WesVar uses repeated replication variance estimation methods exclusively and as a result does not offer the Taylor Series Linearization approach.
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1 CHAPTER 9 ANALYSIS EXAMPLES REPLICATION WesVar 4.3 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for analysis of complex sample survey data and assume all data management and other preliminary work is done. In some software packages certain procedures or options are not available but we have made every attempt to demonstrate how to match the output produced by Stata 10+ in the textbook. Check the ASDA website for updates to the various software tools we cover. NOTES ABOUT GENERALIZED LINEAR MODELS USING WesVar 4.3 WesVar uses repeated replication variance estimation methods exclusively and as a result does not offer the Taylor Series Linearization approach. WesVar is a point and click tool with log and output files that echo the options and variables selected for the particular analysis. As a result the output presented for WesVar examples consists of the log file and the output file. The exact syntax is not presented since it is not generated by the program nor is it possible to run WesVar with just user-written syntax but Workbook files can be created for a record of the analysis session. The workbook files will be posted on the ASDA web site in the near future and would enhance this output. From the output provided, you can determine the data used, output options, variables analyzed and other details of the analysis. WesVar Regression menus can perform only some of the analysis examples in Chapter 9: Multinomial logit regression is an option but Ordinal logit, Poisson, Negative Binomial and the Zero-Inflated versions of Poisson and Negative Binomial regression are not available. Some of the fine points of this tool are the use of the subpopulation filter in the regression request statement, creation of variables used in the analyses (means, ratios, differences, etc.), various output options to specify the statistics of interest and a number of Repeated Replication variance estimation methods (JK1, JK2, BRR, etc.). For these examples, the JK2 method was used throughout but other methods are available. As in the previous regression examples, use of the reverse coded classification variables is used to match the default reference category of Stata (the lowest category). See the WesVar User s Guide for details.
