This example demonstrates the use of the Stata 11.1 sgmediation command with survey correction and a subpopulation indicator.
|
|
- Lindsey Sherman
- 6 years ago
- Views:
Transcription
1 Analysis Example-Stata 11.0 sgmediation Command with Survey Data Correction March 25, 2011 This example demonstrates the use of the Stata 11.1 sgmediation command with survey correction and a subpopulation indicator. The NCS-R data is used in this example. The model is defined as the dependent variable=household income, independent variable=age and mediation variable=obesity status in 6 categories. This model can be regarded as age (IV) influences obesity (MV) which in turn influences household income (DV). This command performs the complex sample adjusted Sobel-Goodman test to test whether a mediator carries the influence of an independent variable to a dependent variable. See the Stata help for sgmediation for details on this approach. The use of a subpop statement is also included in this example. Use of the svyset command must precede the modeling.. use f:\brahms\applied_analysis_book\ncsrsubset_dec15.dta, clear. * set survey variables. svyset seclustr [pweight=ncsrwtlg], strata(sestrat) vce(linearized) singleunit(missing) pweight: ncsrwtlg VCE: linearized Single unit: missing Strata 1: sestrat SU 1: seclustr FPC 1: <zero>. svydes sestrat seclustr Survey: Describing stage 1 sampling units pweight: ncsrwtlg VCE: linearized Single unit: missing Strata 1: sestrat SU 1: seclustr FPC 1: <zero> #Obs with #Obs with #Obs per included Unit #Units #Units complete missing Stratum included omitted data data min mean max
2 = #Obs with missing values in the survey characteristcs * do analysis of household income predicted by age and mediated by obesity status, with survey correction. sgmediation hhinc, mv(obese6ca) iv(age) prefix(svy:) Model with dv regressed on iv (path c) F( 1, 42) = 7.34 Prob > F = R-squared = age _cons Model with mediator regressed on iv (path a) F( 1, 42) = Prob > F = R-squared = obese6ca Coef. Std. Err. t P> t [95% Conf. Interval] age _cons
3 Model with dv regressed on mediator and iv (paths b and c') F( 2, 41) = 4.75 Prob > F = R-squared = obese6ca age _cons Sobel-Goodman Mediation Tests Coef Std Err Z P> Z Sobel Goodman Goodman Indirect effect = Direct effect = Total effect = Proportion of total effect that is mediated: Ratio of indirect to direct effect:
4 . * repeat analysis with female subpopulation indicator. sgmediation hhinc, mv(obese6ca) iv(age) prefix(svy, subpop(sexf):) Model with dv regressed on iv (path c) Subpop. no. of obs = 3251 Subpop. size = F( 1, 42) = Prob > F = R-squared = age _cons Model with mediator regressed on iv (path a) Subpop. no. of obs = 3251 Subpop. size = F( 1, 42) = Prob > F = R-squared = obese6ca Coef. Std. Err. t P> t [95% Conf. Interval] age _cons Model with dv regressed on mediator and iv (paths b and c') Subpop. no. of obs = 3251 Subpop. size = F( 2, 41) = Prob > F = R-squared = obese6ca age _cons
5 Sobel-Goodman Mediation Tests Coef Std Err Z P> Z Sobel Goodman Goodman Indirect effect = Direct effect = Total effect = Proportion of total effect that is mediated: Ratio of indirect to direct effect:
6
Examples of Using Stata v11.0 with JRR replicate weights Provided in the NHANES data set
Examples of Using Stata v110 with JRR replicate weights Provided in the NHANES 1999-2000 data set This document is designed to illustrate comparisons of methods to use JRR replicate weights sometimes provided
More informationSurvey commands in STATA
Survey commands in STATA Carlo Azzarri DECRG Sample survey: Albania 2005 LSMS 4 strata (Central, Coastal, Mountain, Tirana) 455 Primary Sampling Units (PSU) 8 HHs by PSU * 455 = 3,640 HHs svy command:
More informationFailure to take the sampling scheme into account can lead to inaccurate point estimates and/or flawed estimates of the standard errors.
