An Introduction to JASP: a Free and User-Friendly Statistics Package
Total Page：16
File Type：pdf, Size：1020Kb
An Introduction to JASP: A Free and User-Friendly Statistics Package James Bartlett 1 Introduction 1.1 What is JASP? JASP is a free, open-source alternative to SPSS that allows you to perform both simple and complex analyses in a user-friendly package. The aim is to allow you to conduct complex Classical (stuff with p values) and Bayesian analyses ("What the hell is Bayesian statistics?" you might ask, look in appendix 9.1 if you are interested), but have the advantage of a drag-and-drop interface that is intuitive to use. 1.2 Why JASP? Although many universities predominantly use SPSS, it is expensive and it can be a frustrating experience. JASP is a free alternative that aims to give you exactly what you want when you are analysing data. 1.2.1 Effect sizes Effect sizes are one of the most important values to report when analysing data. However, despite many papers and an APA task force explaining their importance, SPSS only offers a limited number reference of effect size options and many simple effect sizes are required to be calculated manually. On the other hand, JASP allows you to simply tick a box to provide an effect size for each test. 1.2.2 Continuously updated output Imagine you have gone through all of the menus in SPSS to realise you forgot to click one option that you wanted to be included in the output. You would have to go back through the menus and select that one option and rerun the whole analysis, printing it below the first output. This looks incredibly messy and takes a lot of time. In JASP, all of the options and results are presented on the same screen. If you want another option to be presented, all you have to do is tick a box and the results are updated in seconds. 1.2.3 Minimalist design For each statistical test, SPSS provides every value you will ever need and more. This can be very confusing when you are getting to grips with statistics and you can easily report the wrong value. In JASP, minimalism is the aim. You start off with a basic result, and you have the option to select additional information if and when you need it. 1.3 Using JASP 1.3.1 How to download JASP Today, you will opening JASP through the software portal, but you can download it yourself for free on their website for either Windows, OSX, or Linux (if that’s your thing). After installing it and opening the program, you will find the "Welcome to JASP" window shown in Figure 1. 1 Figure 1: JASP startup window 1.3.2 Entering data in JASP The first difference you will find between SPSS and JASP is how you enter data. In SPSS, you enter data manually through the data view screen. In JASP, there is currently no facility to enter data directly (although this may change in future), and you have to load the data in a comma separated values (.csv) file. This is like an Excel spreadsheet but you cannot directly load a normal Excel Workbook (.xlsx), you first have to convert it to a .csv file. The data you will use today is already in a .csv file so you will be able to load it directly. However, if you decide to use JASP in your own time and need to create a .csv file, here is a useful link that explains what it is and how to create one. To start any data analysis, we need to load some data to use. At the top of the "welcome" window, there are two tabs: File and Common. Click on the File tab and you will see the window in Figure 2. Here you will have your recently opened files on the right if you have used it before, but yours should be blank the first time you use it. To open a file, click on Computer > Browse, then select the data file from wherever you saved it on your computer. After it has loaded, the "welcome" window will look slightly different as your data will be visible on the left side of the window like in Figure 3. 1.3.3 Changing data types The next step is to make sure JASP knows what type of data your variables are (e.g. nominal, ordinal). Unlike SPSS, JASP does its best to guess what data type it is. The symbols at the top of the columns in Figure 3 look like three circles for nominal data, three lines like a podium for ordinal data, or a ruler for scale data. Most of the time JASP gets it right and the columns have the right symbol. However, sometimes it is not quite right. If you click on the symbol, you can change it from one data type to another if things are not quite right. Another difference between JASP and SPSS is how you define nominal factor levels. In SPSS, you might remember that to define a factor such as nationality you need to assign each level a value and label. For example, you could list the first participant as German and label them with a 1, the second person could be Chinese and be assigned a 2. Every German participant would be identified by the number 1, and every Chinese participant would be labeled 2. However, in JASP all you need to do is list the labels themselves (no values) and make sure the variable type is nominal (three little circles). An important thing to note is that all the labels need to be exactly the same to be considered the same condition. For example, German could not be spelled as german or GERMAN or JASP would think these are three different conditions. It has to be written exactly the same, capitals and spaces and everything. 2 Figure 2: JASP file tab 2 Today’s session The session is organised like the process you would normally go through when you perform data analysis. You want to get a feel for the data through descriptive statistics and using some plots to visualise the data. The next step is to make sure the type of analysis you want to perform is appropriate for the data you have, so we will look at data screening. Finally, we will go ahead and look at the inferential statistics. The data for all of the examples are from real published research and were made available on the Open Stats Lab (McIntyre 2016). All the analyses you are going to do are the same as what was performed in the original research. 3 Example One: T-test 3.1 Study background The first example that we are going to look at is from a study by Schroeder and Epley (2015). The aim of the study was to investigate whether delivering a short speech to a potential employer would be more effective at landing you a job than writing the speech down and the employer reading it. Thirty-nine professional recruiters were randomly assigned to receive a job application speech as either a transcipt for them to read, or an audio recording of them reading the speech. The recruiters then rated the applicants on intellect, their impression of the application, and whether they would recommend hiring the candidate. All ratings were on a likert scale ranging from 0 (low intellect, impression etc.) to 10 (high impression, recommendation etc.). 3.1.1 Task 1. What would your predictions be? As they are exactly the same words, do you think the applicants would be rated similarly, or do you think the audio recordings would result in higher ratings due to additional indicators of intelligence? It is important to think about this first, so write your prediction down briefly. 3.2 Descriptive statistics 3.2.1 Loading the data Firstly, we need to open the data file for this example. Look back at section 1.3 on how to open a .csv file and open Schroeder-Epley-data.csv from the folder you downloaded at the start of the session. Your window should now look like Figure 3. The next thing to do is to make sure JASP 3 Figure 3: JASP window with data has correctly labeled each column. The variables we are interested in for the first example are Condition, Intellect_Rating, Impression_Rating, and Hire_Rating. Condition is our independent variable and indicates whether the participant has been provided with a transcript (value 0) or an audio recording (value 1). It should be labeled as nominal data and have the three little circles explained in section 1.3.3. The other three variables are our dependent variables and each should be labeled as scale (a little ruler). Intellect_Rating and Impression_Rating are both identified correctly. However, Hire_Rating may have been labeled as a nominal variable and needs changing to scale. Click on the three circles and change it to a ruler. 3.2.2 Getting a first look at the data From the window in Figure 3, click on the Descriptives tab (below file) > Descriptive Statistics to find the new window in Figure 4. From here, we can take a look at the data by ticking the box ’display boxplots’ and dragging all three of our dependent variables into the white box to the right of the full list of variables. This will fill the table in the far right screen with the data for the three dependent variables and provide you with three boxplots. However, this only provides you with the descriptive statistics for the whole sample.