Examining our worst fears

Fifty five graduate students in the 2008 class of Smith College School for Social Work, on average, seek symbolic immortality through existential themes, excluding religion. Additionally, the more students identify with themes that define meaning in their lives, the greater their fear of encountering threats to that meaning. Further, students reported that they daydream about existential fears that threaten symbolic immortality more frequently than they dream, or have memories of them; and that they dream about primal fears more frequently than they daydream, or have memories of them. This study tested the hypotheses that people fear what they imagine might happen to them more than what has actually happened to them (Kunzendorf, et al., 2003-2004; 2006-2007); and that the imagined happening that each individual fears most is not death per se, but something that represents a threat to meaning of life as defined by each individual (Kunzendorf, et al., 2006-2007). Students were surveyed to examine if they preserve their immortality through their work as social workers. Students were invited via e-mail to anonymously participate in this quantitative study that explored an individual\u27s fears through utilization of three self-rating scales. This research may increase awareness that although social work graduate students dream about primal fears, they daydream about their meaningful lives, and that they hope to live on through their positive work with their clients. Additionally, findings suggest that it is important for social workers to attend to their clients\u27 worst fears by listening for existential themes that threaten meaning in life

The effect of freeze /thaw temperature fluctuations on microbial metabolism of petroleum hydrocarbon contaminated Antarctic soil

Petroleum contaminated soil exists at McMurdo Station in Antarctica. These soils were contaminated with historic releases of JP-8 jet fuel. Over time, there does not appear to have been significant reduction in the petroleum concentrations in these soils. This lack of reduction has been attributed to the extremely cold Antarctic environment and the lack of available moisture. Cold temperatures and/or lack of moisture may not be the factors inhibiting biodegradation. Soil temperatures can exceed 20 degrees centigrade (°C) during the austral summer and melt water becomes available. However, the soil temperatures have also been reported to fluctuate rapidly. Swings of soil temperatures have ranged over a 25°C interval several times during a period of hours. Rapid changes in temperature may be most detrimental to microbial activity. This research evaluated the biodegradation of petroleum contaminated Antarctic soil at a low stable temperature (7°C). It also evaluated temperature fluctuations. This was accomplished through experiments using contaminated soil from McMurdo Station, Antarctica. The first experiment indicated that a statistically significant loss of petroleum hydrocarbons occurred at stable temperature. Approximately 440 mg/Kg of the starting average petroleum hydrocarbon concentration (38%) were lost by the 56th day of the experiment. Approximately 163 mg/Kg of was lost from the volatilization control reactors and 97 mg/Kg of from the fluctuating temperature reactors. The second experiment showed a statistically significant 41% reduction of petroleum hydrocarbon concentrations at a stable temperature from a starting average concentration of approximately 13,000 to an ending average concentration of approximately 7,680 mg/Kg. Less than 650 mg/Kg was lost due to volatilization and approximately 333 mg/Kg from the fluctuating temperature reactors. The bulk of the petroleum hydrocarbon loss was due to biotic processes, indicated by increased carbon dioxide in reactor effluent gas at stable temperature. The soils also showed significant growth of petroleum hydrocarbon-degrading microorganisms. No carbon dioxide above background concentrations was measured for the sterile, volatilization controls or the fluctuating temperature reactors. Therefore, it appears that temperature fluctuations have an inhibitory effect on biodegradation of petroleum hydrocarbons in Antarctic soils

Snowfall phases in analysis of a snow cover in Hornsund, Spitsbergen

Conditions influencing formation of a snow cover in southern Spitsbergen in Homsund during the winters 1988/1989 and 1989/1990 are presented. Winter snow cover consists of several overlaid layers which correspond to particular snowfall phases, distinguished on the basis of analysis of occurrence of winter precipitation and development of a snow cover in numerous snow pits. Five snowfall phases during the winter 1988/1989 and three during the winter 1989/1990 were determined. A genetic classification, including specific features of a snow cover in Spitsbergen, was applied to describe snow layers in pits. The classification covers metamorphic changes of snow: dry metamorphosis, wet metamorphosis without freezing, wet metamorphosis with freezing, and aeolian metamorphosis. Precipitation, strong winds, and winter thaws are the factors which mostly influence formation of a snow cover in the Hornsund region. Most winter precipitation is connected with inflow of relatively warm air masses from the Norwegian Sea. Short term winter thaws which occur afterwards, result in formation of a characteristic ice-crust on a snow cover. The ice-crust layer protects a snow cover against deflation. It is also a marker band which enables dating of snow. Ice crust layers are almost always the borders between particular snowfall phases. Strong winds (V > 8 m/s) significantly transform a surface layer of snow. Snow deflation, which is locally quite intensive, occurs mainly at seashore plains, mountain ridges and convex slopes