Analyzing Complex Survey Data: Some key issues to be aware of Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 20, 2018 Be sure to read the Stata Manual s
More informationA Survey on Survey Statistics: What is done, can be done in Stata, and what s missing?
A Survey on Survey Statistics: What is done, can be done in Stata, and what s missing? Frauke Kreuter & Richard Valliant Joint Program in Survey Methodology University of Maryland, College Park fkreuter@survey.umd.edu
More informationAll analysis examples presented can be done in Stata 10.1 and are included in this chapter s output.
Chapter 9 Stata v10.1 Analysis Examples Syntax and Output General Notes on Stata 10.1 Given that this tool is used throughout the ASDA textbook this chapter includes only the syntax and output for the
More informationAnalyzing CHIS Data Using Stata
Analyzing CHIS Data Using Stata Christine Wells UCLA IDRE Statistical Consulting Group February 2014 Christine Wells Analyzing CHIS Data Using Stata 1/ 34 The variables bmi p: BMI povll2: Poverty level
More informationESS Round 8 Sample Design Data File: User Guide
ESS Round 8 Sample Design Data File: User Guide Peter Lynn INSTITUTE FOR SOCIAL AND ECONOMIC RESEARCH, UNIVERSITY OF ESSEX 07 February 2019 v2 Contents Page Number 1. Introduction 1 2. Variables 2 2.1
More informationCHAPTER 10 ASDA ANALYSIS EXAMPLES REPLICATION IVEware
CHAPTER 10 ASDA ANALYSIS EXAMPLES REPLICATION IVEware GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis
More informationCHAPTER 5 ASDA ANALYSIS EXAMPLES REPLICATION-SAS v9.2
CHAPTER 5 ASDA ANALYSIS EXAMPLES REPLICATION-SAS v9.2 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis
More informationNotes on PS2
17.871 - Notes on PS2 Mike Sances MIT April 2, 2012 Mike Sances (MIT) 17.871 - Notes on PS2 April 2, 2012 1 / 9 Interpreting Regression: Coecient regress success_rate dist Source SS df MS Number of obs
More information(LDA lecture 4/15/08: Transition model for binary data. -- TL)
(LDA lecture 4/5/08: Transition model for binary data -- TL) (updated 4/24/2008) log: G:\public_html\courses\LDA2008\Data\CTQ2log log type: text opened on: 5 Apr 2008, 2:27:54 *** read in data ******************************************************
More informationPSC 508. Jim Battista. Dummies. Univ. at Buffalo, SUNY. Jim Battista PSC 508
PSC 508 Jim Battista Univ. at Buffalo, SUNY Dummies Dummy variables Sometimes we want to include categorical variables in our models Numerical variables that don t necessarily have any inherent order and
More informationSOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis
SOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis In any longitudinal analysis, we can distinguish between analyzing trends vs individual change that is, model
More informationUnit 5 Logistic Regression Homework #7 Practice Problems. SOLUTIONS Stata version
Unit 5 Logistic Regression Homework #7 Practice Problems SOLUTIONS Stata version Before You Begin Download STATA data set illeetvilaine.dta from the course website page, ASSIGNMENTS (Homeworks and Exams)
More informationCHAPTER 6 ASDA ANALYSIS EXAMPLES REPLICATION SAS V9.2
CHAPTER 6 ASDA ANALYSIS EXAMPLES REPLICATION SAS V9.2 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis
More informationLecture 2a: Model building I
Epidemiology/Biostats VHM 812/802 Course Winter 2015, Atlantic Veterinary College, PEI Javier Sanchez Lecture 2a: Model building I Index Page Predictors (X variables)...2 Categorical predictors...2 Indicator
