ingest cdrApp 2017-07-06T12:13:02.314Z 082b3de9-6030-4a3e-a983-035a47fc699e modifyDatastreamByValue RELS-EXT cdrApp 2017-07-06T12:28:25.483Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:49:58.181Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:49:58.968Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2017-07-06T12:49:59.562Z Adding technical metadata derived by FITS modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:50:08.118Z Setting exclusive relation addDatastream MD_FULL_TEXT fedoraAdmin 2017-07-06T12:50:16.847Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T12:50:32.931Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-25T03:52:45.495Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-27T04:29:13.591Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-03-14T00:28:05.827Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-05-16T21:53:40.032Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-10T22:58:26.569Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-17T19:03:17.231Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-08T18:30:10.305Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-15T15:38:12.331Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-16T18:41:17.244Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-21T16:07:54.466Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-26T19:12:00.791Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-11T19:55:50.484Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-20T13:04:08.954Z Nathan Markiewitz Author Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. Spring 2017 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. Spring 2017 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. Spring 2017 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017-05 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 University of North Carolina at Chapel Hill Degree granting institution Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use; Count; Factor Analysis; Item Response Theory; Latent Variable; Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences The Ordinal Count Factor Model: An Improved Latent Variable Model For Ordinal Count Items Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use, Count, Factor Analysis, Item Response Theory, Latent Variable, Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology and Neuroscience Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Nathan Markiewitz Creator Department of Psychology and Neuroscience College of Arts and Sciences Much of the measurement of human behaviors relies on the reporting of a rate of behavior. Common measures use items that ask participants to select between given intervals of counts—these items are called ordinal count items. I present the ordinal count factor model (OCFM) as a latent variable model for ordinal count item responses in a single population and across multiple groups. OCFMs represent the underlying latent response as a count, instead of the logistic or normal distribution used by current latent variable models for ordinal data. In addition to representing the data generating process more faithfully, OCFMs allow for inferences on the metric of the underlying rate of behavior. I evaluate the OCFM through two empirical examples using the Rutgers Alcohol Problem Index. These studies demonstrate that OCFMs may fit better than standard models, produce more precise factor scores, and may be fit using widely available, open-source software. 2017 Quantitative psychology Clinical psychology Alcohol Use; Count; Factor Analysis; Item Response Theory; Latent Variable; Measurement Invariance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Daniel Bauer Thesis advisor Kenneth Bollen Thesis advisor Patrick Curran Thesis advisor text 2017-05 Markiewitz_unc_0153M_16943.pdf uuid:c4a91c82-e1dd-4a21-a440-8bf4ecab6903 2019-07-06T00:00:00 2017-04-25T11:39:48Z proquest yes application/pdf 5707984