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SPSS is an application that lets you manage your statistics data and research. This development software provides advanced predictive analytics, text analysis, and machine learning algorithms for researchers, government agencies, and survey companies. It can integrate and deploy information to data processing apps and allows organizations to address research problems, predict outcomes, and improve their efficiency. Note, however, that this is the 64bit version. The 32bit is available on a different download link.
Using analysis weights is important to get the point estimates right. Users must consider the weighting, clustering, and stratification of the survey design to produce correct standard errors (and degrees of freedom). 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. This statistical analysis plan (SAP), in turn, results in two subpopulation analyses of proportions for each level of gender. 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. Gender is determined using the categorical variable, IRSEX. Both of the variables in the NSDUH PUF file are numeric in downloadable SAS, Stata, SPSS, and R specific datasets. 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. IRSEX is imputation revised gender for missing values and is coded numeric as 1 for male and 2 for female. 153554b96e