DOCSLIB.ORG

An Introduction to JASP: a Free and User-Friendly Statistics Package

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Introduction to JASP: a Free and User-Friendly Statistics Package An Introduction to JASP: A Free and User-Friendly Statistics Package James E 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 statistics (outlined in section 8), but have the advantage of a drag-and-drop interface that is intuitive to use. 1.2 The development of JASP JASP is still in development with new features being added almost on a monthly basis. This means you should constantly be checking their Twitter (@JASPStats) or Facebook (JASPStats) accounts to see if there is a new version available. This guide currently supports the features available in version 0.8.6 (as of February 28, 2018). If this is slightly out of date and there is a new feature you are confused about, feel free to email me and remind me to update it. 1.3 Why JASP? Although many universities predominantly use SPSS, it is extremely expensive which means you probably cannot use it unless you are affiliated with a university, and even then the licensing means it is often a nightmare to use on your own computer. JASP is a free, open-source alternative that aims to give you a simple and user-friendly output, making it ideal for students who are still getting to grips with statistics in psychology. Here are just a few benefits of using JASP: 1.3.1 Effect sizes Effect sizes are one of the most important values to report when analysing data. However, despite many articles and an APA task force (1999...no one ever listens) explaining their importance, SPSS only offers a limited number of effect size options and many simple effect sizes are requiredtobe calculated manually. On the other hand, JASP allows you to simply tick a box to provide an effect size for each test, and even provides multiple options for some statistical tests. 1.3.2 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. 1.3.3 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 as SPSS also has their own naming conventions (e.g. sig. instead of p value). In JASP, the aim is minimalism. You start off with the bare bones result, and you have the option to select additional information if and when you need it. 1 1.3.4 Reproducible analyses A large number of errors being reported in psychological research has led to calls to improve the reproducibility of research findings (Munafo et al. 2017). This means that you can show someone exactly how you got to the results you included in your report. In JASP, you have the opportunity to save your data and analyses together as a .jasp file. This preserves the analyses you performed to show yourself (thinking of your future self is probably the most important factor as even you will probably forget which options you selected) and others months or years after conducting the analyses. In SPSS, you can save the output file, but this relies on you reverse engineering allthe options that you selected, providing room for error if you miss one of the options. SPSS also creates unnecessary barriers to accessing data as you can not open .sav files without having a valid SPSS license. Therefore, your data would not be accessible to anyone who did not have access to SPSS. 1.4 Using JASP 1.4.1 How to download JASP JASP can be downloaded for free on their website for either Windows, OSX, or Linux (if that’s your thing). Installing it should be pretty straightforward, just follow the instructions it provides you. After installing it and opening the program, you will find the ”Welcome to JASP” window shown in Figure 1. Figure 1: The JASP startup window 1.4.2 Entering data in JASP The first difference you will find between SPSS and JASP is how you enter data. InSPSS,you enter data manually through the data view screen. In JASP, there is currently no facility to enter data directly, and you have to load the data after it has been created in a different program. JASP currently supports SPSS .sav files (this is useful if you already have data in SPSS as you donot need to use SPSS to view or analyse it), Excel .csv files, and Open Office .osd files. If youdatais a normal Excel Workbook (.xlsx), you first have to convert it to a .csv file. If you need tocreatea .csv file from a .xlsx file, here is a useful link that explains how to create one. One feature in JASP is what they call ’data synchronisation’. Although the data is still hosted as a .csv or a .sav file, you can double click on a data point in JASP and it will open the data file in either Excel, SPSS, or Open Office (the only downside to this is if you no longer have access to SPSS you wouldnotbe 2 able to edit the original data file). You can then make changes to the file and it will automatically update in JASP when you have saved it. For further details, see the JASP tutorial. 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 ofthe window like in Figure 3. Figure 2: Opening a data file in JASP. You can choose from different files in your computer, or open a file directly from the Open Science Framework 1.4.3 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, and a little 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. InSPSS, you might remember that to define an independent groups factor such as nationality you needto 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 labeled2 etc. However, in JASP you have the choice of using values as factor levels or using labels as factor levels. You just need to make sure the variable type is nominal (three little circles) at the top of the column. An important thing to note is that if you use labels, all of them need to be exactly the same to be considered the same condition throughout the dataset. 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. We will come back to creating a label for values in section 3.2.1. 3 Figure 3: An empty window with a data file loaded 2 Guide Organisation This guide currently covers three basic statistical tests: T-Tests, correlations, and ANOVA. The first part of this guide focuses on how these can be analysed using the classical approach tostatistics through the use of Null Hypothesis Significance Testing (NHST). The Bayesian equivalent of the T-Test is then introduced in section 7. Throughout the guide, the aim is to demonstrate show how basic analyses that you may be familiar with performing in other statistical packages can be performed in JASP, and offer some practical recommendations. There are some digressions where the topics are not usually discussed in normal textbooks such as using the Student or Welch’s T-Test, or outlining different types of effect size for ANOVA. However, the main focus isonthe process of performing the analyses, and not on the rationale and background to using them.
