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How 'Should' Tech Do in Recruiting Rank - Analysis
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<blockquote data-quote="takethepoints" data-source="post: 504523" data-attributes="member: 265"><p>Three comments:</p><p></p><p>1. Great work. And, I might add, the third post (mine, yours, and Augusta's) based on what passes for reliable recruiting data that has come to the same conclusion: Tech does ok in recruiting, given our limitations and the resources we put into it, and it will take awhile and a lot of money to move up the stalk. Some methodological points, however.</p><p></p><p>2. It's ok to calculate a Pearson correlation using a dummy variable. They are continuous (sorta). If you use them as dependent variables in regressions there can be trouble and they are hard to interpret, but …</p><p></p><p>3. I would have put my indicators into a principle components analysis and formed an index using the loadings. That would weight the contribution of each indicator to explained variance more evenly. Then you could correlate the index with the recruiting rankings.</p><p></p><p>4. Augusta and I used average "stars" instead of rankings since they aren't subject to variance due to the number of slots you have open reach year. But … given that the star ratings are so squirrelly, your choice is probably just as good.</p><p></p><p>And, if you are looking for a terrific free data analysis application, try Jamovi (<a href="https://www.jamovi.org" target="_blank">https://www.jamovi.org</a>). It is based on R, surprisingly complete, completely menu driven. I still use R, but Jamovi is growing on me.</p></blockquote><p></p>
[QUOTE="takethepoints, post: 504523, member: 265"] Three comments: 1. Great work. And, I might add, the third post (mine, yours, and Augusta's) based on what passes for reliable recruiting data that has come to the same conclusion: Tech does ok in recruiting, given our limitations and the resources we put into it, and it will take awhile and a lot of money to move up the stalk. Some methodological points, however. 2. It's ok to calculate a Pearson correlation using a dummy variable. They are continuous (sorta). If you use them as dependent variables in regressions there can be trouble and they are hard to interpret, but … 3. I would have put my indicators into a principle components analysis and formed an index using the loadings. That would weight the contribution of each indicator to explained variance more evenly. Then you could correlate the index with the recruiting rankings. 4. Augusta and I used average "stars" instead of rankings since they aren't subject to variance due to the number of slots you have open reach year. But … given that the star ratings are so squirrelly, your choice is probably just as good. And, if you are looking for a terrific free data analysis application, try Jamovi ([URL]https://www.jamovi.org[/URL]). It is based on R, surprisingly complete, completely menu driven. I still use R, but Jamovi is growing on me. [/QUOTE]
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How 'Should' Tech Do in Recruiting Rank - Analysis
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