A new Big Five: fundamental principles for an integrative science of personality

Despite impressive advances in recent years with respect to theory and research, personality psychology has yet to articulate clearly a comprehensive framework for understanding the whole person. In an effort to achieve that aim, the current article draws on the most promising empirical and theoretical trends in personality psychology today to articulate 5 big principles for an integrative science of the whole person. Personality is conceived as (a) an individual's unique variation on the general evolutionary design for human nature, expressed as a developing pattern of (b) dispositional traits, (c) characteristic adaptations, and (d) self-defining life narratives, complexly and differentially situated (e) in culture and social context. The 5 principles suggest a framework for integrating the Big Five model of personality traits with those self-defining features of psychological individuality constructed in response to situated social tasks and the human need to make meaning in culture.     

Beyond statistical inference: A decision theory for science

Traditional null hypothesis significance testing does not yield the probability of the null or its alternative and, therefore, cannot logically ground scientific decisions. The decision theory proposed here calculates the expected utility of an effect on the basis of (1) the probability of replicating it and (2) a utility function on its size. It takes significance tests—which place all value on the replicability of an effect and none on its magnitude—as a special case, one in which the cost of a false positive is revealed to be an order of magnitude greater than the value of a true positive. More realistic utility functions credit both replicability and effect size, integrating them for a single index of merit. The analysis incorporates opportunity cost and is consistent with alternate measures of effect size, such as r2 and information transmission, and with Bayesian model selection criteria. An alternate formulation is functionally equivalent to the formal theory, transparent, and easy to compute.     

Beyond mind-reading: multi-voxel pattern analysis of fMRI data

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.     

Computer-managed inferential statistical analysis of EEG data

Much of the neuroelectrical/psychophysiologic research of the past has involved the use of “group-type” statistics. Often, this has resulted in large variances that make it difficult, if not impossible, to find significant differences. Using the INNOVATE technique of single-subject design, we are able to consistently demonstrate significant differences in EEG spectra related to experimental and clinical effects previously masked.     

Beyond the Big Five: Increases in Understanding and Practical Utility

The articles in this special issue provide presentations of measures of three new personality constructs. Initial results from these three programs of research indicate that each of these measures has a firm theoretical base and that the authors have engaged in careful consideration of the set of relations that theory suggests ought to be displayed by their new measure. Further empirical research by these authors and others ought to provide greater scientific understanding of the role personality plays in work satisfaction and various dimensions of performance, a better sense of the practical usefulness of these measures, and better information about the situational constraints that apply when each is used to predict various personal and organizational outcomes.     

The effects of collaborative practice on statistical problem solving: Benefits and boundaries

In recent years, understanding the effects of collaboration on learning and memory has emerged as a major topic of investigation. Findings from applied educational research and from basic cognitive research demonstrate a complex view of how collaboration affects learning. The present laboratory study bridged these two domains of research to address the question of how collaborative learning affects statistical problem solving. After viewing a lecture, participants completed two statistics tests. They either completed the tests collaboratively and then individually, or completed both tests individually. Results showed an immediate benefit of collaboration, but this benefit did not persist on a subsequent individual test. Repeated practice by those who worked individually increased performance to the level of those who had previously collaborated. These results were qualified by gender as females showed a consistent benefit from prior collaboration on the post-collaborative test, particularly on conceptual problems. Implications for education are discussed.     

Missing data: prevalence and reporting practices

Results are described for a survey assessing prevalence of missing data and reporting practices in studies with missing data in a random sample of empirical research journal articles from the PsychINFO database for the year 1999, two years prior to the publication of a special section on missing data in Psychological Methods. Analysis indicates missing data problems were found in about one-third of the studies. Further, analytical methods and reporting practices varied widely for studies with missing data. One may consider these results as baseline data to assess progress as reporting standards evolve for studies with missing data. Some potential reporting standards are discussed.     

Optimism and the Big Five factors of personality: Beyond Neuroticism and Extraversion

The purpose of the present study was to investigate the relationship between the Big Five factors of personality and dispositional optimism. Data from five samples were collected (Total N = 4332) using three different measures of optimism and five different measures of the Big Five. Results indicated strong positive relationships between optimism and four of the Big Five factors: Emotional Stability, Extraversion, Agreeableness, and Conscientiousness. Agreeableness and Conscientiousness explained additional variance in dispositional optimism over and above Neuroticism and Extraversion, providing evidence for the complexity of optimism. The position of optimism in the larger web of human personality constructs is discussed.     

Optimism and the Big Five factors of personality: Beyond Neuroticism and Extraversion

The purpose of the present study was to investigate the relationship between the Big Five factors of personality and dispositional optimism. Data from five samples were collected (Total N = 4332) using three different measures of optimism and five different measures of the Big Five. Results indicated strong positive relationships between optimism and four of the Big Five factors: Emotional Stability, Extraversion, Agreeableness, and Conscientiousness. Agreeableness and Conscientiousness explained additional variance in dispositional optimism over and above Neuroticism and Extraversion, providing evidence for the complexity of optimism. The position of optimism in the larger web of human personality constructs is discussed.     

The statistical analysis of data from small groups

The authors elaborate the complications and the opportunities inherent in the statistical analysis of small-group data. They begin by discussing nonindependence of group members' scores and then consider standard methods for the analysis of small-group data and determine that these methods do not take into account this nonindependence. A new method is proposed that uses multilevel modeling and allows for negative nonindependence and mutual influence. Finally, the complications of interactions, different group sizes, and differential effects are considered. The authors strongly urge that the analysis model of data from small-group studies should mirror the psychological processes that generate those data.     

Beyond competence: The significance of performance for conceptual development

Conceptual constraints must change with age if they are to account for children's acquisition of kinds of knowledge that do not fall within the initial constraints. A bi-directional relation between competence and performance is therefore hypothesized, such that cognitive competences not only guide performance but also are shaped by it. This hypothesis offers a solution to the difficulties that current competence models have in accounting for developmental change. Goals are proposed as a potential source of changing constraints because they change with age, they shape what children do, and they also influence what children learn from what they do. These ideas are illustrated with examples drawn primarily from research on children's quantitative concepts.     

Commentary on high-performance computing and statistical analysis: From large-scale data analysis to smoothing functions

High-performance computing becomes essential to statistical analysis when the database is massive or the number of computations per data element is large. Albert F. Anderson (1997) discusses the application of high-performance computing to massive databases; J. O. Ramsay’s (Ramsay, Heckman, & Silverman, 1997) estimation problems potentially require large numbers of computations per data element.     

A manifesto for the new MCDA era

The following manifesto has been prepared by five prominent workers in the field of MCDA. We are currently preparing a special issue on the issues that they raise (see Call for Papers on p. 128). We invite you to contribute to the debate in that issue, either in the form of a paper or a letter to the Editor.