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JASP - Free and User-Friendly Statistical Software

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Students like working with JASP, and the ability to annotate output provides new and exciting teaching opportunities; for instance, with JASP it is easy to pose questions, present interpretations, guide students through a chain of analyses, grade answers, and provide feedback.

These are just a few of the reasons why statistics lecturers at universities around the world are using JASP in their statistics classes as an alternative to traditional statistical software such as SPSS. The JASP team and JASP community are dedicated to providing teachers with all the resources they need.

The Data Library

JASP comes with a data library of over 50 data sets to illustrate various kinds of analyses. All data sets are accompanied by a .jasp-file that describes the data set and the associated analysis in detail, which makes the JASP Data Library a great tool for teaching. Watch how to access the JASP Data Library (GIF).
A book that documents the data sets and provides example analyses is under development and can be freely accessed here. Teachers and researchers who wish to use the JASP Data Documentation Format can find a LaTeX template here.

The JASP Website

Students new to JASP and/or statistics in general will find guidance on the Getting Started and How to Use JASP pages on our website, where the basic functionality of JASP as well as many of the analyses are explained in detail.

Teaching Material on the OSF

The OSF folder 'Undergraduate Statistics with JASP' by Erin Buchanan and colleagues contains a lot of useful material that teachers can use for teaching statistics with JASP.

JASP Workshop Materials

The JASP team has organized many workshops to teach a broader audience about (Bayesian) statistics with JASP. The workshop materials are listed on the JASP Workshop Materials page. The OSF folder 'Generic JASP Workshop' contains various workshop materials that have been used in different workshops.

JASP Manuals

There exist various manuals on how to use JASP for statistical inference. Bayesian Inference in JASP: A Guide for Students is a 120-page long, in-depth guide on how to perform various forms Bayesian analyses in JASP. Statistical Analysis in JASP. A Guide for Students by Mark Goss-Sampson (PDF) is a 159-page long, in-depth guide on how to perform various forms of frequentist analyses in JASP. Teachers are free to use the guides and share it with their students. For older versions and other JASP manuals, see JASP Materials.
Quantitative Analysis with JASP open-source software is a manual by Chris Halter that provides an introduction to quantitative analysis and is available through Amazon.

JASP Textbook

Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners is a free textbook by Danielle J. Navarro, David R. Foxcroft, and Thomas J. Faulkenberry that covers the basics of statistical inference with JASP.

Other JASP Materials

For more JASP-related material (e.g., other online resources, papers, or books about JASP), you can head over to JASP Materials. This blogpost provides an overview of JASP video tutorials available on YouTube, including videos in Traditional Chinese, Malay, German, Spanish, French, and Arabic.

Help from the JASP Community

The JASP community is ready to help you. For statistical questions, our Twitter page and the JASP forum are the places to be. For any software-related issues, the best option is to visit our GitHub repository. Of course, you can also send us an e-mail.

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Teaching with JASP - JASP - Free and User-Friendly Statistical Software

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Students like working with JASP, and the ability to annotate output provides new and exciting teaching opportunities; for instance, with JASP it is easy to pose questions, present interpretations, guide students through a chain of analyses, grade answers, and provide feedback.

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JASP - Free and User-Friendly Statistical Software

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Before you do anything else, we strongly recommend that you visit the JASP YouTube channel and the 'How to Use JASP' page, where you can find introductory blog posts and videos. Below is some general information about JASP that may be helpful as you analyze your first data set. We invite you to post any remaining questions on the JASP forum or on the JASP GitHub page.

Data Sets

JASP comes with a series of example data sets that can be accessed from the 'File → Open' tab. Selecting an example data set will open it in JASP for inspection, editing, and analysis. In addition to its own .jasp format, JASP can open data sets in formats such as .csv (comma-separated values), .txt (plain text), .sav (IBM's SPSS), and .ods (OpenDocument Spreadsheet). Note that all files need to have a header row that contains names for each of the columns or variables. Missing values can either just be missing (i.e., an empty cell) or be denoted by "Nan", "." (period), or " " (space). When opening a file, JASP makes a best guess to assign variable types, as explained below.

