Jamovi: A Powerful R-based Statistical Analyses Tool
- Description
- Curriculum
- FAQ
- Reviews
Complete Beginner Course
If you’re looking for a tool that can help you get started on your research and analyses and have decided to go with Jamovi, then this is the right course for you. This course will give you an introductory overview of how to use Jamovi and what it’s main features are. Anyone can take this course. You don’t have to be a stats expert or some kind of special researcher. It’s mainly about how to use the tool, Jamovi.
Get Started with R Programming
If you’re learning the R programming language, this is a handy tool to have under your belt. Its syntax mode can act like a little cheat code generator for you to use in R and instantaneously be able to do everything that you can do in Jamovi in RStudio.
In This Course
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You’ll get some information about Jamovi’s user interface and how you can get it to do stuff. It’s a pretty straight forward interface, there’s actually not much explanation needed. You’ll be amazed and surprised at just how easy to use and intutitive Jamovi is. It’s beatifully designed and great to use.
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You’ll find out just how easy it to bring your data into Jamovi, set your variables to ordinal, nominal, continuous, add some new computed and transformed variables, and use filters to focus on specific areas of your dataset.
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Then we’ll take a detour and install R and RStudio and have a look at how you can bring in datasets open in Jamovi into RStudio just by typing two words, and how you can then easily copy/paste the R code from Jamovi. You just copy and paste it!
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Another great feature in Jamovi is that you can install Modules wich are like add-ons or R packages. These allow you to do more cool stuff with Jamovi. We’ll have a look at how to install them and I’ll give you some tips on how to use two of them scatr and my favourite, Flexplot!
Analyses Demonstrations:
We’re going to have a look at each of the analyses in Jamovi, with an example. The focus of this part of the course is just to give you a rudementary look at each of the analyses, and kinda get a feel of what you can do with Jamovi’s suite of analyses.
It includes all the basic good stuff:
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T-Test: Independent, Paired, and One-Sample
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ANOVAS (one-way, factoral, repeated measures), ANCOVA (and MANCOVA), Non-parametric one way and repeated meausers ANOVA
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Correlation & Regression, Logistic Regression (Binomial, Multinomial, Ordinal)
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Friequencies: One Sample Proportion Tests(Binomial and Chi Goodness of Fit), Contingency Tables (Chi test of association and McNemar), Log-Linear Regression)
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Factor Analyses (Reliability, Principle Component, Exploratory, and Confirmatory Anlyses)
But remember, that’s not all. Jamovi’s modules let you do a lot more. Everything from JASP’s suite of Baysein statistics to Survival Analyses. Check it out, and explore.
Practicing is Easy
As far as practice activities go, I encourage you to follow along with the same examples and repeat them using your own combination of variables and to experiment with using the code in R and seeing where it takes you. All these demonstration examples are pretty simple. It’s really easy for anyone to swap out the variables used in any of the demonstrations with other variables in the same dataset or to go through other datasets and repeat the same analyses. Just give it a shot. There’s even a module called R Datasets which installs a bunch of datasets for you to practice on. Explore, experiment and be creative!
What are you waiting for? Get started with Jamovi today!!!
More about the Jamovi:
Data Science is really cool and awesome. As with anything you really want to get done, there’s nothing more helpful than a good tool that can help you do the job. Jamovi pushes the envelope when it comes to something that’s free, open, user-friendly and fun. It’s features and specifications rival that of paid programs like SPSS, STATA and SASS.
It includes really awesome stuff and the best part is that it’s all built on top of R. Hadley Wickham, a big name in data science, spoke about how GUIs aren’t really great for data science. It’s true, but in that same talk in the 2016 conference he mentions that, it’s okay to copy/paste code. You don’t have to be a coding dictionary to be good at R. That’s another area where Jamovi shines and flexes its muscles. It’s got syntax mode! It’s like an R code cheat sheet generator. You can easily start to code in R and do everything you can do in Jamovi, in R, just by copy/pasting the code.
It’s really great for a beginner who is just learning R. No one wants to start learning something and feel like it’s so hard and like it’ll take ages for them to actually start learning and be able to actually do anything. But with Jamovi’s syntax mode, you can easily just start copy/pasting code into R and getting results right away. Who wouldn’t want that?
And since Jamovi’s code is based on other packages as well, it’s a really nice package to start working with and start learning with. It can help you get started.
The visualizations in Jamovi are stunning, amazing, and easy. The analyses are the best, so neatly organized and the changes show up in the results immediately.
After seeing all these features of Jamovi, I tototally lost it. I said to myself how can something so cool, awesome, fun and insightful be free? Yes! It’s all free.
I asked myself, where has this tool been all my life? Why didn’t I know about? I thought of all the opportunities that I had missed in my career. All the cool visualizations I could made to convey my points to my bosses and colleagues and other people. I thought of all the things that I could have understood better in the past if I had just known about Jamovi and been able to use it.
So, then I decided to create a course about this cool awesome Jamovi software so that everyone could benefit from it.
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IntroductionIn this lecture you'll see a snapshot of each of the sections.
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Installation
Installation is pretty simple. Just visit Jamovi.org, hit download, and then hit install.
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Navigation
The user interface is so simple, elegant and self-explanatory, I don't even know what this lecture is about... It's about navigating Jamovi.
