DOCSLIB.ORG

Statistical Analysis in JASP

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Analysis in JASP DOI: 10.6084/m9.figshare.9980744 4th Edition JASP v0.14 2020 Copyright © 2020 by Mark A Goss-Sampson. Licenced as CC BY 4.0 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 research, education or private study. CONTENTS PREFACE .................................................................................................................................................. 1 USING THE JASP ENVIRONMENT ............................................................................................................ 2 DATA HANDLING IN JASP ........................................................................................................................ 8 JASP ANALYSIS MENU ........................................................................................................................... 11 DESCRIPTIVE STATISTICS ....................................................................................................................... 14 DESCRIPTIVE PLOTS IN JASP .............................................................................................................. 19 SPLITTING DATA FILES ....................................................................................................................... 23 EXPLORING DATA INTEGRITY ................................................................................................................ 25 DATA TRANSFORMATION ..................................................................................................................... 34 EFFECT SIZE ........................................................................................................................................... 38 ONE SAMPLE T-TEST ............................................................................................................................. 40 BINOMIAL TEST ..................................................................................................................................... 43 MULTINOMIAL TEST .............................................................................................................................. 46 CHI-SQUARE ‘GOODNESS-OF-FIT’ TEST............................................................................................. 48 MULTINOMIAL AND Χ2 ‘GOODNESS-OF-FIT’ TEST. ........................................................................... 49 COMPARING TWO INDEPENDENT GROUPS .......................................................................................... 50 INDEPENDENT T-TEST ....................................................................................................................... 50 MANN-WITNEY U TEST ..................................................................................................................... 54 COMPARING TWO RELATED GROUPS ................................................................................................... 56 PAIRED SAMPLES T-TEST ................................................................................................................... 56 WILCOXON’S SIGNED RANK TEST...................................................................................................... 59 CORRELATION ANALYSIS ....................................................................................................................... 61 REGRESSION .......................................................................................................................................... 67 SIMPLE REGRESSION ......................................................................................................................... 70 MULTIPLE REGRESSION ..................................................................................................................... 73 LOGISTIC REGRESSION .......................................................................................................................... 80 COMPARING MORE THAN TWO INDEPENDENT GROUPS .................................................................... 85 ANOVA .............................................................................................................................................. 85 KRUSKAL-WALLIS .............................................................................................................................. 92 COMPARING MORE THAN TWO RELATED GROUPS ............................................................................. 95 RMANOVA ......................................................................................................................................... 95 FRIEDMAN’S REPEATED MEASURES ANOVA .................................................................................. 100 COMPARING INDEPENDENT GROUPS AND THE EFFECTS OF COVARIATES ........................................ 103 ANCOVA .......................................................................................................................................... 103 TWO-WAY INDEPENDENT ANOVA ...................................................................................................... 111 TWO-WAY REPEATED MEASURES ANOVA ........................................................................................ 119 MIXED FACTOR ANOVA ....................................................................................................................... 127 CHI-SQUARE TEST FOR ASSOCIATION ................................................................................................. 135 META-ANALYSIS .................................................................................................................................. 142 EXPERIMENTAL DESIGN AND DATA LAYOUT IN EXCEL FOR JASP IMPORT. ........................................ 150 Independent t-test .......................................................................................................................... 150 Paired samples t-test ...................................................................................................................... 151 Correlation ...................................................................................................................................... 152 Logistic Regression .......................................................................................................................... 154 One-way Independent ANOVA ....................................................................................................... 155 One-way repeated measures ANOVA ............................................................................................. 156 Two-way Independent ANOVA ....................................................................................................... 157 Two-way Repeated measures ANOVA ............................................................................................ 158 Two-way Mixed Factor ANOVA ....................................................................................................... 159 Chi-squared - Contingency tables ................................................................................................... 160 SOME CONCEPTS IN FREQUENTIST STATISTICS .................................................................................. 161 WHICH TEST SHOULD I USE? ............................................................................................................... 165 Comparing one sample to a known or hypothesized population mean. ........................................ 165 Testing relationships between two or more variables ................................................................... 165 Predicting outcomes ....................................................................................................................... 166 Testing for differences between two independent groups ............................................................ 166 Testing for differences between two related groups ..................................................................... 167 Testing for differences between three or more independent groups ............................................ 167 Testing for differences between three or more related groups ..................................................... 168 Test for interactions between 2 or more independent variables ................................................... 168 PREFACE JASP stands for Jeffrey’s Amazing Statistics Program in recognition of the pioneer of Bayesian inference Sir Harold Jeffreys. This is a free multi-platform open-source statistics package, developed and continually updated by a group of researchers at the University of Amsterdam. They aimed to develop a free, open-source programme that includes both standard and more advanced statistical techniques with a major emphasis on providing a simple intuitive user interface. In contrast to many statistical packages, JASP provides a simple drag and drop interface, easy access menus, intuitive analysis with real-time computation and display of all results. All tables and graphs are presented in APA format and can be copied directly and/or saved independently. Tables can also be exported from JASP in LaTeX format JASP can be downloaded free from the website https://jasp-stats.org/ and is available for Windows, Mac OS X and Linux. You can also download a pre-installed Windows version that will run directly from a USB or external hard drive without the need to install it locally.