Variable Types

JASP distinguishes four variable types:

Nominal Text variables are categorical variables without numeric value (i.e., strings). An example might be a variable called "Favorite Dutch Soccer Club", with three possible entries: "Ajax", "PSV", and "Feyenoord".

Nominal variables are categorical variables that are represented by numeric values. For example, a variable "Group" may have levels "1" and "2". Even though these are numbers, they do not imply an order, and the distance between them is not meaningful.

Ordinal variables are categorical variables with an inherent order. An example might be a Likert preference rating scale with levels 1 ("hate it"), 2 ("yuk"), 3 ("meh"), 4 ("cool"), and 5 ("in love"). Note that the distance between the numbers is not meaningful. JASP assumes that all ordinal variables have been assigned numeric values.

Continuous variables are variables with values that allow a meaningful comparison of distance. Examples include income, IQ, or weight.

Variable types in JASP are often enforced; for instance, you are not allowed to conduct a t-test when your dependent measure is nominal.

Variable Type Assignment

When loading a file, JASP automatically assigns variable types according to the following rules:

1. If the variable contains only integer values and missing values, and contains fewer than 25 unique values, then it is assigned a variable type of Nominal.

2. If the variable contains only integer values, floating point numbers, missing values, and +/- infinities, then it is assigned a type of Continuous.

3. Otherwise the variable is assigned a type of Nominal Text.

Changing Variable Types

If need be, the automatic variable type assignment can be corrected manually. When you click the icon representing the variable type at the top of the column, a menu is produced that allows you to choose a different variable type. Values incompatible with the new variable type are automatically converted to missing values. But be careful! JASP presently does not feature an "undo" facility, so if you change a Nominal Text column full of text values to a type such as Continuous, the entire column will be converted to missing values. At present, there is no way to undo this action and it will be necessary to reload the data set.

Analyses

Having loaded a data set, it is now possible to run analyses. After selecting an analysis from the ribbon along the top you will see input options for that analysis in the left panel, and the associated output in the right panel. As the input options are specified, the analysis results automatically update, providing immediate feedback.

When the analysis has been specified to your satisfaction, you can return to the data view by clicking the "OK" button. You can return to an earlier analysis simply by clicking on the output of interest. This brings up the options that were used to generate that analysis, and allows you to make adjustments or additions.

There is much more to say about JASP: its ability to annotate output, to interact with the Open Science Framework, to conduct sophisticated analyses using modules, to save APA tables and publication-ready figures, and much more. These options are discussed in our YouTube videos. We are also in the process of writing a JASP manual.

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Getting Started - JASP - Free and User-Friendly Statistical Software

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Before you do anything else, we strongly recommend that you visit the JASP YouTube channel and the 'How to Use JASP' page, where you can find introductory blog posts and videos. Below is some general information about JASP that may... Continue reading →

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JASP - Free and User-Friendly Statistical Software

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How to Use JASP - JASP - Free and User-Friendly Statistical Software

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Welcome to the JASP Tutorial section. Below you can find all the analyses and functions available in JASP, accompanied by explanatory media like blog posts, videos and animated GIF-files. Click on the JASP-logo to go to a blog post, on... Continue reading →

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Replacing p-values with Bayes-Factors A Miracle Cure for the Replicability Crisis in Psychological Science  Replicability-Inde

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Authors:

Jonathon Love, Ravi Selker, Maarten Marsman, Tahira Jamil, Damian Dropmann, Josine Verhagen, Alexander Ly, Quentin F. Gronau, Martin Šmíra, Sacha Epskamp, Dora Matzke, Anneliese Wild, Patrick Knight, Jeffrey N. Rouder, Richard D. Morey, Eric-Jan Wagenmakers

Title:
JASP: Graphical Statistical Software for Common Statistical Designs

Abstract:

This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attractive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a "syntax". Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting practice. The JASP analyses are implemented in R and a series of R packages.

