Jamovi
 | |
| Stable release | 2.2.50
/ 22 November 2021 |
|---|---|
| Preview release | 2.3.9
/ 28 May 2022 |
| Repository | Jamovi Github page |
| Written in | C++, R, JavaScript |
| Engine | |
| Operating system | Microsoft Windows, Mac OS X, Linux, and ChromeOS |
| Type | Statistics |
| License | GNU Affero General Public License and GNU General Public License |
| Website | www |
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Jamovi ( officially spelt: jamovi ) is a free and open-source computer program for data analysis and performing statistical tests. The core developers of Jamovi are Jonathon Love, Damian Dropmann, and Ravi Selker who were previously developers for the JASP project.[1] Jamovi has a GUI which is powered by HTML5 and javascript[2] and includes the R package jmv which allows for code taken from Jamovi to run natively in an R environment.[3] Data is entered into a spreadsheet interface similar to programs such as JASP and SPSS.[4] Further transformation and analysis of the data can be conducted through the base program with additional extensionality added though community crafted add on modules.[5] As of May 2022 Jamovi and it's add on modules have been cited in over 9,000 academic articles across multiple academic disciplines.
Analyses
Jamovi is a neutral platform in terms of statistical philosophies allowing equally for the inclusion of both frequentists and Bayesian statistical frameworks. The software allows for descriptive statistics and their graphical display,[6] as well as a range of inferential tests, including paired and independent t tests, Wilcoxon, Mann-Whitney, one-sample t tests, ANOVA, Kruskal-Wallis, repeated measures ANOVA,[7][8] Friedman, ANCOVA, MANCOVA, correlations, partial correlations, semipartial correlations, Bayesian regression,[9] linear regression,[10] binomial logistic regression,[11] multinomial logistic regression, ordinal logistic regression, binomial test, multinomial (Chi Square Goodness of Fit), Chi Squared (test of association/independence), McNemar test, log-linear regression, principal components analysis, exploratory factor analysis,[12] confirmatory factor analysis [13] and reliability analysis.[14] Other functions, such as cluster analysis, structural equation modeling,[15] meta-analysis, power analysis, and survival analysis, are available as downloadable modules.
Interface
Menu-driven, the results screen is split vertically from the analysis input. The results and options are updated as the input data and options change. Data may be imported from text files (as comma-separated values), spreadsheets or as data files from SPSS, SAS and Stata.[16]
Modules
There are number modules which are all created and maintained by members of the Jamovi Community. These additional modules help extend the functionality of Jamovi and are installed though the program in the Jamovi library. All modules are programmed in R with a wrapper used to create menus and the analysis output.
| Module Name | Author(s) | Description |
| Advanced Mediation Models | Marcello Gallucci | Mediational models, including multiple mediators, and conditional mediation |
| Analysis Of Data From Q-Methodology | Thomas Leblay, Juliette Victoire, and Sébastien Lê | Analyze Q-methodology data. |
| Base R | Jonathon Love, Damian Dropmann, and Ravi Selker | A simple module which makes the analyses from the stats package (included with R) useable from jamovi. |
| Bland-Altman Method Comparison | Deepankar Datta | Bland-Altman method comparison analysis. |
| Cluster Analysis | Hyunsoo Seol | This module allows users to analyze k-means and hierarchical clustering,Correspondence Analysis, Multiple Factor Analysis, Discriminant Analysis, Multidimensional Scaling, and various visualization results. |
| Conducts A One-Sample Z-Test | W. Joel Schneider | Conducts a one-sample z-test |
| Continuous Norming | Wolfgang Lenhard and Alexandra Lenhard | Conventional and regression-based continuous norming methods for psychometric, biometric and medical test construction. |
| Correlations Suite For Jamovi | Hyunsoo Seol | Tool for calculating correlations such as Pearson, Partial, Tetrachoric, Polychoric, Spearman, Intraclass correlation, Bootstrap agreement, Multilevel correlation, Concordance correlation, and Analytic Hierarchy Process. |
| Death Watch | Alberto Alvarez-Iglesias and Jonathon Love | Survival analysis |
| Demonstrations | Gasper Cankar | Simple simulations to help students visualize first lessons in probability. |
| Descriptives Functions For Clinicopathological Research | Serdar Balci | Descriptives Functions for Clinicopathological Research Descriptive functions from ClinicoPath jamovi module. |
| Distraction - Quantiles And Probabilities Of Continuous And Discrete Distributions | Michael Rihs and Boris Mayer | A tool for calculating and plotting the cumulative distribution function (CDF) and the quantile function (Inverse CDF) for a number of discrete and continuous distributions. |
| Effect Sizes And Confidence Intervals For R And Jamovi | Bob Calin-Jageman and Geoff Cumming | Analyses that focus on effect sizes, uncertainty, and synthesis. |
| Flexible And Robust Agreement And Reliability Analyses | Aaron R. Caldwell | Agreement and Reliability Analyses for nested designs. |
