Data Analysis Using JAMOVI|Part 1|Statistics Without Complexity|Data Analysis|NLD скачать в хорошем качестве

Data Analysis Using JAMOVI|Part 1|Statistics Without Complexity|Data Analysis|NLD

24 January 2026 (7:00pm to 9:00pm) Resource Person: Dr. Smruti Vakil, Associate Professor, Narayana Business School, Ahmedabad This workshop provides a complete, hands-on introduction to data analysis using JAMOVI, a powerful, user-friendly, and open-source statistical software widely used in academic and professional research. Participants will learn how to import datasets, clean and prepare data, run descriptive statistics, conduct hypothesis testing, perform correlation and regression, and generate publication-ready tables and graphs. The session emphasizes practical application, enabling participants to analyze real datasets step-by-step without coding. JAMOVI’s intuitive interface, integrated statistical output, and APA-style reporting make it an ideal tool for research scholars, teachers, and students who seek accuracy without complex programming. By the end of the workshop, participants will be confident in applying JAMOVI for academic projects, data-driven decision-making, dissertations, surveys, and research publications. Outcomes: After attending this workshop, participants will be able to  Understand the interface and features of JAMOVI.  Import, clean, and manage datasets efficiently.  Perform descriptive statistics: mean, median, mode, frequency tables, charts.  Conduct hypothesis testing (t-test, ANOVA, Chi-square, etc.) using JAMOVI’s statistical modules.  Execute correlation and regression analyses for relationship testing.  Use visualization tools to create bar charts, histograms, boxplots, and scatterplots.  Interpret statistical outputs in APA-style format for academic writing.  Export results and generate research-ready reports with clarity and accuracy.  Apply JAMOVI to real research projects, dissertations, surveys, and decision-making tasks. Details: Introduction to Jamovi Interface and Functionality Dr. Smruti Vakil introduced the Jamovi interface, explaining that variables are visible on the left, data is displayed in the center similar to Excel, and analyses can be performed on the right. They highlighted that users can add more analyses through the modules and the Jamovi library, noting that while the software offers SEM, they still recommend Smart PLS for that specific analysis. Dr. Smruti Vakil praised Jamovi for its immediate display of results and ease of exporting tables or images, making changes instantly visible upon selecting or deselecting options. Core Workshop Objectives and Statistical Test Overview The primary goal of the workshop is to cover fundamental Jamovi analyses, including frequency distribution, t-test, ANOVA, and regression, along with data interpretation. Dr. Smruti Vakil presented a crucial table outlining parametric and non-parametric tests based on the desired analysis (association, pre-post condition, relationship, or impact) and the type of data available. Hypothesis Formulation for Key Statistical Tests Dr. Smruti Vakil provided specific guidance on framing null hypotheses for various tests. For the Chi-square test (association), the hypothesis should state "there is no significant association between X and Y". For t-test and ANOVA (testing variances or differences), the hypothesis should state "there is no significant difference". Hypothesis Formulation for Correlation and Regression For correlation analysis (relationship), using either Pearson or Spearman depending on data normality, the hypothesis is "there is no significant relationship between X and Y". For regression analysis (impact), the hypothesis should state "there is no significant impact of X on Y". Samrat Bandopadhyay, a scholar new to the topic, received a brief explanation of the basic concept of null (H0, negative) and alternative (H1, positive) hypotheses. Setting Up Data in Jamovi for Analysis Dr. Smruti Vakil instructed participants on how to open a data file and modify variable types, specifically advising that all Likert scale questions must be converted to a "continuous" variable type for ANOVA and other advanced tests in Jamovi. Shuchi Mishra raised a concern about changing variables for Chi-square, and Dr. Smruti Vakil clarified that variables do not need to be changed for Chi-square, but Likert scales must be continuous for t-test and ANOVA. Performing and Interpreting the Chi-Square Test Dr. Smruti Vakil demonstrated the Chi-square test (for association) using age and the number of platforms used. They explained that if the p-value is less than 0.05, the null hypothesis of no significant association is rejected, indicating a significant association. Rakesh Pise successfully framed the hypothesis and interpreted a p-value less than 0.05 from an analysis of educational level and platform usage, concluding there is a significant association. Nidhi Pandey also correctly interpreted a Chi-square test on headache and anxiety level, rejecting the null hypothesis.