2 Summary Information of Table Request EX 9.1 BIVARIATE TABLES WESVAR VERSION NUMBER : 4.3 TIME THE JOB EXECUTED : 10:07:41 04/06/2010 INPUT DATASET NAME : C:\Program Files\Westat\WesVar\Data\final_ncsr_part2weight_JK2.var TIME THE INPUT DATASET CREATED : 10:07:11 04/06/2010 FULL SAMPLE WEIGHT : NCSRWTLG REPLICATE WEIGHTS : RPL01...RPL42 VARIANCE ESTIMATION METHOD : JK2 OPTION COMPLETE : ON OPTION FUNCTION LOG : ON OPTION VARIABLE LABEL : ON OPTION VALUE LABEL : ON OPTION OUTPUT REPLICATE ESTIMATES : OFF FINITE POPULATION CORRECTION FACTOR : VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) DEGREES OF FREEDOM : 42 t VALUE : ANALYSIS VARIABLES : None Specified. COMPUTED STATISTIC : None Specified. TABLE(S) : WKSTAT3C*SEX WKSTAT3C*ald WKSTAT3C*mde WKSTAT3C*ED4CAT WKSTAT3C*ag4cat WKSTAT3C*MAR3CAT FACTOR(S) : 1.00 NUMBER OF REPLICATES : 42 NUMBER OF OBSERVATIONS READ : 5692 WEIGHTED NUMBER OF OBSERVATIONS READ : Work Status 3 categories Sex STATISTIC EST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed Male SUM_WTS PERCENT Employed Female SUM_WTS PERCENT Employed MARGINALSUM_WTS PERCENT Unemployed Male SUM_WTS PERCENT Unemployed Female SUM_WTS PERCENT NLF Male SUM_WTS PERCENT NLF Female SUM_WTS PERCENT MARGINAL Male SUM_WTS PERCENT MARGINAL Female SUM_WTS PERCENT PEARSON RS RS
3 Work Status 3 categories ald STATISTIC EST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed No SUM_WTS PERCENT Employed Yes SUM_WTS PERCENT Unemployed No SUM_WTS PERCENT Unemployed Yes SUM_WTS PERCENT NLF No SUM_WTS PERCENT NLF Yes SUM_WTS PERCENT MARGINAL No SUM_WTS PERCENT MARGINAL Yes SUM_WTS PERCENT PEARSON RS RS Work Status mde STATISTIC EST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed No SUM_WTS PERCENT Employed Yes SUM_WTS PERCENT Unemployed No SUM_WTS PERCENT Unemployed Yes SUM_WTS PERCENT NLF No SUM_WTS PERCENT NLF Yes SUM_WTS PERCENT MARGINAL No SUM_WTS PERCENT MARGINAL Yes SUM_WTS PERCENT PEARSON RS RS
4 Work Status Years of education STATISTICEST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed 0-11 SUM_WTS PERCENT Employed 12 SUM_WTS PERCENT Employed SUM_WTS PERCENT Employed 16+ SUM_WTS PERCENT Unemployed 0-11 SUM_WTS PERCENT Unemployed 12 SUM_WTS PERCENT Unemployed SUM_WTS PERCENT Unemployed 16+ SUM_WTS PERCENT NLF 0-11 SUM_WTS PERCENT NLF 12 SUM_WTS PERCENT NLF SUM_WTS PERCENT NLF 16+ SUM_WTS PERCENT MARGINAL 0-11 SUM_WTS PERCENT MARGINAL 12 SUM_WTS PERCENT MARGINAL SUM_WTS PERCENT MARGINAL 16+ SUM_WTS PERCENT PEARSON RS RS Work Status ag4cat STATISTIC EST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed SUM_WTS PERCENT Employed SUM_WTS PERCENT Employed SUM_WTS PERCENT Employed 60+ SUM_WTS PERCENT Unemployed SUM_WTS PERCENT Unemployed SUM_WTS PERCENT Unemployed SUM_WTS PERCENT Unemployed 60+ SUM_WTS PERCENT NLF SUM_WTS PERCENT NLF SUM_WTS PERCENT NLF SUM_WTS PERCENT NLF 60+ SUM_WTS PERCENT MARGINAL SUM_WTS PERCENT MARGINAL SUM_WTS PERCENT MARGINAL SUM_WTS PERCENT MARGINAL 60+ SUM_WTS PERCENT PEARSON RS RS
