Course Introduction
Statistical Analysis with Jamovi Training Course provides a practical and accessible approach to conducting, interpreting, and reporting statistical analyses using jamovi's point-and-click statistical environment. Designed for researchers, analysts, students, educators, and professionals, the course develops the ability to move from raw datasets and research questions to appropriate statistical methods, interpretable results, effect sizes, visualizations, and professionally presented findings without requiring extensive programming knowledge. jamovi is a free and open statistical spreadsheet powered by the R statistical language.
The training begins with the foundations of statistical analysis, including data types, measurement levels, research questions, hypotheses, descriptive statistics, distributions, sampling concepts, and appropriate method selection. Participants learn how to import and organize datasets, define variables, identify missing observations, create computed variables, apply filters, and inspect data before selecting an inferential procedure. jamovi supports direct opening of common formats including CSV, SPSS, SAS, and Stata files, allowing participants to work with datasets from a variety of research and analytical environments.
Participants develop practical skills in descriptive and exploratory analysis before progressing to hypothesis testing and statistical modeling. The course covers frequency distributions, measures of central tendency and variability, confidence intervals, data visualization, correlations, t-tests, analysis of variance, contingency-table analysis, and regression. jamovi's core analysis suite includes one-sample, independent-samples, and paired-samples t-tests, factorial and repeated-measures ANOVA, ANCOVA, correlation, linear and logistic regression, and frequency-based analyses.
A strong emphasis is placed on statistical assumptions and responsible interpretation. Participants learn to evaluate normality, homogeneity, independence, linearity, influential observations, multicollinearity, and other assumptions relevant to particular statistical procedures. Rather than relying solely on p-values, the course introduces effect sizes, confidence intervals, practical significance, graphical evidence, and the relationship between statistical findings and the underlying research question. This approach helps participants communicate results more accurately and avoid common errors in statistical interpretation.
The course also develops skills for producing clear and reproducible statistical reports. jamovi allows users to annotate results directly within analyses and save data, analyses, and results together in a single .omv file, supporting a reproducible workflow that can be reopened and examined by another user. Participants learn how to organize analyses, annotate outputs, copy tables and plots into reports, document analytical decisions, and present findings using appropriate statistical terminology and reporting conventions.
Advanced sessions introduce the broader jamovi ecosystem and demonstrate how specialized analyses can be added through community modules. The jamovi library currently provides more than 70 additional modules covering specialized statistical methods and analytical workflows, allowing users to extend the core environment when research requirements go beyond its built-in procedures. Participants learn how to evaluate and responsibly use additional modules while maintaining transparent analytical workflows. By the end of the course, participants will be able to plan, execute, interpret, validate, and report complete statistical analyses using jamovi.
Duration
5 days
Who Should Attend
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Researchers who need a practical point-and-click environment for quantitative statistical analysis.
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Data analysts performing descriptive, inferential, and predictive statistical analysis on structured datasets.
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Academic researchers preparing statistical analyses for dissertations, theses, journal articles, and research reports.
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Students and postgraduate researchers developing practical skills in applied statistics and quantitative research.
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Social science professionals analyzing survey, experimental, behavioral, or observational datasets.
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Health and medical researchers conducting statistical analysis of clinical, epidemiological, or public-health data.
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Business analysts evaluating customer, operational, financial, marketing, or organizational datasets.
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Monitoring and evaluation professionals analyzing program indicators, survey results, and intervention outcomes.
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Educators and instructors who need a practical environment for teaching statistical concepts and applied analysis.
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Professionals transitioning from spreadsheets toward dedicated statistical analysis software.
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Analysts who want to understand statistical methods without depending on extensive programming or coding skills.
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R users and researchers interested in using jamovi as a graphical interface while maintaining a pathway toward R-based analysis.
Course Objectives
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Understand fundamental statistical concepts, measurement levels, distributions, sampling, hypotheses, and research questions required for selecting appropriate analytical methods.
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Import, inspect, structure, filter, transform, and prepare datasets in jamovi while identifying missing values, incorrect measurement types, and potential data-quality problems.
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Conduct comprehensive descriptive analyses using frequencies, measures of central tendency, variability, confidence intervals, and appropriate graphical representations.
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Select and perform appropriate statistical tests based on research questions, variable types, study designs, assumptions, and the structure of the available data.
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Conduct and interpret one-sample, independent-samples, and paired-samples t-tests while evaluating assumptions, effect sizes, confidence intervals, and practical implications.
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Perform ANOVA and related analyses for comparing multiple groups while interpreting main effects, interactions, post-hoc comparisons, effect sizes, and assumption diagnostics.
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Analyze relationships among variables using correlation and regression techniques while interpreting coefficients, model fit, uncertainty, predictors, and practical meaning.
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Apply categorical-data methods including contingency tables and chi-square analyses while evaluating associations, expected frequencies, effect measures, and appropriate interpretation.
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Produce transparent statistical reports by combining analytical results, tables, plots, annotations, methodological explanations, and evidence-based interpretations.
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Develop a complete reproducible statistical analysis workflow in jamovi that progresses from research question and data preparation through analysis, validation, interpretation, and reporting.
Comprehensive Course Outline
Module 1: Statistical Analysis Foundations and Jamovi Environment
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Understanding statistical analysis concepts including populations, samples, variables, measurement levels, parameters, statistics, hypotheses, and sources of analytical uncertainty.
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Exploring the jamovi interface, spreadsheet structure, analysis ribbon, results panel, variable editor, filters, computed variables, and core workflow for statistical analysis.
