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David Wallace
United States
เข้าร่วมเมื่อ 7 ก.ค. 2020
My name is Dave Wallace. I am a leadership practitioner, researcher, teacher, and coach. I also use and teach statistical analysis to support leadership and organizational performance. This channel captures the lectures I use across my teaching as a way to share my knowledge with b
Contemporary Theories Summary
Some take-aways about leadership theories and what we can learn from them.
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There are lots of buzzwords about leadership theories - it's not just jargon. Here's how it fits.
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Great video explaining suppression! Thank you!
thank you!
Well explained in only 1:27 thank you so much!
Very well described and explained David. Thank you for creating great examples that explain TFL in an understandable inspiring and visual way.
love it. can you do a dedicated econometrics series?
Thanksss
One question about three-way interaction terms. Let's label each variable A(main variable), B(1st moderator), C (2nd moderator). I'm interested in (hypothesize) the relationships A-B and A-B-C. Should all two-way (AB, AC, BC) and three-way interaction terms (A * B * C) be included in a regression model and result or would be it fine to include some of interest (AB, ABC) only?
easy & simple, thanks
Excellent
thank you! very helpful to see it with an image
Super
thanks so much !
what happens if you only standardize the dependet variable?? . how do you interpret it then?
easy and simple. Thank you
Very lucid explanation! Thank you so much :)
Great video!
Brilliant explanation for college!
Good one
simple, easy to understand thanks
I have one question: how did you calculate the correlation? Did you use Pearson, Spearman or what?
I have the same question. Have you solved the problem?
Thank you
This is a great explanation, thank you!
your an absolute legend i am a second year psychology student trying to figure this out in stats and it helped so much
Are you THE David Wallace? CEO of Dunder Mifflin?
very helpful, thank you :)
thank you for explaining the term "partial"!!
Will you please post your references.
The most important reference here is probably: Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254-284. doi.org/10.1037/0033-2909.119.2.254
This was a very lucid explanation! Thank you:)
wow thanks
Thank you so much for this! Psyc student with an arts background... very helpful
Glad it was helpful!
Great video! Would have loved a PPT in addition.
Thank you! Very simplified understandable explanation...
Good overview explanation.
thank you!
Thank you!
How its affect the interpret if you include reference variable? If you include reference variable, the parameters(b) will assign accordingly. so, how its different from excluding reference variable? Kindly clarify my doubt...
Hence, multicollinearity doesn't affect the accuracy instead it will affect the coefficient of individuals right?
It's not about the accuracy for the overall sample or the individual, it's really about making the regression results more difficult to interpret. The weights still mean the same thing, but I will have a hard time “eye-balling” the regression and understanding how they relate to each other. If the collinearity is big enough, one of the predictors may actually change signs from its predictor with Y. This DOES NOT MEAN that there is a negative relationship (esp. if it is contrary to what the correlation is telling us); this is then just an artifact of the way regression handles highly redundant factors. Generally speaking, regression gives most of the "credit" to the predictor with the stronger correlation with Y. The predictor with the weaker correlation with Y will have a weaker B, to the point that it may change signs if the collinearity is high enough. This is because by holding X1 constant, I’m actually holding a lot of X2 constant. The key is, if multicollinearity is going on, be very careful about interpreting regression coefficients individually - you have to look at the big picture of all the predictors in the variate.
@@davidwallace3411 thank you so much for your clear explanation sir.....
Thank you
Great explanation!!
Thank you for sharing your wisdom, Dave!
Thank you very much, you solve a big doubt that i had!
Great! Thank you for your class.