The Positive Effects of Easy Goals on Decision Quality and Risk Propensity in an MCPLP Task

In this study the potential performance benefits of easy goals were examined within the multiple cue probability learning paradigm (MCPLP). Specifically, the effects of varying levels of goal difficulty on performance and risk propensity (used to define a form of commitment) were investigated. With few exceptions, previous studies demonstrated support for difficult goals. In this study, contrary to the majority of past evidence, as goals became easier decision quality significantly improved. Moreover, risk propensity increased with easier goals and, as suggested by a post-hoc analysis, had more direct impact on decision makers’ behaviors than goal levels. Goal-related behavior in complex MCPLP tasks appears to significantly differ from the majority of goal evidence in other types of research due to the tendency for subjects to view complex task properties in the same context as goal levels.     

Using Multiple Methods to Provide Prediction Bands of K-12 Enrollment Projections

Population Research and Policy Review - Often, demographers charged with projecting enrollments for school districts are asked to provide a range of future enrollments, as point estimates are not...     

Australia’s national urban policy: The smart cities agenda in perspective

The Australian Government's attempts at national urban policy have been sporadic and inconsistent, under the constraints of constitutional, financial, ideological and political factors. This paper concerns the recent resurgence of national urban policy under the rubric of “smart cities” adopted by the Turnbull Government and carried on by the incumbent Morrison Government. It investigates how this round of smart cities agenda articulates with Australia's political tradition of national urban policy to seek continuity and change, through comparing historical policies, unpacking major smart cities programmes, identifying explanatory factors and critically commenting on its innovation and legacy. Drawing upon these analyses, this paper argues that understanding the smart cities agenda needs to move beyond the political, ideological “to-and-fro” pendulum to national urban policy observed in history. Rather, it resonates with a globalised policy norm established upon the city-based global integration and competition, driven by an imperative of transitioning to a knowledge economy and pursuing innovation capacity. It is too early to judge whether the smart cities agenda will bring changes to Australian cities in the way it wishes, since it involves long-term infrastructure investment and urban development projects. However, a comprehensive and consistent national urban policy to govern the Australian system of cities and towns has not been established yet.     

The functional patterns of adolescent mothers leaving foster care: Results from a cluster analysis

Few studies have explored the outcomes of adolescent mothers leaving foster care, especially using person‐oriented methods. The current study employed a cluster analysis to identify unique patterns of functioning among adolescent mothers aged 19 (n = 777). Data from the National Youth in Transition Database and the Adoption and Foster Care Analysis and Reporting System were utilized. Findings revealed five subpopulations characterized by distinct constellations of outcomes at age 19. The largest group (43%) exhibited competent functioning across all the domains studied—its members were connected to school and/or employment and did not experience homelessness, substance abuse referrals, or incarceration during the past 2 years (i.e., “resilient”). A relatively small group (12%) exhibited challenges across all the above‐referenced domains, whereas the remaining groups presented challenges in some domains, but not in others. Follow‐up analyses revealed that adolescent mothers classified as resilient at age 19 had the lowest rates of congregate care placements and the highest rates of nonrelative foster care placements at age 17. Moreover, they had lower placement instability and higher rates of extended foster care as compared with members of the other clusters. Implications for practice, policy, and research are discussed.     

Industrial Policy Capture in Taiwan and South Korea

Whatever the theoretical merits of an industrial policy, in practice state incentives in support of infant industry are difficult to withdraw once they have been granted. Infant maturation is thereby delayed, resulting in ‘policy capture’ which confers rents on the assisted firms at the expense of consumers and/or taxpayers. The process is well-documented in poorly managed economies pursuing autarkic industrial policies (Auty, 1994a). This article examines evidence of policy capture in well-managed economies pursuing competitive industrial policies. Examples are drawn from the heavy (and chemical) industry drives of South Korea (hereafter Korea) and Taiwan, but the research is first set in the context of the industrial policy debate.     

The effect of omitted covariates in marginal and partially conditional recurrent event analyses

There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies.

     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.