Qualitative telephone interviews: Strategies for success

The use of the telephone in qualitative interviews is discouraged by traditionalists who view it as an inferior data collection instrument. However these claims have not been supported by empirical evidence and qualitative researchers who have used and compared the telephone to the face-to-face mode of interviewing present a different story. This study attempts to build on the limited existing research comparing the issues involved and the data collected using the telephone and face-to-face interview modes. The study evaluates the criticisms of traditionalists in the light of existing research. The study then presents the observations of the researcher based on a research project that involved 43 telephone, 1 Skype and 6 face-to-face interviews. These observations as well as the limited prior research are used to develop strategies for the effective use telephone interviews in qualitative research. The study concludes that for certain studies the telephone if used with the strategies recommended here provides qualitative researchers with a sound data collection instrument

Analysis of distracted pedestrians' waiting time: Head-Mounted Immersive Virtual Reality application

This paper analyzes the distracted pedestrians' waiting time before crossing the road in three conditions: 1) not distracted, 2) distracted with a smartphone and 3) distracted with a smartphone in the presence of virtual flashing LED lights on the crosswalk as a safety measure. For the means of data collection, we adapted an in-house developed virtual immersive reality environment (VIRE). A total of 42 volunteers participated in the experiment. Participants' positions and head movements were recorded and used to calculate walking speeds, acceleration and deceleration rates, surrogate safety measures, time spent playing smartphone game, etc. After a descriptive analysis on the data, the effects of these variables on pedestrians' waiting time are analyzed by employing a cox proportional hazard model. Several factors were identified as having impact on waiting time. The results show that an increase in initial walk speed, percentage of time the head was oriented toward smartphone during crossing, bigger minimum missed gaps and unsafe crossings resulted in shorter waiting times. On the other hand, an increase in the percentage of time the head was oriented toward smartphone during waiting time, crossing time and maze solving time, means longer waiting times for participants.Comment: Published in the proceedings of Pedestrian and Evacuation Dynamics 201

Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling

Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, non-parametric artificial neural networks. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation