Examples of SPSS Command Files

  • senic97a.sps: Read in free format, declare missing values, do frequencies & descriptives
  • senic97b.sps: Read in free format, declare missing values, do frequencies for categorical & examine for continuous variables.
  • senic97c.sps: Read in fixed format, declare missing values, variable labels, value labels, recode, compute, if, do some frequencies
  • senicread97.sps: Read in free format, declare missing values, variable labels, value labels, recode, compute, if; no statistical procedures. This file is to be used by other jobs with include.
  • senic97d.sps: Include senicread97.sps, do independent t-test, matched t-test, one-way anova, crosstabs with chisquare test of independence, plot with simple regression, correlation matrix.
  • senic97e.sps: Include senicread97.sps, make composite variables, do planned comparisons with ONEWAY.
  • senicreg97a.sps: Basic multiple regression with 2 independent variables.
  • senicreg97b.sps: Multiple regression with indicator dummy variables, save and analyze residuals.
  • senicreg97c.sps: Test a block of variables controlling for another block using multiple regression.
  • senicreg97d.sps: Multiple regression with Z-scores.
  • senicreg97e.sps: Multiple Regression with Interactions between quantitative variables.
  • cars97a.sps: Interactions between indicators and quantitative variables (cars data).
  • senicreg97f.sps: Dummy Var Regression = ANOVA (senic data).
  • senicancova97a.sps: Basic analysis of covariance with senic data.
  • potato97a.sps: Three-way anova with nice table of means from crossbreak (potato data).
  • senicmv97a.sps: Multivariate regression, blocks of variables controlling for other blocks (senic data).
  • senicmv97b.sps: Multivariate analysis of variance and covariance on senic data.
  • chd97a.sps: Simple logistic regression on Coronary Heart Disease Data.
  • plant97a.sps: Logistic regression with categorical independent variables and interactions. (Plant data)
  • bweight97a.sps: Logistic regression test of a block of variables controlling for another block. (Low birthweight data)