Paper:

JASP: Graphical Statistical Software for Common Statistical Designs Download PDF (Downloads: 3063)

Supplements:

jasp-desktop-0.9.1.zip: Source code

Download (Downloads: 123; 50MB)

EyeMovements.csv: Supplementary data (CSV format)

Download (Downloads: 120; 832B)

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jasp; statistical software; bayesian inference; graphical user interface; basic statistics

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JASP: Graphical Statistical Software for Common Statistical Designs | Love | Journal of Statistical Software

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Authors: Jonathon Love, Ravi Selker, Maarten Marsman, Tahira Jamil, Damian Dropmann, Josine Verhagen, Alexander Ly, Quentin F. Gronau, Martin Šmíra, Sacha Epskamp, Dora Matzke, Anneliese Wild, Patrick Knight, Jeffrey N. Rouder, Richard D.

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gli ideatori

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Association for Psychological Science - APS

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The last few decades have witnessed the growth of SPSS as the statistical software of choice across a wide range of academic disciplines. Its pedigree reaches back as far as the 1960s, when SPSS was first programmed with punch cards. As computing advanced, SPSS continued to keep pace; next, it became available for mainframe environments, and then, with the rise of the "personal computer," SPSS became available for command-based DOS environments. Finally, with the ascent of Microsoft Windows and other graphical operating systems, SPSS became equipped with a graphical user interface. Over the years, this ability to adapt and change has allowed SPSS to remain relevant and helpful for both teaching and research.

More recently, however, SPSS has not been as actively developed as it once was. Although statisticians have proposed many useful analyses in the last few years, many of these proposals remain unavailable in SPSS. Similarly, SPSS has not kept pace with trends toward ease and simplicity in user-interface design. The SPSS menu system remains deep and complex, analysis options are not always intuitively labeled, and SPSS often produces substantial output, requiring users to engage in a quest to find the values of interest.

These emerging issues are unfortunate for the SPSS user base, but at the same time they create an opportunity for the scientific community to produce a compelling alternative. With a European Research Council grant, our project "Bayes or Bust: Sensible Hypothesis Tests for Social Scientists" has allowed us to develop JASP, a free and open statistical software package designed to be familiar to users of SPSS. JASP distinguishes itself from SPSS by being as simple, intuitive, and approachable as possible, and by making accessible some of the latest developments in Bayesian analyses. At time of writing, JASP version 0.6 implements the following analysis tools in both their classical and Bayesian manifestations:

Descriptive statistics

Independent samples ANOVA

Repeated measures ANOVA

Correlation

Linear regression

Contingency tables

This set of analysis tools provides a solid base for most students and researchers. Additional analyses are continually added to JASP, such that it will quickly become a comprehensive alternative to SPSS.

JASP maintains a minimalist design philosophy. In JASP, analyses are clearly labeled; for instance, the ANOVA is available through a menu item called "ANOVA." And results are user-friendly: a minimalist, attractive ANOVA table, already in APA format and waiting to be copied and pasted into Word (see Figure 1 for an example).

This simplicity reflects the design philosophy that good software gets out of the users' way, letting them focus on what they want to achieve without first having to "learn the software." Far from dumbing down statistics through "point-and-click," we believe that JASP allows students and researchers to pay attention to what matters: not the software, but the analyses and the results.

As mentioned, JASP implements a number of Bayesian analyses, making heavy use of Morey and Rouder's (2015) BayesFactor R package, which implements a range of Bayesian hypothesis tests. Compared with classical hypothesis tests that produce p values, Bayesian hypothesis tests have a number of distinct practical advantages. Most notably, Bayesian hypothesis tests can quantify evidence in favor of the null hypothesis, and they allow users to monitor evidence continually, as the data accumulate.

