3 LAB I: Introduction to Jamovi – Basic Statistics for the Social Sciences

3.1 Why Jamovi?

Jamovi is a new fee open “3rd generation” statistical software that is built on top of the programming language R (Figure 3.1). Designed from the ground up to be easy to use, Jamovi is a compelling alternative to costly statistical products such as SPSS and SAS.

Figure 3.1: Jamovi is free and open statistical software

Some other advantages are:

Point-and-click environment
It provides informative tables and neat visuals
Enables integration with R
Gives access to a user guide and community resources from the Jamovi website

3.2 Downloading and installing Jamovi

Jamovi is available for Windows (64-bit), macOS, Linux and ChromeOS. Installation on desktop is quite straight-forward. Just go to the Jamovi download page https://www.jamovi.org/download.html, and download the latest version (current release) for your operating system.

3.3 Navigating Jamovi

When jamovi first opens, we will see a screen something like in Figure 3.2.

To the left is the spreadsheet view, and to the right is where the results of statistical tests appear. Down the middle is a bar separating these two regions, and this can be dragged to the left or the right to change their sizes.

Let’s take a quick look at the Jamovi Main Menu, referred to hereafter as the Menu, as shown in Figure 3.3. This Menu is displayed at the very top of the Jamovi screen:

Figure 3.3: The menu bar provides access to all functions of the program.

There are six tabs in the Menu (from left to right): File (a layer with three horizontal levels $\equiv$ ), Variables, Data, Analyses, Edit, and Settings (the three dots $⋮$ at the top right of the window) tabs. A toolbar appears whenever we click on a Menu tab (Table 3.1).

Menu tab	Toolbar
File tab ( $\equiv$ ) The file tab allows us to open/import existing files, save and export our files.
Variables tab This allows us to view and search our variables in a list view.	This view allows us to easily navigate our variables and do the following: Search for a variable by scrolling through the list or search for one by name. Edit the variable names and descriptions by double-clicking in the relevant field. Edit our variable details by double-clicking on the data symbol (the screen will appear for us to add all the necessary information). Create a new variable by clicking on the in the bottom right corner. \|
Data tab Here we will see our raw data which are organised like Excel in rows and columns. We can also manipulate our data and add new variables when necessary.	Specifically, this tab allows us to do the following: Rename and add details to existing variables. Click on the Setup button, or double-click on the variable we want to manage. Compute and transform variables Add and/or Delete variables (columns) Add Filters Add and/or Delete Rows
Analyses tab It includes the available statistical analyses that can be performed by Jamovi.	We will spend most of our time in the Analyses Tab. The following six modules are pre-installed: Exploration T-Tests ANOVA Regression Frequencies Factor For example, if we want to perform regression analysis, we simply click the ’’Regression” button. All other modules need to be installed using the Modules button (Plus button) in our top-right \|
Edit tab It includes a toolbar similar to a word processor.	We can add extra information to our results using the buttons that are very similar to what we would find in Word (though there are fewer options).
Settings tab (the three dots $⋮$ at the top right of the window) It includes the application settings that can be manged by the users according to their preferences.	We can apply our preferences for a number of settings such as: How many decimal numbers we want. If we want to learn R, we can also display the R syntax. Our graph colour scheme Our default missing value.

3.4 Types of Variables in Jamovi

Data variables can be one of four measure types:

Nominal: This type is for nominal categorical variables.
Ordinal: This type is for ordinal categorical variables.
Continuous : this type is for variables with numeric values which are considered to be of Interval or Ratio scales.
ID: This will usally be our first column. This can be text or numbers, but it should be unique to each row.

Additionally, data variables can be one of three data types:

Integer: These are full numbers e.g. 1, 2, 3, … 100, etc. - Integers can be used for all three measure types . When used for Nominal/Ordinal data numbers will represent labels e.g. male=1; female=2.
Decimal: These are numbers with decimal points. e.g. 1.3, 5.6, 7.8, etc. - This will usually only be used for continuous data.
Text: This can be used for ordinal and nominal data.

The measure types are designated by the symbol in the header of the variable’s column. Note that some combinations of data-type and measure-type don’t make sense, and Jamovi won’t let us choose these.