Load more

Recommended publications
  • Anomalous Perception in a Ganzfeld Condition - a Meta-Analysis of More Than 40 Years Investigation [Version 1; Peer Review: Awaiting Peer Review]
    F1000Research 2021, 10:234 Last updated: 08 SEP 2021 RESEARCH ARTICLE Stage 2 Registered Report: Anomalous perception in a Ganzfeld condition - A meta-analysis of more than 40 years investigation [version 1; peer review: awaiting peer review] Patrizio E. Tressoldi 1, Lance Storm2 1Studium Patavinum, University of Padua, Padova, Italy 2School of Psychology, University of Adelaide, Adelaide, Australia v1 First published: 24 Mar 2021, 10:234 Open Peer Review https://doi.org/10.12688/f1000research.51746.1 Latest published: 24 Mar 2021, 10:234 https://doi.org/10.12688/f1000research.51746.1 Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the Abstract article can be found at the end of the article. This meta-analysis is an investigation into anomalous perception (i.e., conscious identification of information without any conventional sensorial means). The technique used for eliciting an effect is the ganzfeld condition (a form of sensory homogenization that eliminates distracting peripheral noise). The database consists of studies published between January 1974 and December 2020 inclusive. The overall effect size estimated both with a frequentist and a Bayesian random-effect model, were in close agreement yielding an effect size of .088 (.04-.13). This result passed four publication bias tests and seems not contaminated by questionable research practices. Trend analysis carried out with a cumulative meta-analysis and a meta-regression model with Year of publication as covariate, did not indicate sign of decline of this effect size. The moderators analyses show that selected participants outcomes were almost three-times those obtained by non-selected participants and that tasks that simulate telepathic communication show a two- fold effect size with respect to tasks requiring the participants to guess a target.
    [Show full text]
  • Dentist's Knowledge of Evidence-Based Dentistry and Digital Applications Resources in Saudi Arabia
    PTB Reports Research Article Dentist’s Knowledge of Evidence-based Dentistry and Digital Applications Resources in Saudi Arabia Yousef Ahmed Alomi*, BSc. Pharm, MSc. Clin Pharm, BCPS, BCNSP, DiBA, CDE ABSTRACT Critical Care Clinical Pharmacists, TPN Objectives: Drug information resources provide clinicians with safer use of medications and play a Clinical Pharmacist, Freelancer Business vital role in improving drug safety. Evidence-based medicine (EBM) has become essential to medical Planner, Content Editor, and Data Analyst, Riyadh, SAUDI ARABIA. practice; however, EBM is still an emerging dentistry concept. Therefore, in this study, we aimed to Anwar Mouslim Alshammari, B.D.S explore dentists’ knowledge about evidence-based dentistry resources in Saudi Arabia. Methods: College of Destiney, Hail University, SAUDI This is a 4-month cross-sectional study conducted to analyze dentists’ knowledge about evidence- ARABIA. based dentistry resources in Saudi Arabia. We included dentists from interns to consultants and Hanin Sumaydan Saleam Aljohani, those across all dentistry specialties and located in Saudi Arabia. The survey collected demographic Ministry of Health, Riyadh, SAUDI ARABIA. information and knowledge of resources on dental drugs. The knowledge of evidence-based dental care and knowledge of dental drug information applications. The survey was validated through the Correspondence: revision of expert reviewers and pilot testing. Moreover, various reliability tests had been done with Dr. Yousef Ahmed Alomi, BSc. Pharm, the study. The data were collected through the Survey Monkey system and analyzed using Statistical MSc. Clin Pharm, BCPS, BCNSP, DiBA, CDE Critical Care Clinical Pharmacists, TPN Package of Social Sciences (SPSS) and Jeffery’s Amazing Statistics Program (JASP).
