How can researchers and universities make better use of smartphones?
Programming dedicated apps for different research needs is costly both in the time and resources needed. It also requires deeper programming skills not always available to the average researcher.
Pollion, a new service for researchers, tries to answer this problem by providing a flexible data collection platform for different research needs.
Pollion is able to collect deep behavioural data from respondents through short surveys and the smartphone's various sensors (e.g. GPS signal). This makes it possible to collect individuals' behaviour across geographic space for example.
Pollion can also be used for more elaborate designs using the day reconstruction method or simpler tasks such as getting instantaneous feedback from a study group (e.g. course evaluations).
Pollion will be launching across major smartphone operating systems soon. Get more information on the Pollion website.
Showing posts with label self-reported data. Show all posts
Showing posts with label self-reported data. Show all posts
Wednesday, March 06, 2013
Monday, October 04, 2010
The Lake Wobegon Effect in Student Self-Reported Data
The Lake Wobegon effect, as described by Wikipedia, is also known as illusory superiority or the above average effect. It is a "cognitive bias that causes people to overestimate their positive qualities and abilities and to underestimate their negative qualities, relative to others. This is evident in a variety of areas including intelligence, performance on tasks or tests and the possession of desirable characteristics or personality traits." The phraseology of the Lake Wobegon effect comes from Garrison Keillor's fictional town, Lake Wobegon, where "all the children are above average".
In 1994, Maxwell and Lopus published an article in the American Economic Review: The Lake Wobegon Effect in Student Self-Reported Data. According to the authors, overstated achievement may produce biased estimates of the relationship between achievement and educational inputs, if overstatement is correlated with achievement. Maxwell and Lopus produce evidence for biases which stem from two sources. First, below-average students tend to inflate their academic achievements, and second, they often fail to report their inferior accomplishments.
In a more recent paper (Journal of Economic Education, 2010), Haley, Johnson and McGee examine whether using student-survey data in place of official records data meaningfully biases regression estimates. They motivate their contribution by noting a useful statistical feature of overreporting on bounded variables such as grade point average. "Specifically, the misreports will be negatively correlated with the true grade point average, yielding a form of nonclassical measurement error that actually counteracts the bias." The authors connect this observation to reliability ratios used in labour economics. In two applications, they find that it is unnecessary to correct for the bias from the Lake Wobegon effect because it is so small.
In 1994, Maxwell and Lopus published an article in the American Economic Review: The Lake Wobegon Effect in Student Self-Reported Data. According to the authors, overstated achievement may produce biased estimates of the relationship between achievement and educational inputs, if overstatement is correlated with achievement. Maxwell and Lopus produce evidence for biases which stem from two sources. First, below-average students tend to inflate their academic achievements, and second, they often fail to report their inferior accomplishments.
In a more recent paper (Journal of Economic Education, 2010), Haley, Johnson and McGee examine whether using student-survey data in place of official records data meaningfully biases regression estimates. They motivate their contribution by noting a useful statistical feature of overreporting on bounded variables such as grade point average. "Specifically, the misreports will be negatively correlated with the true grade point average, yielding a form of nonclassical measurement error that actually counteracts the bias." The authors connect this observation to reliability ratios used in labour economics. In two applications, they find that it is unnecessary to correct for the bias from the Lake Wobegon effect because it is so small.
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