Interaction effects - Two Way ANOVA (Part 1)
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11 سال پیش
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Interaction effects - Two Way
Interaction effects - Two Way ANOVA (Part 1). How to calculate and interpret the interaction effect for a two way ANOVA is explained in this video.
Video Transcript: In this video we'll take a look, a little deeper look, at the interaction effect to try and get a little bit of a better idea of what is really going on when there's a significant interaction. Now recall from our last video, that our interaction was in fact significant, our p-value was .047, so that was less than .05, so it was significant. And we had the plot of the interaction effect given here, and let's go ahead and take a look at this in more detail. And we'll also look at these means as well, the marginal means, for the interaction effect. So here are the written results again for the interaction effect and once again there's that p-value .047. And then next, I've pasted the table of marginal means from SPSS into this document here and I also have the interaction effect with a few labels that we'll talk about in just a moment or two. In this plot recall that we had our four conditions plotted and you can see here that we have volume on the X-axis, just as a review here, with no music and high-volume and then the different lines are the different levels of study method, where we had cramming at the bottom here, the green line, and then spaced was the blue line. And what we saw here was that for high-volume, if you look at this level here, for high-volume notice how the two circles, which represent the means for cramming and spaced, respectively, how these two are very close together. And for no music these two circles or means are very far apart. And recall that one way to talk about the interaction effect was that the lines were significantly non-parallel. And they appear to be non-parallel here, and in fact they are, but even if they look non-parallel, you want to make sure that the p-value is significant, that is less than .05. In other words, that it's beyond sampling error, so we can feel confident that the lines truly are non-parallel. And that is the case here. And if we look at these means in this table up here, what you can see is that this first mean is the none, or no music for volume, and its spaced for study method, that's a mean of 84. So no music, spaced. if you look here we have no music, and then the blue is spaced, so that's this value right here, this mean is 84. And then next we have no music and cramming, that mean is 74.167, and that's shown right here, no music and cramming. And this difference, if you take 84-74.167, that's a 9.833 point difference, which I show right here. So this difference between these two points is almost 10 points, if we just rounded up. That's about a 10 point difference.
Click here to check out our ANOVA course: http://tinyurl.com/ltcgqx4
Interaction
2-way interaction
ANOVA interaction video
ANOVA video
SPSS videos statisticsinstructor/Quantitative Specialists:
YouTube channel: statisticsinstructor
Udemy inferential course: https://www.udemy.com/inferential-sta...
Udemy descriptives course: https://www.udemy.com/descriptive-sta...
Udemy ANOVA course: https://www.udemy.com/anova-spss
Udemy MANOVA course: https://www.udemy.com/manova-spss
Video Transcript: In this video we'll take a look, a little deeper look, at the interaction effect to try and get a little bit of a better idea of what is really going on when there's a significant interaction. Now recall from our last video, that our interaction was in fact significant, our p-value was .047, so that was less than .05, so it was significant. And we had the plot of the interaction effect given here, and let's go ahead and take a look at this in more detail. And we'll also look at these means as well, the marginal means, for the interaction effect. So here are the written results again for the interaction effect and once again there's that p-value .047. And then next, I've pasted the table of marginal means from SPSS into this document here and I also have the interaction effect with a few labels that we'll talk about in just a moment or two. In this plot recall that we had our four conditions plotted and you can see here that we have volume on the X-axis, just as a review here, with no music and high-volume and then the different lines are the different levels of study method, where we had cramming at the bottom here, the green line, and then spaced was the blue line. And what we saw here was that for high-volume, if you look at this level here, for high-volume notice how the two circles, which represent the means for cramming and spaced, respectively, how these two are very close together. And for no music these two circles or means are very far apart. And recall that one way to talk about the interaction effect was that the lines were significantly non-parallel. And they appear to be non-parallel here, and in fact they are, but even if they look non-parallel, you want to make sure that the p-value is significant, that is less than .05. In other words, that it's beyond sampling error, so we can feel confident that the lines truly are non-parallel. And that is the case here. And if we look at these means in this table up here, what you can see is that this first mean is the none, or no music for volume, and its spaced for study method, that's a mean of 84. So no music, spaced. if you look here we have no music, and then the blue is spaced, so that's this value right here, this mean is 84. And then next we have no music and cramming, that mean is 74.167, and that's shown right here, no music and cramming. And this difference, if you take 84-74.167, that's a 9.833 point difference, which I show right here. So this difference between these two points is almost 10 points, if we just rounded up. That's about a 10 point difference.
Click here to check out our ANOVA course: http://tinyurl.com/ltcgqx4
Interaction
2-way interaction
ANOVA interaction video
ANOVA video
SPSS videos statisticsinstructor/Quantitative Specialists:
YouTube channel: statisticsinstructor
Udemy inferential course: https://www.udemy.com/inferential-sta...
Udemy descriptives course: https://www.udemy.com/descriptive-sta...
Udemy ANOVA course: https://www.udemy.com/anova-spss
Udemy MANOVA course: https://www.udemy.com/manova-spss
11 سال پیش
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