400 research outputs found
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
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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
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