Michael P. Wilmot

Self-Monitoring and the Metatraits.

Prior attempts at locating self-monitoring within general taxonomies of personality traits have largely proved unsuccessful. However, past research has typically neglected (a) the bidimensionality of the Self-Monitoring Scale and (b) the hierarchical nature of personality. The objective of this study was to test hypotheses that the two self-monitoring factors are located at the level of the metatraits. Using data from two large multi-informant samples, one community (Sample 1: N = 552, Mage  = 51.26, 61% female; NPeers  = 1,551, Mage  = 48.61, 37% female) and one online (Sample 2: N = 3,726, Mage  = 24.89, 59% female; NPeers  = 17,868, Mage  = 26.23, 64% female), confirmatory factor analysis was used to test the hypotheses. Results confirmed hypotheses that acquisitive self-monitoring would have a strong positive relation to metatrait Plasticity, whereas protective self-monitoring would have a moderate negative relation to metatrait Stability. In both samples, constraining the correlation between acquisitive self-monitoring and Plasticity to unity did not alter model fit indices, indicating that the two putatively distinct constructs are identical. Findings have wide-ranging implications, including integration of the construct of self-monitoring into the mainstream of personality research, as the latter moves toward the development of broad explanatory theories.

A Contemporary Taxometric Analysis of the Latent Structure of Self-Monitoring

One of the most provocative findings in the personality psychology literature is evidence that the latent structure of self-monitoring is categorical. That is, individuals can be classified as either high or low self-monitors (Gangestad & Snyder, 1985). Surprisingly, in the three decades since its original publication, this study has never been replicated. Using the sample from the original study (N = 1,918) and a replication sample (N = 2,951), the latent structure of self-monitoring was retested using contemporary taxometric procedures. Preliminary analyses indicated that the eight-item indicator set used in the original study lacked sufficient indicator validities for unambiguously detecting latent categorical structure. In addition, the Other-Directedness subscale, one of the three factor analytically derived subscale indicators used in the original investigation, was likewise found to be unsuitable, because of a combination of low validity and relative orthogonality vis-à-vis its fellow subscales. The 2 remaining subscales, Acting and Extraversion, had excellent properties as indicators, and were subsequently subjected to multiple taxometric procedures and consistency tests. Results failed to support the original taxonic claim; to the contrary, multiple comparison curves and a grand mean comparison curve fit index (CCFI) of .214 provided strong, convergent evidence that the latent structure of self-monitoring is dimensional rather than categorical. Dimensional findings indicate that the conventional model of self-monitoring may merit reexamination, and that theoretical models, measurement practices, and data analytic procedures that assume taxonicity should be replaced by dimensional conceptualizations and corresponding statistical procedures. Findings underscore the importance of replication in psychological science.

Using Item Response Theory to Develop Measures of Acquisitive and Protective Self-Monitoring From the Original Self-Monitoring Scale.

For the past 40 years, the conventional univariate model of self-monitoring has reigned as the dominant interpretative paradigm in the literature. However, recent findings associated with an alternative bivariate model challenge the conventional paradigm. In this study, item response theory is used to develop measures of the bivariate model of acquisitive and protective self-monitoring using original Self-Monitoring Scale (SMS) items, and data from two large, nonstudent samples (Ns = 13,563 and 709). Results indicate that the new acquisitive (six-item) and protective (seven-item) self-monitoring scales are reliable, unbiased in terms of gender and age, and demonstrate theoretically consistent relations to measures of personality traits and cognitive ability. Additionally, by virtue of using original SMS items, previously collected responses can be reanalyzed in accordance with the alternative bivariate model. Recommendations for the reanalysis of archival SMS data, as well as directions for future research, are provided.

Direct and conceptual replications of the taxometric analysis of type a behavior.

We present direct and conceptual replications of the influential taxometric analysis of Type A Behavior (TAB; Strube, 1989), which reported evidence for the latent typology of the construct. Study 1, the direct replication (N = 2,373), duplicated sampling and methodological procedures of the original study, but results showed that the item indicators used in the original study lacked sufficient validity to unambiguously determine latent structure. Using improved factorial subscale indicators to further test the question, multiple taxometric procedures, in combination with parallel analyses of simulated data, failed to replicate the original typological finding. Study 2, the conceptual replication, tested the latent structure of the wider construct of TAB using the sample from the Caerphilly Prospective Study (N = 2,254), which contains responses to the three most widely used self-report measures of TAB: the Jenkins Activity Survey, Bortner scale, and Framingham scale. Factorial subscale indicators were derived from the measures and submitted to multiple taxometric procedures. Results of Study 2 converged with those of Study 1, providing clear evidence of latent dimensional structure. Overall, results suggest there is no evidence for the type in TAB. Findings imply that theoretical models of TAB, assessment practices, and data analytic procedures that assume a typology should be replaced by dimensional models, factorial subscale measures, and corresponding statistical approaches. Specific subscale measures that tap multiple Big Five trait domains, and show evidence of predictive utility, are also recommended.