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]
  • 14. Non-Parametric Tests You Know the Story. You've Tried for Hours To
    14. Non-Parametric Tests You know the story. You’ve tried for hours to normalise your data. But you can’t. And even if you can, your variances turn out to be unequal anyway. But do not dismay. There are statistical tests that you can run with non-normal, heteroscedastic data. All of the tests that we have seen so far (e.g. t-tests, ANOVA, etc.) rely on comparisons with some probability distribution such as a t-distribution or f-distribution. These are known as “parametric statistics” as they make inferences about population parameters. However, there are other statistical tests that do not have to meet the same assumptions, known as “non-parametric tests.” Many of these tests are based on ranks and/or are comparisons of sample medians rather than means (as means of non-normal data are not meaningful). Non-parametric tests are often not as powerful as parametric tests, so you may find that your p-values are slightly larger. However, if your data is highly non-normal, non-parametric test may detect an effect that is missed in the equivalent parametric test due to falsely large variances caused by the skew of the data. There is, of course, no problem in analyzing normally distributed data with non-parametric statistics also. Non-parametric tests are equally valid (i.e. equally defendable) and most have been around as long as their parametric counterparts. So, if your data does not meet the assumption of normality or homoscedasticity and if it is impossible (or inappropriate) to transform it, you can use non-parametric tests.
    [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]
  • Arxiv:1908.07390V1 [Stat.AP] 19 Aug 2019
    An Overview of Statistical Data Analysis Rui Portocarrero Sarmento Vera Costa LIAAD-INESC TEC FEUP, University of Porto PRODEI - FEUP, University of Porto [email protected] [email protected] August 21, 2019 Abstract The use of statistical software in academia and enterprises has been evolving over the last years. More often than not, students, professors, workers and users in general have all had, at some point, exposure to statistical software. Sometimes, difficulties are felt when dealing with such type of software. Very few persons have theoretical knowledge to clearly understand software configurations or settings, and sometimes even the presented results. Very often, the users are required by academies or enterprises to present reports, without the time to explore or understand the results or tasks required to do a optimal prepara- tion of data or software settings. In this work, we present a statistical overview of some theoretical concepts, to provide a fast access to some concepts. Keywords Reporting Mathematics Statistics and Applications · · 1 Introduction Statistics is a set of methods used to analyze data. The statistic is present in all areas of science involving the collection, handling and sorting of data, given the insight of a particular phenomenon and the possibility that, from that knowledge, inferring possible new results. One of the goals with statistics is to extract information from data to get a better understanding of the situations they represent. Thus, the statistics can be thought of as the science of learning from data. Currently, the high competitiveness in search technologies and markets has caused a constant race for the information.
    [Show full text]
  • Discriminant Function Analysis
    Discriminant Function Analysis Discriminant Function Analysis ● General Purpose ● Computational Approach ● Stepwise Discriminant Analysis ● Interpreting a Two-Group Discriminant Function ● Discriminant Functions for Multiple Groups ● Assumptions ● Classification General Purpose Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups. For example, an educational researcher may want to investigate which variables discriminate between high school graduates who decide (1) to go to college, (2) to attend a trade or professional school, or (3) to seek no further training or education. For that purpose the researcher could collect data on numerous variables prior to students' graduation. After graduation, most students will naturally fall into one of the three categories. Discriminant Analysis could then be used to determine which variable(s) are the best predictors of students' subsequent educational choice. A medical researcher may record different variables relating to patients' backgrounds in order to learn which variables best predict whether a patient is likely to recover completely (group 1), partially (group 2), or not at all (group 3). A biologist could record different characteristics of similar types (groups) of flowers, and then perform a discriminant function analysis to determine the set of characteristics that allows for the best discrimination between the types. To index Computational Approach Computationally, discriminant function analysis is very similar to analysis of variance (ANOVA ). Let us consider a simple example. Suppose we measure height in a random sample of 50 males and 50 females. Females are, on the average, not as tall as http://www.statsoft.com/textbook/stdiscan.html (1 of 11) [2/11/2008 8:56:30 AM] Discriminant Function Analysis males, and this difference will be reflected in the difference in means (for the variable Height ).