More informationThe Multivariate Regression Model
The Multivariate Regression Model Example Determinants of College GPA Sample of 4 Freshman Collect data on College GPA (4.0 scale) Look at importance of ACT Consider the following model CGPA ACT i 0 i
More informationNever Smokers Exposure Case Control Yes No
Question 0.4 Never Smokers Exosure Case Control Yes 33 7 50 No 86 4 597 29 428 647 OR^ Never Smokers (33)(4)/(7)(86) 4.29 Past or Present Smokers Exosure Case Control Yes 7 4 2 No 52 3 65 69 7 86 OR^ Smokers
More informationGuideline on evaluating the impact of policies -Quantitative approach-
Guideline on evaluating the impact of policies -Quantitative approach- 1 2 3 1 The term treatment derives from the medical sciences and has more meaning when is used in that context. However, this term
More information* STATA.OUTPUT -- Chapter 5
* STATA.OUTPUT -- Chapter 5.*bwt/confounder example.infile bwt smk gest using bwt.data.correlate (obs=754) bwt smk gest -------------+----- bwt 1.0000 smk -0.1381 1.0000 gest 0.3629 0.0000 1.0000.regress
More informationTable. XTMIXED Procedure in STATA with Output Systolic Blood Pressure, use "k:mydirectory,
Table XTMIXED Procedure in STATA with Output Systolic Blood Pressure, 2001. use "k:mydirectory,. xtmixed sbp nage20 nage30 nage40 nage50 nage70 nage80 nage90 winter male dept2 edu_bachelor median_household_income
More informationCategorical Data Analysis
Categorical Data Analysis Hsueh-Sheng Wu Center for Family and Demographic Research October 4, 200 Outline What are categorical variables? When do we need categorical data analysis? Some methods for categorical
More informationCOMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION
PLS 802 Spring 2018 Professor Jacoby COMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION This handout shows the log of a STATA session that compares alternative estimates of
More informationSoci Statistics for Sociologists
University of North Carolina Chapel Hill Soci708-001 Statistics for Sociologists Fall 2009 Professor François Nielsen Stata Commands for Module 11 Multiple Regression For further information on any command
More informationECON Introductory Econometrics Seminar 6
ECON4150 - Introductory Econometrics Seminar 6 Stock and Watson EE10.1 April 28, 2015 Stock and Watson EE10.1 ECON4150 - Introductory Econometrics Seminar 6 April 28, 2015 1 / 21 Guns data set Some U.S.
More informationGroup Comparisons: Using What If Scenarios to Decompose Differences Across Groups
Group Comparisons: Using What If Scenarios to Decompose Differences Across Groups Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015 We saw that the
More informationInterpreting and Visualizing Regression models with Stata Margins and Marginsplot. Boriana Pratt May 2017
Interpreting and Visualizing Regression models with Stata Margins and Marginsplot Boriana Pratt May 2017 Interpreting regression models Often regression results are presented in a table format, which makes
More informationCompartmental Pharmacokinetic Analysis. Dr Julie Simpson
Compartmental Pharmacokinetic Analysis Dr Julie Simpson Email: julieas@unimelb.edu.au BACKGROUND Describes how the drug concentration changes over time using physiological parameters. Gut compartment Absorption,
More informationThis is a quick-and-dirty example for some syntax and output from pscore and psmatch2.