    [Show full text]
  • 11. Correlation and Linear Regression
    11. Correlation and linear regression The goal in this chapter is to introduce correlation and linear regression. These are the standard tools that statisticians rely on when analysing the relationship between continuous predictors and continuous outcomes. 11.1 Correlations In this section we’ll talk about how to describe the relationships between variables in the data. To do that, we want to talk mostly about the correlation between variables. But first, we need some data. 11.1.1 The data Table 11.1: Descriptive statistics for the parenthood data. variable min max mean median std. dev IQR Dan’s grumpiness 41 91 63.71 62 10.05 14 Dan’s hours slept 4.84 9.00 6.97 7.03 1.02 1.45 Dan’s son’s hours slept 3.25 12.07 8.05 7.95 2.07 3.21 ............................................................................................ Let’s turn to a topic close to every parent’s heart: sleep. The data set we’ll use is fictitious, but based on real events. Suppose I’m curious to find out how much my infant son’s sleeping habits affect my mood. Let’s say that I can rate my grumpiness very precisely, on a scale from 0 (not at all grumpy) to 100 (grumpy as a very, very grumpy old man or woman). And lets also assume that I’ve been measuring my grumpiness, my sleeping patterns and my son’s sleeping patterns for - 251 - quite some time now. Let’s say, for 100 days. And, being a nerd, I’ve saved the data as a file called parenthood.csv.
    [Show full text]
  • Statistical Analysis in JASP
    Copyright © 2018 by Mark A Goss-Sampson. All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author except for the purposes of research, education or private study. CONTENTS PREFACE .................................................................................................................................................. 1 USING THE JASP INTERFACE .................................................................................................................... 2 DESCRIPTIVE STATISTICS ......................................................................................................................... 8 EXPLORING DATA INTEGRITY ................................................................................................................ 15 ONE SAMPLE T-TEST ............................................................................................................................. 22 BINOMIAL TEST ..................................................................................................................................... 25 MULTINOMIAL TEST .............................................................................................................................. 28 CHI-SQUARE ‘GOODNESS-OF-FIT’ TEST............................................................................................. 30 MULTINOMIAL AND Χ2 ‘GOODNESS-OF-FIT’ TEST. ..........................................................................
    [Show full text]
  • Resources for Mini Meta-Analysis and Related Things Updated on June 25, 2018 Here Are Some Resources That I and Others Find
    Resources for Mini Meta-Analysis and Related Things Updated on June 25, 2018 Here are some resources that I and others find useful. I know my mini meta SPPC article and Excel were not exhaustive and I didn’t cover many things, so I hope this resource page will give you some answers. I’ll update this page whenever I find something new or receive suggestions. Meta-Analysis Guides & Workshops • David Wilson, who wrote Practical Meta-Analysis, has a website containing PowerPoints and macros (for SPSS, Stata, and SAS) as well as some useful links on meta-analysis o I created an Excel template based on his slides (read the Excel page to see how this template differs from my previous SPPC template) • McShane and Böckenholt (2017) wrote an article in the Journal of Consumer Research similar to my mini meta paper. It comes with a very cool website that calculates meta- analysis for you: https://blakemcshane.shinyapps.io/spmetacase1/ • Courtney Soderberg from the Center for Open Science gave a mini meta-analysis workshop at SIPS 2017. All materials are publicly available: https://osf.io/nmdtq/ • Adam Pegler wrote a tutorial blog on using R to calculate mini meta based on my article • The founders of MetaLab at Stanford made video tutorials on meta-analysis in general • Geoff Cumming has a video tutorial on meta-analysis and other “new statistics” or read his Psychological Science article on the “new statistics” • Patrick Forscher’s meta-analysis syllabus and class materials are available online • A blog post containing 5 tips to understand and carefully
    [Show full text]
  • Sequence Ambiguity Determined from Routine Pol Sequencing Is a Reliable Tool for Real-Time Surveillance of HIV Incidence Trends