    [Show full text]
  • Factor Analysis
    Factor Analysis Qian-Li Xue Biostatistics Program Harvard Catalyst | The Harvard Clinical & Translational Science Center Short course, October 27, 2016 1 Well-used latent variable models Latent Observed variable scale variable scale Continuous Discrete Continuous Factor Discrete FA analysis IRT (item response) LISREL Discrete Latent profile Latent class Growth mixture analysis, regression General software: MPlus, Latent Gold, WinBugs (Bayesian), NLMIXED (SAS) Objectives § What is factor analysis? § What do we need factor analysis for? § What are the modeling assumptions? § How to specify, fit, and interpret factor models? § What is the difference between exploratory and confirmatory factor analysis? § What is and how to assess model identifiability? 3 What is factor analysis § Factor analysis is a theory driven statistical data reduction technique used to explain covariance among observed random variables in terms of fewer unobserved random variables named factors 4 An Example: General Intelligence (Charles Spearman, 1904) Y1 δ1 δ Y2 2 δ General Y3 3 Intelligence Y4 δ4 F Y5 δ5 5 Why Factor Analysis? 1. Testing of theory § Explain covariation among multiple observed variables by § Mapping variables to latent constructs (called “factors”) 2. Understanding the structure underlying a set of measures § Gain insight to dimensions § Construct validation (e.g., convergent validity) 3. Scale development § Exploit redundancy to improve scale’s validity and reliability 6 Part I. Exploratory Factor Analysis (EFA) 7 One Common Factor Model: Model Specification Y δ1 λ1 1 Y1 = λ1F +δ1 λ2 Y δ F 2 2 Y2 = λ2 F +δ 2 λ3 Y δ 3 3 Y3 = λ3F +δ3 § The factor F is not observed; only Y1, Y2, Y3 are observed § δi represent variability in the Yi NOT explained by F § Yi is a linear function of F and δi 8 One Common Factor Model: Model Assumptions λ Y δ1 1 1 Y1 = λ1F +δ1 λ2 Y δ F 2 2 Y2 = λ2 F +δ 2 λ3 Y δ 3 3 Y3 = λ3F +δ3 § Factorial causation § F is independent of δj, i.e.
    [Show full text]
  • Principal Component Analysis (PCA) As a Statistical Tool for Identifying Key Indicators of Nuclear Power Plant Cable Insulation
    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2017 Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation Chamila Chandima De Silva Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Materials Science and Engineering Commons, Mechanics of Materials Commons, and the Statistics and Probability Commons Recommended Citation De Silva, Chamila Chandima, "Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation" (2017). Graduate Theses and Dissertations. 16120. https://lib.dr.iastate.edu/etd/16120 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation by Chamila C. De Silva A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Materials Science and Engineering Program of Study Committee: Nicola Bowler, Major Professor Richard LeSar Steve Martin The student author and the program of study committee are solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred.
    [Show full text]
  • Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning
    medRxiv preprint doi: https://doi.org/10.1101/2020.06.01.20119560; this version posted June 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning Serge Dolgikh1[0000-0001-5929-8954] Abstract. Based on a subset of Covid-19 Wave 1 cases at a time point near TZ+3m (April, 2020), we perform an analysis of the influencing factors for the epidemics impacts with several different statistical methods. The consistent con- clusion of the analysis with the available data is that apart from the policy and management quality, being the dominant factor, the most influential factors among the considered were current or recent universal BCG immunization and the prevalence of smoking. Keywords: Infectious epidemiology, Covid-19 1 Introduction A possible link between the effects of Covid-19 pandemics such as the rate of spread and the severity of cases; and a universal immunization program against tuberculosis with BCG vaccine (UIP, hereinafter) was suggested in [1] and further investigated in [2]. Here we provide a factor analysis based on the available data for the first group of countries that encountered the epidemics (Wave 1) by applying several commonly used factor-ranking methods. The intent of the work is to repeat similar analysis at several different points in the time series of cases that would allow to make a confident conclusion about the epide- miological and social factors with strong influence on the course of the epidemics.
