anonymous reviewers for their comments on earlier drafts. Direct correspondence to Robert

This paper develops a procedure for measuring how much is gained, and at what precision, by students in a pre-test and post-test situation against a target score on the post-test. We define our productivity index, M j , for teacher j as the ratio of estimated gains to an estimated standard that is the distance between an estimate of the pre-test score and the target score. Using language, mathematics, and reading scores on the SAT 9 for 1999 and 2000 from 75 public elementary classrooms (grades 3, 4, 5, and 6 in 2000), we employ a Bayesian implementation of a multivariate mixed model for repeated test scores from individual students who in turn are nested within teachers. Our analysis point to statistically significant gains on the whole for grades 3, 4, and 6. The strength of the approach lies in a straightforward estimation of the productivity index. Using the simulated sampling distribution of the posterior mean of the productivity index, we introduce a fuller depiction of progress in the productivity curve, or productivity profile, by calculating the probability that the index exceeds set proportions of the estimated standard. The basic model employed in this study thus contributes three essential components for sound accountability decisions. First, it estimates correlated measurement errors when using multiple measures. In doing so, we take full advantage of the informational redundancy in the measures. Second, it estimates initial status and value-added gains simultaneously. Lastly, it proposes a productivity index along with new procedures for representing the uncertainty in individual productivity estimates in the form of a productivity profile. This approach also facilitates a Bayesian e#ect-size analysis free from frequentist appeals to non-central t- or F- d...

data analyzed in Study 3 are derived from a larger longitudinal study reported in Montes and Irving (2008). We would like to thank Greg Irving for his valuable contributions in the role of doctoral dissertation advisor to Samantha Montes, Lisa Lambert and Jeff Edwards for their guidance with our analyses in Study 3, and the Co-operative Education Program at the University of Waterloo for their assistance in conducting Study 3. Correspondence concerning this article should be addressed to Samantha D. Montes,

In this chapter, we discuss current trends in the assessment of intelligence and personality that we believe have implications for the future of these disciplines. However, the present is always illuminated by the past; indeed, sometimes it is comprehensible only when seen in the context of antecedent events. Therefore, when possible, we identify some of the threads that tie current controversies to previous debates. Although we believe that the issues we have identified will help shape future developments, we refrain, for the most part, from specific speculations about the future of intelligence and personality assessment. Very near term predictions are easy: things will stay much the same as they are now. In some cases, we might even make reasonable predictions slightly further out by extrapolation. Our reading of others ’ past predictions about the future of psychological theory and research, though, is that interesting predictions (i.e., those that are more than simple extrapolations) usually look at best charmingly naive in retrospect. We present this chapter in three major sections, one focusing primarily on the assessment of intelligence, one focusing primarily on the assessment of personality, and one addressing issues at the intersections of intelligence and personality. The juxtaposition of our discussions of assessment in intelligence and personality illustrates both points of contact and points of real difference between the two domains, which we discuss in a final section. The structures of this chapter reflect our personal, no doubt somewhat idiosyncratic, views of what is important to say about each domain. As it turns out, we find ourselves with a little to say about a lot in the intelligence domain and a lot to say about a little in the personality domain. Others would certainly choose other emphases.

Some methodological and statistical issues in the study of change processes in psychotherapy ☆

This paper focuses on interpreting the major conceptual features of California's Academic Performance Index (API) as a coherent set of statistical procedures. To facilitate a characterization of its statistical properties, we first cast the index as a simple weighted average of the subjective worth of students' normative performance and present its estimation in the form of a linear model. In the process, we illustrate with an example several problems with this index for the study of a school's year-to-year progress. In its current usage the API lacks realistic estimates of precision and, on closer examination, further misrepresents conceptually student and school performance. We present an alternative analysis of the API index, based on a Bayesian meta-analysis of results from school-specific multilevel models of longitudinal student test scores. We introduce a display for the precision of estimated relative gains of each school in the form of a profile that represents the probability that a gain estimate exceeds set fractions of the distance the pretest is from the statewide target of 800. Along with estimates of their reliabilities, we also produce rank estimates of school API gains rather than simply ranking schools.

by Kenneth Kelley III Standard methods for analyzing change generally assume that the population of interest is homogeneous or that heterogeneity is known. When a population consists of unknown subpopulations, the parameters within each of the latent classes may be unique to that particular class. In such a situation the results of standard techniques for analyzing change are misleading, because such methods ignore unobserved heterogeneity and treat the population as if it were homogeneous. The growth mixture model (GMM; Muthén, 2001a; Muthén, 2001b; Muthén, 2002) partly addresses the problem of unknown heterogeneity because the parameters of the GMM are conditional on latent class membership. However, the GMM is necessarily restricted to models of change linear in their parameters (such as polynomial change models). The latent classification

by Kenneth Kelley III The average rate of change is a key concept in longitudinal analyses that examine change over time. However, this concept has been misunderstood both implicitly and explicitly in the literature. The present work attempts to clarify the concept and show unequivocally the mathematical definition and meaning of the average rate of change. Oftentimes the slope from the straight-line growth model is interpreted as though it were the average rate of change. It is shown, however, that this is generally not the case and holds true in only a limited number of situations. General equations are presented for the bias and discrepancy factor when the slope from the straight-line growth model is used to estimate the average rate of change. The importance of fitting an appropriate individual growth model is discussed, as are the benefits provided by nonlinear models for longitudinal data. CONTENTS

Despite much research, debate continues about the impact of risk taking on a firm’s future performance. Unlike prior studies, we propose that risk-return relationships evolve as firms age and learn, particularly in high-velocity settings where accumulated knowledge affects how firms respond to technological change. Discerning this requires three things absent from prior analyses: (1) studying an entire population; (2) modeling evolutionary processes; and (3) using separate models to capture how a firm’s gains and losses (i.e., its strong and weak performances) unfold across time. Using this framework, we found that (a) risk-return relationships generally evolved from positive to negative as firms aged; because (b) firms learned to avoid large losses at younger ages than they learned to sustain large gains; yet (c) the risk taking that followed below-aspiration performance moderated those effects such that major setbacks prompted large future gains and large future losses among older firms and downward spirals among younger ones. 1 Relationships between risk and return are central to our lives. In the hope of emotional or monetary rewards, some people take risks by climbing mountains, changing employers, or switching careers. Some executives take risks in pursuit of better pay and enhanced reputations, and some firms pursue risky strategies in a quest for higher sales and profits.

TMT COORDINATION NEEDS AND THE CEO PAY GAP: