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The 10 (3 helpful, 7 problematic) characteristics of big data (cf. Salganik 2.3)

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The 10 (3 helpful, 7 problematic) characteristics of big data (cf. Salganik 2.3)

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What is the robot.txt and how should it be interpreted?

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What is the robot.txt and how should it be interpreted?

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When repurposing data, we need to be aware of..

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When repurposing data, we need to be aware of..

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what can it be used for? what do you need to be aware of?

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.. is good for [...]

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Whether or not the researcher intervenes and affects what is being observed : obtrusive if they do have an effect - this includes people just being aware of observation.
Examples:
obtrusive: interviews, surveys, field experiments, lab experiments
Unobtrusive: archives, primary source data collection

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Things to control for within the experiment, such as people opting out of the newsletter re. the power experiment

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Assigning treatments randomly within an experiment

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Difference between desired and actual privacy (?)

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A type of ethics that emphasizes following a set of rules: simply following these can be seen as sufficient for ethical practice

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Salganik discusses this. Inspired by rules and ad-hoc approaches: takes aspects from both and tries to find a middle way. Follow guiding principles, but some flexibility and ability to trade off between principles

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This describes how an event (e.g. ban of Instagram) can lead to further, sometimes unexpected/more serious events (people circumventing censorship and finding political materials as well as Instagram)

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Gamification is about creating similar expierience in an survey or something else which is similar to game-design to motivate and engage the participant

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Distinguish ready- from custom made data 

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Distinguish ready- from custom made data 

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Ready made data is every data source that is not initially designed to use it for reasearch e.g. Webdata, official records, text data etc. Thus it is often very messy and also does not contain the sufficient variables to operationalize theoretical constructs. Because of this, it is often neccesary and usefull to combine ready- with custommade data.

Custom made data in comparison is data that is designed "from researchers for research" e.g. surveys, experiments, interviews. The keyvalue of this data is that one can in theory include what one needs. THus, this data is valuable to operationalize certain research questions and relate it to theoretical assumptions. But, custom made data is often not sufficient to answer all questions, because the methods are often obtrusive, which could lead to biased results etc. Also, asking people is sometimes not the best way to get valid results, because other dimensions such as social desireability can bias responses.
(Salganik 2019 "Bit by bit": p.7 ff..)

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The granularity of data is about the data's level of detail. For example weather data for every second of an day is more granular then weather data for every minute of an day.

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Unobtrusive data collection methods like scraping require reflections on ethical implications. Good to have Salganiks 3 Rs in mind when working with digital traces:
• Replace
• Refine
• Reduce

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Digital traces are NOT digital data (yet): need to be turned into it by researchers with a purpose. Digital traces refers to info we leave behind via activity interacting with the digital world: e.g. the internet, smart sensors, etc
Weaknesses: incomplete, indistinguishable, incomprehensible
Strengths: organic, big, always-on, non-reactive

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Netnography is an online research method to understand social interaction in contemporary digital communications contexts