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The Savvy Survey #16: Data Analysis and Survey Results1 Milton G
AEC409 The Savvy Survey #16: Data Analysis and Survey Results1 Milton G. Newberry, III, Jessica L. O’Leary, and Glenn D. Israel2 Introduction In order to evaluate the effectiveness of a program, an agent or specialist may opt to survey the knowledge or Continuing the Savvy Survey Series, this fact sheet is one of attitudes of the clients attending the program. To capture three focused on working with survey data. In its most raw what participants know or feel at the end of a program, he form, the data collected from surveys do not tell much of a or she could implement a posttest questionnaire. However, story except who completed the survey, partially completed if one is curious about how much information their clients it, or did not respond at all. To truly interpret the data, it learned or how their attitudes changed after participation must be analyzed. Where does one begin the data analysis in a program, then either a pretest/posttest questionnaire process? What computer program(s) can be used to analyze or a pretest/follow-up questionnaire would need to be data? How should the data be analyzed? This publication implemented. It is important to consider realistic expecta- serves to answer these questions. tions when evaluating a program. Participants should not be expected to make a drastic change in behavior within the Extension faculty members often use surveys to explore duration of a program. Therefore, an agent should consider relevant situations when conducting needs and asset implementing a posttest/follow up questionnaire in several assessments, when planning for programs, or when waves in a specific timeframe after the program (e.g., six assessing customer satisfaction. -
Evolution of the Infographic
EVOLUTION OF THE INFOGRAPHIC: Then, now, and future-now. EVOLUTION People have been using images and data to tell stories for ages—long before the days of the Internet, smartphones, and Excel. In fact, the history of infographics pre-dates the web by more than 30,000 years with the earliest forms of these visuals being cave paintings that helped early humans find food, resources, and shelter. But as technology has advanced, so has our ability to tell meaningful stories. Here’s a look into the evolution of modern infographics—where they’ve been, how they’ve evolved, and where they’re headed. Then: Printed, static infographics The 20th Century introduced the infographic—a staple for how we communicate, visualize, and share information today. Early on, these print graphics married illustration and data to communicate information in a revolutionary way. ADVANTAGE Design elements enable people to quickly absorb information previously confined to long paragraphs of text. LIMITATION Static infographics didn’t allow for deeper dives into the data to explore granularities. Hoping to drill down for more detail or context? Tough luck—what you see is what you get. Source: http://www.wired.co.uk/news/archive/2012-01/16/painting- by-numbers-at-london-transport-museum INFOGRAPHICS THROUGH THE AGES DOMO 03 Now: Web-based, interactive infographics While the first wave of modern infographics made complex data more consumable, web-based, interactive infographics made data more explorable. These are everywhere today. ADVANTAGE Everyone looking to make data an asset, from executives to graphic designers, are now building interactive data stories that deliver additional context and value. -
Using Survey Data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1
ukdataservice.ac.uk Using survey data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1 Acknowledgement/Citation These pages are based on the following workbook, funded by the Economic and Social Research Council (ESRC). Williamson, Lee, Mark Brown, Jo Wathan, Vanessa Higgins (2013) Secondary Analysis for Social Scientists; Analysing the fear of crime using the British Crime Survey. Updated version by Sarah King-Hele. Centre for Census and Survey Research We are happy for our materials to be used and copied but request that users should: • link to our original materials instead of re-mounting our materials on your website • cite this as an original source as follows: Buckley, Jen and Sarah King-Hele (2015). Using survey data. UK Data Service, University of Essex and University of Manchester. UK Data Service – Using survey data Contents 1. Introduction 3 2. Before you start 4 2.1. Research topic and questions 4 2.2. Survey data and secondary analysis 5 2.3. Concepts and measurement 6 2.4. Change over time 8 2.5. Worksheets 9 3. Find data 10 3.1. Survey microdata 10 3.2. UK Data Service 12 3.3. Other ways to find data 14 3.4. Evaluating data 15 3.5. Tables and reports 17 3.6. Worksheets 18 4. Get started with survey data 19 4.1. Registration and access conditions 19 4.2. Download 20 4.3. Statistics packages 21 4.4. Survey weights 22 4.5. Worksheets 24 5. Data analysis 25 5.1. Types of variables 25 5.2. Variable distributions 27 5.3. -