    Infection, Genetics and Evolution 69 (2019) 146–152 Contents lists available at ScienceDirect Infection, Genetics and Evolution journal homepage: www.elsevier.com/locate/meegid Research paper Sequence ambiguity determined from routine pol sequencing is a reliable T tool for real-time surveillance of HIV incidence trends ⁎ Maja M. Lunara, Snježana Židovec Lepejb, Mario Poljaka, a Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška 4, Ljubljana 1105, Slovenia b Dr. Fran Mihaljevič University Hospital for Infectious Diseases, Mirogojska 8, Zagreb 10000, Croatia ARTICLE INFO ABSTRACT Keywords: Identifying individuals recently infected with HIV has been of great significance for close monitoring of HIV HIV-1 epidemic dynamics. Low HIV sequence ambiguity (SA) has been described as a promising marker of recent Incidence infection in previous studies. This study explores the utility of SA defined as a proportion of ambiguous nu- Sequence ambiguity cleotides detected in baseline pol sequences as a tool for routine real-time surveillance of HIV incidence trends at Mixed base calls a national level. Surveillance A total of 353 partial HIV-1 pol sequences obtained from persons diagnosed with HIV infection in Slovenia from 2000 to 2012 were studied, and SA was reported as a percentage of ambiguous base calls. Patients were characterized as recently infected by examining anti-HIV serological patterns and/or using commercial HIV-1 incidence assays (BED and/or LAg-Avidity assay). A mean SA of 0.29%, 0.14%, and 0.19% was observed for infections classified as recent by BED, LAg, or anti-HIV serological results, respectively. Welch's t-test showed a significant difference in the SA of recent versus long-standing infections (p < 0.001).
    [Show full text]
  • 9. Categorical Data Analysis
    9. Categorical data analysis Now that we’ve covered the basic theory behind hypothesis testing it’s time to start looking at specific tests that are commonly used in psychology. So where should we start? Not every textbook agrees on where to start, but I’m going to start with “χ2 tests” (this chapter, pronounced “chi-square”1)and “t-tests” (Chapter 10). Both of these tools are very frequently used in scientific practice, and whilst they’re not as powerful as “regression” (Chapter 11) and “analysis of variance” (Chapter 12)they’re much easier to understand. The term “categorical data” is just another name for “nominal scale data”. It’s nothing that we haven’t already discussed, it’s just that in the context of data analysis people tend to use the term “categorical data” rather than “nominal scale data”. I don’t know why. In any case, categorical data analysis refers to a collection of tools that you can use when your data are nominal scale. However, there are a lot of different tools that can be used for categorical data analysis, and this chapter covers only a few of the more common ones. 9.1 The χ2 (chi-square) goodness-of-fit test The χ2 goodness-of-fit test is one of the oldest hypothesis tests around. It was invented by Karl Pearson around the turn of the century (Pearson 1900), with some corrections made later by Sir Ronald Fisher (Fisher 1922a). It tests whether an observed frequency distribution of a nominal variable matches an expected frequency distribution.
    [Show full text]
  • JASP: Graphical Statistical Software for Common Statistical Designs
    JSS Journal of Statistical Software January 2019, Volume 88, Issue 2. doi: 10.18637/jss.v088.i02 JASP: Graphical Statistical Software for Common Statistical Designs Jonathon Love Ravi Selker Maarten Marsman University of Newcastle University of Amsterdam University of Amsterdam Tahira Jamil Damian Dropmann Josine Verhagen University of Amsterdam University of Amsterdam University of Amsterdam Alexander Ly Quentin F. Gronau Martin Šmíra University of Amsterdam University of Amsterdam Masaryk University Sacha Epskamp Dora Matzke Anneliese Wild University of Amsterdam University of Amsterdam University of Amsterdam Patrick Knight Jeffrey N. Rouder Richard D. Morey University of Amsterdam University of California, Irvine Cardiff University University of Missouri Eric-Jan Wagenmakers University of Amsterdam Abstract This paper introduces JASP, a free graphical software package for basic statistical pro- cedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specifically, results are provided immediately as the user makes changes to options, output is attrac- tive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a “syntax”. Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting prac- tice. The JASP analyses are implemented in R and a series of R packages. Keywords: JASP, statistical software, Bayesian inference, graphical user interface, basic statis- tics.
