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.

**Results**

**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.