Data 101 Workshop
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The document outlines a workshop conducted by Joshua H. Morrill, PhD, focusing on essential research questions, data interpretation, and the importance of reliable data collection methods. It emphasizes understanding the driving need for research, constructing effective survey questions, and recognizing potential pitfalls in data interpretation. Additionally, it highlights various information-gathering techniques and the significance of skepticism when analyzing data results.
![Misc. Big Data
Interactive
Techniques
Methods
Surveys
3
What are options
for getting
information?
How reliable is reliable
enough?
Bad Ques8on
The length of 8me I spent in the
Emergency room was…
- Excellent
- Good
- Fair
-Poor
Be]er Ques8on
The length of 8me I spent wai8ng in
the Emergency room before seeing a
physician was…
- Longer than I expected
- About what I expected
- Faster than I expected
The library hours were easy to find
on the site.
- Strongly Agree
- Agree
-Indifferent
-Disagree
How easy were the library hours to
find on the site.
- Very easy to find
- Somewhat easy to find
- Somewhat difficult to find
-Very difficult to find
- I did not noHce if the hours were easy or difficult to find](https://image.slidesharecdn.com/data101-160805171228/85/Data-101-Workshop-14-320.jpg)
![How do I
interpret what I
have?
What are some pitfalls
for understanding data?
No
Variance
What can
you do?
Cul1vate your inner skep1c
The curious case of: “Big data / Li]le e-Text, & The
Time-on-Task gang”
They don’t take things
at face value.](https://image.slidesharecdn.com/data101-160805171228/85/Data-101-Workshop-26-320.jpg)
![The value of [information]
lies in the using of it.
-Thomas Edison](https://image.slidesharecdn.com/data101-160805171228/85/Data-101-Workshop-27-320.jpg)
Data 101 Workshop
- 2. Why are you doing this research? What is the driving need? What do you really want to know? How can I ask the right research question? 1 2 3 4 We are going to be working through some of the WHY- WHAT-WHAT- & HOWs of any research project What are options for getting information? How reliable is reliable enough? How do I interpret what I have? What are some pitfalls for understanding data?
- 5. What do you really want to know? How can I ask the right research question? 2 The NEED should drive a series of questions that may -or may not - require additional data collection. My library is thinking about renova8ng a space What are trends in space redesign? How have other libraries renovated spaces? What do current patrons think about the space? • Conferences • Ar8cles • Web Search • Vendor Presenta8ons • Call other libraries • Tours • Past / older studies or feedback • A new study
- 6. What do you really want to know? How can I ask the right research question? 2 Assuming you want to do a new study…There is a different set of questions to consider. What do current patrons think about the space? • What do patrons think about the exis8ng space? ATTITUDES BEHAVIORS • What do patrons use the exis8ng space for? What are you prepared to act on? What informa8on is holding up your decision? ASPIRATION • What would patrons like to be able to do? • Are there other people who would use the space?
- 7. What do you really want to know? How can I ask the right research question? 2 EXERCISE TIME! • What is your need? • Does this really require new research? • What do you need to know that is holding up a decision?
- 9. Misc. Big Data Interactive Techniques Methods Surveys IF DONE WELL – Can provide a broad range of responses. Can be a good way to capture low motivation individuals in your population. Good for tracking/ documenting of change Really powerful when you have a comparison point for your data. 3 What are options for getting information? How reliable is reliable enough?
- 10. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? 43% 25% 19% 13% Very Well PreQy Well Not too well Not well at all My library serves my learning and educa3onal needs…
- 11. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? 43% 25% 19% 13% 49% 39% 6% 4% Very Well PreQy Well Not too well Not well at all My library serves my learning and educa3onal needs… PEW
- 12. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? Good Survey Ques3ons…. Should be things individuals can reliably answer. (“Please rate how efficiently the staff at your library are managed.” = BAD) Can be 4, 5, 6 or 7 point scales. (Think about when you want to force a choice / what a mid-response means) Are sensitive to people who are taking the survey (Empathy will get you better/ more reliable information)
- 15. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? Interac8ve Techniques (Interviews, Focus Groups, etc.) As a rule of thumb - Keep things to 1 hour or less Can get interesting information if you have participants “create” something. (IL Building Project ) Think about WHO you want to talk to and why. Generates a LOT of information that can take time to get in a useable form.