Table 3.2: Types of data and measures

		Measure
Data	Nominal	Ordinal	Continuous
Integer	$✓$	$✓$	$✓$
Decimal			$✓$
Text	$✓$	$✓$

3.5 Importing data

3.5.1 The dataset

It is possible to simply begin typing values into the Jamovi spreadsheet as we would with any other spreadsheet software. Alternatively, existing datasets in a range of formats (OMV, Excel, CSV, SPSS, R data, Stata, SAS) can be opened in Jamovi. We will use the following dataset as an example (Figure 3.4).

sex	age	time_spent	q1	q2	q3	q4	q5	q6	q7	q8	q9	q10
2	18	9	3	3	0	2	0	2	3	1	1	0
2	19	3	1	2	1	2	1	1	0	2	2	2
1	18	3	2	2	0	1	2	2	1	1	1	1
1	17	12	2	2	0	1	1	2	1	1	1	1
1	17	3	2	2	1	2	2	2	1	1	1	1
1	18	3	2	2	1	3	1	2	1	1	1	1
2	18	7	1	1	1	1	1	2	2	1	1	1
1	17	3	2	1	1	2	1	2	2	1	1	1
2	18	2	2	1	1	1	1	2	3	1	1	1
2	14	3	3	2	1	2	0	3	3	1	1	1
2	17	3	3	3	1	3	1	3	2	0	0	0
2	20	1	3	2	1	2	1	2	2	1	1	1
1	17	3	2	1	1	1	2	2	1	1	2	1
2	18	7	1	1	2	1	1	1	1	1	1	1
2	17	3	1	0	2	1	2	0	2	2	2	2
2	17	3	1	1	3	1	1	2	0	1	3	3
2	18	9	2	1	1	1	1	2	3	1	1	1
2	18	13	0	1	2	1	2	1	0	2	3	3
2	22	1	1	1	2	1	1	1	1	1	1	1
2	14	1	2	2	1	2	2	2	1	1	1	1
1	16	1	1	1	2	1	1	1	1	1	1	1
2	14	5	2	2	2	3	1	1	2	1	1	1
1	17	1	0	1	2	1	3	1	0	3	3	3
1	18	2	2	1	1	2	2	2	2	2	1	2
2	15	3	1	1	1	1	1	2	2	1	1	1
1	18	3	3	3	0	3	0	2	1	1	1	0
2	18	5	3	2	1	2	1	2	2	1	2	2
2	17	9	2	1	1	1	2	2	1	1	2	1
1	16	5	2	1	1	2	2	2	2	2	1	2
2	20	5	2	2	1	2	2	2	1	1	1	1
2	18	9	1	1	2	1	3	1	1	2	2	2
2	20	7	2	2	1	3	1	1	2	1	1	1
2	16	5	1	1	2	0	2	1	1	3	2	3
2	20	3	2	1	2	1	2	1	1	2	2	2
2	16	3	2	3	1	2	1	2	3	1	1	1
1	18	3	2	2	1	2	2	2	1	1	1	1
2	17	7	1	1	2	1	1	1	1	1	1	1
2	19	5	1	1	2	1	2	2	1	2	2	2
2	20	3	2	2	1	2	1	2	2	1	1	1
2	17	3	1	0	2	1	2	0	2	2	2	2
1	20	5	1	1	1	1	1	2	2	1	1	1
2	18	3	2	1	1	2	2	2	2	2	1	2
2	17	3	0	1	3	1	3	1	0	2	2	2
1	20	4	3	2	1	3	2	2	1	1	0	0
2	16	15	1	1	1	1	1	2	2	1	1	1
2	14	0	2	2	1	2	1	2	2	1	1	1
2	20	0	2	2	1	1	1	2	2	1	1	1
1	18	0	1	1	1	1	1	2	2	1	1	1
1	18	0	2	2	0	2	2	1	1	1	1	1
2	18	3	1	1	2	1	1	1	1	1	1	1
1	21	3	3	2	0	2	1	1	2	1	1	1
2	17	4	2	1	1	1	2	2	1	1	2	1
2	18	5	2	1	2	1	1	1	1	2	2	2
1	14	1	2	1	2	1	1	1	1	2	2	2
1	22	3	2	2	0	3	1	2	2	2	2	2
2	16	1	2	2	1	2	2	2	1	1	1	1
1	16	3	3	2	1	2	1	2	2	1	1	0
2	19	5	1	1	1	1	1	2	2	1	1	1
2	17	3	1	1	1	1	1	2	2	1	1	1
1	17	1	2	2	1	2	1	2	2	1	1	1
2	22	5	2	3	1	2	1	2	1	1	2	2
1	21	5	2	2	0	2	1	3	2	0	1	0
2	19	4	1	1	2	0	2	1	1	3	2	3
2	21	3	2	2	1	2	1	2	2	1	0	1
2	19	4	2	1	3	1	3	1	1	3	2	2
2	16	5	1	1	1	2	2	2	1	1	2	1
1	17	5	1	2	1	2	1	1	1	1	1	1
2	16	5	1	1	2	1	3	1	1	2	2	2
1	19	1	2	1	1	2	1	2	2	1	1	1
2	21	3	1	1	2	1	3	1	1	2	2	2
1	17	5	1	1	1	1	1	2	2	1	1	1
1	18	3	2	2	1	2	1	2	2	1	1	1
2	17	4	1	1	2	1	1	1	1	1	1	1
1	18	5	2	2	1	2	1	2	2	1	1	1
2	16	7	2	1	1	2	1	2	2	1	1	1
1	16	5	2	2	1	2	1	2	2	1	0	1
2	18	1	2	1	1	2	2	2	2	2	1	2
1	17	3	2	1	1	2	1	2	2	1	1	1
2	18	3	2	2	1	2	2	2	1	1	1	1
2	17	3	2	2	1	2	2	2	1	1	1	1
1	17	5	2	1	2	1	1	1	1	2	2	2
2	18	8	0	1	3	0	2	1	0	2	2	2
2	20	3	2	2	1	2	1	2	2	1	1	1
1	20	1	3	3	0	3	0	3	2	0	1	1
2	21	3	1	1	3	2	3	1	1	3	2	2
2	18	5	1	1	1	1	1	2	2	1	1	1
1	21	3	1	1	2	1	1	1	1	1	1	1
2	20	5	2	1	1	1	2	2	1	1	2	1
2	18	3	1	1	1	2	1	2	2	2	1	2
1	16	3	1	2	1	2	2	1	1	2	2	2