5 Work Status Marital Status-3 categories STATISTICEST_TYPE ESTIMATE STDERROR LOWER 95% UPPER 95% DEFF Employed Married SUM_WTS PERCENT Employed Previously Married SUM_WTS PERCENT Employed Never Married SUM_WTS PERCENT Unemployed Married SUM_WTS PERCENT Unemployed Previously Married SUM_WTS PERCENT Unemployed Never Married SUM_WTS PERCENT NLF Married SUM_WTS PERCENT NLF Previously Married SUM_WTS PERCENT NLF Never Married SUM_WTS PERCENT MARGINAL Married SUM_WTS PERCENT MARGINAL Previously Married SUM_WTS PERCENT MARGINAL Never Married SUM_WTS PERCENT PEARSON RS RS
6 ANALYSIS EXAMPLE: MULTINOMIAL LOGIT (TABLES 9.2 AND 9.3 OF ASDA) Summary Information of Regression WESVAR VERSION NUMBER : 4.3 TIME THE JOB EXECUTED : 10:07:08 03/28/2010 INPUT DATASET NAME : C:\Program Files\Westat\WesVar\Data\final_ncsr_part2weight_JK2.var TIME THE INPUT DATASET CREATED : 16:17:19 03/27/2010 FULL SAMPLE WEIGHT : NCSRWTLG REPLICATE WEIGHTS : RPL01...RPL42 VARIANCE ESTIMATION METHOD : JK2 TYPE OF ANALYSIS : MULTINOMIAL CONVERGENCE CRITERION : 1e-06 MAXIMUM NUMBER OF ITERATIONS : 25 VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): WKSTAT_REV = SEXM ALD MDE ED12 ED1315 ED16 AGECAT_REV[4] MAR3CAT_REV[3] NUMBER OF REPLICATES : 42 NUMBER OF OBSERVATIONS READ : 5692 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : WKSTAT_REV = SEXM ALD MDE ED12 ED1315 ED16 AGECAT_REV[4] MAR3CAT_REV[3] Class Variable Index : AGECAT_REV.1 : 1 AGECAT_REV.2 : 2 AGECAT_REV.3 : 3 AGECAT_REV.4 : 4 MAR3CAT_REV.1 : 1 MAR3CAT_REV.2 : 2 MAR3CAT_REV.3 : 3 MODEL : WKSTAT_REV = SEXM ALD MDE ED12 ED1315 ED16 AGECAT_REV[4] MAR3CAT_REV[3] OPTIONS : Intercept, No Standardized Coefficient, Degrees of Freedom = 42 t VALUE : STARTING VALUES : WKSTAT_REV.1 INTERCEPT : WKSTAT_REV.1 SEXM : WKSTAT_REV.1 ALD : WKSTAT_REV.1 MDE : WKSTAT_REV.1 ED12 : WKSTAT_REV.1 ED1315 : WKSTAT_REV.1 ED16 : WKSTAT_REV.1 AGECAT_REV.1 : WKSTAT_REV.1 AGECAT_REV.2 : WKSTAT_REV.1 AGECAT_REV.3 : WKSTAT_REV.1 MAR3CAT_REV.1 : WKSTAT_REV.1 MAR3CAT_REV.2 : WKSTAT_REV.2 INTERCEPT : WKSTAT_REV.2 SEXM : WKSTAT_REV.2 ALD : WKSTAT_REV.2 MDE : WKSTAT_REV.2 ED12 : WKSTAT_REV.2 ED1315 : WKSTAT_REV.2 ED16 :
7 WKSTAT_REV.2 AGECAT_REV.1 : WKSTAT_REV.2 AGECAT_REV.2 : WKSTAT_REV.2 AGECAT_REV.3 : WKSTAT_REV.2 MAR3CAT_REV.1 : WKSTAT_REV.2 MAR3CAT_REV.2 : TEST(S) : TEST1 : ALD@1=0, ALD@2=0 TEST2 : MDE@1=0, MDE@2=0 TEST3 : SEXM@1=0, SEXM@2=0 TEST4 : ED12@1=0, ED12@2=0, ED1315@1=0, ED1315@2=0, ED16@1=0, ED16@2=0 TEST5 : AGECAT_REV.1@1=0, AGECAT_REV.1@2=0, AGECAT_REV.2@1=0, AGECAT_REV.2@2=0, AGECAT_REV.3@1=0, AGECAT_REV.3@2=0 TEST6 : MAR3CAT_REV.1@1=0, MAR3CAT_REV.1@2=0, MAR3CAT_REV.2@1=0, MAR3CAT_REV.2@2=0 TEST7 : ED12@1-ED12@2=0, ed1315@1-ed1315@2=0, ed16@1-ed16@2=0 ODDS RATIO(S) : OddsRatio1 : AGECAT_REV.1@1 OddsRatio2 : AGECAT_REV.1@2 OddsRatio3 : AGECAT_REV.2@1 OddsRatio4 : AGECAT_REV.2@2 OddsRatio5 : AGECAT_REV.3@1 OddsRatio6 : AGECAT_REV.3@2 OddsRatio7 : ALD@1 OddsRatio8 : ALD@2 OddsRatio9 : ED12@1 OddsRatio10 : ED12@2 OddsRatio11 : ED1315@1 OddsRatio12 : ED1315@2 OddsRatio13 : ED16@1 OddsRatio14 : ED16@2 OddsRatio15 : MAR3CAT_REV.1@1 OddsRatio16 : MAR3CAT_REV.1@2 