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Social Media Evaluation Webinar for NAEPSDP
1. Evaluating Social Media in
Extension Programming
National Association of Extension Program and Staff
Development Professionals
October 21, 2014
Sarah Baughman, Ph.D. & Brigitte Scott, Ph.D.
Military Families Learning Network
Virginia Tech
Sarah Baughman, Ph.D.
eXtension
2. Photo credit: Douglas Wrey on http://www.geek.com/wp-content/uploads/2012/02/social_media_donut.jpg
8. • Support F2F workshops with information on
Facebook
• Use a FB page to encourage discussion on
educational information presented F2F
• Provide FB incentives for person who increases the
most or tries something new
• Have participants each take a different day to
share one recipe they have tried or something
from their food log that is working (or maybe not
working)
• Invite family members to the FB page to encourage
participants
•Highlight a different family member every week and
how they have helped support healthier eating
18. Photo by LauraGilchrist4 - Creative Commons Attribution-NonCommercial-ShareAlike License https://www.flickr.com/photos/76060406@N07 Created with Haiku Deck
21. Basic Text Analysis: Inductive
Use data to discover concepts, themes,
or models
Evaluator as interpreter; highly involved
22. Basic Text Analysis: Inductive
Use data to discover concepts, themes, or
models
Evaluator as interpreter; highly involved
Emergent, “bottom up”
23. Basic Text Analysis: Inductive
Use data to discover concepts, themes, or
models
Evaluator as interpreter; highly involved
Emergent, “bottom up”
Qualitative outcome: key themes or categories
relevant to evaluation/research questions
33. Basic Text Analysis: Deductive
Data is analyzed according to prior
assumptions
Evaluator is “independent” from data
34. Basic Text Analysis: Deductive
Data is analyzed according to prior
assumptions
Evaluator is “independent” from data
A-priori; “top down”
35. Basic Text Analysis: Deductive
Data is analyzed according to prior
assumptions
Evaluator is “independent” from data
A-priori; “top down”
Quantitative outcome: metrics relevant to
evaluation/research objectives
36. Application: Deductive Analysis
Category comparison, comparison over
time
Analyzing webinar chat pods
Analyzing how a hashtag is leveraged in
Tweets
Facebook/LinkedIn audience
engagement
37. Basic Deductive Analysis: 4 Steps
1. Develop data categories.
2. Clearly define those categories.
3. Read through all raw data and apply
categories.
4. Count.
38. Chat Pod Engagement Metrics
21
0
17
10
5
0 5 10 15 20 25
Unique participant to participant exchanges
Participant questions
Resources shared by MFLN
Resources shared by participants
Unique chat pod participants
39. The fine print….
Only DCO viewers can participate in the chat pod; percentage of chat pod participants based on
total number of DCO viewers and total number of unique participants.
Resources shared by participants include shared links, authors, studies, books, etc.; demonstrates
high-level engagement because participants are contributing to the co-construction of knowledge
during webinar.
Resources shared by MFLN include links, peer-reviewed studies and books, etc., from both MFLN
and non-MFLN authors; demonstrates direct CA engagement with participants by further
supporting and contextualizing knowledge construction by situating webinar presentation within
the larger disciplinary area.
Participant questions are those listed in the chat pod; demonstrates intent to pursue two-way
engagement in webinar and therefore high-level engagement.
Unique participant to participant exchanges are those in which chat pod participants respond
directly to one another’s comments; demonstrates high-level engagement through realized
reactive (two-way) and interactive (dependent) discourse patterns.
Chat pod text related to webinar content is not captured as an engagement measure due to its
discursive category as declarative (one-way) communication. (It is noted, however, that
declarative text is still understood to indicate webinar engagement, and MFLN encourages and
values such participant engagement.)
Chat pod text related to technical issues and/or CEUs is not included in MFLN evaluation.
42. Storytelling
Identify narratives that connect to your
evaluation aims
Be strategic and leverage stories for
evaluation task at hand
43. Storytelling
Identify narratives that connect to your
evaluation aims
Be strategic and leverage stories for
evaluation task at hand
Contextualize your stories with other data to
show a larger picture
44. Storytelling
Identify narratives that connect to your
evaluation aims
Be strategic and leverage stories for
evaluation task at hand
Contextualize your stories with other data to
show a larger picture
Ethics, ethics, ethics
46. From the Master Gardeners…
“On a Celebrex commercial a guy is
shown bent over in some beets or chard
and he raises up with a beautiful eggplant!
The first time I laughed at it my wife
thought I was crazy.”
47. Application: Storytelling and
Evaluation
Use stories in your reports, and include an executive
summary of those stories
Incorporate compelling stories with facts and figures
Include stories with direct quotes in press releases, on
Web sites
Include stories and quotes in newsletters, brochures,
annual reports