3.3 Opening, Saving, and Annotating Files

Before we can analyze data, we need to get the data into jamovi and make sure the file is organized correctly.

In most datasets you will use in this book, each row represents one and each column represents one . That spreadsheet-like structure is important because jamovi expects data to be organized in a consistent way.

Opening Existing Data Files

jamovi can open several kinds of data files, including Excel spreadsheets and SPSS files.

When you open a file, take a moment to look through the data and ask:

Are the rows and columns organized the way I expect?
Are the variables labeled clearly?
Are the variable types set correctly?
Are there missing values or unusual entries?

That quick first look can save you a lot of problems later.

Alexander Swan has a video on opening files in jamovi.

Opening SPSS Files

If you are working with SPSS files, which usually have a .sav extension, jamovi can usually open them directly. This is useful when you are working with older datasets, shared research files, or data from someone who uses SPSS.

Alexander Swan has an additional video on opening .sav files in jamovi.

Entering Data Manually

Sometimes you may need to enter data manually rather than opening an existing file. This is more common for small practice datasets than for larger research projects.

If you enter data manually:

make sure each row is one case, participant, response, or observation;
make sure each column is one variable;
use clear and consistent variable names;
enter values consistently; and
check the measure type and data type for each variable.

This is another place where Chapter 2 matters. A variable coded with numbers is not automatically continuous, and a dataset that looks like a spreadsheet is not automatically ready for analysis.

Alexander Swan has a video on entering data manually in jamovi.

Saving jamovi Files

After opening or entering data, save your work as a jamovi file (.omv). Saving as an .omv file preserves more than just the raw data. It can also preserve analyses, settings, output, and notes.

A good habit is to save a new copy before making major changes to a dataset. For example, you might keep one copy of the original dataset and another copy for your analysis work.

Good File Habit

Use file names that will still make sense later. A file named homework.omv may seem clear today, but topic-4-descriptives-practice.omv or study-data-cleaned.omv will be much easier to understand later.

Before You Start Working

Before you begin an analysis assignment or project, take a minute to set yourself up well:

Download the dataset and save it somewhere you can find again.
Open the dataset in jamovi.
Save the file as a .omv file before you do much work.
Check that your variables have meaningful names.
Check the data type and measure type for each variable you plan to use.
Save again after major changes, especially after computing or transforming variables.

This sounds simple, but it prevents a lot of frustration. Future you will be grateful.

Annotating Output

Sometimes you may need to submit output files from jamovi with annotations. Annotations can help you show your work, point out key results, or explain how you interpreted an analysis.

If you have not done this before, it is worth reviewing this short tutorial by Alexander Swan on annotating output in jamovi.

For Dana’s Students

When an assignment asks you to submit a jamovi file, submit the .omv file unless the directions say otherwise. If I ask you to annotate output or upload a screenshot, the assignment directions in Canvas will explain that.