- 17. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? “Big Data” (data from databases other sta1s1cs) Buzzword now – but “Big Data ≠ Error Free” With Big data, motivations are rarely collected. Still useful, but very little “WHY” answers in big data. Big Data requires a greater vigilance to understand limitations of the data (i.e., rural areas), and make judgments on applicability and utility.
- 18. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? MISC - SEGMENTATION A way to understand subgroups in your data Varies in sophistication but helps fight monolithic perceptions Can be useful, but can also leave people feeling overwhelmed Does the segmentation pass the “smell test” for you?
- 20. Misc. Big Data Interactive Techniques Methods Surveys 3 What are options for getting information? How reliable is reliable enough? Ambivalent+ Learners+ 48%$of$Sample$ This segment addresses learning problems using a plan (at least they believe that they have a plan). But, mostly, they do not feel strongly about their learning. They are confident in their ability to find information, but do not enjoy studying nor do they have a need to learn. This is the largest learner segment from the sample. Adap1ve+ Learners+ 26%$of$Sample$ This segment exhibits a lot of characteristics of “ideal” learners (They solve problems with a plan, they are systematic, they set goals, they ask for help if they experience a problem, they enjoy studying and have a need to learn). A differentiator in this group is that there is more variance around setting specific times to study. For example, this could be a learner who studies in a hallway whenever they had some free time. Free+Form+ Learners++ 13%$of$Sample$ This group is not systematic in their learning, and do not solve problems with plans. But they are willing to change what they do when presented with new information (may speak to an experiential type of learner). This group also feels like they have a need to learn, but are among the least likely to set aside specific time to study. Time+Sensi1ve+ Learners+ 11%$of$Sample$ This segment is similar to the adaptive learners in many ways (use a plan, are systematic, etc), but they are just not quite as strong in these skills. Directionally they are identical to adaptive learners. The other key difference is that this group is the most likely to set specific times to study, and least likely to ask for assistance with a problem. This is also the smallest learner segment. NSF Digital Resource User SegmentaHon (2014)
- 22. 4 How do I interpret what I have? What are some pitfalls for understanding data? Small samples, Biased samples, and unrepresentative samples. Bad Sample! Spurious associations & phantom causations… sometimes wishful thinking. No Variance Overreach A Few Common Perils and Pitfalls of Research Can be a symptom of bad samples, bad questions, or something else. Be skeptical of uniform sameness.
- 23. How do I interpret what I have? What are some pitfalls for understanding data? Small samples, Biased samples, and unrepresentative samples. Bad Sample! “We asked people who came into the library and en3ced them with doughnuts.” Depending on what decision you are making …is this who you want to privilege hearing from? Doughnut-eaters? “We talked to three people to solicit different community perspec3ves about the library.” Is this public rela8ons…or research? Can these people accurately represent your “community”? “We sent the survey to several email lists and posted on facebook to get our sample.” Again, this might be OK, but who are you privileging/ who are you missing? What are the limits of your generalizability?
- 24. How do I interpret what I have? What are some pitfalls for understanding data? “100% of community members we asked supported the library!” This so patently does not pass the “smell test” that it suggests a flawed ques8on, sample or simple propaganda. “An average for par3cipants yielded a mean score of 3.2 on a 5-point scale.” Means without Standard Devia8ons are MEANINGLESS. No Variance Can be a symptom of bad samples, bad questions, or something else. Be skeptical of uniform sameness.
- 25. How do I interpret what I have? What are some pitfalls for understanding data? “Students behaviors indicated a posi3ve opinion of the new technology” What if people are being polite? If you want to know an opinion—ask. Do not assume. “A $1 investment in the library returns $14 back to the community.” These are slippery claims... “So why not put the whole budget in the library? A 14x return is beZer than gold!” No Variance Can be a symptom of bad samples, bad questions, or something else. Be skeptical of uniform sameness. Spurious associations & phantom causations… sometimes wishful thinking. Overreach “Our library programs reduced unemployment in the community by 12%” Helping ___ number of people look for a job – yes! But there are a TON of factors that influence unemployment..
- 26. How do I interpret what I have? What are some pitfalls for understanding data? No Variance What can you do? Cul1vate your inner skep1c The curious case of: “Big data / Li]le e-Text, & The Time-on-Task gang” They don’t take things at face value.
- 28. Thank You. Contact Joshua H. Morrill, PhD joshua@morrillsolutions.com 608-588-2874