Variables Affecting Readiness to Benefit From Career Interventions

This article identifies and briefly describes the broad range of variables that may influence clients’ readiness to benefit from career interventions. The article also discusses consequences of low readiness for effective use of career interventions and addresses implications for practice as well as for future research. Variables contributing to low readiness for effective use of career interventions include personal characteristics and circumstances; knowledge of self, options, and decision making; and prior experience with career interventions. Consequences of low readiness for using career interventions include premature disengagement, negative perception of skills and interests, selective acquisition of incomplete information, premature choice foreclosure, protracted exploration, dependent decision‐making style, and poor evaluation of options.     

Growth curve analysis of complete and incomplete longitudinal data

Growth curve analysis is reviewed for both complete and incomplete data structures. Specific attention is paid to the incomplete data model of Kleinbaum and its applicability to the mixed longitudinal study. Design considerations and efficiency for various incomplete longitudinal studies are also discussed.     

REVISITING KEY ISSUES ABOUT COLLECTIVE BEHAVIOR,ORGANIZING, AND ROLE ENACTMENT

This article addresses the benefits of using a structural code and a role analysis framework for conducting organizational research in the context of a disaster. In the present article, I apply that code and framework to Thomas Forrest's earlier analysis of a organization, the Interfaith Emergency Center (IEC), that emerged during the Detroit civil disturbance of 1967. Kreps' structural code is applied to the same set of interviews and documents that constituted the database for Forrest's original case study (Disaster Research Center data archives). I first document the origins and restructuring of the IEC using the structural code. I then perform a role level analysis that builds on Bosworth and Kreps' three dimensional framework. That framework addresses the expectational, relational, and behavioral dimensions of role enactment. Revisiting Forrest's original study results in a more theoretically rich analysis of how and why the IEC emerged in the way that it did during the emergency period.     

Effects of a single outlier on arma identification

Fox (1972), Box and Tiao (1975), and Abraham and Box (1979) have proposed methods for detecting outliers in time series whose ARMA form is known (or identified). We show that the existence of a single aberrant observation, innovation, or intervention causes an ARMA model to be misidentified using unadjusted autocorrelation (acf) and partial autocorrelation estimates. The magnitude, location, type of outlier, and in some cases the ARMA's parameters, affect the identification outcome. We use variance inflation, signal-to-noise ratios, and acf critical values to determine an ARMA model's susceptibility to misidentifi-cation. Numerical and simulation examples suggest how to iteratively use the outlier detection methods in practice.     

Research Notes

In the summer of 1995, a required MSW course was taught simultaneously to two sections of students at the University of Georgia, 11 on the main campus and 9 at a branch campus. Each section was alternately taught using live instruction or distance learning (two-way interactive television). At the conclusion of the course, students evaluated each method of teaching using Biner’s (1993) instrument for measuring attitudes toward televised courses. Live instruction was rated significantly higher than distance learning. Although distance learning technology has potential for augmenting social work educational resources, it has not yet demonstrated comparable outcomes in terms of student learning.     

Multiple hypothesis test for parameter constancy based on recursive residuals

This article presents a multiple hypothesis test procedure that combines two well known tests for structural change in the linear regression model, the CUSUM test and the recursive t test. The CUSUM test is run through the sequence of recursive residuals as usual; if the CUSUM plot does not violate the critical lines, one more step is taken to perform the t test for hypothesis of zero mean based on all recursive residuals. The asymptotic size of this multiple hypothesis test is derived; power simulation results suggest that it outperforms the traditional CUSUM test and complements other tests that are currently stressed in econometrics.