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Everything exists in time. “Survival analysis” is a statistical technique long used in the health sciences. As “time-to-event analysis,” it enables the asking of questions like: How much time passes before (an event) occurs, if it occurs, and what does this data suggest about various in-world phenomena?
In an online learning context, with LMS data, questions such as the following may be answered:
How long does it take for an online learner to (find his or her rhythm) in an online course? (if it happens)
How long does it take for an online instructor (to get to know) a particular student in a more person-to-person way? (if it happens)
How long does it take for an online learner to (form basic facility) with a new software tool? (if it happens)
How long does it take for a student researcher to (achieve breakout capacity) in (a particular skill)? (if it happens)
How long does it take for a doctoral student to (publish his / her first peer-reviewed paper)? (if it happens)
And what are observable variables that may affect whether the particular observed “state” is achieved or not? And if achieved, whether the occurrence is “early” or “late” in comparison with other comparable events?
This presentation will introduce survival analysis, its basic assumptions, its practice (using SPSS), its strengths and limitations, data “censoring” (to avoid “survivorship bias”), and ways to interpret related linegraphs and other related data visualizations.