dc.contributor.author	Morris, Dana Halley
dc.coverage.spatial	United States	en_US
dc.date.accessioned	2020-11-19T14:30:33Z
dc.date.available	2020-11-19T14:30:33Z
dc.date.issued	2020-09
dc.identifier.other	BA9A5E38-71AF-7BB9-4C75-105AF7303B43	en_US
dc.identifier.uri	https://hdl.handle.net/10428/4542
dc.description.abstract	The problem of high school dropouts has been studied for decades, but utilizing readily obtainable student data and data mining can aid school leaders to more accurately detect which students will likely drop out. This early warning information can be used by educators as early as sixth grade to help identify potential high school dropouts and students who will not graduate on time and intervene more efficiently and effectively with those students. The purpose of this nonexperimental correlational study was to use longitudinal data from a mid-sized school district from two cohorts to support the creation of a dropout early warning system to predict both sixth-grade and ninth-grade students who are at risk for not graduating on time. The statistical models utilized to identify the most accurate indicators were logistics regression, linear discriminate analysis, and quadratic discriminate analysis. The variables identified in the ninth-grade models as able to predict students who would not complete high school within four years were: if a student did not receive enough credits to advance to the tenth grade, did not attend school at least 90% of the time, was suspended from school, had multiple school moves in the ninth grade, and gender. The sixth-grade variables identified as able to predict students who would not complete high school within four years were: if the student was suspended from school, had multiple school moves, did not pass English, and gender. The model identified as having the lowest false-positive rate and a relatively high accuracy for ninth grade was the downsampled QDA model with the lowest false-positive rate of 0.43 and an accuracy of 75%. For sixth grade, the downsampled LDA model had the lowest false-positive rate at 0.34 and an accuracy of 67%. Keyword 1: dropout Keyword 2: late graduate Keyword 3: predict dropouts Keyword 4: early warning	en_US
dc.description.tableofcontents	Chapter I: INTRODUCTION 1 -- Impact of Dropping Out 3 -- Early Warning Signs 4 -- Statement of the Problem 7 -- Purpose of the Study 8 -- Research Questions 9 -- Research Methodology 10 -- Significance of the Study 13 -- Conceptual Framework of the Study 14 -- Limitations of the Study 16 -- Definition of Terms 16 -- Organization of the Study 18 -- Chapter II: LITERATURE REVIEW 20 -- National Graduation Rate 20 -- Georgia’s Graduation Rate 21 -- Consequences of Dropping Out 21 -- Individual and School Level Factors 23 -- Academic Success 24 -- Low Attendance Rates 33 -- School Failure 38 -- Behavior Problems 41 -- Gender 45 -- Low Socioeconomic Status 48 -- English Language Learner Status 51 -- Special Needs Status 52 -- Standardized Test Scores 55 -- School-level Variables 57 -- Major Studies 58 -- Summary 68 -- Chapter III: METHODOLOGY 69 -- Research Design 70 -- Participants 72 -- Instrumentation 73 -- Validity 76 -- Reliability 77 -- Data Collection 78 -- Variables 80 -- Data Analysis 84 -- Class Imbalance 88 -- Assumptions 89 -- Logistic Regression Assumptions 89 -- Continuous Predictors Linearly Correlation to Logit of Outcome 89 -- Pearson Correlation Coefficients 90 -- Variance Inflation Factor (VIF) 91 -- Linear Discriminant Analysis and Quadratic Discriminant Analysis Assumptions 92 -- Multivariate Normality 92 -- Homoscedasticity 93 -- Outliers 93 -- Multicollinearity 93 -- Summary 94 -- Chapter IV: RESULTS 96 -- Missing Data 98 -- Descriptive Statistics 99 -- Pearson Correlation Coefficients 107 -- RQ1 120 -- Logistic Regression 121 -- Linear Discriminant Analysis 127 -- Quadratic Discriminant Analysis 132 -- Model Comparisons and Variable Evaluations 136 -- RQ2 138 -- Receiver Operating Characteristic (ROC) Curve and Confusion Matrix 138 -- Choosing the Best Model 150 -- RQ3 151 -- Logistic Regression 152 -- Linear Discriminant Analysis 157 -- Quadratic Discriminant Analysis 161 -- Model Comparisons and Variable Evaluations 165 -- RQ4 166 -- Receiver Operating Characteristic (ROC) Curve and Confusion Matrix 167 -- Choosing the Best Model 177 -- Summary 177 -- Chapter V: SUMMARY, CONCLUSIONS, AND IMPLICATIONS 182 -- Summary of Findings 183 -- Conclusions for Ninth Grade 185 -- Conclusions for Sixth Grade 193 -- Limitations 201 -- Implications 203 -- Conclusion 204 -- REFERENCES 207 -- APPENDIX A: Institutional Review Board Protocol Exemption Report 223.	en_US
dc.format.extent	1 electronic document, 239 pages.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en_US	en_US
dc.rights	This dissertation is protected by the Copyright Laws of the United States (Public Law 94-553, revised in 1976). Consistent with fair use as defined in the Copyright Laws, brief quotations from this material are allowed with proper acknowledgement. Use of the materials for financial gain with the author's expressed written permissions is not allowed.	en_US
dc.subject	Dissertations, Academic--United States	en_US
dc.subject	Dropout behavior, Prediction of	en_US
dc.subject	High school dropouts--Prevention	en_US
dc.subject	High school dropouts	en_US
dc.subject	High school graduates	en_US
dc.title	Using Ninth Grade and Sixth Grade Indicators within an Early Warning System to Predict Students at Risk for Graduating Late or Dropping Out	en_US
dc.type	Dissertation	en_US
dc.contributor.department	Department of Curriculum, Leadership, and Technology of the Dewar College of Education and Human Services	en_US
dc.description.advisor	Brockmeier, Lantry L.
dc.description.committee	Pate, James L.
dc.description.committee	Siegrist, Gerald R.
dc.description.degree	Ed.D.	en_US
dc.description.major	Education in Curriculum and Instruction	en_US