The motivational paradise: How extreme contextual differences fail to emerge in extreme motivational differences

Abstract

This master thesis investigates the accuracy and precision of survey-based instruments. We will present previous research by highlighting the limitations of survey-based research with a five-point Likert scale and the problems with interpreting such results. To investigate these relations, we have chosen to use motivational theory and the Job Characteristics Model as a case. Our study has a cross-sectional research design and we have chosen an explorative approach in light of measurement theory. Our sample is divided in three; the first sample was a self-rating sample (N= 399), which consists of participants within 18 different occupations or work contexts. These participants were recruited to conduct a motivational survey. The second sample was an external evaluation panel (N= 30), which conducted a survey rating of the work contexts on several job characteristics. Our third sample (N=27) participated in indepth interviews to complement the quantitative information. The majority of these participants were also a part of the self-rating sample. The 18 occupations were selected to reflect an undisputably broad range of situations that should elicit different types of motivation such as intrinsic and extrinsic motivation. Since the different work contexts presumptively seem to be extremely different, one would assume that the motivational configuration should be quite different. However, according to our findings, the differences between the motivational configurations for the occupations were small. Our results indicate that the differences in measurements of motivation are minimal between the 18 occupations. From the results it is therefore difficult to distinguish between the motivational state for a priest or a sex seller. Because of this, one might argue that what is measured is not necessarily what we believe is measured. Consequently, one may argue that there is a need to consider qualitative data in order to understand and interpret quantitative data.