Features

On this page:

General

Usability

  • Easy-to-use Windows® interface
  • No coding of procedure statements needed
  • Simple workbook structure for organizing analysis requests
  • Workbook output can be shared without having the source data sets
  • Multiple files with replicate weights can be imported in one run
  • Preferences control default settings for output, tables, regression models, and file locations

Data Import/Export

  • SAS V7/V8/V9
  • SAS transport file
  • S-Plus
  • Microsoft Access®
  • Paradox
  • Quattro Pro
  • ASCII
  • SAS for PC/DOS
  • SPSS
  • STATA
  • Microsoft Excel®
  • Lotus
  • Minitab
  • ODBC
  • Many more

Working with WesVar Data Sets

  • Add variable labels
  • Perform recodes
  • Subset populations
  • Log file for tracking the history of changes made to a data file

Weighting

Create Weights

  • Import existing replicate weights or create them in WesVar
  • Adjust input base weights using:
    • Nonresponse adjustments
    • Post-stratification
    • Raking
  • Make adjustments for the full sample and separately for each replicate

Replication Methods of Variance Estimation Supported

  • Stratified two primary sampling units (PSUs)/stratum
    • Balanced repeated replication (BRR)
    • Fay’s BRR method (Fay)
    • Jackknife 2 (JK2)
  • Stratified >2 PSUs/stratum
    • Jackknife n (JKn)
  • Unstratified
    • Jackknife 1 (JK1)

Maximum Replicates

  • BRR, Fay—up to 512 with bundled orthogonal matrices, up to 9,984 with user supplied matrices
  • Jackknife (JK1, JK2, JKn)—up to 9,999

Tables

General Features

  • Specify up to eight-way tables
  • Easily generate all two-way and three-way tables
  • Analyze data sets with multiple imputations

Generated Statistics

  • Estimates
  • Standard errors
  • Coefficients of variation
  • Confidence intervals
  • Wilson method for confidence intervals on extreme proportions
  • Unweighted sample size in each table cell
  • Design effect for estimate
  • Effective sample size
  • t statistic for test that estimate is zero, associated p values
  • Standardized rates

Computed Statistics

  • A statistic that is a function of one or more survey variables or of statistics derived from a survey (e.g., RATIO=INCOME/ASSET). For example, the ratio of two means, the log-odds in a two-way table, and the difference in two ratios can be calculated using computed statistics.
  • Other functions include standard operators (+, -, x, /), Log, Exp, Sqrt, Mean, Median, Quantile, and Geometric Mean
  • Plausible values can be computed for education assessment tests or for multiple imputation analysis

Tests of Independence

  • Pearson’s chi-square
  • RS2—Rao-Scott statistic using a design effect adjustment to the Pearson chi-square
  • RS3—Rao-Scott statistic based on Satterthwaite approximation

Options for Tables

  • Set alpha for confidence intervals
  • Set finite population correction factor for variance estimates
  • Set degrees of freedom for estimated variances
  • Choose method for handling missing values
    • Listwise deletion (complete case analysis)
    • Separate treatment of each analysis variable and computed statistic (available case analysis)
  • Control method for computing medians or quantiles
    • Group method
    • No group method (default)
    • Compute direct replicate estimates of standard errors
  • Print diagnostic to measure whether replicate contributes heavily to variance estimate

Function Statistics

  • A statistic computed as a function of the cell entries in a table, such as the difference between males and females (up to four-way tables)

Output for Tables

  • Requested statistics for all cells and marginals of table
  • Statistic type
  • Sum of weights
  • Percentage of total
  • Row percentages
  • Column percentages
  • Labels for variables and values
  • Fixed decimal or scientific notation, with ability to control number of decimal places
  • Replicate estimates saved in auxiliary output file

Table Viewer

  • Allows users to view the output of a Table Request in a tabular format and copy the data into Microsoft Excel or other programs

Regression

General Features

  • Point-and-click selection of dependent and independent variables
  • Independent variables—continuous and categorical variables are allowed (class variables can have up to 256 categories)
  • Automatic creation of dummy variables
  • By variable for subgroup modeling
  • Models with or without intercept
  • Interactions can be specified among predictors
  • Significance of linear combinations of parameters can be tested
  • Degrees of freedom can be set for estimated variances

Types of Models

  • Linear regression
  • ANOVA—up to three-way interactions
  • Dichotomous logistic regression
  • Multinomial logistic regression (unordered categories)

Linear Regression

  • Specify a transformation of a source variable as the dependent variable
  • Find parameter estimates using weighted least squares algorithm
  • Analyze data sets with multiple imputations

Logistic Regression

  • Use modified Newton-Raphson iteration procedure to find parameter estimates
  • Specify maximum number of iterations
  • Set convergence criterion—relative difference of log-likelihood
  • Control starting values for parameter estimates
  • Define success value for the dependent variable in dichotomous logistic regression
  • Define reference category for the dependent variable in multinomial logistic regression
  • Compute odds ratios—estimates and confidence intervals

Output

  • Model, error, and total sums of squares
  • Parameter estimates
  • Standardized linear regression coefficients
  • Standard error of parameter estimates
  • Confidence intervals for parameters
  • t tests on individual parameters
  • F tests on main effects and interactions
  • User-defined tests on linear combinations of parameters
  • Score tests for logistic parameters
  • p values for hypothesis tests
  • R2 values
    • Linear—squared multiple correlation coefficient
    • Logistic—Cox-Snell, entropy, Estrella
  • Correlations of parameter estimates
  • Covariances of parameter estimates
  • Estimates and confidence intervals for odds ratios in dichotomous and multinomial logistic regression
  • Fixed decimal or scientific notation, ability to control number of decimal places
  • Replicate estimates saved in auxiliary output file
  • Iteration history for logistic saved in auxiliary output file

Descriptive Statistics

Generated Statistics

  • Extreme observations—five smallest and five largest unweighted observations
  • Influential observations—five most influential weighted observation with respect to the mean and five most influential observations with respect to the total
  • Univariate statistics:
    • Weighted and unweighted count of observations, sum, mean, variance, skewness, kurtosis, percentiles, and geometric mean
    • Standard errors for weighted statistics
    • Both replication and Woodruff standard errors for percentiles
    • Minimum, maximum, and unweighted count of missing observations
  • Weighted and unweighted correlation coefficients
  • Replication standard errors for weighted correlation coefficients