Overview

The aim of the following section is to provide short, non-technical tutorials on how to conduct common statistical procedures in jamovi, and was originally created by Jonas Rafi under the name jamovi guide. However, this is only intended as a brief overview. For a more in-depth treatment of these analyses, we’d recommend you take a look at the many community resources available.

A Note On Measurement Types

The following documentation describes each of the analyses, and describes the variable measure types required. For example, it might explain that a particular analysis requires a continuous  dependent variable, and a nominal  grouping variable. However it’s possible that the columns in your data set, for a number of different reasons, aren’t set up with the correct measure types (as indicated in the column header). You can adjust the measure type of columns using the variable editor, however (for the most part) these measure types are really just a guide and don’t need to be set correctly. If you assign a  continuous variable to an analysis expecting a  nominal one, the analysis will simply treat the variable as nominal.

All this to say, if the documentation says that a particular measure type is required, the data simply needs to be the correct ‘shape’, and you can save yourself the labour of setting the measure types correctly if you don’t want to.

Descriptives

• Descriptives

t-tests

The t-test is used to test whether the mean value in a normally distributed data set deviates significantly from a null hypothesis (which assumes that there is no difference).

There are three types of t-tests:

• t-test: Independent samples

• t-test: Paired samples

• t-test: One sample

Analyses of Variance (ANOVA)

The Analysis of variance (ANOVA) is a statistical method that examines how the impact of one or more factors affects an outcome (dependent) variable. These factors are the predictor variables and are categorical. Typically, such factors reflect an experimental manipulation (e.g. with or without treatment), but a factor can also represent groups for whose influence one would like to control (e.g. gender: men or women). The analysis of variance is based on a concept similar to that of the t-test, but it goes beyond that, in that factors can have several levels (t-tests only allow two) and in that several factors can be examined simultaneously.

There are different types of analysis of variance, which differ in the number of factors examined - one or more factors - or whether they compare between people or within a person - repeated measurements:

• ANOVA: One-Way

• ANOVA

• ANOVA: Repeated Measures

• ANOVA: Mixed

Correlation and regression

Correlation and regression analyses are statistical methods for assessing the relationships between a dependent / outcome variable and one or more independent / predictor variables. While the correlation analysis examines the relationship between one predictor and one outcome variable, regression analysis mainly focuses on prediction (how well can one or more variables predict another (outcome) variable). Often, a distinction is made between linear and non-linear (e.g., logistic regression).

• Linear Regression

• Logistic Regression

user guide

Welcome to the jamovi user-guide. This contains everything you need to know about getting up and running for jamovi. Note that there are also video tutorials available from , and the learning statistics with jamovi textbook for those who prefer those formats.

Jump to...

1. Installation

   1. Windows

   2. macOS

   3. iPad

   4. Linux and Chromebooks

      1. Text not appearing in plots under linux

      2. Blank screen at startup on Chromebooks

   5. Cloud

2. Getting Started

3. Analyses

4. The Spreadsheet

   1. Data Variables

   2. Computed Variables

      1. V-functions

   3. Recoded Variables

5. Filters

6. Updating data

   1. Single-file Import

   2. Multi-file Import

   3. Templates

7. Copy and Paste

8. Syntax Mode

The most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package.

PSPP is a stable and reliable application. It can perform descriptive statistics, T-tests, anova, linear and logistic regression, measures of association, cluster analysis, reliability and factor analysis, non-parametric tests and more. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.

A brief list of some of the PSPP's features follows below. We also made available a page with screenshots and sample output. PSPP has:

• Support for over 1 billion cases.

• Support for over 1 billion variables.

• Syntax and data files which are compatible with those of SPSS.

• A choice of terminal or graphical user interface.

• A choice of text, postscript, pdf, opendocument or html output formats.

• Inter-operability with Gnumeric, LibreOffice, OpenOffice.Org and other free software.

• Easy data import from spreadsheets, text files and database sources.

• The capability to open, analyse and edit two or more datasets concurrently. They can also be merged, joined or concatenated.

• A user interface supporting all common character sets and which has been translated to multiple languages.

• Fast statistical procedures, even on very large data sets.

• No license fees.

• No expiration period.

• No unethical “end user license agreements”.

• A fully indexed user manual.

• Freedom ensured; It is licensed under the GPLv3 or later.

• Portability; Runs on many different computers and many different operating systems (GNU or GNU/Linux are the prefered platforms, but we have had many reports that it runs well on other systems too).

PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data.

Downloading PSPP

As with most GNU software, PSPP can be found on the main GNU ftp server: http://ftp.gnu.org/gnu/pspp/ (via HTTP) and ftp://ftp.gnu.org/gnu/pspp/ (via FTP). It can also be found on the GNU mirrors; please use a mirror if possible.

There are some additional ways you can download or otherwise obtain PSPP.

Documentation

Documentation for PSPP is available online, as is documentation for most GNU software. You may also find more information about PSPP by running info pspp or man pspp, or by looking at /usr/share/doc/pspp/, /usr/local/doc/pspp/, or similar directories on your system. A brief summary is available by running pspp --help.

A developer's manual is also available in various formats. Developers of software designed to interoperate with PSPP or SPSS will find this manual's appendices particularly valuable, because they specify the data file formats in great detail.

A tutorial independently published by Prof. Gary Fisk may also be helpful to those first starting out with PSPP.

Further information

For further information, please browse our list of frequently asked questions to see if your issue is mentioned there. If it is not, you might also want to peruse the archives of our mailing list, pspp-users; the issue may have been discussed there. Failing that, you are welcome to subscribe to the list, and send a question of your own.

If you believe you have found a bug in PSPP, please report it either by sending a message to the mailing list bug-gnu-pspp or by using the bug tracker. To privately report a security vulnerability in GNU PSPP, please send your report to the pspp-security mailing list.

Announcements about PSPP are made on pspp-announce as well as (in common with most other GNU software) info-gnu.