Course Introduction
Statistical Analysis with JASP Training Course provides comprehensive practical training in quantitative data analysis using JASP, an open-source statistical application designed around an intuitive graphical interface. The course helps participants progress from raw datasets and research questions to descriptive analysis, hypothesis testing, statistical modeling, effect-size estimation, interpretation, and professional reporting. JASP supports both classical frequentist and Bayesian statistical approaches, making it particularly useful for analysts and researchers who want to understand and compare complementary approaches to statistical inference.
The training begins with the foundations required to conduct sound statistical analysis, including measurement levels, variables, distributions, sampling, research questions, hypotheses, confidence intervals, statistical significance, effect sizes, and assumptions. Participants learn how to import datasets, define variable types, inspect data quality, recode variables, create computed variables, apply filters, and explore distributions before selecting an appropriate statistical procedure. JASP supports a broad range of common data formats, including CSV, SPSS, SAS, Excel, Stata, and R data files, enabling participants to work with datasets originating from different analytical environments.
Participants develop practical skills in descriptive and exploratory statistics before progressing to inferential methods. The course covers descriptive statistics, visual exploration, one-sample and independent-samples t-tests, paired-samples tests, ANOVA, ANCOVA, repeated-measures ANOVA, MANOVA, correlation, linear and logistic regression, contingency tables, and other statistical procedures. JASP's current feature set provides many of these analyses in both classical and Bayesian forms, allowing participants to understand how different inferential frameworks address research questions.
A distinctive part of the course is its treatment of Bayesian statistics. Participants learn the conceptual foundations of prior distributions, likelihood, posterior distributions, credible intervals, and Bayes factors before applying Bayesian t-tests, ANOVA, regression, and contingency-table analyses. The objective is not simply to operate JASP menus, but to understand what the statistical output means, how evidence should be interpreted, and how Bayesian results differ conceptually from conventional p-value-based inference. JASP provides dedicated Bayesian analyses alongside its frequentist procedures, making this comparison practical within a single analytical environment.
The course places substantial emphasis on statistical assumptions, effect sizes, uncertainty, and responsible interpretation. Participants learn how to evaluate normality, homogeneity, independence, linearity, multicollinearity, influential observations, and other assumptions relevant to particular procedures. They also learn why statistical significance should be interpreted alongside effect sizes, confidence or credible intervals, graphical evidence, sample size, research design, and substantive context. JASP provides dynamically updated output and annotated results, supporting an interactive workflow in which analytical choices and results can be reviewed as analyses develop.
The final component focuses on reproducible and publication-oriented statistical reporting. Participants learn how to annotate, organize, export, and communicate statistical results, including APA-formatted tables and publication-ready figures. JASP also provides access to analysis-specific R syntax and supports integration with the Open Science Framework, creating opportunities to connect graphical statistical workflows with reproducible research practices. By the end of the course, participants will be able to design, execute, validate, interpret, and report complete statistical analyses in JASP using appropriate frequentist and Bayesian methods.
Duration
5 days
Who Should Attend
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Researchers who need a practical graphical environment for conducting quantitative statistical analysis.
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Data analysts working with descriptive, inferential, predictive, or exploratory statistical methods.
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Academic researchers preparing statistical analyses for dissertations, theses, journal articles, and research reports.
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Postgraduate students developing applied statistics and quantitative research skills.
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Social science researchers analyzing survey, behavioral, experimental, or observational datasets.
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Health and medical researchers working with clinical, epidemiological, or public-health datasets.
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Business analysts evaluating customer, marketing, financial, operational, or organizational data.
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Monitoring and evaluation professionals analyzing survey results, program indicators, and intervention outcomes.
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Educators and instructors teaching applied statistics, research methods, or quantitative analysis.
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Professionals transitioning from spreadsheets or basic statistical tools to dedicated statistical software.
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Analysts who want to understand both frequentist and Bayesian approaches through an accessible graphical interface.
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Researchers interested in reproducible statistical workflows and publication-ready analytical reporting.
Course Objectives
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Understand core statistical concepts including variables, measurement levels, distributions, sampling, hypotheses, uncertainty, effect sizes, and statistical inference.
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Import, inspect, prepare, transform, filter, and organize datasets in JASP while identifying data-quality and measurement-level issues.
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Conduct comprehensive descriptive analyses using appropriate numerical summaries, distributions, confidence intervals, and graphical exploratory techniques.
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Select appropriate statistical procedures based on research questions, study designs, variable types, assumptions, and characteristics of the available data.
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Perform and interpret one-sample, independent-samples, and paired-samples t-tests while evaluating assumptions, effect sizes, confidence intervals, and substantive implications.
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Conduct ANOVA, ANCOVA, repeated-measures ANOVA, and related analyses while interpreting main effects, interactions, post-hoc comparisons, and effect sizes.
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Apply correlation and regression techniques to investigate relationships and predictive models while interpreting coefficients, model fit, uncertainty, and assumptions.
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Understand and apply Bayesian statistical methods including prior distributions, posterior distributions, credible intervals, Bayes factors, and Bayesian model comparisons.
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Produce clear statistical reports using appropriate tables, figures, annotations, effect sizes, statistical notation, interpretations, and methodological explanations.
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Complete a reproducible end-to-end JASP analysis project that integrates data preparation, exploratory analysis, statistical testing, Bayesian inference, interpretation, and professional reporting.
