Jasp Statistics Download
Jeff
These resources were created to complement our undergraduate statistics lab manual, Applied Data Analysis in Psychology: Exploring Diversity with Statistics, published by Kendall Hunt publishing company. Like our lab manual, these JASP walk-through guides meaningfully and purposefully integrate and highlight diversity research to teach students how to analyze data in an open-source statistical program. The data sets utilized in these guides are from open-access databases (e.g., Pew Research Center, PLoS One, ICPSR, and more). Guides with step-by-step instructions, including annotated images and examples of how to report findings in APA format, are included for the following statistical tests: independent samples t test, paired samples t test, one-way ANOVA, two factor ANOVA, chi-square test, Pearson correlation, simple regression, and multiple regression. Additionally, you will find instructor resources added with our Summer 2023 update. Please feel free to use our added instructor PowerPoint slides highlighting additional research focused on equity, diversity, and inclusion topics. If you are new to incorporating diversity-focused research, you may benefit from some of the resources shared in our newly added instructor resources list. If you are interested in partnering with us to adapt these resources for other statistical software programs, have suggestions for revisions or additions, or would like additional information on how to best integrate these materials into your courses, please contact ruth-...@utc.edu
JASP is a simplified point-and-click interface for R. I have never been a fan of these interfaces for statistical packages because for me it is quicker and easier just to write commands. When I first learned SAS, SPSS and other packages, I already knew how to program, so learning was quick. Dropdown menus did not exist at the time, so there was no other choice but to learn the command language. Today, however, there are many options that do not require you to learn commands, even for the traditional statistics packages. The design of these interfaces is user friendly, none more so than JASP.
I find the design of JASP to be very intuitive. When you start it up, you see several categories of statistics across the top of the screen, including Descriptives, T-Tests, ANOVA, and Regression. Click one and a pull-down list appears with more choices. For example, if I click Regression, I will have choice of linear or logistic regression. Click linear regression and in the left-hand text box there will be a series of topics, such as model, plots, and statistics. Click one of these and additional options appear. As you click options, the analysis instantly appears in the right-hand text box. You can perform a series of analyses very quickly.
As I reflected on how the semester was going, I realized that JASP is not only a good choice for executive education where there is a tight class schedule. It is a good choice for more traditional courses when learning a command language is not an important class objective. This would be the case with most undergraduate introductory statistics classes. I have found that learning statistics can be challenging for students, especially those who do not have a strong math background. Likewise, learning a command language can be challenging for students who do not have a background in computer programming. A better approach would be to limit the statistics course to the statistics, and if analyses are to be done with software, use JASP or other point-and-click options. This enables students to focus their attention on learning the statistics without the distraction, and potential frustration, of learning how to program at the same time.
Statistics is an important topic in many undergraduate and graduate programs in business, health science, natural science, and social science. In some cases, students need to also learn command languages so they will be prepared to handle complicated data analysis situations, such as merging data from different sources. Most students have no need for that expertise, so point-and-click interfaces like JASP are a better choice because learning the software does not steal time from the main course objective. This past semester I found that I like teaching statistics with JASP and that students liked learning it, as well.
Given the data loaded, we explore data via descriptive statistics and data visualization. We further explain how to perform correlation analysis, multiple linear regression, t-test, and one-way analysis of variance from a frequentist perspective and draw conclusions from outputs.
Since we continuously improve the tutorials, let us know if you discover mistakes, or if you have additional resources we can refer to. If you want to be the first to be informed about updates, follow Rens on Twitter.
The dataset is based on a study that investigates an association between popularity status and antisocial behavior from at-risk adolescents (n = 1491), where gender and ethnic background are moderators under the association. The study distinguished subgroups within the popular status group in terms of overt and covert antisocial behavior.
For more information on the sample, instruments, methodology, and research context, we refer the interested readers to the paper (see references). A brief description of the variables in the dataset follows. The variable names in the table below will be used in the tutorial, henceforth.
JASP is a free and open-source statistics package that targets beginners looking to point-and-click their way through analyses. This article is one of a series of reviews that aim to help non-programmers choose the Graphical User Interface (GUI) for R which best meets their needs. Most of these reviews also include cursory descriptions of the programming support that each GUI offers.
I have joined the BlueSky Statistics development team and have written the BlueSky User Guide (online here), but you can trust this series of reviews, as I describe here. All my comments below are easily verifiable. There is no perfect user interface for everyone; each GUI for R has features that appeal to different people.
IDE = Integrated Development Environment, which helps programmers write code. I do not include point-and-click style menus and dialog boxes when using this term. IDE users are people who prefer to write R code to perform their analyses.
You start JASP directly by double-clicking its icon from your desktop or choosing it from your Start Menu (i.e. not from within R itself). It interacts with R in the background; you never need to be aware that R is running.
A data editor is a fundamental feature in data analysis software. It puts you in touch with your data and lets you get a feel for it, if only in a rough way. A data editor is such a simple concept that you might think there would be hardly any differences in how they work in different GUIs. While there are technical differences, to a beginner what matters the most are the differences in simplicity. Some GUIs, including BlueSky and jamovi, let you create only what R calls a data frame. They use more common terminology and call it a data set: you create one, you save one, later you open one, then you use one. Others, such as RKWard trade this simplicity for the full R language perspective: a data set is stored in a workspace. So the process goes: you create a data set, you save a workspace, you open a workspace, and choose a dataset from within it.
Clicking on the name of a factor will open a small window on the top of the data viewer where you can overwrite the existing labels. Variable names however, cannot be changed without going back to Excel, or whatever editor you used to enter the data.
A critically important aspect of data management is the ability to transform many variables at once. For example, social scientists need to recode many survey items, biologists need to take the logarithms of many variables. Doing these types of tasks one variable at a time is tedious.
The goal of pointing and clicking your way through an analysis is to save time by recognizing menu settings rather than performing the more difficult task of recalling programming commands. Some GUIs, such as BlueSky and jamovi, make this easy by sticking to menu standards and using simpler dialog boxes; others, such as RKWard, use non-standard menus that are unique to it and hence require more learning.
While nearly all GUIs keep your dialog box settings during your session, JASP keeps those settings in its main file. This allows you to return to a given analysis at a future date and try some model variations. You only need to click on the output of any analysis to have the dialog box appear to the right of it, complete with all settings intact.
The JASP Materials web page provides links to a helpful array of information to get you started. The How to Use JASP web page offers a cornucopia of training materials, including blogs, GIFs, and videos. The free book, Statistical Analysis in JASP: A Guide for Students, covers the basics of using the software and includes a basic introduction to statistical analysis.
The help files are very well done, explaining what each choice means, its assumptions, and even journal citations. While there is no reference to the R functions used, nor any link to their help files, the overall set of R packages JASP uses is listed here.
Other GUIs, such as BlueSky and R Commander, do modeling using a two-step process. First, you generate and save a model, then use it for scoring new datasets, calculating model-level measures of fit or observation-level scores of influence, diagnostic plotting, testing differences between models, and so on.
Another way in which R GUIs differ is the model formula builder. Some, like jamovi and RKWard, offer only the most popular model types, providing interactions and allowing you to force the y-intercept through zero. Others, such as R Commander and BlueSky, offer maximum power by including buttons to control nested factors, polynomials, splines, and so on.
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