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Research in work and organizational psychology frequently conducts studies based on self-report questionnaires. Evidence of the reliability and validity of these measures has to be provided based on thorough research in order to be certain that meaningful conclusions can be reached. Recently, latent variable approaches have been introduced that provide new opportunities to examine the instruments and determine if they are suitable to obtain meaningful results. They also offer new approaches to investigate the relationships between constructs, particularly when assessed over time. The research conducted in this PhD thesis and reported in three papers aims at utilizing these opportunities to examine the measurement properties of a selection of self-report questionnaires and to address conceptual questions regarding the validity of these instruments. In the first paper, the structure of two five-factor personality inventories was examined using Confirmatory Factor Analyses (CFA) and Exploratory Structural Equation Modeling (ESEM). Both methods were applied to construct better-fitting, though more complex models based on data from two questionnaires (NEO PI-R and 16PF) completed by 620 respondents. The impact on the construct validity of the inventories was assessed. Generally, scores derived from either method did not differ substantially. When applying ESEM, convergent validity declined but discriminant validity improved. When applying CFA, convergent and discriminant validity decreased. We conclude that using current personality questionnaires that utilize a simple structure is appropriate. In the second paper, the nature of and reason for the relationship between a presence of a calling and three aspects of career preparation (career planning, decidedness, and self-efficacy) were investigated. Data were collected in three waves of a diverse sample of German university students (N = 846) over one year. Latent growth analyses revealed that calling was positively related with all career preparation measures. The slope of calling was positively related to those of decidedness and self-efficacy but not to planning. Cross-lagged analyses showed that calling predicted a subsequent increase in planning and self-efficacy. Planning and decidedness predicted an increase in the presence of a calling. In the third paper, the measurement properties of an adapted protean career orientation scale were examined. We present a series of studies that (1) establish the scale’s unidimensionality and measurement invariance across gender within separate samples of students and working professionals as well as measurement invariance between both samples; (2) demonstrate measurement invariance and differential stability over six months among students and professionals; (3) show that a protean career orientation partially mediates the relationship between personality dispositions (i.e., proactive personality, core self-evaluations) and proactive career behaviors and career satisfaction among students and employees; (4) demonstrate that a protean career orientation possesses incremental predictive validity regarding proactive career behaviors and career satisfaction beyond personality dispositions among students and employees; and (5) based on a cross-lagged study among employees, we show that career satisfaction predicts a protean career orientation but not vice versa. In summary, the research presented here provides researchers in the field of work and organizational psychology with a thorough assessment of the measurement properties and aspects of validity of these self-report questionnaires. The findings demonstrate their suitability for future research studies conducted in work and organizational psychology as well as for practical applications.
Evaluating another person´s personality is an essential part of human life. How an individual reacts to a certain trigger, let it be a statement, strongly depends on his personality. Therefore, knowledge about the personality of a conversational counterpart is crucial to predict how he or she will react to a question or an answer. Personality is commonly understood as ´patterns of thought, emotion, and behavior that are relatively consistent over time and across situations´ (Funder 2012). If personality is as aforementioned defined as stable ´over time and across situations´, then it has to be differentiated from the character, which might change as an actor plays a role. A large proportion of an individual´s outer behavior can be explained by the inner personality. The outer behavior as a result of the personality determines various socio-demographic attributes, like job satisfaction (Furnham et al. 2002), the success of romantic relationships (Noftle, Shaver 2006), job performance (Barrik, Mount 1991) or high income, conservative political attitudes, early life adjustment to challenges, and social relationships (Soldz, Vaillant 1999). Humans can infer another person´s personality pretty precise. A first impression like a short video in many cases is enough to asses a personality (Carney et al. 2007). However, personality assessment is not limited to the social-cognitive domain of human brains - machine learning models attempt to predict personalities as well, or even better than humans. The internet provides a vast amount of data regarding personal information about its users - to so-called digital footprint. Especially social networks offer personal data in a very condensed form, the social-media footprint. Social media networks, which are online platforms, where people create a profile of themselves and communicate with other users or artificial persons like newspaper, offer a wide range of personal data to the broad community, as well as the network and its developers. In the year 2014 49.7 % of the German internet participated in social media networks (Statistisches Bundesamt 3/16/2015) with an upward trend. Furthermore, social media networks, like Facebook, provide the possibility to ´like´ something, which means at first: the user starts to follow a certain page and therefore receives updates and messages from the page and secondly: that the user publicly declares that he or she likes the page, visible to other users. However, it has been shown that the profile of a social network user indeed reflects the individual user and his personality and not an ´idealized´ version of 5 themselves (Back et al. 2010). Hence, these profiles seem to be unbiased, or at least as biased as the personality tests themselves. On the other side are the Facebook pages. A page in this case can be related to anything that a user started, let it be a political attitude, an artificial person, a company or a special kind of food. Any page can be created, and every user can give it a ´Like´. Facebook, as the biggest social media network as of today (Statista 2017) offers the possibility to collect data about a user´s Facebook likes, if the user agrees to the request. Due to the generic nature of Facebook likes and the relevance of personality assessment as a crucial part of social living, this paper focuses onto machine personality prediction based on Facebook likes. However, listening to music from a certain group in a web browser or reading a certain online newspaper can be easily translated into the Facebook like analogy and vice versa, which means that findings from this study are unlikely limited to the domain of Facebook likes.