This is a quick-and-dirty example for some syntax and output from pscore and psmatch2. It is critical that when you run your own analyses, you generate your own syntax. Both of these procedures have very
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationYou can find the consultant s raw data here:
Problem Set 1 Econ 475 Spring 2014 Arik Levinson, Georgetown University 1 [Travel Cost] A US city with a vibrant tourist industry has an industrial accident (a spill ) The mayor wants to sue the company
More informationEFA in a CFA Framework
EFA in a CFA Framework 2012 San Diego Stata Conference Phil Ender UCLA Statistical Consulting Group Institute for Digital Research & Education July 26, 2012 Phil Ender EFA in a CFA Framework Disclaimer
More informationBios 312 Midterm: Appendix of Results March 1, Race of mother: Coded as 0==black, 1==Asian, 2==White. . table race white
Appendix. Use these results to answer 2012 Midterm questions Dataset Description Data on 526 infants with very low (
More informationTabulate and plot measures of association after restricted cubic spline models
Tabulate and plot measures of association after restricted cubic spline models Nicola Orsini Institute of Environmental Medicine Karolinska Institutet 3 rd Nordic and Baltic countries Stata Users Group
More informationPREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY
Page22 PREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY Wilson Cordova wilson.cordova@cksc.edu.ph Chiang Kai Shek College, Philippines Abstract There are varied sources
More informationCHAPTER 5 ASDA ANALYSIS EXAMPLES REPLICATION-SUDAAN
CHAPTER 5 ASDA ANALYSIS EXAMPLES REPLICATION-SUDAAN GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis
More informationUnit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users
Unit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users Data Set for this Assignment: Download from the course website: Stata Users: framingham_1000.dta Source: Levy (1999) National
More informationWeek 11: Collinearity
Week 11: Collinearity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Regression and holding other
More informationExperiment Outcome &Literature Review. Presented by Fang Liyu
Experiment Outcome &Literature Review Presented by Fang Liyu Experiment outcome 1. Data from JD Sample size: 1) Data contains 3325 products in 8 days 2) There are 2000-3000 missing values in each data
More informationCHECKING INFLUENCE DIAGNOSTICS IN THE OCCUPATIONAL PRESTIGE DATA
PLS 802 Spring 2018 Professor Jacoby CHECKING INFLUENCE DIAGNOSTICS IN THE OCCUPATIONAL PRESTIGE DATA This handout shows the log from a Stata session that examines the Duncan Occupational Prestige data
More information. *increase the memory or there will problems. set memory 40m (40960k)
Exploratory Data Analysis on the Correlation Structure In longitudinal data analysis (and multi-level data analysis) we model two key components of the data: 1. Mean structure. Correlation structure (after
More informationThe Effect of Occupational Danger on Individuals Wage Rates. A fundamental problem confronting the implementation of many healthcare
The Effect of Occupational Danger on Individuals Wage Rates Jonathan Lee Econ 170-001 Spring 2003 PID: 703969503 A fundamental problem confronting the implementation of many healthcare policies is the
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationCenter for Demography and Ecology
Center for Demography and Ecology University of Wisconsin-Madison A Comparative Evaluation of Selected Statistical Software for Computing Multinomial Models Nancy McDermott CDE Working Paper No. 95-01
More informationIntroduction of STATA
Introduction of STATA News: There is an introductory course on STATA offered by CIS Description: Intro to STATA On Tue, Feb 13th from 4:00pm to 5:30pm in CIT 269 Seats left: 4 Windows, 7 Macintosh For
More informationTechnical note The treatment effect: Comparing the ESR and PSM methods with an artificial example By: Araar, A.: April 2015:
Technical note The treatment effect: Comparing the ESR and PSM methods with an artificial example By: Araar, A.: April 2015: In this brief note, we propose to use an artificial example data in order to
More informationEco311, Final Exam, Fall 2017 Prof. Bill Even. Your Name (Please print) Directions. Each question is worth 4 points unless indicated otherwise.
Your Name (Please print) Directions Each question is worth 4 points unless indicated otherwise. Place all answers in the space provided below or within each question. Round all numerical answers to the
More informationTopics in Biostatistics Categorical Data Analysis and Logistic Regression, part 2. B. Rosner, 5/09/17
Topics in Biostatistics Categorical Data Analysis and Logistic Regression, part 2 B. Rosner, 5/09/17 1 Outline 1. Testing for effect modification in logistic regression analyses 2. Conditional logistic
More informationLecture (chapter 7): Estimation procedures
Lecture (chapter 7): Estimation procedures Ernesto F. L. Amaral February 19 21, 2018 Advanced Methods of Social Research (SOCI 420) Source: Healey, Joseph F. 2015. Statistics: A Tool for Social Research.
More information17.871: PS3 Key. Part I
17.871: PS3 Key Part I. use "cces12.dta", clear. reg CC424 CC334A [aweight=v103] if CC334A!= 8 & CC424 < 6 // Need to remove values that do not fit on the linear scale. This entails discarding all respondents
More informationUsing Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015
Using Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015 NOTE: The routines spost13, lrdrop1, and extremes
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationSUGGESTED SOLUTIONS Winter Problem Set #1: The results are attached below.
