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The COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities have all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chain. As a result, we examine the food supply chain, which is one of the key industries COVID-19 has detrimentally affected, impacting, indeed, on the entire business process from the supplier all the way to the customer. Retail businesses are thus facing supply issues, which affect consumer behavior by creating stress regarding the availability of food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. This study examines the impact of COVID-19 on the United Arab Emirates food distribution systems and how consumer behavior changed in reaction to interruptions in the food supply chain and the food security problem. Hypothesis testing was used in the study’s quantitative methodology to assess consumer behavior, and participants who were consumers were given a descriptive questionnaire to ascertain whether the availability and security of food had been impacted. The study used JASP 0.17.2 software to develop a model of food consumption behavior and to reveal pertinent connections between each construct. Results show that consumer food stress and consumption behavior are directly impacted by food access, food quality and safety, and food pricing. Furthermore, food stress has an impact on how consumers behave when it comes to consumption. Food stress, however, is not significantly influenced by food supply.

This study aimed to thoroughly describe the reading learning models in junior high school through a careful meta-analysis and from a cultural perspective that affects student learning achievement. Articles selected for investigation were restricted from 2012 to 2021. Quantitative analysis encompassed essential measures such as sample size, standard deviations, and mean. The JASP software was used for analysis, which has various calculations, including population correlation means, variation coefficient of variance, sampling error variance, population correlation variance estimation, correlation-based interval calculation, and publication bias analysis. The findings revealed the existence of 12 heterogeneous articles, each displaying a solid effect size value. The identified effective reading learning models and strategies, namely Numbered Heads Together (NHT), Think Pair Share (TPS), Teams Games Tournament (TGT), Cooperative Integrated Reading and Composition (CIRC), and Team Assisted Individualization (TAI), highlighted the clear boundaries between conventional and non-conventional learning approaches. In addition, there was no visible publication bias, as evidenced by the funnel plot analysis. The findings were further bolstered in trustworthiness and reliability, making them suitable as references for instructional purposes.

Background: In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable. Methods and Material: A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys's Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability. Results and Conclusions: The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems.

Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this ‘typical CFA’, leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan  software platforms. Supplementary materials such as videos, files, and scripts are freely available.

Background Although null hypothesis significance testing (NHST) is the agreed gold standard in medical decision making and the most widespread inferential framework used in medical research, it has several drawbacks. Bayesian methods can complement or even replace frequentist NHST, but these methods have been underutilised mainly due to a lack of easy-to-use software. JASP is an open-source software for common operating systems, which has recently been developed to make Bayesian inference more accessible to researchers, including the most common tests, an intuitive graphical user interface and publication-ready output plots. This article provides a non-technical introduction to Bayesian hypothesis testing in JASP by comparing traditional tests and statistical methods with their Bayesian counterparts. Results The comparison shows the strengths and limitations of JASP for frequentist NHST and Bayesian inference. Specifically, Bayesian hypothesis testing via Bayes factors can complement and even replace NHST in most situations in JASP. While p -values can only reject the null hypothesis, the Bayes factor can state evidence for both the null and the alternative hypothesis, making confirmation of hypotheses possible. Also, effect sizes can be precisely estimated in the Bayesian paradigm via JASP. Conclusions Bayesian inference has not been widely used by now due to the dearth of accessible software. Medical decision making can be complemented by Bayesian hypothesis testing in JASP, providing richer information than single p -values and thus strengthening the credibility of an analysis. Through an easy point-and-click interface researchers used to other graphical statistical packages like SPSS can seemlessly transition to JASP and benefit from the listed advantages with only few limitations.

ObjectivesMost pharmaceutical investigations have relied on p values to infer conclusions from their study findings. Central to this paradigm is the concept of null hypothesis significance testing. This approach is however fraught with overuse and misinterpretations. Several alternatives have already been proposed, yet uptake remains low. In this study, we aimed to discuss the pitfalls of p value-based testing and to provide readers with the basics to apply Bayesian statistics.MethodsJeffreys’s Amazing Statistical Package (JASP) was used to evaluate the effect of a clinical pharmacy (CP) intervention (opposed to usual care) on the number of emergency department (ED) visits without hospital admission. Basic Bayesian terminology was explained and compared with classical p value-based testing. In the study example, a Cauchy prior distribution was used to determine the effect size with a scale parameter r=0.707 at location=0 and Bayes factors (BF) were subsequently estimated. A robustness analysis was then performed to visualise the impact of different r values on the BF value.ResultsA BF of 4.082 was determined, indicating that the observed data were about four times more likely to occur under the alternative hypothesis that the CP intervention was effective. The median effect size of the CP intervention on ED visits was found to be 0.337 with a 95% credible interval of 0.074 to 0.635. A robustness check was performed and all BF values were in favour of the CP intervention.ConclusionBayesian inference can be an important addition to the statistical armamentarium of pharmacists, who should become more acquainted with the basic terminology and rationale of such testing. To prove our point, Jeffreys’ approach was applied to a CP study example, using an easy-to-use software program JASP.

SummaryThis paper describes the language for a statistical system named Jasp (JAva based Statistical Processor). Even if a statistical system has an advanced graphical user interface for operations, a language for it is still important in order to have complete control of it. The language is also used to implement new statistical procedures that are not realized in the system. For simplifying these, the Jasp language is designed as a procedural function-based scripting language especially for statistical analysis. The language, at the same time, can treat class-based objects for gathering related functions without difficulty. In addition, it can “glue” Java classes and routines written in native languages, and make them available simply.