OddsRatio17 : MAR3CAT_REV.2@1 OddsRatio18 : MAR3CAT_REV.2@2 OddsRatio19 : MDE@1 OddsRatio20 : MDE@2 OddsRatio21 : SEXM@1 OddsRatio22 : SEXM@2 BY : None Specified. MISSING : 13 (UNWEIGHTED) (WEIGHTED) NONMISSING : 5679 (UNWEIGHTED) (WEIGHTED) Records in category 1 : 1630 (UNWEIGHTED) (WEIGHTED) Records in category 2 : 283 (UNWEIGHTED) (WEIGHTED) Records in the reference category (3) : 3766 (UNWEIGHTED) (WEIGHTED) ITERATIONS REQUIRED FOR FULL SAMPLE : 8 MAXIMUM ITERATIONS FOR REPLICATE SAMPLE : 8-2 LOG LIKELIHOOD FOR FULL SAMPLE : LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY :
8 PARAMETER STANDARD ERROR TEST FOR H0: CATEGORY PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% WKSTAT_REV.1 INTERCEPT WKSTAT_REV.1 SEXM WKSTAT_REV.1 ALD WKSTAT_REV.1 MDE WKSTAT_REV.1 ED WKSTAT_REV.1 ED WKSTAT_REV.1 ED WKSTAT_REV.1 AGECAT_REV WKSTAT_REV.1 AGECAT_REV WKSTAT_REV.1 AGECAT_REV WKSTAT_REV.1 MAR3CAT_REV WKSTAT_REV.1 MAR3CAT_REV WKSTAT_REV.2 INTERCEPT WKSTAT_REV.2 SEXM WKSTAT_REV.2 ALD WKSTAT_REV.2 MDE WKSTAT_REV.2 ED WKSTAT_REV.2 ED WKSTAT_REV.2 ED WKSTAT_REV.2 AGECAT_REV WKSTAT_REV.2 AGECAT_REV WKSTAT_REV.2 AGECAT_REV WKSTAT_REV.2 MAR3CAT_REV WKSTAT_REV.2 MAR3CAT_REV TEST F VALUE NUM. DF DENOM. DF PROB>F OVERALL FIT TEST TEST TEST TEST TEST TEST TEST NOTE: CODES FOR WKSTAT3C 1=EMPLOYED 2=UNEMPLOYED 3=NOT IN LABOR FORCE, CODES FOR SEX 1=MALE 2=FEMALE, CODES FOR ALD 0=NO 1=YES, CODES FOR MDE 0=NO 1=YES, CODES FOR EDUCATION 1=0-11 2=12 3= =16+ YEARS OF EDUCATION. REVERSE CODING USED IN MODEL IS SIMPLY THE REVERSE OF THE CODES ABOVE.
9 ODDS RATIO PARAMETER ESTIMATE LOWER 95% UPPER 95% WKSTAT_REV.1 vs. WKSTAT_REV.3 SEXM WKSTAT_REV.1 vs. WKSTAT_REV.3 ALD WKSTAT_REV.1 vs. WKSTAT_REV.3 MDE WKSTAT_REV.1 vs. WKSTAT_REV.3 ED WKSTAT_REV.1 vs. WKSTAT_REV.3 ED WKSTAT_REV.1 vs. WKSTAT_REV.3 ED WKSTAT_REV.1 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.1 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.1 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.1 vs. WKSTAT_REV.3 MAR3CAT_REV WKSTAT_REV.1 vs. WKSTAT_REV.3 MAR3CAT_REV WKSTAT_REV.2 vs. WKSTAT_REV.3 SEXM WKSTAT_REV.2 vs. WKSTAT_REV.3 ALD WKSTAT_REV.2 vs. WKSTAT_REV.3 MDE WKSTAT_REV.2 vs. WKSTAT_REV.3 ED WKSTAT_REV.2 vs. WKSTAT_REV.3 ED WKSTAT_REV.2 vs. WKSTAT_REV.3 ED WKSTAT_REV.2 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.2 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.2 vs. WKSTAT_REV.3 AGECAT_REV WKSTAT_REV.2 vs. WKSTAT_REV.3 MAR3CAT_REV WKSTAT_REV.2 vs. WKSTAT_REV.3 MAR3CAT_REV NOTE: CODES FOR WKSTAT3C 1=EMPLOYED 2=UNEMPLOYED 3=NOT IN LABOR FORCE, CODES FOR SEX 1=MALE 2=FEMALE, CODES FOR ALD 0=NO 1=YES, CODES FOR MDE 0=NO 1=YES, CODES FOR EDUCATION 1=0-11 2=12 3= =16+ YEARS OF EDUCATION. REVERSE CODING USED IN THE MODEL IS SIMPLY THE REVERSE OF THE CODES ABOVE.
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