Hi I'm the Devansh Saxena PhD student
in the Department of Computer Science at Marquette
University.
Today my colleague Patrick Skeba,
PhD student in a department of Computer Science
and Engineering at Lehigh University.
And I will be presenting research
we are connecting with our mentors, Dr. Shian
Guha at Marquette University.
Dr. Eric P.S Baumer at Lehigh University
on the methods for generating typologies of non-use.
Growing body of research has shown
that a binary distinction between users and non users,
is insufficient and that would be observed myriad forms
of disengagement in practice.
This makes it hard to develop a single definitive typology that
account for all these on-use behaviors.
We often find ourselves caught between rich,
but unquantifiable collection of practices
recognized by quoted of methods and oversimplified
classification schemes developed by a quantitive matters.
So can we possibly combine the two methods to generate
more robust typologies?
We conducted a survey about Facebook non-use via Quatricts
and ask questions of unused practices derived
from prior literature.
We also ask questions derived from five psychometric scales
as depicted here.
We use a data-driven approach to find
the most prevalent types of non-use
and focus on the top six on use types.
C2 for instance, is an on use type where the participants
apparently have a Facebook account,
but at some point, while literally
took a break from Facebook.
This method uses all 7 on use questions
but limits the sample size by excluding
data points, the long tail of the distribution.
The taxonomic key method is a data-driven process
that allows for qualitative interventions based
on the research criteria.
In our case, we devised valuation criteria
based on the intensity of non-use behaviors,
but permanently deleting your account being the most intense.
And voluntarily taking a break being the least intense form
of non-use this method preserves the sample size
or only uses four non-use questions.
A theoretical mapping exists between the two typologies,
for instance, comforting type C6 maps on to the taxonomic tree
type key 5, plus participants who
aren't engage in the more intense point of my news
also more likely to engage in the less
intense forms of my news.
Our that shows that participants were permanently
deleted their Facebook account had also
engaged in other non-use behaviors.
In order to compare the effects of applying these two
strategies, we built two logistic regression models
with classes from either the curve fitting
or the taxonomic tree typologies.
In our paper, we discuss in detail the process
of future engineering.
But basically, we used factor analysis
to extract features from the five psychometric scales
and then combine this with the demographic features
from the survey as well.
For each of these models, we used a step-wise process
to remove features which did not significantly
improve the model fit.
This table shows the odds ratios for four factors
that were significant in both typologies
where the base class and C 1 in T1
were current users with no non-use behaviors.
What we want to emphasize here are the convergences
and divergences between the statistical relationships
and the two typologies.
For two features, relapse and age,
the odd to ratios are in the same direction
and approximately it's to the same magnitude.
On the other hand, daily routine and effective gratification
have odds ratios in opposite directions.
Positive for curve fitting and negative for taxonomic treat.
These similarities and especially the differences
can reveal insights into the data,
which would be hidden if only a single topology were used.
To investigate these divergences more closely
we made one final comparison using
a typology from Bamurael 2019 that was constructed manually.
We found significant alignment with the taxonomic tree model,
but not the curve fitting.
This hints at a potentially interesting but subtle
difference between our two typologies that would
make for good future research.
The main goal of our study is to offer
a holistic methodological approach
for typology generation which can reveal insights
that might be missed if a single, a priori typology
were chosed.
We generate two separate typologies
through different statistical means,
train models to predict those classes
and compare the results which features
have similar significant effects as with relapse
and age in our study, it can increase our confidence
in the predictive power of those features.
When the models do not agree with each other,
it can suggest subtle differences
in the typologies that need revised
or add skepticism to those features significance
to their lack of stability.
This process of inspection can help
to further refine our typologies potentially leading
to more robust results with more stable outcomes and higher
effect sizes.
In summary, the curve fitting in taxonomic true methods
presented here offered different ways
to operationalize complex theoretical constructs
in a data-driven manner each of these methods on their own
have potential to generate typologies for theoretically
tricky or immature constructs.
But results which align with both
are likely to be more consistent with reality
than those that align to only one.
Thank you.
We hope to get some questions from the audience
and our contact info's on the screen to reach us later.