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John L. Horn (1928-2006) was a pioneer in multivariate thinking and the application of multivariate methods to research on intelligence and personality. His key works on individual differences in the methodological areas of factor analysis and the substantive areas of cognition are reviewed here. John was also our mentor, teacher, colleague, and friend. We overview John Horn's main contributions to the field of intelligence by highlighting 3 issues about his methods of factor analysis and 3 of his substantive debates about intelligence. We first focus on Horn's methodological demonstrations describing (a) the many uses of simulated random variables in exploratory factor analysis; (b) the exploratory uses of confirmatory factor analysis; and (c) the key differences between states, traits, and trait-changes. On a substantive basis, John believed that there were important individual differences among people in terms of cognition and personality. These sentiments led to his intellectual battles about (d) Spearman's g theory of a unitary intelligence, (e) Guilford's multifaceted model of intelligence, and (f) the Schaie and Baltes approach to defining the lack of decline of intelligence earlier in the life span. We conclude with a summary of John Horn's unique approaches to dealing with common issues.

A mixed effects location scale model was used to model and explain individual differences in within-person variability of negative and positive affect across 7 days (N=178) within a measurement burst design. The data come from undergraduate university students and are pooled from a study that was repeated at two consecutive years. Individual differences in level and change in mood was modeled with a random intercept and random slope where the residual within-person variability was allowed to vary across participants. Additionally changes in within-person variability were explained by the inclusion of a time-varying predictor indicating the severity of daily stressors. This model accounted for 2 location and 2 scale effects and provided evidence that individuals who reported higher severity in daily stressors also exhibited greater variability in affect-but only for participants who showed low overall affect variability and who reported low average negative affect. Those who were more variable in their affect reports overall were less reactive to daily stressors in the sense that their high levels of affect variability remained high. We describe the utility of this model for further research on individual variation and change.

The effect of time-related mean differences on estimates of association in cross-sectional studies has not been widely recognized in developmental and aging research. Cross-sectional studies of samples varying in age have found moderate to high levels of shared age-related variance among diverse age-related measures. These findings may be misleading because high levels of association between time-dependent processes can result simply from average population age differences and not necessarily from associations between individual "rates of aging." This is demonstrated both analytically and in a simulation involving cross-sectional sampling of individual trajectories. An alternative cross-sectional narrow age-cohort design is shown to provide a useful alternative for evaluating the interdependence of time-related processes.

Researchers often face a dilemma: Should they collect little data and emphasize quality, or much data at the expense of quality? The utility of the 3-form design coupled with maximum likelihood methods for estimation of missing values was evaluated. In 3-form design surveys, four sets of items. X, A, B, and C are administered: Each third of the subjects receives X and one combination of two other item sets - AB, BC, or AC. Variances and covariances were estimated with pairwise deletion, mean replacement, single imputation, multiple imputation, raw data maximum likelihood, multiple-group covariance structure modeling, and Expectation-Maximization (EM) algorithm estimation. The simulation demonstrated that maximum likelihood estimation and multiple imputation methods produce the most efficient and least biased estimates of variances and covariances for normally distributed and slightly skewed data when data are missing completely at random (MCAR). Pairwise deletion provided equally unbiased estimates but was less efficient than ML procedures. Further simulation results demonstrated that nun-maximum likelihood methods break down when data are not missing completely at random. Application of these methods with empirical drug use data resulted in similar covariance matrices for pairwise and EM estimation, however, ML estimation produced better and more efficient regression estimates. Maximum likelihood estimation or multiple imputation procedures. which are now becoming more readily available, are always recommended. In order to maximize the efficiency of the ML parameter estimates, it is recommended that scale items be split across forms rather than being left intact within forms.