Comprehensive Course Outline
Module 1: Statistical Foundations and JASP Environment
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Understanding populations, samples, variables, measurement scales, parameters, statistics, hypotheses, uncertainty, and the role of statistical inference in research.
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Exploring the JASP interface, data grid, analysis menus, results panel, variable editor, filters, computed variables, and interactive analytical workflow.
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Importing common data formats and configuring variable types, measurement levels, labels, missing values, and categorical levels before analysis.
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Establishing a complete analytical workflow that connects research questions, data preparation, method selection, statistical output, interpretation, and reporting.
Module 2: Data Preparation and Exploratory Statistics
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Identifying missing observations, duplicate records, invalid values, unusual observations, inconsistent coding, and incorrect measurement levels before statistical analysis.
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Creating computed variables, recoding categories, applying filters, and transforming variables to prepare datasets for specific research questions.
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Exploring distributions through descriptive statistics, histograms, boxplots, scatterplots, frequency tables, and other graphical techniques.
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Using exploratory analysis to identify patterns, outliers, potential assumptions violations, and relationships that should inform subsequent statistical procedures.
Module 3: Descriptive Statistics and 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 descriptive summaries according to variable type, distribution characteristics, research objectives, and analytical context.
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Developing effective graphical summaries using histograms, boxplots, scatterplots, raincloud plots, bar charts, and other available visualization techniques.
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Using descriptive statistics and visual evidence together to communicate distributions, variability, group differences, relationships, and unusual observations.
Module 4: Frequentist 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 common hypothesis-testing errors.
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Performing one-sample t-tests to compare sample means against reference values while interpreting assumptions, confidence intervals, effect sizes, and practical meaning.
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Conducting independent-samples t-tests to compare groups while evaluating variance assumptions, descriptive differences, uncertainty, and standardized effect sizes.
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Applying paired-samples t-tests to repeated or matched observations while interpreting within-subject changes, confidence intervals, effect sizes, and research implications.
Module 5: ANOVA and Advanced Group Comparisons
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Understanding analysis of variance through between-group and within-group variation, F-statistics, significance testing, effect sizes, and post-hoc comparisons.
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Performing one-way and factorial ANOVA while interpreting main effects, interactions, estimated marginal means, contrasts, and relevant effect-size measures.
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Applying repeated-measures ANOVA and ANCOVA to appropriate research designs while evaluating assumptions and interpreting complex group and within-subject effects.
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Introducing MANOVA and multivariate group-comparison concepts while considering multiple dependent variables, assumptions, and appropriate interpretation.
Module 6: Correlation, Regression, and Predictive Analysis
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Conducting Pearson correlation analyses to evaluate relationships between variables while interpreting direction, magnitude, confidence intervals, significance, and practical relevance.
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Building linear regression models using predictors and outcomes while interpreting coefficients, model fit, residuals, confidence intervals, and explanatory measures.
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Applying logistic regression concepts to binary outcomes and understanding how predictor variables can be used to model probabilities and classification-related questions.
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Evaluating regression assumptions including linearity, residual behavior, influential observations, independence, and multicollinearity before interpreting model results.
Module 7: Bayesian Statistics with JASP
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Understanding Bayesian inference through prior distributions, likelihoods, posterior distributions, credible intervals, and the conceptual meaning of updating beliefs with evidence.
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Performing Bayesian one-sample, independent-samples, and paired-samples t-tests while interpreting Bayes factors, posterior estimates, and credible intervals.
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Applying Bayesian ANOVA and regression to investigate group differences and predictor relationships while comparing evidence for competing statistical models.
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Comparing frequentist and Bayesian results and identifying situations where Bayes factors, posterior distributions, or credible intervals provide useful complementary evidence.
Module 8: Categorical Data, Factor Analysis, and Advanced Methods
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Analyzing categorical variables using contingency tables, binomial tests, multinomial methods, chi-square-related procedures, and appropriate association measures.
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Introducing principal component analysis, exploratory factor analysis, and confirmatory factor analysis for investigating underlying structures among measured variables.
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Exploring advanced analytical extensions including mixed models, reliability analysis, mediation, meta-analysis, survival analysis, and time-series methods where appropriate.
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Evaluating specialized JASP modules and understanding how additional modules can extend the platform into advanced research and statistical-analysis domains.
Module 9: Assumptions, Effect Sizes, and Statistical Interpretation
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Evaluating normality, homogeneity, independence, linearity, multicollinearity, influential observations, and other assumptions relevant to selected statistical procedures.
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Understanding effect sizes as measures of practical magnitude and using them alongside p-values, confidence intervals, credible intervals, and graphical evidence.
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Identifying common statistical interpretation problems involving multiple comparisons, p-value misuse, causal claims, sample size, statistical power, and generalization.
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Developing evidence-based statistical conclusions that integrate numerical output, assumptions, uncertainty, research design, limitations, and substantive research questions.
Module 10: Reproducible Analysis and Statistical Reporting Capstone
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Developing a complete research or business analysis plan that connects research questions and hypotheses to variables, statistical procedures, assumptions, and reporting requirements.
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Performing data preparation, exploratory analysis, frequentist testing, Bayesian analysis, effect-size estimation, assumption assessment, and interpretation within a structured JASP project.
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Creating publication-ready tables and figures, annotating analytical results, and preparing statistical findings using appropriate reporting conventions and clear methodological explanations.
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Presenting the completed statistical analysis and demonstrating how JASP can support transparent, reproducible, interpretable, and professionally communicated quantitative research.
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.
