The Empirical and Content Validity of Assessment Centers vs. Traditional Methods for Predicting Managerial Success

1977 ◽  
Vol 2 (3) ◽  
pp. 442 ◽  
Author(s):  
Steven D. Norton
10.12737/5798 ◽  
2014 ◽  
Vol 3 (5) ◽  
pp. 62-66
Author(s):  
Никитина ◽  
M. Nikitina

Technology of assessment centers has been showing a good performance as a method of recruitment since the early stages of implementation in large foreign companies. However in Russian experience of recruitment traditional methods still prevail. Assessment-center is a time-consuming and therefore quite expensive method, which is an obstacle for being carried out by domestic organizations. This article proposes an alternative to carrying out technology of assessment-center – an express-assessment-center. Unlike the traditional assessment-center, the express method allows assessing candidates within a few hours without reducing the quality of an original method. The paper describes the order of carrying out the express-assessment-center, proposes competency profi les and methods of assessing its demonstration as exemplifi ed in company’s line manager position, provides examples of simulation exercise to discover announced competencies.


2005 ◽  
Vol 4 (4) ◽  
pp. 181-186 ◽  
Author(s):  
Antonia Püschel
Keyword(s):  

Zusammenfassung. Die mit Urteils- und Entscheidungsprozessen verbundene Unsicherheit bildet ein ernstes Problem hinsichtlich der Qualität der Beurteilung oder des Risikos einer Fehlentscheidung. Zur Quantifizierung der Unsicherheit wird der in der physikalischen Metrologie gebräuchliche, international genormte Begriff der Messunsicherheit interdisziplinär auf die psychologische Praxis am Beispiel des Assessment Centers (AC) übertragen. Dabei werden zu bewertende Merkmale von Personen oder Alternativen in Analogie zu physikalischen Messgrößen betrachtet. Daraus folgt ein Auswerteverfahren, das zu den Ergebnissen der Merkmalsbewertungen jeweils auch quantitativ die Messunsicherheiten liefert. Außerdem lassen sich bei umfangreichem Datenmaterial Unsicherheitskennwerte berechnen, die konventionell ermittelte Korrelationskoeffizienten zur Beurteilung der Konstruktvalidität eines ACs ergänzen.


2014 ◽  
Vol 35 (4) ◽  
pp. 236-244 ◽  
Author(s):  
Atsushi Oshio ◽  
Shingo Abe ◽  
Pino Cutrone ◽  
Samuel D. Gosling

The Ten Item Personality Inventory (TIPI; Gosling, Rentfrow, & Swann, 2003 ) is a widely used very brief measure of the Big Five personality dimensions. Oshio, Abe, and Cutrone (2012) have developed a Japanese version of the TIPI (TIPI-J), which demonstrated acceptable levels of reliability and validity. Until now, all studies examining the validity of the TIPI-J have been conducted in the Japanese language; this reliance on a single language raises concerns about the instrument’s content validity because the instrument could demonstrate reliability (e.g., retest) and some forms of validity (e.g., convergent) but still not capture the full range of the dimensions as originally conceptualized in English. Therefore, to test the content validity of the Japanese TIPI with respect to the original Big Five formulation, we examine the convergence between scores on the TIPI-J and scores on the English-language Big Five Inventory (i.e., the BFI-E), an instrument specifically designed to optimize Big Five content coverage. Two-hundred and twenty-eight Japanese undergraduate students, who were all learning English, completed the two instruments. The results of correlation analyses and structural equation modeling demonstrate the theorized congruence between the TIPI-J and the BFI-E, supporting the content validity of the TIPI-J.


Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.


2011 ◽  
Vol 10 (2) ◽  
pp. 61-69 ◽  
Author(s):  
Nicolas Becker ◽  
Stefan Höft ◽  
Marcus Holzenkamp ◽  
Frank M. Spinath

As previous meta-analyses have focused almost solely on English-speaking regions, this study presents the first systematic meta-analytical examination of the predictive validity of assessment centers (ACs) conducted in German-speaking regions. It summarizes 24 validity coefficients taken from 19 studies (N = 3,556), yielding a mean corrected validity of ρ = .396 (80% credibility interval .235 ≤ ρ ≤ .558). ACs with different purposes and different kinds of criterion measures were analyzed separately. Furthermore, target group (internal vs. external candidates), average age of the assessees, inclusion of intelligence measures, number of instruments used, AC duration, as well as time elapsed between AC and criterion assessment were found to moderate the validity.


Author(s):  
V. Srinivasan ◽  
Allan D. Shocker ◽  
Alan G. Weinstein
Keyword(s):  

2011 ◽  
Author(s):  
Kevin R. Murphy ◽  
Paige J. Deckert ◽  
Ted B. Kinney ◽  
Mei-Chuan Kung
Keyword(s):  

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