GLM Options
Optional statistics are available from this dialog box. Statistics are calculated using a fixed-effects model.
Display. Select Descriptive statistics to produce observed means, standard deviations, and counts for all of the dependent variables in all cells. Estimates of effect size gives a partial eta-squared value for each effect and each parameter estimate. The eta-squared statistic describes the proportion of total variability attributable to a factor. Select Observed power to obtain the power of the test when the alternative hypothesis is set based on the observed value. Select Parameter estimates to produce the parameter estimates, standard errors, t tests, confidence intervals, and the observed power for each test. Select Contrast coefficient matrix to obtain the L matrix.
Homogeneity tests produces Levene tests of the homogeneity of variance for each dependent variable across all level combinations of the between-subjects factors, for between-subjects factors only. The spread-versus-level and residual plots options are useful for checking assumptions about the data. This item is disabled if there are no factors. Select Residual plot to produce an observed-by-predicted-by-standardized residual plot for each dependent variable. These plots are useful for investigating the assumption of equal variance. Select Lack of fit to check if the relationship between the dependent variable and the independent variables can be adequately described by the model. General estimable function(s) allows you to construct custom hypothesis tests based on the general estimable function(s). Rows in any contrast coefficient matrix are linear combinations of the general estimable function(s).
Heteroskedasticity Tests are available for testing whether the variance of the errors (for each dependent variable) depends on the values of the independent variables. For the Breusch-Pagan test, Modified Breusch-Pagan test, and F test you can specify the model on which the test is based. By default, the model consists of a constant term, a term that is linear in the predicted values, a term that is quadratic in the predicted values, and an error term.
Parameter estimates with robust standard errors displays a table of parameter estimates, along with robust or heteroskedasticity-consistent (HC) standard errors; and t statistics, significance values, and confidence intervals that use the robust standard errors. Five different methods are available for the robust covariance matrix estimation.
- HC0
- Based on the original asymptotic or large sample robust, empirical, or "sandwich" estimator of the covariance matrix of the parameter estimates. The middle part of the sandwich contains squared OLS (ordinary least squares) or squared weighted WLS (weighted least squares) residuals.
- HC1
- A finite-sample modification of HC0, multiplying it by N/(N-p), where N is the sample size and p is the number of non-redundant parameters in the model.
- HC2
- A modification of HC0 that involves dividing the squared residual by 1-h, where h is the leverage for the case.
- HC3
- A modification of HC0 that approximates a jackknife estimator. Squared residuals are divided by the square of 1-h.
- HC4
- A modification of HC0 that divides the squared residuals by 1-h to a power that varies according to h, N, and p, with an upper limit of 4.
Significance level. You might want to adjust the significance level used in post hoc tests and the confidence level used for constructing confidence intervals. The specified value is also used to calculate the observed power for the test. When you specify a significance level, the associated level of the confidence intervals is displayed in the dialog box.
Specifying Options for GLM Univariate
This feature requires the Statistics Base option.
- From the
menus choose:
- In the GLM Univariate dialog box, click Options.
Select the statistics and diagnostics.