The high rates of mobility in the U.S. can produce negative consequences for children's academic achievement. The purpose of this study was to determine relationships among math and reading academic achievement, mobility characteristics, student characteristics, and school characteristics in order to develop a model to predict achievement using these variables. Using such a model, educational stakeholders could identify students that are at risk for academic failure. The study included 523 third grade students from a high poverty, predominantly Latino, suburban district. Correlation analyses, factor analyses, ordered linear regression, and forward regression analyses were used to determine the relationships among variables as well as the power of variables to predict math and reading Transitional Colorado Assessment Program scale scores (TCAPSS).
In the correlation analyses, four predictor variables (including one mobility variable) had significant correlations with math TCAPSS, while six predictor variables (with no mobility variables) had significant correlations with reading TCAPSS. An initial factor analyses showed that the variables in the study had low proportion of variance that could be caused by underlying factors. A factor analysis, therefore, was not considered useful for building a model, and was not conducted.
The single block and ordered two set block regression analyses revealed that student characteristics, as a block of variables, significantly predicts TCAPSS for both math and reading, while mobility characteristics did not.
A forward regression analysis was conducted to determine the best model for predicting TCAPSS. In the math regression, six variables (including two mobility characteristics) were accepted into the model, reaching a low predictive value (adjusted R2= .21). In the reading regression, four variables (with no mobility variables) were accepted into the model, also reaching a low predictive value (adjusted R2 = .26).
The conclusions of this study are that most mobility characteristics are not useful as predictors of academic achievement for the population of this study when student characteristics are present or absent. However, two binary mobility variables, moving to a better school ( R2 change = .006) and moving between school years ( R2 change = .008), were accepted in the math forward regression model with small but significant predictive value.
","abstract_html":"<p>The high rates of mobility in the U.S. can produce negative consequences for children's academic achievement. The purpose of this study was to determine relationships among math and reading academic achievement, mobility characteristics, student characteristics, and school characteristics in order to develop a model to predict achievement using these variables. Using such a model, educational stakeholders could identify students that are at risk for academic failure. The study included 523 third grade students from a high poverty, predominantly Latino, suburban district. Correlation analyses, factor analyses, ordered linear regression, and forward regression analyses were used to determine the relationships among variables as well as the power of variables to predict math and reading Transitional Colorado Assessment Program scale scores (TCAPSS).</p> <p>In the correlation analyses, four predictor variables (including one mobility variable) had significant correlations with math TCAPSS, while six predictor variables (with no mobility variables) had significant correlations with reading TCAPSS. An initial factor analyses showed that the variables in the study had low proportion of variance that could be caused by underlying factors. A factor analysis, therefore, was not considered useful for building a model, and was not conducted.</p> <p>The single block and ordered two set block regression analyses revealed that student characteristics, as a block of variables, significantly predicts TCAPSS for both math and reading, while mobility characteristics did not.</p> <p>A forward regression analysis was conducted to determine the best model for predicting TCAPSS. In the math regression, six variables (including two mobility characteristics) were accepted into the model, reaching a low predictive value (adjusted <em>R<sup>2</sup></em>= .21). In the reading regression, four variables (with no mobility variables) were accepted into the model, also reaching a low predictive value (adjusted <em>R<sup>2</sup></em> = .26).</p> <p>The conclusions of this study are that most mobility characteristics are not useful as predictors of academic achievement for the population of this study when student characteristics are present or absent. However, two binary mobility variables, moving to a better school ( <em>R<sup>2</sup></em> change = .006) and moving between school years ( <em>R<sup>2</sup></em> change = .008), were accepted in the math forward regression model with small but significant predictive value.</p>","abstract_has_math":false,"creators":["Hicks, John Christopher"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Communication Disorders & Special Education","degree_department":null,"school":null,"contributors":["Katherine Kersey","Jennifer Kidd","Steve Myran"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-07-01T07:00:00Z","date_published":"2014-07-01T07:00:00Z","updated_at":"2026-07-24T03:35:00Z","subjects":["Academic achievement","Changing to a new school","Hispanic","Latino","Moving schools","School mobility","Standardized assessments","Standardized tests","Early Childhood Education","Elementary Education"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9781321316599"],"render_values":[{"text":"9781321316599","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/earlychildhood_etds/12","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Katherine Kersey","Jennifer Kidd","Steve Myran"]},{"key":"dc:creator","label":"Author","values":["Hicks, John Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-08-27T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Communication Disorders & Special Education"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Academic achievement","Changing to a new school","Hispanic","Latino","Moving schools","School mobility","Standardized assessments","Standardized tests","Early Childhood Education","Elementary Education"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9781321316599","https://digitalcommons.odu.edu/earlychildhood_etds/12"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The high rates of mobility in the U.S. can produce negative consequences for children's academic achievement. The purpose of this study was to determine relationships among math and reading academic achievement, mobility characteristics, student characteristics, and school characteristics in order to develop a model to predict achievement using these variables. Using such a model, educational stakeholders could identify students that are at risk for academic failure. The study included 523 third grade students from a high poverty, predominantly Latino, suburban district. Correlation analyses, factor analyses, ordered linear regression, and forward regression analyses were used to determine the relationships among variables as well as the power of variables to predict math and reading Transitional Colorado Assessment Program scale scores (TCAPSS).