450-2 Winter 2008 Problem Set #1: SUGGESTED SOLUTIONS The results are attached below. 1. The balanced panel contains larger firms (sales 120-130% bigger than the full sample on average), which are more
More informationİnsan Tunalı November 29, 2018 Econ 511: Econometrics I. ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement
İnsan Tunalı November 29, 2018 Econ 511: Econometrics I STATA Exercise 1 ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement TASK 1: --- name: log: g:\econ511\heter_housinglog log type: text opened
More information(February draft)
For an International NGO Background statistics, cross tabs, summaries, graphs, t-tests and regression analysis for Nepal response survey data (February 2017 - draft) Contents Confidence level/statistical
More informationFoley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling
Foley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling Amber Barnato MD MPH MS University of Pittsburgh Scott Halpern MD PhD University of Pennsylvania Learning objectives 1. List
More informationThe study obtains the following results: Homework #2 Basics of Logistic Regression Page 1. . version 13.1
Soc 73994, Homework #2: Basics of Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 14, 2018 All answers should be typed and mailed to
More informationMilk Data Analysis. 1. Objective: analyzing protein milk data using STATA.
1. Objective: analyzing protein milk data using STATA. 2. Dataset: Protein milk data set (in the class website) Data description: Percentage protein content of milk samples at weekly intervals from each
More informationA Little Stata Session 1
A Little Stata Session 1 Following is a very basic introduction to Stata. I highly recommend the tutorial available at: http://www.ats.ucla.edu/stat/stata/default.htm When you bring up Stata, you will
More informationMultilevel/ Mixed Effects Models: A Brief Overview
Multilevel/ Mixed Effects Models: A Brief Overview Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 27, 2018 These notes borrow very heavily, often/usually
More informationApplication: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).)
Application: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).) There are two data sets; each as the same treatment group of 185 men. JTRAIN2 includes 260
More informationSUDAAN Analysis Example Replication C6
SUDAAN Analysis Example Replication C6 * Sudaan Analysis Examples Replication for ASDA 2nd Edition * Berglund April 2017 * Chapter 6 ; libname d "P:\ASDA 2\Data sets\nhanes 2011_2012\" ; ods graphics off
More informationInteractions made easy
Interactions made easy André Charlett Neville Q Verlander Health Protection Agency Centre for Infections Motivation Scientific staff within institute using Stata to fit many types of regression models
More informationSurvey Data Analysis in Stata 10: Accessible and Comprehensive
Survey Data Analysis in Stata 10: Accessible and Comprehensive Christine Wells Statistical Consulting Group Academic Technology Services University of California, Los Angeles Thursday, October 25, 2007
More informationApplied Econometrics
Applied Econometrics Lecture 3 Nathaniel Higgins ERS and JHU 20 September 2010 Outline of today s lecture Schedule and Due Dates Making OLS make sense Uncorrelated X s Correlated X s Omitted variable bias
More informationMidterm Exam. Friday the 29th of October, 2010
Midterm Exam Friday the 29th of October, 2010 Name: General Comments: This exam is closed book. However, you may use two pages, front and back, of notes and formulas. Write your answers on the exam sheets.