The thermal condition of the active layer in the permafrost at Hornsund, Spitsbergen

Ground temperature variations have been analysed to the depth of 160 cm,with respect to meteorological elements and short−wave radiation balance. The database of the ground temperature covers a thirteen month−long period (May 1992 – June 1993), which in− cluded both the seasons of complete freezing of the ground and thaw. Special attention has been given to the development of perennial permafrost and its spatial distribution. In summer, the depth of thawing ground varied in different types of ground—at the Polish Polar Station, this was ca. 130 cm. The ground froze completely in the first week of October. Its thawing started in June. The snow cover restrained heat penetration in the ground, which hindered the ground thawing process. Cross−correlation shows a significant influence of the radiation bal− ance (K*) on the values of near−surface ground temperatures (r2 = 0.62 for summer)

Szacunek naturalnej stopy bezrobocia dla Polski

This paper presents alternative estimates of the natural rate of unemployment (NAWRU, NAIRU) for Poland for the years 1990–2008. The estimation process utilizes sequentially procedures based on the classical and the modified Phillips curve, the structural price-wage models as well as approach that uses the reduced form of the Phillips curve. The comparison of the results leads to the conclusion that the natural rates of unemployment estimated by different methods are generally close to each other and do not differ significantly from the observed values. The conducted analysis indicates that the relation of the natural rate of unemployment and the rate of registered unemployment may signal a change of the inflation pressures, which in turn can be used by the Monetary Policy Council

Optimal Experimental Designs for Nonlinear Conjoint Analysis

Estimators of choice-based multi-attribute preference models have a covariance matrix that depends on both the design matrix as well as the unknown parameters to be estimated from the data. As a consequence, researchers cannot optimally design the experiment (minimizing the variance). Several approaches have been considered in the literature, but they require prior assumptions about the values of the parameters that often are not available. Furthermore, the resulting design is neither optimal nor robust when the assumed values are far from the true parameters. In this paper, we develop efficient worst-case designs for the choice-based conjoint analysis which accounts for customer heterogeneity. The contributions of this method are manifold. First, we account for the uncertainty associated with ALL of the unknown parameters of the mixed logit model (both the mean and the elements in covariance matrix of the heterogeneity distribution). Second, we allow for the unknown parameters to be correlated. Third, this method is also computationally efficient, which in practical applications is an advantage over e.g. fully Bayesian designs. We conduct multiple simulations to evaluate the performance of this method. The worst case designs computed for the logit and mixed logit models are indeed more robust than the local and Bayesian benchmarks, when the prior guess about the parameters is far from their true values

Distribution of snow accumulation on some glaciers of Spitsbergen

We describe the spatial variability of snow accumulation on three selected gla− ciers in Spitsbergen (Hansbreen, Werenskioldbreen and Aavatsmarkbreen) in the winter seasons of 1988/89, 1998/99 and 2001/2002 respectively. The distribution of snow cover is determined by the interrelationships between the direction of the glacier axes and the domi− nant easterly winds. The snow distribution is regular on the glaciers located E−W, but is more complicated on the glaciers located meridionally. The western part of glaciers is more predisposed to the snow accumulation than the eastern. This is due to snowdrift intensity. Statistical relationships between snow accumulation, deviation of accumulation from the mean values and accumulation variability related to topographic parameters such as: alti− tude, slope inclination, aspect, slope curvature and distance from the edge of the glacier have been determined. The only significant relations occured between snow accumulation and altitude (r = 0.64–0.91)

Three essays on conjoint analysis : optimal design and estimation of endogenous consideration sets