In the correlation analyses, four predictor variables (including one mobility variable) had significant correlations with math TCAPSS, while six predictor variables (with no mobility variables) had significant correlations with reading TCAPSS. An initial factor analyses showed that the variables in the study had low proportion of variance that could be caused by underlying factors. A factor analysis, therefore, was not considered useful for building a model, and was not conducted.
The single block and ordered two set block regression analyses revealed that student characteristics, as a block of variables, significantly predicts TCAPSS for both math and reading, while mobility characteristics did not.
A forward regression analysis was conducted to determine the best model for predicting TCAPSS. In the math regression, six variables (including two mobility characteristics) were accepted into the model, reaching a low predictive value (adjusted R2= .21). In the reading regression, four variables (with no mobility variables) were accepted into the model, also reaching a low predictive value (adjusted R2 = .26).
The conclusions of this study are that most mobility characteristics are not useful as predictors of academic achievement for the population of this study when student characteristics are present or absent. However, two binary mobility variables, moving to a better school ( R2 change = .006) and moving between school years ( R2 change = .008), were accepted in the math forward regression model with small but significant predictive value.
"]},{"key":"dc:title","label":"Title","values":["The Relationship of Mobility, Child Characteristics and School Characteristics to the Academic Achievement of Third Grade Students in a Predominantly Latino District"]}]}],"canonical_facts":{"dc:contributor":["Katherine Kersey","Jennifer Kidd","Steve Myran"],"dc:creator":["Hicks, John Christopher"],"dc:date.available":["2019-08-27T07:00:00Z"],"dc:description.abstract":["The high rates of mobility in the U.S. can produce negative consequences for children's academic achievement. The purpose of this study was to determine relationships among math and reading academic achievement, mobility characteristics, student characteristics, and school characteristics in order to develop a model to predict achievement using these variables. Using such a model, educational stakeholders could identify students that are at risk for academic failure. The study included 523 third grade students from a high poverty, predominantly Latino, suburban district. Correlation analyses, factor analyses, ordered linear regression, and forward regression analyses were used to determine the relationships among variables as well as the power of variables to predict math and reading Transitional Colorado Assessment Program scale scores (TCAPSS).
In the correlation analyses, four predictor variables (including one mobility variable) had significant correlations with math TCAPSS, while six predictor variables (with no mobility variables) had significant correlations with reading TCAPSS. An initial factor analyses showed that the variables in the study had low proportion of variance that could be caused by underlying factors. A factor analysis, therefore, was not considered useful for building a model, and was not conducted.
The single block and ordered two set block regression analyses revealed that student characteristics, as a block of variables, significantly predicts TCAPSS for both math and reading, while mobility characteristics did not.
A forward regression analysis was conducted to determine the best model for predicting TCAPSS. In the math regression, six variables (including two mobility characteristics) were accepted into the model, reaching a low predictive value (adjusted R2= .21). In the reading regression, four variables (with no mobility variables) were accepted into the model, also reaching a low predictive value (adjusted R2 = .26).
The conclusions of this study are that most mobility characteristics are not useful as predictors of academic achievement for the population of this study when student characteristics are present or absent. However, two binary mobility variables, moving to a better school ( R2 change = .006) and moving between school years ( R2 change = .008), were accepted in the math forward regression model with small but significant predictive value.
"],"dc:identifier":["9781321316599","https://digitalcommons.odu.edu/earlychildhood_etds/12"],"dc:subject":["Academic achievement","Changing to a new school","Hispanic","Latino","Moving schools","School mobility","Standardized assessments","Standardized tests","Early Childhood Education","Elementary Education"],"dc:title":["The Relationship of Mobility, Child Characteristics and School Characteristics to the Academic Achievement of Third Grade Students in a Predominantly Latino District"],"thesis:degree_discipline":["Communication Disorders & Special Education"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:00Z"}