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Importing common datasets and understanding how data structures, variable types, measurement levels, labels, missing values, and categorical levels influence statistical procedures.
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Establishing a structured analytical workflow that connects research questions, data preparation, method selection, statistical output, interpretation, and reporting.
Module 2: Data Preparation and Exploratory Analysis
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Preparing analytical datasets by identifying missing observations, duplicates, invalid values, unusual records, incorrect variable types, and inconsistent categorical coding.
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Creating computed variables, recoding categories, transforming measurements, applying filters, and documenting analytical decisions before conducting inferential analyses.
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Using descriptive statistics and exploratory plots to understand distributions, central tendency, variability, outliers, skewness, and relationships among variables.
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Developing an exploratory analysis strategy that uses both numerical summaries and graphical evidence to identify important characteristics of the dataset.
Module 3: Descriptive Statistics and Data Visualization
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Calculating and interpreting means, medians, modes, standard deviations, variances, ranges, percentiles, frequencies, proportions, and other descriptive measures.
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Selecting appropriate summaries according to variable type, distribution characteristics, research objectives, and the intended audience for the statistical report.
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Creating and interpreting histograms, boxplots, scatterplots, bar charts, and other graphical summaries to communicate patterns and potential anomalies.
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Using confidence intervals and uncertainty measures to provide more informative descriptions of sample estimates and avoid relying exclusively on point estimates.
Module 4: Hypothesis Testing and T-Tests
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Understanding null and alternative hypotheses, significance levels, p-values, confidence intervals, test statistics, statistical power, and the distinction between statistical and practical significance.
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Performing one-sample t-tests to compare sample means with specified reference values while interpreting assumptions, uncertainty, effect sizes, and substantive meaning.
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Conducting independent-samples t-tests for comparing two groups while evaluating group characteristics, variance assumptions, confidence intervals, and standardized effects.
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Applying paired-samples t-tests to repeated or matched observations while interpreting within-subject changes, effect sizes, assumptions, and research implications.
Module 5: ANOVA, ANCOVA, and Group Comparisons
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Understanding the logic of analysis of variance, between-group and within-group variation, F-statistics, significance testing, and the interpretation of group differences.
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Performing one-way and factorial ANOVA while evaluating main effects, interactions, estimated differences, post-hoc comparisons, and effect-size measures.
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Applying repeated-measures ANOVA to within-subject designs while considering measurement structure, sphericity-related issues, interactions, and appropriate interpretation.
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Introducing ANCOVA and related modeling concepts for comparing groups while accounting for relevant continuous covariates and evaluating model assumptions.
Module 6: Correlation and Regression Analysis
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Exploring Pearson and related correlation approaches to quantify relationships between variables while considering direction, magnitude, uncertainty, and underlying assumptions.
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Building and interpreting linear regression models using predictors, outcomes, coefficients, confidence intervals, model fit, residual diagnostics, and practical interpretation.
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Understanding multiple regression concepts including predictor adjustment, multicollinearity, incremental relationships, standardized effects, and interpretation of model coefficients.
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Introducing logistic regression concepts for binary outcomes and explaining how predictor variables can be used to model probabilities and classification-related outcomes.
Module 7: Categorical Data and Non-Parametric Analysis
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Creating contingency tables and applying chi-square tests to evaluate relationships between categorical variables while checking expected frequencies and interpreting associations.
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Understanding when categorical-data methods are appropriate and how research design, measurement level, sample size, and expected cell counts influence analytical choices.
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Introducing non-parametric alternatives for situations where distributional assumptions or measurement characteristics make conventional parametric procedures inappropriate.
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Comparing analytical approaches based on research questions, assumptions, measurement scales, robustness, interpretability, and the characteristics of the observed dataset.
Module 8: Statistical Assumptions, Effect Sizes, and Robust Interpretation
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Evaluating assumptions such as normality, homogeneity of variance, independence, linearity, influential observations, and multicollinearity before interpreting statistical model results.
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Understanding effect sizes and confidence intervals as complementary evidence that helps communicate the magnitude and uncertainty of observed statistical relationships.
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Identifying common interpretation errors involving p-values, multiple comparisons, statistical significance, causal language, sample size, and generalization beyond the study design.
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Developing balanced statistical conclusions that integrate numerical results, graphical evidence, assumptions, uncertainty, research context, limitations, and practical relevance.
Module 9: Advanced Jamovi Modules and Reproducible Statistical Reporting
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Exploring the jamovi module library and understanding how community-developed modules can extend the platform with specialized statistical procedures and analytical capabilities.
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Understanding the relationship between jamovi and R, including how jamovi analyses are powered by R and how syntax mode can provide visibility into corresponding R analysis code.
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Creating annotated statistical outputs and organizing tables, plots, notes, and analytical results so that statistical findings can be incorporated efficiently into professional reports.
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Saving complete jamovi projects and establishing reproducible analytical workflows in which datasets, analyses, results, and methodological decisions remain available for review and future use.
Module 10: End-to-End Statistical Analysis Capstone Project
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Defining a realistic research or business question and developing an analytical plan that identifies variables, hypotheses, appropriate statistical methods, assumptions, and reporting requirements.
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Importing and preparing the project dataset, conducting exploratory analysis, selecting appropriate statistical procedures, and documenting analytical decisions throughout the workflow.
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Performing the required descriptive, inferential, and modeling analyses while evaluating assumptions, effect sizes, confidence intervals, statistical significance, and practical interpretation.
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Producing and presenting a complete statistical report containing appropriate tables, visualizations, statistical findings, interpretations, limitations, and reproducibility documentation.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
