#SPSS CODE EXAMPLE HOW TO#
#SPSS CODE EXAMPLE SOFTWARE#
#SPSS CODE EXAMPLE CODE#
#SPSS CODE EXAMPLE SERIES#

SAS's SURVEYMEANS always analyzes character variables as categorical. Stata's SVY: PROPORTION produces estimates of proportions, along with SEs, for character or numeric variables. In the SAP, RSKPKCIG is the analysis variable with four valid and missing values and IRSEX will be used to define the domains or subpopulations. Statistical procedures and requested statistics. See the 'How are complex sampling variances computed by Taylor linearization method?' FAQ on the use of MISSUNIT option in SUDAAN, SINGLEUNIT(centered) option in Stata, and the NOMCAR procedure option in SAS.

#SPSS CODE EXAMPLE CODE#

The first block of code in the SPSS program syntax, below, for the CSPLAN ANALYSIS procedure will create such an analysis plan file. SPSS requires an analysis plan design file for the data file to perform a complex survey analysis. The above can be implemented by the SVYSET command in Stata as 'SVYSET VEREP, STRATA( VESTR) SINGLEUNIT(centered)'. For example, PROC SURVEYLOGISTIC in SAS and PROC LOGISTIC in standalone SUDAAN. One should use the above block of statements (specific to NSDUH complex design) in any survey procedure program code for any SAP. Note that the order of the variables in the NEST statement is important. For example, 'NEST VESTR VEREP / MISSUNIT WEIGHT ANALWT_C ' in SUDAAN and 'STRATA VESTR CLUSTER VEREP WEIGHT ANALWT_C 'in SAS.

#SPSS CODE EXAMPLE SOFTWARE#

The stratification and clustering of the complex sample design in the NSDUH PUF are described by specifying the variance estimation variables (and also the analysis weight) via the statements in the analysis procedure program code for SUDAAN and SAS software packages. The WR design method is the default with the Taylor method for all but SPSS software packages. For variance estimation, each sample program code uses the Taylor linearization method for this example SAP.

The statistical procedure along with requested statistics andД®ach of these three considerations is discussed below.

#SPSS CODE EXAMPLE HOW TO#

How to correctly specify the variance estimation variables including analysis weights.IRSEX is imputation revised gender for missing values and is coded numeric as 1 for male and 2 for female.ДЇor analysis of the NSDUH PUF file, one should consider three important thingsВ before preparing program code in a statistical software package: RSKPKCIG is coded numeric as 1 to 4 for no risk, slight risk, moderate risk, and great risk for valid values and as system missing for invalid values. Both of the variables in the NSDUH PUF file are numeric in downloadable SAS, Stata, SPSS, and R specific datasets. Gender is determined using the categorical variable, IRSEX. The dependent or outcome variable is the risk of smoking one or more packs of cigarettes per day and is determined using the categorical variable, RSKPKCIG. This statistical analysis plan (SAP), in turn, results in two subpopulation analyses of proportions for each level of gender. The example code provided below shows how to specify these variables correctly, using an individual year of the NSDUH PUF, and also indicates how to calculate the proportions, standard errors (SE), and confidence intervals of the risk of smoking one or more packs of cigarettes per day by gender. Users must consider the weighting, clustering, and stratification of the survey design to produce correct standard errors (and degrees of freedom). Using analysis weights is important to get the point estimates right. Note that users should read the help document (of her/his respective statistical package) regarding how missing values are being handled if any exist in the analysis variables.

#SPSS CODE EXAMPLE SERIES#

The WR design is the default design, except in SPSS and Taylor series linearization is also the default method for variance estimation of them. SUDAAN, all survey procedures in SAS, Stata, R and the survey add-on module in SPSS can handle data from complex sampling designs. However, analysis variables can have missing values. There are no missing values in the variance estimation variables and final analysis weight, VESTR, VEREP and ANALWT_C. It is therefore considered that the complex survey method for NSDUH PUF is a single-stage stratified clustering design, where the clusters are sampled with replacement (WR). The NSDUH public-use file (PUF) includes the variance estimation variables (which were derived from the complex sample designs 2): variance estimation stratum (VESTR), variance estimation cluster replicates (VEREP) and final analysis weight (ANALWT_C). National Survey on Drug Use and Health (NSDUH) 1 employs a multistage (stratified cluster) sample design for the selection of a representative sample from non-institutional members of United States households aged twelve and older.

Spss code example