Another advantage of JASP for students and universities is that it comes free of charge. In contrast, campus licenses for SPSS can cost upwards of €30,000 per year. This leads us to estimate that SPSS costs the scientific and university communities in the order of $50 million each year. Consistent with this estimate, SPSS was purchased by IBM in 2009 for $1.2 billion. Over time, the recurring annual licensing fees sum to a truly staggering amount of money; one can only dream of the alternative projects and educational initiatives that this money could support.

But arguably more important than being free, JASP is open source. That is, the source code, the underlying algorithms that determine its behavior, are publicly available with permission for people to use as they see fit. Anyone can take the code, inspect it, modify it, and build on it. Software that is not open source prevents others from building on that work and leaves users at the mercy of the owner. In contrast, JASP will always be available to the scientific community to adapt to their needs.

The fact that JASP is open source brings another advantage: community. Due to the open nature of development, open-source projects such as JASP can create a community around them and take advantage of work produced by other open-source software communities. An example of this is JASP's use of the R programming language "under the hood," with many analyses in JASP provided through open-source R packages. Similarly, development is underway to allow people to develop and publish their own analyses in JASP.

Given the ambition and promise of JASP, a question remains: When will JASP be ready? This is a difficult question with two answers: now and never. In a sense, JASP development will always be ongoing; there are always new features that can be added to software, and there are always additional analyses that can be incorporated, possibly through separate JASP modules. So, in that sense, JASP will never be "ready." But then, for many researchers and institutions, JASP provides many standard analyses, and it is already a useful tool for both teaching and research. Anyone can evaluate for themselves whether and when JASP will meet their needs.

Despite its advantages over SPSS, at the time of publication, JASP has a number of substantial omissions. At present, there are no facilities for editing, cleaning, or restructuring data. Similarly, it is possible only to export the results from analyses, and at present there is no facility for saving and reopening analyses performed earlier. Moreover, some important analysis methods are missing, there is no help functionality, and graphical output is limited. These omissions will be addressed in the coming months.

We encourage people to begin exploring and evaluating JASP today. It is simple and minimalist. Additionally, given the growing importance of Bayesian statistics in psychology, it is a "future-proof" solution. Using JASP, the transition to Bayesian statistics will be straightforward and relatively painless.

For those who wish to learn more about JASP and Bayesian statistics in JASP, a number of workshops are available in the upcoming months, all listed on the JASP website. The first workshop, "Bayesian Hypothesis Testing Using JASP," will be held March 13 at the International Convention of Psychological Science in Amsterdam.
JASP is available for Windows, Mac OS X, and Linux. Jeffrey N. Rouder and Richard D. Morey will speak at the 2015 APS Annual Convention in New York City, May 21-24.
References and Further Reading

Morey, R. D., & Rouder, J. N. (2015). BayesFactor: Computation of Bayes factors for common designs. R package version 0.9.10-1. Retrieved from http://CRAN.R-project.org/package=BayesFactor

Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. Psychonomic Bulletin & Review, 21, 301-308.

Ryan, R. S., Wilde, M., & Crist, S. (2013). Compared to a small, supervised lab experiment, a large, unsupervised Web-based experiment on a previously unknown effect has benefits that outweigh its potential costs. Computers in Human Behavior, 29, 1295-1301.

Wagenmakers, E. J. (2007). A practical solution to the pervasive problem of p values. Psychonomic Bulletin and Review, 14, 779-804.

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The last few decades have witnessed the growth of SPSS as the statistical software of choice across a wide range of academic disciplines. Its pedigree reaches back as far as the 1960s, when SPSS was first programmed with punch cards.

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il sito ufficiale

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JASP - Free and User-Friendly Statistical Software

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JASP is an open-source statistics program that is free, friendly, and flexible. Armed with an easy-to-use GUI, JASP allows both classical and Bayesian analyses.