0

Figure 3.4: Table with raw data.

The meta-data (data about the data) for this dataset are as following:

sex: sex (1 = male, 2 = female)
age: age in years
time_spend: hours spent on social media
$q_{1} . . . q_{10}$ : Ten questions (items) of Rosenberg Self-Esteem Scale (RSES). The 10 items are answered on a four point scale ranging from strongly disagree to strongly agree.(0 = Strongly Disagree, 1 = Disagree, 2 = Agree, 3 = Strongly Agree)

3.5.2 Opening the file

To open this csv file, click on the File tab at the top left hand corner (just left of the Variables tab) (Figure 3.5).

This will open the menu shown in Figure 3.6. Select ‘Open’ and then ‘This PC’. Choose the downloaded file from the files listed on ‘Browse’ which are stored on our computer folders:

Figure 3.6: Open an existing file stored on our computer into Jamovi.

The flowchart of the process is:

We should see data now in the Spreadsheet view (Figure 3.7).

As we can see this is a data set with 258 observations and 13 variables. JAMOVI has classified all the variables as nominal ; however, only the sex variable is actually nominal.

3.6 Transforming data

3.6.1 Changing the measure type

First, we will change the measure type from nominal to continuous for all variables except sex. For example, double-click on the variable name age. This will open an additional menu at the top of the JAMOVI screen, as shown in Figure 3.8:

Figure 3.8: Extra menu for data variables.

From the drop-down list of “Measure type” we select the continuous type , as shown in Figure 3.9.

Figure 3.9: Extra menu for data variables.

The categorical variable sex is coded as 1 for males and 2 for females, and it is stored as nominal .