Monte Carlo Likelihood Inference for Missing Data Models
MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS By Yun Ju Sung and Charles J. Geyer University of Washington and University of Minnesota Abbreviated title: Monte Carlo Likelihood Asymptotics We describe a Monte Carlo method to approximate the maximum likeli- hood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer ¤ of the Kullback- Leibler information, as both a Monte Carlo sample size and an observed data sample size go to in¯nity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing con¯dence regions for ¤. We give Logit-Normal generalized linear mixed model examples, calculated using an R package that we wrote. AMS 2000 subject classi¯cations. Primary 62F12; secondary 65C05. Key words and phrases. Asymptotic theory, Monte Carlo, maximum likelihood, generalized linear mixed model, empirical process, model misspeci¯cation 1 1. Introduction. Missing data (Little and Rubin, 2002) either arise naturally|data that might have been observed are missing|or are intentionally chosen|a model includes random variables that are not observable (called latent variables or random e®ects). A mixture of normals or a generalized linear mixed model (GLMM) is an example of the latter. In either case, a model is speci¯ed for the complete data (x; y), where x is missing and y is observed, by their joint den- sity f(x; y), also called the complete data likelihood (when considered as a function of ). -
Interactive Statistical Graphics/ When Charts Come to Life
Titel Event, Date Author Affiliation Interactive Statistical Graphics When Charts come to Life [email protected] www.theusRus.de Telefónica Germany Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 2 www.theusRus.de What I do not talk about … Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 3 www.theusRus.de … still not what I mean. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. 1973 PRIM-9 Tukey et al. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. -
Improving Data Preparation for Business Analytics Applying Technologies and Methods for Establishing Trusted Data Assets for More Productive Users
BEST PRACTICES REPORT Q3 2016 Improving Data Preparation for Business Analytics Applying Technologies and Methods for Establishing Trusted Data Assets for More Productive Users By David Stodder Research Sponsors Research Sponsors Alation, Inc. Alteryx, Inc. Attivio, Inc. Datameer Looker Paxata Pentaho, a Hitachi Group Company RedPoint Global SAP / Intel SAS Talend Trifacta Trillium Software Waterline Data BEST PRACTICES REPORT Q3 2016 Improving Data Table of Contents Research Methodology and Demographics 3 Preparation for Executive Summary 4 Business Analytics Why Data Preparation Matters 5 Better Integration, Business Definition Capture, Self-Service, and Applying Technologies and Methods for Data Governance. 6 Establishing Trusted Data Assets for Sidebar – The Elements of Data Preparation: More Productive Users Definitions and Objectives 8 By David Stodder Find, Collect, and Deliver the Right Data . 8 Know the Data and Build a Knowledge Base. 8 Improve, Integrate, and Transform the Data . .9 Share and Reuse Data and Knowledge about the Data . 9 Govern and Steward the Data . 9 Satisfaction with the Current State of Data Preparation 10 Spreadsheets Remain a Major Factor in Data Preparation . 10 Interest in Improving Data Preparation . 11 IT Responsibility, CoEs, and Interest in Self-Service . 13 Attributes of Effective Data Preparation 15 Data Catalog: Shared Resource for Making Data Preparation Effective . 17 Overcoming Data Preparation Challenges 19 Addressing Time Loss and Inefficiency . 21 IT Responsiveness to Data Preparation Requests . 22 Data Volume and Variety: Addressing Integration Challenges . 22 Disconnect between Data Preparation and Analytics Processes . 24 Self-Service Data Preparation Objectives 25 Increasing Self-Service Data Preparation . 27 Self-Service Data Integration and Catalog Development . -
Automating Exploratory Data Analysis Via Machine Learning: an Overview