More informationDealing with missing data in practice: Methods, applications, and implications for HIV cohort studies
Dealing with missing data in practice: Methods, applications, and implications for HIV cohort studies Belen Alejos Ferreras Centro Nacional de Epidemiología Instituto de Salud Carlos III 19 de Octubre
More informationlog: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56
log: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56 (20 cities >100k pop). de obs: 20 20 cities >100k pop vars: 13 size: 1,040 storage display value variable name type format label variable
More informationSurvey Analysis: Options for Missing Data
Survey Analysis: Options for Missing Data Paul Gorrell, IMPAQ International, LLC, Columbia, MD Abstract A common situation researchers working with survey data face is the analysis of missing data, often
More informationfor var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty
for var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty -> reg trstprl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS
More informationStata v 12 Illustration. One Way Analysis of Variance
Stata v 12 Illustration Page 1. Preliminary Download anovaplot.. 2. Descriptives Graphs. 3. Descriptives Numerical 4. Assessment of Normality.. 5. Analysis of Variance Model Estimation.. 6. Tests of Equality
More informationMixed Mode Surveys in Business Research: A Natural Experiment. Dr Andrew Engeli March 14 th 2018
Mixed Mode Surveys in Business Research: A Natural Experiment Dr Andrew Engeli March 14 th 2018 Structure of todays presentation The general context The natural experiment Resources Conclusion Coverage
More informationWeek 10: Heteroskedasticity
Week 10: Heteroskedasticity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline The problem of (conditional)
More informationECON Introductory Econometrics Seminar 9
ECON4150 - Introductory Econometrics Seminar 9 Stock and Watson EE13.1 May 4, 2015 Stock and Watson EE13.1 ECON4150 - Introductory Econometrics Seminar 9 May 4, 2015 1 / 18 Empirical exercise E13.1: Data
More informationExploring Functional Forms: NBA Shots. NBA Shots 2011: Success v. Distance. . bcuse nbashots11
NBA Shots 2011: Success v. Distance. bcuse nbashots11 Contains data from http://fmwww.bc.edu/ec-p/data/wooldridge/nbashots11.dta obs: 199,119 vars: 15 25 Oct 2012 09:08 size: 24,690,756 ------------- storage
More informationThe relationship between innovation and economic growth in emerging economies
Mladen Vuckovic The relationship between innovation and economic growth in emerging economies 130 - Organizational Response To Globally Driven Institutional Changes Abstract This paper will investigate
More informationNumber of obs = R-squared = Root MSE = Adj R-squared =
Appendix for the details of statistical test results Statistical Package used:stata/se 11.1 1. ANOVA result with dependent variable: current level of happiness, independent variables: sexs, ages, and survey
More informationFlorida. Difference-in-Difference Models 8/23/2016
Florida Difference-in-Difference Models Bill Evans Health Economics 8/25/1997, State of Florida settles out of court in their suits against tobacco manufacturers Awarded $13 billion over 25 years Use $200m
More informationCHAPTER 10 ASDA ANALYSIS EXAMPLES REPLICATION-SUDAAN
CHAPTER 10 ASDA ANALYSIS EXAMPLES REPLICATION-SUDAAN 10.0.1 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis
More informationDAY 2 Advanced comparison of methods of measurements
EVALUATION AND COMPARISON OF METHODS OF MEASUREMENTS DAY Advanced comparison of methods of measurements Niels Trolle Andersen and Mogens Erlandsen mogens@biostat.au.dk Department of Biostatistics DAY xtmixed:
More informationSupplementary Materials for
advances.sciencemag.org/cgi/content/full/2/2/e1500599/dc1 Supplementary Materials for How many cents on the dollar? Women and men in product markets The PDF file includes: Tamar Kricheli-Katz and Tali
More informationChecking the model. Linearity. Normality. Constant variance. Influential points. Covariate overlap
Checking the model Linearity Normality Constant variance Influential points Covariate overlap 1 Checking the model: linearity Average value of outcome initially assumed to be linear function of continuous
More informationModeling Contextual Data in. Sharon L. Christ Departments of HDFS and Statistics Purdue University
Modeling Contextual Data in the Add Health Sharon L. Christ Departments of HDFS and Statistics Purdue University Talk Outline 1. Review of Add Health Sample Design 2. Modeling Add Health Data a. Multilevel
More informationSociology 7704: Regression Models for Categorical Data Instructor: Natasha Sarkisian. Preliminary Data Screening
