The purpose of this research was to develop a self-evaluating instrument to assess family and social values . It can be used individually or in small group s of students at the senior high school or Junior college level . The variables rated are: familial, religious, sexual, and social values. The instrument is self-scoring and a schema is provided at the end of the test booklet to ind1cate the pattern of values as they are compared to the median or average scheme for the instrument. The 36 items in the scale are responded to by a forced choice design of alternate answer boxes which appear under different combinations of value variables . An intensity of response is measured in the form of alternates ranging from zero to three. Each item must total to three points . This method of responding to the items produces data that are curvilinear; therefore, advanced factorial analysis of items is necessary to determine the exact nature of data that might be collected from such an instrument. Hence, the instrument is limited for research purposes . However, it is not limited for use as an individual or self-administering evaluative tool . The interdependent nature of the values can be studied by the individual and the instrument becomes a valuable teaching device. A pretest situation involving 100 lower-division students at Utah State University reported the following results . The mean on the familial variable was highest, the intensity and frequency of response was greatest on the familial variable, and the intercorrelations on each variable ranged no lower than r = .59 to a high of r = .78. As a classroom tool, the Cole Scale of Values developed in this study should prove helpful for use in teaching concepts related to individual conceptions of familial and social values. Because of the interdependent nature of responses, and the indiscrete nature of any data collected from it, this instrument is only recommended for research when suitable methods of analysis are used.

Value-items from the Schwartz scale of Values have been added to travel data to investigate if the value-items can be used to model mode choice. Two kinds of mode choice models, both discrete choice models, multinomial models (MNL) and the Machine Learning Models Random Forests (RF) were constructed, using Travel Diary data (RVU) and additional data from European Social Survey (ESS). The additional data was connected to the base data by grouping the individuals using three key variables: gender, age, and household income. Models were then created with and without any data from the value-items to screen for variables that had an impact on the model. The RF model predicted the correct modes for all but the smaller groups, car passengers and biking. While the MNL model had less success accurately assessing which mode someone had chosen. The MNL with additional grouped value-items improved, while the models created using Random Forest had no difference in accuracy based on the addition. Even though there were some significant value-items in the MNL-models, the expected consequences from them small, as the base model specification might be insufficient in incorporating more relevant variables. Based on the Random Forest having no use from the value-items along with them being of similar importance no value-items stood out for further testing. The main findings were thus that no value-items of particular interest could be found with the RF model while the results for the MNL-model were inconclusive. Suggested improvements for further similar studies would be to perform grouping using data for a longer time frame and or to use a value-model as input for the mode choice modelling. It is deemed appropriate to study what values people associate with specific modes directly, and to investigate if other models such as car ownership models or models of choices between different versions of the same mode could be more suitable for additional value-data. / Värdeobjekt från Schwartz värderingsskala har kombinerats med resedata för att undersöka om värdeobjekten kan användas vid modellering av färdmedelsval. Två typer av färdmedelsmodeller, multinomiala modeller (MNL) och Random Forests konstruerades. Den data som användes var Resvanedata (RVU), med kompletterande värderingsdata från European Social Survey (ESS). ESS-datan kopplades till basdatan genom att gruppera individerna med hjälp av tre nyckelvariabler: kön, ålder och hushållsinkomst. Sedan skapades modeller med och utan den kompletterande datan för att se om modellerna påverkades. RF-modellens resultat överensstämde väl med de faktiska valen förutom för de mindre grupperna: bilpassagerare och cyklister. MNL-modellen hade mindre framgång med att bedöma vilket färdmedel en individ hade valt. MNL-modellen med ytterligare grupperade värdeobjekt förbättrades i jämförelse med grundmodellen, medan modellerna skapade med Random Forest inte skilde sig märkbart från varandra. Även om värdeobjekten i MNL-modellerna var signifikanta är de förväntade konsekvenserna av dem små, eftersom specifikationen för basmodellen tros saknar andra mer relevanta variabler. RF-modellen gynnades inte av värdeobjekten och inga värdeobjekt var betydande för modellen. De huvudsakliga fynden var att inga värdeobjekt av särskilt intresse kunde hittas med RF-modellen medan resultaten för MNL-modellen var ofullständiga. Föreslagna förbättringar för ytterligare liknande studier skulle vara att utföra gruppering med hjälp av data för ett längre tidsspann eller att introducera en värdemodell som indata för modelleringen av färdmedelsval. Det bedöms lämpligt att studera vilka värderingar människor förknippar med specifika färdmedel direkt samt att undersöka om andra modeller såsom av bilägande eller i val mellan olika versioner av samma färdmedel skulle var mer passande för att modelleras med hjälp av data med värderingar.