Sampling Participants’ Experience in Laboratory Experiments: Complementary Challenges for More Complete Data Collection

Sampling Participants’ Experience in Laboratory Experiments: Complementary Challenges for More Complete Data Collection

Speelman and McGann's (2013) examination of the uncritical way in which the mean is often used in psychological research raises questions both about the average's reliability and its validity. In the present paper, we argue that interrogating the validity of the mean involves, amongst other things, a better understanding of the person's experiences, the meaning of their actions, at the time tha...

Data Collection and Analysis Methods for Data from Field Experiments

Big Data? More Challenges!

Recent advances in data acquisition technologies have led to massive amount of data being collected routinely in the physical, chemical, and engineering sciences as well as information sciences and technology. In addition to volume, the data often have complicated structure. Examples of such Big Data include the data streams obtained from complex engineering systems, image sequences, climate da...

Integrated Data Analysis of Complementary Experiments

The Integrated Data Analysis (IDA) concept allows to combine data from different experiments to obtain improved results [1,2]. Heterogeneous and complementary experimental data as well as various kinds of physical prior information are easily integrated employing Bayesian probability theory. The concepts of IDA are compared to classical approaches. In contrast to classical inversion techniques ...

Exchanging More than Complete Data

In the traditional data exchange setting source instances are restricted to be complete, in the sense that every fact is either true or false in these instances. Although natural for a typical database translation scenario, this restriction is gradually becoming an impediment to the development of a wide range of applications that need to exchange objects that admit several interpretations. In ...