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These two emphases mark the endpoints of an epistemological continuum of conceptual breadth versus conceptual specificity (Millon, 1987). Open concepts are more abstract and hypothetical, reflecting the nature of personality constructs as less rigidly organized than many constructs in the hard sciences. That is, in the field of personology, we have few oneto-one relationships among personality, behavior, experience, and development, and we have many more feedback, feedforward, stochastic, and transactional processes involving indeterminate or inferential intervening concepts. As suitable as open concepts are to personology, Millon et al. (2001) noted that conceptualizing personality disorder strictly in terms of open concepts runs the risk of growing so circuitous in references as to become tautological and imply no links to anything observable. This undermines the scientific contribution of theory by rendering it both untestable and inapplicable to clinical diagnosis (Millon, 1991). Operational definitions seek to anchor personality disorder directly in the empirical world of observation, linking each personological attribute to an indicator in a one-to-one fashion. The goal is to reduce inference and maximize the relationship between attribute and method of measurement. In clinical assessment, operational definitions allow for diagnostic indicators to be directly translatable into assessment guidelines that maximize precision (reliability); however, such approaches can be deficient in scope (validity) if they are based on any single methodological procedure (e.g., factor analysis).

C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research (pp. 420 438). Thousand Oaks, CA: Sage. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17(Suppl.), 100. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461 464. Spiel, C. (1994). Latent trait models for measuring change. In A. von Eye & C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research (pp. 274 293). Thousand Oaks, CA: Sage. Stern, H. S., Arcus, D., Kagan, J., Rubin, D. B., & Snidman, N. (1995). Using mixture models in temperament research. International Journal of Behavioral Development, 18, 407 423. Sullivan, P. F., Kessler, R. C., & Kendler, K. S. (1998). Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. American Journal of Psychiatry, 155, 1398 1406. Tracy, A. J., Collins, L. M., & Graham, J. W. (1997). Exposure to adult substance use as a risk factor in adolescent substance use

Performance Figure 27.2 Peak moments quadrant of the feeling and performance model. Adapted and expanded from Measurement of Experience: Construct and Content Validity of the Experience Questionnaire, by G. Privette and C. M. Bundrick, 1987, Perceptual and Motor Skills, 65, p. 318. Copyright 1987 by C. H. Ammons and R. B. Ammons. Adapted with permission.

Finally, Appendix C shows how this testbed can be utilized to evaluate the impact of various IP network impairments such as delay jitter, packet loss, and bandwidth constraints on voice quality and transmission of DTMF messages over an IP network. HVB language is used to implements the test scripts. A brief description of the testbed is presented in the next section, followed by a detailed discussion of each of its major components. The test and measurements procedures, and associated HVB scripts, are available in the respective appendixes.

In Figure 33-5, the clear property of the nonfloating element (P) is set to left. This prevents anything from floating to the left of the P element. In this example, however, because both images (being the same image) are the same height, the second image (with a class of icon2) floats to the right on the page, parallel to the first image.

The ti.xy in Equation 25.31 are de ned by summing the tihxy in a row of the (n n) symmetric matrix. For the tihxy in Table 25.1, we have 0 1 1 = i 1 1 1 0 +1 +1 +1 h 1 1 1 +1 +1 +1 0 1 1 , 1 0 0 1 0 0