r's age when 1st child born 2 4 6 Density.2.4.6.8 Density.5.1 Sociology 774: Regression Models for Categorical Data Instructor: Natasha Sarkisian Preliminary Data Screening A. Examining Univariate Normality
More informationTrunkierte Regression: simulierte Daten
Trunkierte Regression: simulierte Daten * Datengenerierung set seed 26091952 set obs 48 obs was 0, now 48 gen age=_n+17 gen yhat=2000+200*(age-18) gen wage = yhat + 2000*invnorm(uniform()) replace wage=max(0,wage)
More informationPropensity Scores for Multiple Treatments
C O R P O R A T I O N Propensity Scores for Multiple Treatments A Tutorial on the MNPS Command for Stata Users Matthew Cefalu, Maya Buenaventura For more information on this publication, visit www.rand.org/t/tl170z1
More informationNested or Hierarchical Structure School 1 School 2 School 3 School 4 Neighborhood1 xxx xx. students nested within schools within neighborhoods
Multilevel Cross-Classified and Multi-Membership Models Don Hedeker Division of Epidemiology & Biostatistics Institute for Health Research and Policy School of Public Health University of Illinois at Chicago
More informationX. Mixed Effects Analysis of Variance
X. Mixed Effects Analysis of Variance Analysis of variance with multiple observations per patient These analyses are complicated by the fact that multiple observations on the same patient are correlated
More informationCode Enforcement in the United States. Kyle Onda April 23, 2014 Alexandria, VA 2014 Emerging Technology Symposium
Assessment of Plumbing Code Enforcement in the United States Kyle Onda April 23, 2014 Alexandria, VA 2014 Emerging Technology Symposium Introduction Health Risks Health and Safety Introduction Health Risks
More informationMethods for Multilevel Modeling and Design Effects. Sharon L. Christ Departments of HDFS and Statistics Purdue University
Methods for Multilevel Modeling and Design Effects Sharon L. Christ Departments of HDFS and Statistics Purdue University Talk Outline 1. Review of Add Health Sample Design 2. Modeling Add Health Data a.
More information********************************************************************************************** *******************************
1 /* Workshop of impact evaluation MEASURE Evaluation-INSP, 2015*/ ********************************************************************************************** ******************************* DEMO: Propensity
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON4137 Applied Micro Econometrics Date of exam: Thursday, May 31, 2018 Grades are given: June 15, 2018 Time for exam: 09.00 to 12.00 The problem set covers
More informationWind Turbines and Coastal Recreation Demand
Wind Turbines and Coastal Recreation Demand Craig E. Landry, Tom Allen, Todd Cherry, and John Whitehead Appalachian Energy Center ECU Center for Sustainable Tourism Electric Power Generation Increasing
More informationBiostatistics 208. Lecture 1: Overview & Linear Regression Intro.
Biostatistics 208 Lecture 1: Overview & Linear Regression Intro. Steve Shiboski Division of Biostatistics, UCSF January 8, 2019 1 Organization Office hours by appointment (Mission Hall 2540) E-mail to
More information3. The lab guide uses the data set cda_scireview3.dta. These data cannot be used to complete assignments.
Lab Guide Written by Trent Mize for ICPSRCDA14 [Last updated: 17 July 2017] 1. The Lab Guide is divided into sections corresponding to class lectures. Each section should be reviewed before starting the
More information*STATA.OUTPUT -- Chapter 13
*STATA.OUTPUT -- Chapter 13.*small example of rank sum test.input x grp x grp 1. 4 1 2. 35 1 3. 21 1 4. 28 1 5. 66 1 6. 10 2 7. 42 2 8. 71 2 9. 77 2 10. 90 2 11. end.ranksum x, by(grp) porder Two-sample
More informationLogistic Regression, Part III: Hypothesis Testing, Comparisons to OLS
Logistic Regression, Part III: Hypothesis Testing, Comparisons to OLS Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 22, 2015 This handout steals heavily
More informationIntroduction to Survey Data Analysis. Focus of the Seminar. When analyzing survey data... Young Ik Cho, PhD. Survey Research Laboratory
Introduction to Survey Data Analysis Young Ik Cho, PhD Research Assistant Professor University of Illinois at Chicago Fall 2008 Focus of the Seminar Data Cleaning/Missing Data Sampling Bias Reduction When
More information(R) / / / / / / / / / / / / Statistics/Data Analysis
Series de Tiempo FE-UNAM Thursday September 20 14:47:14 2012 Page 1 (R) / / / / / / / / / / / / Statistics/Data Analysis User: Prof. Juan Francisco Islas{space -4} Project: UNIDAD II ----------- name:
More information