8: Entering, Importing, and Managing Data

8.1 Entering Data

Entering data manually in Jamovi is intuitive and efficient, especially when working with small datasets or during the early stages of data collection. Upon opening Jamovi, the Data Pane appears, resembling a spreadsheet where data can be entered directly. To begin, define your variables by clicking on a column header and typing the variable name. Once variables are named, you can enter data into the corresponding cells by clicking an empty cell and typing the value for that observation.

Each row in the Data Pane represents a single case or observation. To add a new case, scroll to the bottom of the table and begin entering data in the next available row. To edit a value, simply click on the cell and type the updated information. You can navigate between cells using the arrow keys or by clicking directly on the desired cell.

8.2 Importing Data

One of the first steps in using Jamovi for statistical analysis is importing your dataset. Jamovi supports a range of file formats, making it easy to bring in data from various sources and software programs. Once imported, the data appears in the Data Pane, ready for analysis and manipulation.

Jamovi supports several common formats, including CSV (Comma-Separated Values), Excel (.xls, .xlsx), and SPSS (.sav) files. CSV files store plain-text data where each value is separated by a comma and each row represents an observation. Excel files with .xls or .xlsx extensions can also be imported, and Jamovi preserves the formatting and sheet structure during the process. For users transitioning from SPSS, Jamovi allows direct import of .sav files while retaining variable information and metadata such as value labels.

To import a file, go to File > Open, locate your file, and Jamovi will automatically display the data, with columns as variables and rows as cases. Once the file is loaded, the data will be visible in the Data Pane, where you can immediately begin editing, transforming, and analyzing your dataset.

8.3 Managing Imported Data

Once your data is imported, it’s important to review the format of each variable to ensure that Jamovi has correctly identified its type. This step is essential for producing accurate and reliable statistical analyses. Jamovi automatically assigns variable types based on the data content during import, but these settings may need to be reviewed or adjusted.

Variables in Jamovi are typically classified as nominal, ordinal, or continuous. Nominal variables represent categories without a meaningful order. Ordinal variables have a meaningful order but no consistent intervals between categories. Continuous variables are numerical and have consistent, meaningful intervals between values.

To adjust a variable’s settings, double-click on the variable name in the Data Pane to open the Variable Settings. From there, you can change the variable type or modify the measurement level (and other details) as needed.

8.4 Editing Data 

After importing and organizing your data, you may need to make edits to correct errors, add variables, or manage missing or irrelevant information. Jamovi provides flexible tools that make modifying datasets both intuitive and efficient.

To add a new variable, right-click on a column header in the Data Pane and select Add Variable. You can then manually enter values. To delete a variable, right-click its column header and select Delete Variable.

To add a new case, click on the last empty row of the data table and enter values for each variable. To remove a case, right-click on the row number and select Delete Case. If you want to exclude specific cases based on certain conditions.

8.5 Filtering Cases

Filtering cases allows you to focus on a subset of your data that meets specific criteria, which is especially useful in research when targeting particular groups or excluding irrelevant or incomplete data.

Filtering helps you narrow your analysis to the most relevant cases, improving precision and avoiding potential biases introduced by irrelevant or outlier data. It’s also an effective tool for managing missing data or conducting subgroup analyses.