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Lack of effective selection and training of employees for positioning within a business can have extensive financial repercussions. Fostering mentoring relationships between leaders and employees increases competitive advantage and institutional financial growth. However, there is limited research that supports the role of mentoring leadership styles for improving organizational productivity. The purpose of this single case study was to explore strategies used by business managers to select and individually train employees for opportunities within the organization to achieve job-person fit. The sample included 5 business managers employed within a medium-sized organization in a consultation firm in the mid-Atlantic region of the United States. Strategic human resource management theory formed the conceptual framework for this study. Data collection used semistructured interviews and employee performance evaluations. Yin's 5 steps for qualitative data analysis provided the process for organizing the data and conducting the data analysis. Four themes emerged from the data analysis including job-person fit, organizational culture, employee relationships, and performance management. Potential contributions to positive social change include increasing employee retention rates, which could benefit local business economies due to the retained employees'sustained local buying power and contributions to their communities' charities.

This study investigated the efficacy of the lz person fit statistic for detecting aberrant responding with unidimensional pairwise preference (UPP) measures, constructed and scored based on the Zinnes-Griggs (ZG, 1974) IRT model, which has been used for a variety of recent noncognitive testing applications. Because UPP measures are used to collect both "self-" and "other-" reports, I explored the capability of lz to detect two of the most common and potentially detrimental response sets, namely fake good and random responding. The effectiveness of lz was studied using empirical and theoretical critical values for classification, along with test length, test information, the type of statement parameters, and the percentage of items answered aberrantly (20%, 50%, 100%). We found that lz was ineffective in detecting fake good responding, with power approaching zero in the 100% aberrance conditions. However, lz was highly effective in detecting random responding, with power approaching 1.0 in long-test, high information conditions, and there was no diminution in efficacy when using marginal maximum likelihood estimates of statement parameters in place of the true values. Although using empirical critical values for classification provided slightly higher power and more accurate Type I error rates, theoretical critical values, corresponding to a standard normal distribution, provided nearly as good results.

There are concerns regarding the risk of the inaccurate responses in the personality data. The inaccurate responses negatively affect in the individual selection contexts. Especially, in the military context, the personality score including inaccurate responses results in the selection of inappropriate personnel or allows enlistment dodgers to avoid their military duty. This study conducted IRT-based person-fit analysis with the dichotomous military dataset in the Korean Military Personality Inventory. In order for that, 2PL model was applied for the data and person-fit index l_(z) was used to detect aberrant respondents. Based on l_(z) values of each respondent, potentially inaccurate respondents was identified. In diagnosing possible sources of aberrant response patterns, PRCs was assessed. This study with the military empirical data shows that person-fit analysis using l_(z) is applicable and practical method for detecting inaccurate response patterns in the personnel selection contexts based on the personality measurement.

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Ziel der Arbeit ist die Entwicklung von Modellen zur Entdeckung von Fehlspezifikation im Linear Logistic Test Model ( = LLTM) und verwandten Modellen der Verände\-rungs\-mes\-sung. Fehlspezifikation bedeutet hierbei, dass dem Modell ein unzutreffendes Muster latenter Traits zugrundegelegt wurde. Dies kann, vgl. z.B. [Baker,1993], zu bedeutenden Schätzfehlern führen. Die hier vorgestellten Methoden ermöglichen es unter leicht zu erfüllenden Annahmen, Aussagen über das Ausmaß der Unkorrektheit der verwendeten Modellspezifikation zu machen, ohne die in der Modellschätzung bestimmten Parameterwerte verwenden zu müssen. Zunächst wird eine auf dem Mantel-Haenszel-Test beruhende Methodik vorgestellt, die bei Tests bezüglich der Veränderungsparameter eines LLTMs als direkte Konkurrenz zu den bekannten Likelihood-Ratio-Tests für das LLTM anzusehen ist, wie sie z.B. bei [Fischer,1995a] vorgestellt werden. Weiterhin werden für das LLTM optimierte Personenfittests und daraus abgeleitete Effektgrößen vorgestellt. Diese ermöglichen das Auffinden von Subpopulationen, bei denen eine Abweichung zum angenommenen Modell aufgetreten ist. Es werden die statistischen Eigenschaften dieser Tests resp. Effektgrößen mittels Simulation und Teststärkeberechnung untersucht und Anwendungsbeispiele für diese Methoden vorgestellt. / In this thesis, new methods are developed for the detection of misspecification within Linear Logistic Test Models (=LLTM) and similar model classes for the measurement of change. The phrase "misspecification" will be used if a wrong selection of latent traits is chosen for the estimation of the LLTM. Misspecification can lead to erronious estimation [Baker,1993]). Using the newly developed methods, it is possible to measure the extent of deviations between the proposed model and the data. This can be done without using estimated parameter values. First a method is introduced which is based on the well-known Mantel-Haenszel-test. For some hypotheses, this method can be used instead of a Likelihood Ratio Test (e.g. [Fischer,1995a]). The Main topic of this thesis are uniformly most powerful tests for the measurement of person fit and related effect measures. These effect measures can be used for the identification of subpopulations where the proposed model does not hold. Statistical properties of these tests resp. effect measures are examined by simulations and power calculations using the SAS software. Furthermore, examples of the application of these methods are given.