Using Survey Data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1
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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. -
Regression Summary Project Analysis for Today Review Questions: Categorical Variables
Statistics 102 Regression Summary Spring, 2000 - 1 - Regression Summary Project Analysis for Today First multiple regression • Interpreting the location and wiring coefficient estimates • Interpreting interaction terms • Measuring significance Second multiple regression • Deciding how to extend a model • Diagnostics (leverage plots, residual plots) Review Questions: Categorical Variables Where do those terms in a categorical regression come from? • You cannot use a categorical term directly in regression (e.g. 2(“Yes”)=?). • JMP converts each categorical variable into a collection of numerical variables that represent the information in the categorical variable, but are numerical and so can be used in regression. • These special variables (a.k.a., dummy variables) use only the numbers +1, 0, and –1. • A categorical variable with k categories requires (k-1) of these special numerical variables. Thus, adding a categorical variable with, for example, 5 categories adds 4 of these numerical variables to the model. How do I use the various tests in regression? What question are you trying to answer… • Does this predictor add significantly to my model, improving the fit beyond that obtained with the other predictors? (t-ratio, CI, p-value) • Does this collection of predictors add significantly to my model? Partial-F (Note: the only time you need partial F is when working with categorical variables that define 3 or more categories. In these cases, JMP shows you the partial-F as an “Effect Test”.) • Does my full model explain “more than random variation”? Use the F-ratio from the Anova summary table. Statistics 102 Regression Summary Spring, 2000 - 2 - How do I interpret JMP output with categorical variables? Term Estimate Std Error t Ratio Prob>|t| Intercept 179.59 5.62 32.0 0.00 Run Size 0.23 0.02 9.5 0.00 Manager[a-c] 22.94 7.76 3.0 0.00 Manager[b-c] 6.90 8.73 0.8 0.43 Manager[a-c]*Run Size 0.07 0.04 2.1 0.04 Manager[b-c]*Run Size -0.10 0.04 -2.6 0.01 • Brackets denote the JMP’s version of dummy variables. -
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 ). -
Analysis of Variance with Categorical and Continuous Factors: Beware the Landmines R. C. Gardner Department of Psychology Someti
Analysis of Variance with Categorical and Continuous Factors: Beware the Landmines R. C. Gardner Department of Psychology Sometimes researchers want to perform an analysis of variance where one or more of the factors is a continuous variable and the others are categorical, and they are advised to use multiple regression to perform the task. The intent of this article is to outline the various ways in which this is normally done, to highlight the decisions with which the researcher is faced, and to warn that the various decisions have distinct implications when it comes to interpretation. This first point to emphasize is that when performing this type of analysis, there are no means to be discussed. Instead, the statistics of interest are intercepts and slopes. More on this later. Models. To begin, there are a number of approaches that one can follow, and each of them refers to a different model. Of primary importance, each model tests a somewhat different hypothesis, and the researcher should be aware of precisely which hypothesis is being tested. The three most common models are: Model I. This is the unique sums of squares approach where the effects of each predictor is assessed in terms of what it adds to the other predictors. It is sometimes referred to as the regression approach, and is identified as SSTYPE3 in GLM. Where a categorical factor consists of more than two levels (i.e., more than one coded vector to define the factor), it would be assessed in terms of the F-ratio for change when those levels are added to all other effects in the model. -
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. -
A Computationally Efficient Method for Selecting a Split Questionnaire Design
Iowa State University Capstones, Theses and Creative Components Dissertations Spring 2019 A Computationally Efficient Method for Selecting a Split Questionnaire Design Matthew Stuart Follow this and additional works at: https://lib.dr.iastate.edu/creativecomponents Part of the Social Statistics Commons Recommended Citation Stuart, Matthew, "A Computationally Efficient Method for Selecting a Split Questionnaire Design" (2019). Creative Components. 252. https://lib.dr.iastate.edu/creativecomponents/252 This Creative Component 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 Creative Components by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A Computationally Efficient Method for Selecting a Split Questionnaire Design Matthew Stuart1, Cindy Yu1,∗ Department of Statistics Iowa State University Ames, IA 50011 Abstract Split questionnaire design (SQD) is a relatively new survey tool to reduce response burden and increase the quality of responses. Among a set of possible SQD choices, a design is considered as the best if it leads to the least amount of information loss quantified by the Kullback-Leibler divergence (KLD) distance. However, the calculation of the KLD distance requires computation of the distribution function for the observed data after integrating out all the missing variables in a particular SQD. For a typical survey questionnaire with a large number of categorical variables, this computation can become practically infeasible. Motivated by the Horvitz-Thompson estima- tor, we propose an approach to approximate the distribution function of the observed in much reduced computation time and lose little valuable information when comparing different choices of SQDs. -
Describing Data: the Big Picture Descriptive Statistics Community