    [Show full text]
  • Statistical Analysis in JASP V0.10.0- a Students Guide .Pdf
    2nd Edition JASP v0.10.0 June 2019 Copyright © 2019 by Mark A Goss-Sampson. All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the author except for the purposes of research, education or private study. CONTENTS PREFACE .................................................................................................................................................. 1 USING THE JASP ENVIRONMENT ............................................................................................................ 2 DATA HANDLING IN JASP ........................................................................................................................ 7 JASP ANALYSIS MENU ........................................................................................................................... 10 DESCRIPTIVE STATISTICS ....................................................................................................................... 12 EXPLORING DATA INTEGRITY ................................................................................................................ 21 DATA TRANSFORMATION ..................................................................................................................... 29 ONE SAMPLE T-TEST ............................................................................................................................. 33 BINOMIAL TEST ....................................................................................................................................
    [Show full text]
  • 12. Comparing Several Means (One-Way ANOVA)
    12. Comparing several means (one-way ANOVA) This chapter introduces one of the most widely used tools in psychological statistics, known as “the analysis of variance”, but usually referred to as ANOVA. The basic technique was developed by Sir Ronald Fisher in the early 20th century and it is to him that we owe the rather unfortunate terminology. The term ANOVA is a little misleading, in two respects. Firstly, although the name of the technique refers to variances, ANOVA is concerned with investigating differences in means. Secondly, there are several different things out there that are all referred to as ANOVAs, some of which have only a very tenuous connection to one another. Later on in the book we’ll encounter a range of different ANOVA methods that apply in quite different situations, but for the purposes of this chapter we’ll only consider the simplest form of ANOVA, in which we have several different groups of observations, and we’re interested in finding out whether those groups differ in terms of some outcome variable of interest. This is the question that is addressed by a one-way ANOVA. The structure of this chapter is as follows: in Section 12.1 I’ll introduce a fictitious data set that we’ll use as a running example throughout the chapter. After introducing the data, I’ll describe the mechanics of how a one-way ANOVA actually works (Section 12.2) and then focus on how you can run one in JASP (Section 12.3). These two sections are the core of the chapter.
    [Show full text]
  • A Tutorial on Bayesian Multi-Model Linear Regression with BAS and JASP
    Behavior Research Methods https://doi.org/10.3758/s13428-021-01552-2 A tutorial on Bayesian multi-model linear regression with BAS and JASP Don van den Bergh1 · Merlise A. Clyde2 · Akash R. Komarlu Narendra Gupta1 · Tim de Jong1 · Quentin F. Gronau1 · Maarten Marsman1 · Alexander Ly1,3 · Eric-Jan Wagenmakers1 Accepted: 21 January 2021 © The Author(s) 2021 Abstract Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inference concerning the importance of the predictors. However, such second-stage inference ignores the model uncertainty from the first stage, resulting in overconfident parameter estimates that generalize poorly. These drawbacks can be overcome by model averaging, a technique that retains all models for inference, weighting each model’s contribution by its posterior probability. Although conceptually straightforward, model averaging is rarely used in applied research, possibly due to the lack of easily accessible software. To bridge the gap between theory and practice, we provide a tutorial on linear regression using Bayesian model averaging in JASP, based on the BAS package in R. Firstly, we provide theoretical background on linear regression, Bayesian inference, and Bayesian model averaging. Secondly, we demonstrate the method on an example data set from the World Happiness Report. Lastly,
    [Show full text]
  • A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP
    A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP Don van den Bergh∗1, Johnny van Doorn1, Maarten Marsman1, Tim Draws1, Erik-Jan van Kesteren4, Koen Derks1,2, Fabian Dablander1, Quentin F. Gronau1, Simonˇ Kucharsk´y1, Akash R. Komarlu Narendra Gupta1, Alexandra Sarafoglou1, Jan G. Voelkel5, Angelika Stefan1, Alexander Ly1,3, Max Hinne1, Dora Matzke1, and Eric-Jan Wagenmakers1 1University of Amsterdam 2Nyenrode Business University 3Centrum Wiskunde & Informatica 4Utrecht University 5Stanford University Abstract Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using fre- quentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. How- ever, the broad adoption of Bayesian statistics {and Bayesian ANOVA in particular{ is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples. ∗Correspondence concerning this article should be addressed to: Don van den Bergh University of Amsterdam, Department of Psychological Methods Postbus 15906, 1001 NK Amsterdam, The Netherlands E-Mail should be sent to: [email protected]. 1 Ubiquitous across the empirical sciences, analysis of variance (ANOVA) al- lows researchers to assess the effects of categorical predictors on a continuous outcome variable.
    [Show full text]