Over many years conjoint analysis has become the favourite tool among marketing practitioners and scholars for learning consumer preferences towards new products or services. Its wide acceptance is substantiated by the high validity of conjoint results in numerous successful implementations among a variety of industries and applications. Additionally, this experimental method elicits respondents’ preference information in a natural and effective way. One of the main challenges in conjoint analysis is to efficiently estimate consumer preferences towards more and more complex products from a relatively small sample of observations because respondent’s wear-out contaminates the data quality. Therefore the choice of sample products to be evaluated by the respondent (the design) is as much as relevant as the efficient estimation. This thesis contributes to both research areas, focusing on the optimal design of experiments (essay one and two) and the estimation of random consideration sets (essay three). Each of the essays addresses relevant research gaps and can be of interest to both marketing managers as well as academicians. The main contributions of this thesis can be summarized as follows: • The first essay proposes a general flexible approach to build optimal designs for linear conjoint models. We do not compute good designs, but the best ones according to the size (trace or determinant) of the information matrix of the associated estimators. Additionally, we propose the solution to the problem of repeated stimuli in optimal designs obtained by numerical methods. In most of comparative examples our approach is faster than the existing software for Conjoint Analysis, while achieving the same efficiency of designs. This is an important quality for the applications in an online context. This approach is also more flexible than traditional design methodology: it handles continuous, discrete and mixed attribute types. We demonstrate the suitability of this approach for conjoint analysis with rank data and ratings (a case of an individual respondent and a panel). Under certain assumptions this approach can also be applied in the context of discrete choice experiments. • In the essay 2 we propose a novel method to construct robust efficient designs for conjoint iii experiments, where design optimization is more problematic, because the covariance matrix depends on the unknown parameter. In fact this occurs in many nonlinear models commonly considered in conjoint analysis literature, including the preferred choice-based conjoint analysis. In such cases the researcher is forced to make strong assumptions about unknown parameters and to implement an experimental design not knowing its true efficiency. We propose a solution to this puzzle, which is robust even if we do not have a good prior guess about consumer preferences. We demonstrate that benchmark designs perform well only if the assumed parameter is close to true values, which is rarely the case, otherwise there is no need to implement the experiment. On the other hand, our worst-case designs perform well under a variety of scenarios and are more robust to misspecification of parameters. • Essay 3 contributes with a method to estimate consideration sets which are endogenous to respondent preferences. Consideration sets arise when consumers use decision rules to simplify difficult choices, for example when evaluating a wide assortment of complex products. This happens because rationally bounded respondents often skip potentially interesting options, for example due to lack of information (brand unawareness), perceptual limitations (low attention or low salience), or halo effect. Research in consumer behaviour established that consumers choose in two stages: first they screen off products whose attributes do not satisfy certain criteria, and then select the best alternative according to their preference order (over the considered options). Traditional CA focuses on the second step, but more recently methods incorporating both steps were developed. However, they are always considered to be independent, while the halo effect clearly leads to endogeneity. If the cognitive process is influenced by the overall affective impression of the product, we cannot assume that the screening-off is independent from the evaluative step. To test this behavior we conduct an online experiment of lunch menu entrees using Amazon MTurk sample.A lo largo de los años, el “Análisis Conjunto” se ha convertido en una de las herramientas más extendidas entre los profesionales y académicos de marketing. Se trata de un método experimental para estudiar la función de utilidad que representa las preferencias de los consumidores sobre productos o servicios definidos mediante diversos atributos. Su enorme popularidad se basa en la validez y utilidad de los resultados obtenidos en multitud de estudios aplicados a todo tipo de industrias. Se utiliza regularmente para problemas tales como diseño de nuevos productos, análisis de segmentación, predicción de cuotas de mercado, o fijación de precios. En el análisis conjunto, se mide la utilidad que uno o varios consumidores asocian a diversos productos, y se estima un modelo paramétrico de la función de utilidad a partir de dichos datos usando métodos de regresión en sus diversas variantes. Uno de los principales retos del análisis conjunto es estimar eficientemente los parámetros de la función de