| Flexplot - Graphically Based Data Analysis | Dustin Fife | The flexplot suite is a graphically-based set of tools for doing data analysis. |
| Functions For Medical Decision In Clinicopath Jamovi Module | Serdar Balci | Interobserver and intraobserver reliability and decision tests (sensitivity, specificity, PPV, NPV). |
| General Analyses For Linear Models In Jamovi | Marcello Gallucci | A suite for estimation of linear models, such as the general linear model, linear mixed model, generalized linear models and generalized mixed models. |
| Item Response Theory For Jamovi | Hyunsoo Seol | Item and person Statistics, Model fit, Differential Item Functioning, Wright Map, Expected Scores Curve,and Item Characteristic Curves for DIF using MML estimation of the Rasch measurement model. |
| Jamovi Arcade | Ravi Selker | Play games such as hangman and blackjack. |
| JPOWER: Power Analysis For Common Research Designs | Richard D. Morey and Ravi Selker | Power analysis for common research designs |
| JSQ - Bayesian Methods | The JASP Team, Damian Dropmann, Ravi Selker, and Jonathon Love | Bayesian statistical methods, including t-tests, ANOVAs, linear models, and contingency tables. These tests are a port of the Bayesian analyses from the JASP statistical software. |
| Learning Statistics With Jamovi | Danielle Navarro and David Foxcroft | This module provides examples data sets to accompany the book learning statistics with jamovi. |
| MAJOR - Meta-Analysis For Jamovi R | W. Kyle Hamilton | Meta-analysis and publication bias assessment using the R package metafor[17] |
| MedMod | Ravi Selker | Simple mediation and moderation analysis. |
| Moretests - Adds More Tests To The Jamovi Analyses | Jonathon Love and Victor Moreno | Adds additional normality tests (Kolmogorov-Smirnov and Anderson-Darling) and homogeneity of variances (Bartlett's) to the jamovi t-tests, One-way ANOVA, ANOVA, ANCOVA and linear regression |
| Multivariate Exploratory Data Analysis | Thomas Leblay, Fiona Tuffin, Maxime Saland, and Sébastien Lê | Multivariate exploratory analyses. Principal Component Analysis, Correspondence Analysis and Multiple Correspondence Analysis. |
| Parallel Use Of Statistical Packages In Teaching | Companion to the Rosetta Stats book, containing the example datasets used in the book, | |
| Path Analysis | Marcello Gallucci | Path Analysis based on the R package lavaan[18] |
| Psychometrics & Post-Data Analysis | Lucas Friesen | Post-data analyses in pyschometric research. |
| R Data Sets | The R Core Team | This module provides the example data sets from R |
| Randomize Common Experimental Designs | Michael Bomford | Randomize balanced single factor or full factorial experiments using Completely Randomized, Randomized Complete Block, or Latin Square designs. Generate plot maps, plot lists, and design property tables. |
| Rasch Mixture Model For Jamovi | Hyunsoo Seol | Conduct Latent class analysis, Rasch model, and Rasch mixture model including model information,fit statistics,and bootstrap fit based on JMLE. |
| RJ - Editor To Run R Code Inside Jamovi | Jonathon Love | Provides an editor allowing you to enter R code, and analyse your data using R inside jamovi. |
| Scatr | Ravi Selker | Produce several types of exploratory plots such as scatter plots and pareto charts. |
| SEM | Marcello Gallucci and Sebastian Jentschke | Structural Equation Models based on the R package lavaan[18] |
| Sensory Evaluation Data Analysis | Thomas Leblay, Fiona Tuffin, Thomas Vincent, Maëlle Beaudinet, Maxime Saland, and Sébastien Lê | This module allows you to analyze two types of perception data |
| Statkat - Method Selection Tool | Rivka M. de Vries | This tool will help you to find an appropriate statistical method given your research question and the measurement level of your data. |
| Survey Plots | Ravi Selker | Generates summary plots for your survey data. |
| Survival Module Of Clinicopath For Jamovi | Serdar Balci | Analysis for Clinicopathological Research Survival Module of ClinicoPath. |
| Tools For Behavior Change Researchers And Professionals | Gjalt-Jorn Peters, Rik Crutzen, and Stefan Gruijters | Specialised analyses and visualisation tools for research in and application of behavior change science. |
| TOSTER | Daniel Lakens | Two one-sided tests (TOST) procedures to test equivalence for t-tests and correlations. |
| UFS: Tools For Confidence Intervals And Other Tricks | The ufs module makes functions from the eponymous R package available in jamovi. | |
| Walrus - Robust Statistical Methods | Jonathon Love and Patrick Mair | Robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. |
| Wrapper For Ggstatsplot | Serdar Balci | A wrapper for ggstatsplot: jjstatsplot help researchers to generate plots in jamovi based on ggstatsplot[19] package. |
Literature
- Navarro, DJ and Foxcroft, DR (2022). learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version 0.75).[20]
- Davis, C (2019) Statistical Testing with jamovi and JASP Open Source Software Psychology[21]
- Quintana, D (2020) Reproducible Meta-analysis [Conference presentation] (In)credible Research conference.[22]
External links
References
- ↑ Edelsbrunner, Peter (2017-03-23). "Introducing jamovi: Free and Open Statistical Software Combining Ease of Use with the Power of R". JEPS Bulletin. Retrieved 2022-05-28.
