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Baylor Electronically Accessible Research Data (BEARdata) is the data repository for Baylor University hosted at the Texas Data Repository

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1 to 10 of 879 Results

Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll) Sep 26, 2019 - Blaine McCormick Dataverse McCormick, Blaine, 2019, "Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll)", https://doi.org/10.18738/T8/LXOUVX, Texas Data Repository Dataverse, V1 This is the data set associated with the 2011 expert polling to determine the greatest entrepreneurs and businesspeople in American history. It includes a Excel file with complete results from 41 experts included in the poll. The names and 2011 affiliations are included in a sepa...
Expert Pool as of 2011 Affiliation.docx Sep 26, 2019 - Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll) MS Word (docx) - 22.5 KB - MD5: f51fa7fc3faa83bf12da3ec2625c5cde An alphabetical listing of all 41 experts as of their 2011 affiliation.
Greatest 2011 Public Data.xlsx Sep 26, 2019 - Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll) MS Excel (XLSX) - 57.5 KB - MD5: 64503fcedb4cba6d556d323aeff58afd This Excel file contains detailed data from all 41 expert ballots. The experts remain non-identified with their nominations.
Blaine McCormick Dataverse(Baylor University) Sep 26, 2019Management Dataverse
2019 Fundamentals of Data Research Fellows(Baylor University) Aug 15, 2019Digital Scholarship Contains data generated from projects supported by the 2019 Fundamental of Data Fesearch Fellows Program
Monte Carlo Simulation Results Categorical Latent Class Analysis Mar 25, 2019 - Mixture Modeling with Categorial Responses Padgett, R. Noah;Tipton, Rebecca J., 2019, "Monte Carlo Simulation Results Categorical Latent Class Analysis", https://doi.org/10.18738/T8/CHW28Z, Texas Data Repository Dataverse, V2 This text (.txt, tab delimited) file contains the extracted results from a Monte Carlo simulation experiment investigating model selection in latent class analysis with categorical indicators.
createmodels_datagen - Copy.txt Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis Plain Text - 6.8 KB - MD5: 6d2454837de5d815702019b352723eeb
createmodels_lca.txt Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis Plain Text - 452 B - MD5: ef2208bb426177194d59543bde39de13
createmodels_lpa.txt Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis Plain Text - 430 B - MD5: 87cd28f976312ad4b1338e06050d3cce
createmodels.R Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis R Syntax - 6.6 KB - MD5: 6777e89e61814a83058c197be8a48d1b

Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll)

Sep 26, 2019 - Blaine McCormick Dataverse

McCormick, Blaine, 2019, "Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll)", https://doi.org/10.18738/T8/LXOUVX, Texas Data Repository Dataverse, V1

This is the data set associated with the 2011 expert polling to determine the greatest entrepreneurs and businesspeople in American history. It includes a Excel file with complete results from 41 experts included in the poll. The names and 2011 affiliations are included in a sepa...

Expert Pool as of 2011 Affiliation.docx

Sep 26, 2019 - Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll)

MS Word (docx) - 22.5 KB - MD5: f51fa7fc3faa83bf12da3ec2625c5cde

An alphabetical listing of all 41 experts as of their 2011 affiliation.

Greatest 2011 Public Data.xlsx

Sep 26, 2019 - Greatest Entrepreneurs and Businesspeople in American History (2011 Expert Poll)

MS Excel (XLSX) - 57.5 KB - MD5: 64503fcedb4cba6d556d323aeff58afd

This Excel file contains detailed data from all 41 expert ballots. The experts remain non-identified with their nominations.

Blaine McCormick Dataverse(Baylor University)

Sep 26, 2019Management Dataverse

2019 Fundamentals of Data Research Fellows(Baylor University)

Aug 15, 2019Digital Scholarship

Contains data generated from projects supported by the 2019 Fundamental of Data Fesearch Fellows Program

Monte Carlo Simulation Results Categorical Latent Class Analysis

Mar 25, 2019 - Mixture Modeling with Categorial Responses

Padgett, R. Noah;Tipton, Rebecca J., 2019, "Monte Carlo Simulation Results Categorical Latent Class Analysis", https://doi.org/10.18738/T8/CHW28Z, Texas Data Repository Dataverse, V2

This text (.txt, tab delimited) file contains the extracted results from a Monte Carlo simulation experiment investigating model selection in latent class analysis with categorical indicators.

createmodels_datagen - Copy.txt

Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis

Plain Text - 6.8 KB - MD5: 6d2454837de5d815702019b352723eeb

createmodels_lca.txt

Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis

Plain Text - 452 B - MD5: ef2208bb426177194d59543bde39de13

createmodels_lpa.txt

Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis

Plain Text - 430 B - MD5: 87cd28f976312ad4b1338e06050d3cce

createmodels.R

Mar 25, 2019 - Monte Carlo Simulation Results Categorical Latent Class Analysis

R Syntax - 6.6 KB - MD5: 6777e89e61814a83058c197be8a48d1b

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