    [Show full text]
  • If I Were Doing a Quick ANOVA Analysis
    ANOVA Note: If I were doing a quick ANOVA analysis (without the diagnostic checks, etc.), I’d do the following: 1) load the packages (#1); 2) do the prep work (#2); and 3) run the ggstatsplot::ggbetweenstats analysis (in the #6 section). 1. Packages needed. Here are the recommended packages to load prior to working. library(ggplot2) # for graphing library(ggstatsplot) # for graphing and statistical analyses (one-stop shop) library(GGally) # This package offers the ggpairs() function. library(moments) # This package allows for skewness and kurtosis functions library(Rmisc) # Package for calculating stats and bar graphs of means library(ggpubr) # normality related commands 2. Prep Work Declare factor variables as such. class(mydata$catvar) # this tells you how R currently sees the variable (e.g., double, factor) mydata$catvar <- factor(mydata$catvar) #Will declare the specified variable as a factor variable 3. Checking data for violations of assumptions: a) relatively equal group sizes; b) equal variances; and c) normal distribution. a. Group Sizes Group counts (to check group frequencies): table(mydata$catvar) b. Checking Equal Variances Group means and standard deviations (Note: the aggregate and by commands give you the same results): aggregate(mydata$intvar, by = list(mydata$catvar), FUN = mean, na.rm = TRUE) aggregate(mydata$intvar, by = list(mydata$catvar), FUN = sd, na.rm = TRUE) by(mydata$intvar, mydata$catvar, mean, na.rm = TRUE) by(mydata$intvar, mydata$catvar, sd, na.rm = TRUE) A simple bar graph of group means and CIs (using Rmisc package). This command is repeated further below in the graphing section. The ggplot command will vary depending on the number of categories in the grouping variable.
    [Show full text]
  • Healthy Volunteers (Retrospective Study) Urolithiasis Healthy Volunteers Group P-Value 5 (N = 110) (N = 157)
    Supplementary Table 1. The result of Gal3C-S-OPN between urolithiasis and healthy volunteers (retrospective study) Urolithiasis Healthy volunteers Group p-Value 5 (n = 110) (n = 157) median (IQR 1) median (IQR 1) Gal3C-S-OPN 2 515 1118 (810–1543) <0.001 (MFI 3) (292–786) uFL-OPN 4 14 56392 <0.001 (ng/mL/mg protein) (10–151) (30270-115516) Gal3C-S-OPN 2 52 0.007 /uFL-OPN 4 <0.001 (5.2–113.0) (0.003–0.020) (MFI 3/uFL-OPN 4) 1 IQR, Interquartile range; 2 Gal3C-S-OPN, Gal3C-S lectin reactive osteopontin; 3 MFI, mean fluorescence intensity; 4 uFL-OPN, Urinary full-length-osteopontin; 5 p-Value, Mann–Whitney U-test. Supplementary Table 2. Sex-related difference between Gal3C-S-OPN and Gal3C-S-OPN normalized to uFL- OPN (retrospective study) Group Urolithiasis Healthy volunteers p-Value 5 Male a Female b Male c Female d a vs. b c vs. d (n = 61) (n = 49) (n = 57) (n = 100) median (IQR 1) median (IQR 1) Gal3C-S-OPN 2 1216 972 518 516 0.15 0.28 (MFI 3) (888-1581) (604-1529) (301-854) (278-781) Gal3C-S-OPN 2 67 42 0.012 0.006 /uFL-OPN 4 0.62 0.56 (9-120) (4-103) (0.003-0.042) (0.002-0.014) (MFI 3/uFL-OPN 4) 1 IQR, Interquartile range; 2 Gal3C-S-OPN, Gal3C-S lectin reactive osteopontin; 3MFI, mean fluorescence intensity; 4 uFL-OPN, Urinary full-length-osteopontin; 5 p-Value, Mann–Whitney U-test.
    [Show full text]
  • Violin Plots: a Box Plot-Density Trace Synergism Author(S): Jerry L
    Violin Plots: A Box Plot-Density Trace Synergism Author(s): Jerry L. Hintze and Ray D. Nelson Source: The American Statistician, Vol. 52, No. 2 (May, 1998), pp. 181-184 Published by: American Statistical Association Stable URL: http://www.jstor.org/stable/2685478 Accessed: 02/09/2010 11:01 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=astata. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to The American Statistician. http://www.jstor.org StatisticalComputing and Graphics ViolinPlots: A Box Plot-DensityTrace Synergism JerryL.
    [Show full text]