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Entering Data and Setting Up Fields
Just type or copy/paste your data, go to setting and set the measure type and data type. Yup! It's that simple.
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Example: First Data Entry
Follow along with this example. It's easy!
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Computed Variable Setup
Here you'll see a brief overview about how to create a new computed variable. It's as simple as entering a formula in excel. = and then the forumla.
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Transformed Variable Setup
This is kinda like batch applying a formula to a bunch of different variables.
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Supporting Files -
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Importing Data
Jamovi handles many different types of files. Everyone's favourite, though, is ... .csv.
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Example: Importing the Shot put dataset and creating some new variables
Here's an example of importing a dataset and adding new variables. Try it out yourself. Try out different forumlas.
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Filters
It's pretty cool. It's not like excel. In Jamovi, filters get there own cool little column. How awesome is that.
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Example: Some simple filters on the Shot put dataset
Try out some filters
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Analyeses Setup
Here' you'll learn about the general layout of an Analyses menu in Jamovi. It seems simple at first glance and it doesn't really look like there's anything to learn or pay attention to, but that's just because Jamovi is so elegantly, simply and beatifully designed.
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The Output Window
Here's an overview of the output window. Everything is so neatly and wonderfully displayed and organized. It changes in real time as you put in your variables and select different options. It is amazing. Intutive. Fluid. User-friendly. Easy on the eys. Just amazing.
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Modules
Please install the R datasets module, the scatr module, and flexplot. Those are my favourites, and I'll be using them later on. So, if you're going to be following along, it'll be nice if you already have them installed. Explore the other modules and see if there's anything that interests you.
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Descriptives of Nominal or Ordinal Variables
This is the first thing I always do when I get any dataset. It's a simple routine.
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Descriptives of Continuous Variables
This is the second thing I always do when I get any dataset. It's awesome.
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Example: Descriptives of the Shot Put dataset
Give it a shot. It's super duper easy. In and out. Like clock work. Just do it and have a look at the results and see what you can understand.
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Syntax Mode
Are you learning R? Well then guesss what. You just got a cheatcode generator. You'll go from being able to do nothing in R to being able to do everything you can do in Jamovi, in R. It's a quick win!
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Example: Shot put and first data entry descriptives code used in RStudio
Let's put it to practice.
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Demonstrations Introduction
This is the introduction to the demonstrations. Kinda like some heads up info.
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T-Tests Introduction
This is and introduction to T-tests where I'll be telling you what I'm doing.
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Bugs Dataset Introduction
Here we'll find out about the disgusting and frightening bugs dataset. I'll be running the basic descriptives on this and every other dataset as I explained in the earlier lectures.
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Independent Samples T-Test
The question is, "Are men and women equally afraid of disgusting and frightening bugs?" The answer in this dataset is no. Women are more afraid of frightening bugs, and men are kinda more afraid of disgusting bugs, but it's pretty much the same.
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Paired Samples T-Test
Is there an improvement in some grades?
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One-Sample T-Test
Comparing the mean with the median.
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Tooth growth introduction
Vitamin C or Orange Juice? Which one is better for tooth growth?
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One-Way ANOVA
Different doses but same results?
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ANOVA
This is the factoral ANOVA where we have more than one grouping variable.
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Repeated Measures ANOVA
Here, we'll be using the bugs dataset again to do a RMA.
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Irist Dataset introduction
Here we'll see some details about the famous iris dataset and a little about correlations
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ANCOVA
Analyses of Covariance
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MANCOVA
Multiple Analyses of Covariance
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Installing Flexplot in R + link to guide
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Coorelation
Here we'll be using the Big 5 dataset to create some cool looking awesome correlations
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Air Quality Dataset
Checking out this R dataset
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Linear Regression
Here the outcome variable will be a continous variable and we'll be checking if these other different variables are good predictors or not.
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Models
The model builder is pretty cool and awesome. Here we'll be having a closer look at how to add main effects, interactions and a couple other things.
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Dataset Introduction
Here we'll be looking at an introdcution to a dataset.
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Binomial Logistic Regression
Here the outcome variable is a nominal variable with only two outcomes and we'll be checking out how good a bunch of other variables are as predictors.
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Multinomial Logistic Regression
Here the outcome variable is a nominal variable with three outcomes and we'll be checking out how good a bunch of other variables are as predictors.
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Ordinal Logistic Regression
Here the outcome variable is an ordinal variable and we'll be checking out how good a bunch of other variables are as predictors.
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2 Outcomes - Binomial Test
This is a table that will show us proportions and frequencies.
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N Outcomes - Goodness of Fit
This is a table that will show us a bunch of different levels and proportions, and we can add a summary count variable and set our own expected proportions.
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Independent Samples - Test of Association
Here we'll be getting the coolest and most awesome frequency tables and with best tables. It's like frequency tables on steriods. It's got rows, columns, and much more.
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Paired Samples - McNemar Test
Kinda like a specialty frequency table that has two matched pairs with only two outcomes and you check if the outcomes are in equal proportions. It's so simple.
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Log Linear Regression
Check out this cool awesome bar chart. It's like bar chart 2.0. Check out how Jamovi, being as cool and awesome as it is, automatically color coded the bar chart according to the different eye colors. Mind blown!