utilidad del consumidor hacia productos cada vez más complejos, y hacerlo a partir de una muestra relativamente pequeña de observaciones debido a que en experimentos prolongados la fatiga de los encuestados contamina la calidad de los datos. La eficiencia de los estimadores es esencial para ello, y dicha eficiencia depende de los productos evaluados. Por tanto, la elección de los productos de la muestra que serán evaluados por el encuestado (el diseño) es clave para el éxito del estudio. La primera parte de esta tesis contribuye al diseño óptimo de experimentos (ensayos uno y dos, que se centran respectivamente en modelos lineales en parámetros, y modelos no lineales). Pero la función de utilidad puede presentar discontinuidades. A menudo el consumidor simplifica la decisión aplicando reglas heurísticas, que de facto introducen una discontinuidad. Estas reglas se denominan conjuntos de consideración: los productos que cumplen la regla son evaluados con la función de utilidad usual, el resto son descartados o evaluados con una utilidad diferente (especialmente baja) que tiende a descartarlos. La literatura ha estudiado la estimación de este tipo de modelos suponiendo que la decisión de consideración está dada exógenamente. Pero sin embargo, las reglas heurísticas pueden ser endógenas. Hay sesgos de percepción que relacionan utilidad y la forma en se perciben los atributos. El tercer estudio de esta tesis considera modelos con conjuntos v de consideración endógenos. Cada uno de los ensayos cubre problemas de investigación relevantes y puede resultar de interés tanto para managers de marketing como para académicos. Las principales aportaciones de esta tesis pueden resumirse en lo siguiente: • El primer ensayo presenta una metodología general y flexible para generar diseños experimentales óptimos exactos para modelos lineales, con aplicación a multitud de variantes dentro del análisis conjunto. Se presentan algoritmos para calcular los diseños óptimos mediante métodos de Newton, minimizando el tamaño (traza o determinante) de la matriz de covarianzas de los estimadores asociados. En la mayoría de los ejemplos comparativos nuestro enfoque resulta más rápido que los softwares existentes para Análisis Conjunto, al tiempo que alcanza la misma eficiencia de los diseños. Nuestro enfoque es también más flexible que la metodología de diseño tradicional: maneja tipos de atributos continuos, discretos y mixtos. Demostramos la validez de este enfoque para el análisis conjunto con datos de rango de preferencias y valoraciones (un caso de un encuestado individual y un panel). Bajo ciertos supuestos, este enfoque puede también ser aplicado en el contexto de experimentos de elección discreta. • En el segundo ensayo nos centramos en modelos de preferencia cuyos estimadores tienen matrices de covarianzas no pivotales (dependientes del parámetro a estimar). Esto sucede por ejemplo en modelos de preferencia no lineales en parámetros, así como modelos de elección como el popular Logit Multinomial. En tal caso la minimización de la matriz de covarianzas no es posible. La literatura ha considerado algunas soluciones como suponer una hipótesis acerca de este valor a fin de poder minimizar en el diseño la traza o determinante de la matriz de covarianzas. Pero estos diseños de referencia funcionan bien solo si el parámetro asumido es cercano a los valores reales (esto raramente sucede en la práctica, o de lo contrario no hay necesidad de implementar el experimento). En este ensayo proponemos un método para construir diseños robustos basados en algoritmos minimax, y los comparamos con los que normalmente se aplican en una gran variedad de escenarios. Nuevi stros diseños funcionan son más robustos a errores de los parámetros, reduciendo el riesgo de estimadores altamente ineficientes (que en cambio está presente en los otros métodos). • El ensayo 3 aporta un método para estimar conjuntos de consideración que son endógenos a las preferencias de los encuestados. Conjuntos de consideración surgen cuando los consumidores usan reglas de decisión para simplificar la dificultad de las elecciones, lo cual requiere una significativa búsqueda de información y esfuerzos cognitivos (por ejemplo, evaluar una amplia variedad de productos complejos). Esto ocurre porque racionalmente limitados consumidores a menudo pasan por alto opciones potencialmente interesantes, por ejemplo, debido a una falta de información (desconocimiento de la marca), limitaciones de percepción (baja atención o prominencia), o efecto de halo. La investigación en el comportamiento de los consumidores establece que los consumidores eligen en dos fases: primero eliminan productos que no satisfacen ciertos criterios y luego seleccionan las mejores alternativas de acuerdo a su orden de preferencia (de acuerdo a las opciones consideradas). El Análisis Conjunto convencional, se centra en el segundo paso, pero recientemente, se han desarrollado métodos incorporando ambos pasos. Sin embargo, son siempre considerados independientes, mientras que el efecto de halo claramente lleva a la endogeneidad del proceso de consideración. Si el proceso cognitivo está influenciado por una impresión general afectiva del producto, no podemos asumir que la eliminación sea independiente del proceso evaluativo. Para probar este comportamiento llevamos a cabo un experimento online sobre entrantes en menús de comida usando una muestra desde Amazon MTurk