Tutorials SIGMOD ’20, June 14–19, 2020, Portland, OR, USA Automating Exploratory Data Analysis via Machine Learning: An Overview Tova Milo Amit Somech Tel Aviv University Tel Aviv University [email protected] [email protected] ABSTRACT up-close, better understand its nature and characteristics, Exploratory Data Analysis (EDA) is an important initial step and extract preliminary insights from it. Typically, EDA is for any knowledge discovery process, in which data scien- done by interactively applying various analysis operations tists interactively explore unfamiliar datasets by issuing a (such as filtering, aggregation, and visualization) – the user sequence of analysis operations (e.g. filter, aggregation, and examines the results of each operation, and decides if and visualization). Since EDA is long known as a difficult task, which operation to employ next. requiring profound analytical skills, experience, and domain EDA is long known to be a complex, time consuming pro- knowledge, a plethora of systems have been devised over cess, especially for non-expert users. Therefore, numerous the last decade in order to facilitate EDA. lines of work were devised to facilitate the process, in multi- In particular, advancements in machine learning research ple dimensions [10, 24, 25, 31, 42, 46]: First, simplified EDA 1 have created exciting opportunities, not only for better fa- interfaces (e.g., [25], Tableau ) allow non-programmers to cilitating EDA, but to fully automate the process. In this effectively explore datasets without knowing scripting lan- tutorial, we review recent lines of work for automating EDA. guages or SQL. Second, numerous solutions (e.g., [6, 14, 22]) Starting from recommender systems for suggesting a single were devised to improve the interactivity of EDA, by re- exploratory action, going through kNN-based classifiers and ducing the running times of exploratory operations, and active-learning methods for predicting users’ interestingness displaying preliminary sketches of their results. -
Questionnaire Analysis Using SPSS
Questionnaire design and analysing the data using SPSS page 1 Questionnaire design. For each decision you make when designing a questionnaire there is likely to be a list of points for and against just as there is for deciding on a questionnaire as the data gathering vehicle in the first place. Before designing the questionnaire the initial driver for its design has to be the research question, what are you trying to find out. After that is established you can address the issues of how best to do it. An early decision will be to choose the method that your survey will be administered by, i.e. how it will you inflict it on your subjects. There are typically two underlying methods for conducting your survey; self-administered and interviewer administered. A self-administered survey is more adaptable in some respects, it can be written e.g. a paper questionnaire or sent by mail, email, or conducted electronically on the internet. Surveys administered by an interviewer can be done in person or over the phone, with the interviewer recording results on paper or directly onto a PC. Deciding on which is the best for you will depend upon your question and the target population. For example, if questions are personal then self-administered surveys can be a good choice. Self-administered surveys reduce the chance of bias sneaking in via the interviewer but at the expense of having the interviewer available to explain the questions. The hints and tips below about questionnaire design draw heavily on two excellent resources. SPSS Survey Tips, SPSS Inc (2008) and Guide to the Design of Questionnaires, The University of Leeds (1996). -
STANDARDS and GUIDELINES for STATISTICAL SURVEYS September 2006
OFFICE OF MANAGEMENT AND BUDGET STANDARDS AND GUIDELINES FOR STATISTICAL SURVEYS September 2006 Table of Contents LIST OF STANDARDS FOR STATISTICAL SURVEYS ....................................................... i INTRODUCTION......................................................................................................................... 1 SECTION 1 DEVELOPMENT OF CONCEPTS, METHODS, AND DESIGN .................. 5 Section 1.1 Survey Planning..................................................................................................... 5 Section 1.2 Survey Design........................................................................................................ 7 Section 1.3 Survey Response Rates.......................................................................................... 8 Section 1.4 Pretesting Survey Systems..................................................................................... 9 SECTION 2 COLLECTION OF DATA................................................................................... 9 Section 2.1 Developing Sampling Frames................................................................................ 9 Section 2.2 Required Notifications to Potential Survey Respondents.................................... 10 Section 2.3 Data Collection Methodology.............................................................................. 11 SECTION 3 PROCESSING AND EDITING OF DATA...................................................... 13 Section 3.1 Data Editing ........................................................................................................ -
