars, and financial institutions worldwide. In that regard, this research focuses on Peru to assess the country’s financial inclusion condition, which continues to face significant hurdles in providing financial services to its whole population despite economic improvement. The aim of this article is twofold, based on recent data on demand for financial services and financial culture in the country: (1) to empirically test how machine learning methods, such as decision trees, random forests, artificial neural networks, XGBoost, and support vector machines, can be a valuable complement to standard models (i.e., generalized linear models like logistic regression) for assessing financial inclusion in Peru, and (2) to identify the most influential sociodemographic factors on financial inclusion assessment in the country. The results may catalyze the integration of machine learning techniques into the Peruvian financial system, garnering the interest of finance researchers and policymakers committed to augmenting financial access and utilization among Peruvian consumers.

The growth of small-scale Peruvian farmers is highly dependent on cocoa factor productivity. Agricultural extension programs can help to improve farm productivity using available resources. Thus, the objective of this study is to estimate the productivity of Peruvian cocoa farming and identify if frequent technical assistance impacts on farmers’ technical efficiency. The data came from a survey of 379 cocoa farmers in Tocache, San Martin (177 producers who sell through intermediaries and 202 who are cooperative members), conducted between January and June 2015. This article is supported by the interaction of two associated techniques: the production function and the technical efficiency technique based on stochastic frontier analysis (SFA). The key findings were that the estimated coefficients for labor, capital, land and fertilizer were positive to cocoa production. Our outcomes also show there is a (marginally) significant relationship between technical assistance and technical efficiency (p-value<10%). There is also a positive relationship between efficiency and other socioeconomic characteristics of being a male, having experience in cocoa cultivation and practicing non-diversification in other crops. Policymakers could consider these results to improve farm management systems and, therefore, the competitiveness of the cocoa plantations in the Peruvian Amazonia.

Quinoa is a traditional food grain that originated in the Peruvian Andean region. The United Nations declared 2013 to be the International Year of Quinoa (iyq). This official launch had a great impact around the world. On the contrary, it had minor impact on the consumption of quinoa in Peru, which remained relatively steady in the following years. However, the covid-19 pandemic raised concerns about nutrition and health among consumers. Therefore, this study seeks to analyze quinoa consumption in Peru during the covid-19 pandemic. Primary data were collected between September 2020 and August 2021 in Lima Metropolitan Area, Peru. Exploratory factor analysis with varimax rotation was performed for data analysis, and logistic binomial analysis was then conducted to consolidate the hypothesis of this study. The main outcomes identified were that (i) current quinoa consumers in Peru ate quinoa even before the iyq; (ii) consumers who are concerned about their health and nutrition needs increased quinoa consumption during the pandemic; (iii) women showed a higher probability of daily to weekly trend in quinoa consumption; and (iv) people with the highest income have more probability of purchasing food at supermarkets than those who earn less. The findings of this study can shed some light on consumers’ expectations and perceptions regarding quinoa consumption behavior during covid-19.

This study evaluates financial inclusion in Peru through self-organizing maps. Financial inclusion is a multidimensional issue of great importance on the global agenda and continues to concern various actors internationally. In this context, the objective is to assess the financial inclusion situation in the country and determine how self-organizing maps can complement standard models for this purpose. The empirical aim is to demonstrate how this technique can help identify priority areas and vulnerable groups, thus facilitating decision-making and policy design to improve the access to and use of financial services among Peruvian consumers by finding clearly defined profiles that allow the identification of potential problems within each category. This makes it possible to create customized strategies for each group, such as addressing the financial inclusion barriers faced by rural residents, compounded by low income and educational levels.