The Big Picture Describing Data: Categorical and Quantitative Variables Population Sampling Sample Statistical Inference Exploratory Data Analysis Descriptive Statistics Community Coalitions (n = 175) In order to make sense of data, we need ways to summarize and visualize it. Summarizing and visualizing variables and relationships between two variables is often known as exploratory data analysis (also known as descriptive statistics). The type of summary statistics and visualization methods to use depends on the type of variables being analyzed (i.e., categorical or quantitative). One Categorical Variable Frequency Table “What is your race/ethnicity?” A frequency table shows the number of cases that fall into each category: White Black “What is your race/ethnicity?” Hispanic Asian Other White Black Hispanic Asian Other Total 111 29 29 2 4 175 Display the number or proportion of cases that fall into each category. 1 Proportion Proportion The sample proportion (̂) of directors in each category is White Black Hispanic Asian Other Total 111 29 29 2 4 175 number of cases in category pˆ The sample proportion of directors who are white is: total number of cases 111 ̂ .63 63% 175 Proportion and percent can be used interchangeably. Relative Frequency Table Bar Chart A relative frequency table shows the proportion of cases that In a bar chart, the height of the bar corresponds to the fall in each category. number of cases that fall into each category. 120 111 White Black Hispanic Asian Other 100 .63 .17 .17 .01 .02 80 60 40 All the numbers in a relative frequency table sum to 1. -
Collecting Data
Statistics deal with the collection, presentation, analysis and interpretation of data.Insurance (of people and property), which now dominates many aspects of our lives, utilises statistical methodology. Social scientists, psychologists, pollsters, medical researchers, governments and many others use statistical methodology to study behaviours of populations. You need to know the following statistical terms. A variable is a characteristic of interest in each element of the sample or population. For example, we may be interested in the age of each of the seven dwarfs. An observation is the value of a variable for one particular element of the sample or population, for example, the age of the dwarf called Bashful (= 619 years). A data set is all the observations of a particular variable for the elements of the sample, for example, a complete list of the ages of the seven dwarfs {685, 702,498,539,402,685, 619}. Collecting data Census The population is the complete set of data under consideration. For example, a population may be all the females in Ireland between the ages of 12 and 18, all the sixth year students in your school or the number of red cars in Ireland. A census is a collection of data relating to a population. A list of every item in a population is called a sampling frame. Sample A sample is a small part of the population selected. A random sample is a sample in which every member of the population has an equal chance of being selected. Data gathered from a sample are called statistics. Conclusions drawn from a sample can then be applied to the whole population (this is called statistical inference). -
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). -
Which Statistical Test
Which Statistical Test Using this aid Understand the definition of terms used in the aid. Start from Data in Figure 1 and go towards the right to select the test you want depending on the data you have. Then go to the relevant instructions (S1, S2, S3, S4, S5, S6, S7, S8, S9 or S10) to perform the test in SPSS. The tests are mentioned below. S1: Normality Test S2: Parametric One-Way ANOVA Test S3: Nonparametric Tests for Several Independent Variables S4: General Linear Model Univariate Analysis S5: Parametric Paired Samples T Test S6: Nonparametric Two Related Samples Test S7: Parametric Independent Samples T Tests S8: Nonparametric Two Independent Samples T Tests S9: One-Sample T Test S10: Crosstabulation (Chi-Square) Test Definition of Terms Data type is simply the various ways that you use numbers to collect data for analysis. For example in nominal data you assign numbers to certain words e.g. 1=male and 2=female. Or rock can be classified as 1=sedimentary, 2=metamorphic or 3=igneous. The numbers are just labels and have no real meaning. The order as well does not matter. For ordinal data the order matters as they describe order e.g. 1st, 2nd 3rd. They can also be words such as ‘bad’, ‘medium’, and ‘good’. Both nominal and ordinal data are also refer to as categorical data. Continuous data refers to quantitative measurement such as age, salary, temperature, weight, height. It is good to understand type of data as they influence the type of analysis that you can do. -
Getting Started with the Public Use File: 2015 and Beyond
Getting Started with the Public Use File: 2015 and Beyond LAST UPDATED: MAY 2021 U.S. DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT U.S. CENSUS BUREAU U.S. DEPARTMENT OF COMMERCE Department of Housing and Urban Development | Office of Policy Development and Research Contents 1. Overview ...................................................................................................... 1 2. 2015 Sample Design Review ........................................................................... 1 3. PUF Availability ............................................................................................. 2 3. PUF File Structures ........................................................................................ 3 3.1. PUF Relational File Structure................................................................................... 3 3.2. PUF Flat File Structure ............................................................................................ 3 4. AHS Codebook .............................................................................................. 3 5. PUF and the Internal Use File .......................................................................... 4 6. PUF Respondent Types ................................................................................... 5 7. Variable Types and Names in the PUF ............................................................. 5 8. Replicating PUF Estimates in the AHS Summary Tables ..................................... 6 9. “Not Applicable” and “Not Reported” Codes in the PUF ..................................