Improving Critical Thinking Through Data Analysis*
Midwest Manufacturing Accounting Conference Improving Critical Thinking Through Data Analysis* Kurt Reding May 17, 2018 *This presentation is based on the article, “Improving Critical Thinking Through Data Analysis,” published in the June 2017 issue of Strategic Finance. Outline . Critical thinking . Diagnostic data analysis . Merging critical thinking with diagnostic data analysis 2 Critical Thinking . “Bosses Seek ‘Critical Thinking,’ but What Is That?” (emphasis added) “It’s one of those words—like diversity was, like big data is— where everyone talks about it but there are 50 different ways to define it,” says Dan Black, Americas director of recruiting at the accounting firm and consultancy EY. (emphasis added) “Critical thinking may be similar to U.S. Supreme Court Justice Porter Stewart’s famous threshold for obscenity: You know it when you see it,” says Jerry Houser, associate dean and director of career services at Williamette University in Salem, Ore. (emphasis added) http://www.wsj.com/articles/bosses-seek-critical-thinking-but-what-is-that-1413923730 3 Critical Thinking . Critical thinking is not… − Going-through-the-motions thinking. − Quick thinking. 4 Critical Thinking . Proposed definition: Critical thinking is a manner of thinking that employs curiosity, creativity, skepticism, analysis, and logic, where: − Curiosity means wanting to learn, − Creativity means viewing information from multiple perspectives, − Skepticism means maintaining a ‘trust but verify’ mindset; − Analysis means systematically examining and evaluating evidence, and − Logic means reaching well-founded conclusions. 5 Critical Thinking . Although some people are innately more curious, creative, and/or skeptical than others, everyone can exercise these personal attributes to some degree. Likewise, while some people are naturally better analytical and logical thinkers, everyone can improve these skills through practice, education, and training. -
The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek
CONTRIBUTED RESEARCH ARTICLE 1 The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek Abstract The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of fifteen popular R packages to identify the parts of the analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development. Introduction With the advent of tools for automated model training (autoML), building predictive models is becoming easier, more accessible and faster than ever. Tools for R such as mlrMBO (Bischl et al., 2017), parsnip (Kuhn and Vaughan, 2019); tools for python such as TPOT (Olson et al., 2016), auto-sklearn (Feurer et al., 2015), autoKeras (Jin et al., 2018) or tools for other languages such as H2O Driverless AI (H2O.ai, 2019; Cook, 2016) and autoWeka (Kotthoff et al., 2017) supports fully- or semi-automated feature engineering and selection, model tuning and training of predictive models. Yet, model building is always preceded by a phase of understanding the problem, understanding of a domain and exploration of a data set. -
Data Visualization in Exploratory Data Analysis: an Overview Of
DATA VISUALIZATION IN EXPLORATORY DATA ANALYSIS: AN OVERVIEW OF METHODS AND TECHNOLOGIES by YINGSEN MAO Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN INFORMATION SYSTEMS THE UNIVERSITY OF TEXAS AT ARLINGTON December 2015 Copyright © by YINGSEN MAO 2015 All Rights Reserved ii Acknowledgements First I would like to express my deepest gratitude to my thesis advisor, Dr. Sikora. I came to know Dr. Sikora in his data mining course in Fall 2013. I knew little about data mining before taking this course and it is this course that aroused my great interest in data mining, which made me continue to pursue knowledge in related field. In Spring 2015, I transferred to thesis track and I appreciated Dr. Sikora’s acceptance to be my thesis adviser, which made my academic research possible. Because this is my first time diving myself into academic research, I’m thankful for Dr. Sikora for his valuable guidance and patience throughout the thesis. Without his precious support this thesis would not have been possible. I also would like to thank the members of my Master committee, Dr. Wang and Dr. Zhang, for their insightful advices, and the kind acceptance for being my committee members at the last moment. November 17, 2015 iii Abstract DATA VISUALIZATION IN EXPLORATORY DATA ANALYSIS: AN OVERVIEW OF METHODS AND TECHNOLOGIES Yingsen Mao, MS The University of Texas at Arlington, 2015 Supervising Professor: Riyaz Sikora Exploratory data analysis (EDA) refers to an iterative process through which analysts constantly ‘ask questions’ and extract knowledge from data.