- ↑ "Advanced customisation of the options UI". dev.jamovi.org. Retrieved 2022-05-28.
- ↑ Muenchen, Bob (2018-02-14). "jamovi for R: Easy but Controversial | r4stats.com". r4stats.com. Retrieved 2022-05-28.
- ↑ Şahin, Murat; Aybek, Eren (2019-12-20). "Jamovi: An Easy to Use Statistical Software for the Social Scientists". International Journal of Assessment Tools in Education. 6 (4): 670–692. doi:10.21449/ijate.661803. ISSN 2148-7456. Unknown parameter
|s2cid=ignored (help) - ↑ Davis, C. (ed.) (2019) Statistical testing with jamovi and JASP open source software Psychology. Vor Press.
- ↑ Ahmed, A.A. & Muhammad, R.A. (2021), A Beginners Review of Jamovi Statistical Software for Economic Research. Dutse International Journal of Social and Economic Research, 6 (1).
- ↑ Reyhanlioglu, C. (2022) Jamovi ile Veri Analizi. Pegem Akademi Yayincilik.
- ↑ Strunk, K.K. & Mwavita, M. (2021) Design and Analysis in Educational Research Using jamovi: ANOVA Designs. Routledge.
- ↑ "Advanced Bayesian regression in jamovi". Rens van de Schoot. Retrieved 2022-05-28.
- ↑ Richardson, P. & Machan, L. (2021) Jamovi for Psychologists. Red Globe Press.
- ↑ Friesen, Lucas (2019). Psychometrics & post-data analysis : a software implementation for binary logistic regression in jamovi (Thesis). University of British Columbia. doi:10.14288/1.0380439. Unknown parameter
|s2cid=ignored (help) - ↑ Exploratory factor analysis — jamovi, retrieved 2022-05-28
- ↑ Navarro DJ and Foxcroft DR (2022). Learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version 0.75). DOI: 10.24384/hgc3-7p15
- ↑ Exploratory factor analysis — jamovi, retrieved 2022-05-28
- ↑ Gallucci, Marcello. "SEMLj: Structural Equation Models in jamovi". SEMLj: Structural Equation Models in jamovi. Retrieved 2022-05-28. Unknown parameter
|url-status=ignored (help) - ↑ Hoyt, R. & Muenchen, R. (2020) Data Preparation and Exploration: Applied to Healthcare Data. Informatics Education.
- ↑ Viechtbauer, Wolfgang (2010). "Conducting Meta-Analyses in R with the metafor Package". Journal of Statistical Software. 36 (3). doi:10.18637/jss.v036.i03. ISSN 1548-7660.
- ↑ 18.0 18.1 Rosseel, Yves (2012). "lavaan : An R Package for Structural Equation Modeling". Journal of Statistical Software. 48 (2). doi:10.18637/jss.v048.i02. ISSN 1548-7660.
- ↑ Patil, Indrajeet (2021-05-25). "Visualizations with statistical details: The 'ggstatsplot' approach". Journal of Open Source Software. 6 (61): 3167. Bibcode:2021JOSS....6.3167P. doi:10.21105/joss.03167. ISSN 2475-9066. Unknown parameter
|s2cid=ignored (help) - ↑ Navarro, Danielle J; Foxcroft, David R (2018). Learning statistics with jamovi: a tutorial for psychology students and other beginners. Danielle J. Navarro and David R. Foxcroft. doi:10.24384/hgc3-7p15. Search this book on
- ↑ Statistical testing with jamovi and JASP open source software : psychology. Cole Davis. Norwich. 2019. ISBN 978-1-9164779-9-5. OCLC 1224060553. Search this book on
- ↑ Quintana, Daniel S. (2020-10-29). "Reproducible Meta-analysis". Daniel S. Quintana. Retrieved 2022-05-29.
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