All Effects Results in GLZ

The options described here are available on the GLZ Results dialog box - Summary tab.

Summary of all effects

Click the Summary of all effects button to display a spreadsheet with the Wald statistic and respective p-values for all effects in the model. Significant effects in this spreadsheet will be highlighted. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note in the GLZ Results topic for details.

Note: Models that are not full-rank (e.g., overparameterized models). When redundant columns are detected during the evaluation of the design matrix, some difficulties arise when computing the Wald statistic for the overall model, and when attempting to compute Type 3 LR tests of effects (see below). Specifically, because of the redundancy of some of the parameters in the model, independent tests of effects, controlling for all other parameters in the model (not belonging to the effect under consideration) cannot be computed. Therefore, the Summary of all effects and Type 3 LR test buttons will not be available in that case.

Type 1 LR test

Click the Type 1 LR test (likelihood ratio test) button to display a spreadsheet with the results for the Type 1 sequential tests for the effects in the model. Refer to GLM - Six types of sums of squares for details on how effects can be tested in unbalanced factorial ANOVA designs (in particular, see Type I sums of squares). In short, the spreadsheet will report the log-likelihoods for the model that includes a particular effect (shown in the respective row of the spreadsheet) and all effects that precede it (shown in the previous rows of the spreadsheet); the incremental Chi-square statistic then provides a test of the increment in the log-likelihood, attributable to the respective (current) effect. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note in the GLZ Results topic for details.

Type 3 LR test

Click the Type 3 LR test (likelihood ratio test) button to display a spreadsheet with the results for the Type 3 tests for the effects in the model. Refer to the GLM topic Six types of sums of squares for details on how effects can be tested in unbalanced factorial ANOVA designs (in particular, see Type III sums of squares). In short, the spreadsheet will report the log-likelihoods for the models that include all effects except for the current effect (shown in the respective row of the spreadsheet); the incremental Chi-square statistic for that model, and the full model (that includes all effects) then provides a test of the increment in the log-likelihood, attributable to the respective (current) effect, while controlling for all other effects. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note in GLZ Results for details. See the Models that are not full-rank (e.g., overparameterized models) note above for additional information on this option.

Cell statistics

Click the Cell statistics button to display a spreadsheet of the descriptive statistics for each cell in the design; specifically, descriptive statistics are computed for the dependent (response) variable, as well as any continuous predictors (covariates) in the design, for each column of the overparameterized design matrix for categorical effects. Thus, marginal means and standard deviations are available for each categorical effect in the design. Note that for lower-order effects (e.g., main-effects in designs that also contain interactions involving the main effects), the reported means are weighted marginal means, and as such estimates of the weighted population marginal means (for details, see, for example, Milliken and Johnson, 1984, page 132; see also the discussion of means in the description of the options on the Means tab).

Design term

Click the Design term button to display a spreadsheet of all the labels for each column in the design matrix.

V-C matrix

Click the V-C matrix button to display (asymptotic) variance-covariance matrix for the parameter estimates. Specifically, the values shown in this spreadsheet are the expected parameter variances and covariances computed via the Fisher Scoring method. See the Reading the spreadsheet note (above) for additional information on this option.

Note: Categorical response variables. For models with categorical response variables (i.e., for Poisson, Binomial, Multinomial, or Ordinal multinomial responses, see Distribution and link function) the standard error computations will be affected by the setting of the Overdispersion check box; see Sample options in GLZ for details.

Corr. matrix.

Click the Corr. matrix button to display a (asymptotic) correlation matrix for the parameter estimates. Specifically, the values shown in this spreadsheet are the expected parameter correlations computed via the Fisher Scoring method.

Note: Reading the spreadsheet. Note that each row of the spreadsheet corresponds to a column in the design matrix. When the design includes categorical predictor variables, then the parameter estimates pertain to the coded effects in the design matrix. The GLM topic The Sigma-Restricted and Overparameterized Model discusses in detail how this coding is accomplished (and how, consequently, the parameter estimates can be interpreted). You can also refer to the Design terms and Coefficient options in Summary results for between effects in GLM for details on the labeling of the columns of the design matrix in results spreadsheets.

Estimates

Click the Estimates button to display a spreadsheet with the parameter estimates, their standard errors, and statistical significance. See the Reading the spreadsheet note (above) for information on the proper way to read this spreadsheet. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note in the GLZ Results topic for details. Finally, see the Categorical response variables note (above) for additional information on this option.

Assuming a binomial distribution, a second spreadsheet containing odds ratios and their confidence intervals is produced when the parameter estimates results spreadsheet is requested.

Note: Reference level for categorical dependent (response) variable. The last category (level) that is specified for a categorical dependent (response) variable will be the reference category for the comparisons with the other categories. So, for example, if a multinomial dependent (response) variable with k = 3 levels is analyzed, the k-1 = 2 parameters for each predictor (effect column) pertain to the comparison of (1) the first level with the last level, and (2) the second level with the last level of the dependent (response) variable.

Conf. intervals.

Click the Conf. intervals button to display a spreadsheet with the confidence intervals for the parameter estimates (see also Estimates above); the confidence level (p value) that is to be used for the interval can be specified in the Conf. limit field (see below). See the Reading the spreadsheet note (above) for information on the proper way to read this spreadsheet. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note on the GLZ Results topic for details. Finally, see the Categorical response variables note (above) for additional information on this option.

Iter. results.

Click the Iter. results button to display a spreadsheet that shows the parameter estimates and the model log-likelihood at each iteration. Specifically, each column of the spreadsheet represents one iteration, and the rows show the respective parameter estimates and model log-likelihood at that iteration. See the Reading the spreadsheet note (above) for information on the proper way to read this spreadsheet. Note that these results always pertain to the overall model with all effects, regardless of which effects were selected by any Model building procedures on the Quick Specs Dialog - Advanced tab; see the Results for stepwise or best-subset regression note in GLZ Results for details.

Sign. lev.

Enter the value to be used for all spreadsheets and graphs where statistically significant results are to be highlighted (e.g., in the Summary of all effects spreadsheet) in the Sign. lev field; by default all results significant at the p < .05 level will be highlighted.

Conf. limit.

Enter the value to be used for constructing confidence limits in the respective results spreadsheets or graphs (e.g., in the Confidence intervals of estimates spreadsheet) in the Conf. limit field; by default 95% confidence limits will be constructed.

See also, GLZ - Index.