Demand for parks and perceived accessibility as key determinants of urban park use behavior
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It is almost a cliché that as theories gain in explanatory breadth, they lose in predictive power and as they gain in predictive power, they lose in explanatory breadth. We acknowledge that it is easy to generate examples in psychology that seem to exemplify the tradeoff between explanatory breadth and predictive power. Nevertheless, we believe that the tradeoff is far less clear-cut than psychology researchers have understood. Our argument is based on the necessity to make auxiliary assumptions when traversing the distance between non-observational terms in theories and observational terms in empirical hypotheses.
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In this paper the predictive and explanatory power of value added information was investigated with regards to three external indicators by doing statistical analysis on empirical data of South African listed companies. The analysis indicated that value added information did not have significant predictive and explanatory power additional to that of earnings for the three selected external indicators. It also showed that the high inter-correlation between value added and earnings prevented value added from being used in prediction models in combination with earnings. The predictive and explanatory power of value added information therefore seems to be limited.
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Abstract There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a model with a certain degree of explanatory power can produce vastly different levels of predictive power and vice versa—in the same and different contexts. In a further step, we review all the papers published in three top‐tier human resource management journals between 2014 and 2018 to show that researchers generally confuse explanation and prediction. Specifically, while almost all authors rely solely on explanatory power assessments (i.e., assessing whether the coefficients are significant and in the hypothesised direction), they also derive practical recommendations, which inherently result from a predictive scenario. Based on our results, we provide HRM researchers recommendations on how to improve the rigour of their explanatory studies.
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Young adults 19 through 24 years of age were among the populations that had the highest frequency of infection from the 2009 H1N1 pandemic. However, over the 2009–2010 flu season, H1N1 vaccine uptake among college students nationwide was around 8%. To explore the social cognitive factors that influenced their intentions to get the H1N1 vaccine, this study compares the predictive power of the theory of planned behavior (TPB), the health belief model (HBM), and an integrated model. The final model shows that several HBM variables influenced behavioral intentions through the TPB variables. The results suggest that even though the TPB seemed a superior model for behavior prediction, the addition of the HBM variables could inform future theory development by offering health-specific constructs that potentially enhance the predictive validity of TPB variables.
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This paper explores the impacts of personal characteristics and the spatial structure on travel behaviour, especially mode choice. The spatial structure is described among other things by accessibility measures. The models are estimated using structural equation modelling (SEM). The models are based on the 1992 Upper Austrian travel survey and the Upper Austrian transport model. The results highlight the key roles of car ownership, gender and work status in explaining the observed level and intensity of travel. The most important spatial variable is the number of facilities which can be reached by a household. The municipality based variables and the accessibility measures have rather little explanatory power. The reasons for this low explanatory power are considered. Although the findings in this study indicate that the spatial structure is not a decisive determinant of traffic, the results provide useful hints for possible policy alternatives.
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