Repeated Measure Models

Factorial designs with a repeated (within-subject) factor; use General Linear Models to specify and analyze complex between-within models. Use this module script to specify designs that include main-effects and interactions for categorical predictors (to a specified degree, e.g., two-way effects, three-way effects, etc.), covariates, and a single repeated measures factor. Both univariate (single dependent measure) and multivariate (multiple dependent measures) designs can be analyzed. Default results include the ANOVA/ANCOVA (MANOVA/MANCOVA) table; set the Level of detail parameter to All results to request tables of means and other statistics.

Model and Estimation

Parameterization of effects. Specifies either the sigma-restricted model or the overparameterized model; the sigma restricted parameterization is the default.

Tests factorial to degree. Specify the factorial degree of the between-group design to be tested; Statistica will construct a factorial design for all categorical predictors up to the specified degree (i.e., by default up to degree 2, so that the final model will include all factor main effects and two-way interactions for categorical predictors).

Within effect name. Name of repeated measure (use General Linear Models to specify and analyze complex between-within models).

No. levels for within effect. Number of levels for repeated measure (within-subject) effect; the list of continuous dependent variables will be divided by this number, and the analysis will be performed on the resulting number of dependent measures. For example, if you specified 6 continuous dependent variables, and a repeated measures factor with 3 levels, than the program will perform a repeated measures MANOVA with 6/3=2 dependent measures. Specifically, to assign the consecutive continuous dependent variables to the levels of the repeated measures factors, STATISTICA will cycle through a nested loop, where the number of dependent measures has the fastest moving subscript and the repeated measures factor the next-to-fastest moving subscript. See the Electronic Manual for additional details on how to specify repeated measures factors (see the GLM syntax help for keyword REPEATED).

Type of sums of squares. Specifies how to construct the hypotheses for the tests of main effects and interactions. Note: Type IV sums of squares are not available for sigma-restricted parameterization; Type VI sums of squares are not available for overparameterized parameterization of categorical factor effects.

Intercept. Specifies whether the intercept (constant) is to be included in the model.

Sweep delta 1.E-. Specifies the negative exponent for a base-10 constant Delta (delta = 10^-sdelta); the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.

Inverse delta 1.E-. specify the negative exponent for a base-10 constant Delta (delta = 10^-idelta); the default value is 12. Delta is used to check for matrix singularity in matrix inversion calculations.


Detail of computed results reported. Specifies the detail of computed results reported. If All results is requested, Statistica will also report all univariate results (for multivariate designs), descriptive statistics, details about the design terms, the whole-model R, regression coefficients, and the least-squares means for all effects.

Least square means. Creates the expected marginal means, given the current model; either all marginal means tables can be computed, or only the means for the highest-order effect of the factorial design.

Tests homogeneity of variances. Tests the homogeneity of variances/covariances assumption. One of the assumptions of univariate ANOVA is that the variances are equal (homogeneous) across the cells of the between-groups design. In the multivariate case (MANOVA), this assumption applies to the variance/covariance matrix of dependent variables (and covariates).

Plots of means vs. std. dev. Plots the (unweighted) marginal means (see also the Means tab) for the selected Variables against the standard deviations.

Contrast coefficients. Specifies your contrasts for least squares means; consult the documentation for syntax details.

Normal probability plot. Normal probability plot of residuals.

Generates data source, if N for input less than. Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.