ingest cdrApp 2018-06-13T15:19:58.382Z 51cd2fe2-3fd7-401f-a923-a97bc3db68a2 modifyDatastreamByValue RELS-EXT fedoraAdmin 2018-06-13T15:46:03.917Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2018-06-13T15:46:15.327Z Adding technical metadata derived by FITS addDatastream MD_FULL_TEXT fedoraAdmin 2018-06-13T15:46:38.034Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2018-06-13T15:47:00.511Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-16T21:28:04.497Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-22T15:44:32.535Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-28T18:32:48.776Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-12T17:22:28.061Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-22T20:44:28.074Z Cara Arizmendi Author Department of Psychology and Neuroscience College of Arts and Sciences Diagnostics for detecting low variability Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. Spring 2018 2018 Quantitative psychology gini, model impact, multicollinearity, variability, variance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text Cara Arizmendi Author Department of Psychology and Neuroscience College of Arts and Sciences Diagnostics for detecting low variability Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. Spring 2018 2018 Quantitative psychology gini, model impact, multicollinearity, variability, variance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text Cara Arizmendi Author Department of Psychology and Neuroscience College of Arts and Sciences Diagnostics for detecting low variability Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. Spring 2018 2018 Quantitative psychology gini, model impact, multicollinearity, variability, variance eng Master of Arts Thesis Psychology Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text University of North Carolina at Chapel Hill Degree granting institution Cara Arizmendi Creator Department of Psychology and Neuroscience College of Arts and Sciences Diagnostics for detecting low variability Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. Quantitative psychology gini; model impact; multicollinearity; variability; variance eng Master of Arts Masters Thesis Psychology Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text University of North Carolina at Chapel Hill Degree granting institution 2018 2018-05 Cara Arizmendi Author Department of Psychology and Neuroscience College of Arts and Sciences Diagnostics for detecting low variability Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. Spring 2018 2018 Quantitative psychology gini, model impact, multicollinearity, variability, variance eng Master of Arts Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Psychology Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text Cara Arizmendi Creator Department of Psychology and Neuroscience College of Arts and Sciences Whether to include variables with low variability in analyses is a predicament for researchers. Like the case of multicollinearity in which a correlation matrix has a near-zero determinant, data with low-variability variables will have a near-zero determinant of the covariance matrix. Similar to problems with multicollinearity, we may expect that retaining low-variability variables in our models would result in unstable estimates of the other variables in the model. Despite this predicament: 1. a method for identifying these variables for removal has yet to be developed and 2. the impact of low variability on estimation of other variables has yet to be explored. We present a simulation study exploring the impact of low variability in linear regression, while also assessing indices of low variability. Simulations suggest the stability of estimates of normal-variability variables, despite the presence of a low variability variable and the instability of estimates for variables with low variability. 2018-05 2018 Quantitative psychology gini; model impact; multicollinearity; variability; variance eng Master of Arts Masters Thesis University of North Carolina at Chapel Hill Graduate School Degree granting institution Kathleen Gates Thesis advisor David Thissen Thesis advisor Kenneth Bollen Thesis advisor text Arizmendi_unc_0153M_17679.pdf uuid:ca596dc1-6ab1-47be-aaee-6cf3e7540b8d 2020-06-13T00:00:00 2018-04-12T17:26:02Z proquest application/pdf 2009942