Question about interpret IAT data

Back

Question about interpret IAT data

Author	Message
zphermione	zphermione posted 6 Years Ago Topic Details Share Topic
Group: Forum Members Posts: 2, Visits: 2	Hi everyone, I am encountering a data analysis problem and hope you could help me! I am doing a research about letting participants read three positive or negative articles about female politicians and measuring their IAT before and after reading articles. As I read posts before, the association score between the target and attribute is the expression.d score in the output data, so I compared the means of expression.d scores between the before and after experiment IAT test. The result shows that participants who read positive articles, expression.d changed from-0.25 to -0.12. Does it mean their attitude has been improved? Or it means nothing? Or I am on a wrong way to deal with the data? Thank you very much for your help!
	Reply Like 51
Dave	Dave posted 6 Years Ago ANSWER Post Details Share Post
Group: Administrators Posts: 12K, Visits: 98K	+x zphermione - Tuesday, May 15, 2018 Hi everyone, I am encountering a data analysis problem and hope you could help me! I am doing a research about letting participants read three positive or negative articles about female politicians and measuring their IAT before and after reading articles. As I read posts before, the association score between the target and attribute is the expression.d score in the output data, so I compared the means of expression.d scores between the before and after experiment IAT test. The result shows that participants who read positive articles, expression.d changed from-0.25 to -0.12. Does it mean their attitude has been improved? Or it means nothing? Or I am on a wrong way to deal with the data? Thank you very much for your help! > The result shows that participants who read positive articles, expression.d changed from-0.25 to -0.12 This depends on how you coded your categories. Generally speaking, both -0.25 and -0.12 are in the same direction. Using the conventional breakpoints, -0.25 would indicate a "slight" preference, whereas -0.12 would essentially indicate no preference in either direction (it's below the breakpoint for a slight effect; cf. https://implicit.harvard.edu/implicit/demo/background/raceinfo.html ). As for how to best deal with this kind of design and data, the best advice I can give you is to look at other studies that have employed IATs in pretest-posttest designs.
	Reply Like 48
zphermione	zphermione posted 6 Years Ago ANSWER Post Details Share Post
Group: Forum Members Posts: 2, Visits: 2	+x Dave - Tuesday, May 15, 2018 +x zphermione - Tuesday, May 15, 2018 Hi everyone, I am encountering a data analysis problem and hope you could help me! I am doing a research about letting participants read three positive or negative articles about female politicians and measuring their IAT before and after reading articles. As I read posts before, the association score between the target and attribute is the expression.d score in the output data, so I compared the means of expression.d scores between the before and after experiment IAT test. The result shows that participants who read positive articles, expression.d changed from-0.25 to -0.12. Does it mean their attitude has been improved? Or it means nothing? Or I am on a wrong way to deal with the data? Thank you very much for your help! > The result shows that participants who read positive articles, expression.d changed from-0.25 to -0.12 This depends on how you coded your categories. Generally speaking, both -0.25 and -0.12 are in the same direction. Using the conventional breakpoints, -0.25 would indicate a "slight" preference, whereas -0.12 would essentially indicate no preference in either direction (it's below the breakpoint for a slight effect; cf. https://implicit.harvard.edu/implicit/demo/background/raceinfo.html ). As for how to best deal with this kind of design and data, the best advice I can give you is to look at other studies that have employed IATs in pretest-posttest designs. Hi Dave, Thank you so much! This is very helpful!!! Best, Zhao
	Reply Like 48