English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Implications of Interactions Among Society, Education and Technology: A Comparison of Multiple Regression and Multilevel Modeling in Mathematics Achievement Analyses Pamela Rose Deering This research compares and contrasts two approaches to predictive analysis of three years’ of school district data to investigate relationships between student and teacher characteristics and math achievement as measured by the state-mandated Maryland School Assessment mathematics exam. The sample for the study consisted of 3,514 students taught by 99 teachers in a small Appalachian school district in western Maryland. The first analytic approach, standard multiple linear regression, produced a model in which each of the predictors is statistically significant: student gender, prior math achievement, student performance on school district mathematics benchmark exams, teacher years of experience, and advanced teacher certification. In the second approach—multilevel modeling with students as the level-1 unit of analysis and teachers as the level-2 unit of analysis—student characteristics are significant predictors of math achievement, and teacher characteristics are insignificant predictors. The study is set within a context of an exploration of relationships among society, education, and technology. Implications of the study’s results for K-12 mathematics education practice and policy are discussed including: the need to define teacher effectiveness and to identify teacher characteristics that contribute to student achievement; the promise of benchmarking exam systems; the necessity of effective math education, minimally from early education through Algebra II; the need to evaluate teacher certification criteria and the efficacy of teacher preparation programs; the importance of using appropriate statistical modeling approaches in education research; and a call to put students back into the education equation through student-centered funding models. Running head: COMPARISON OF MLR & MLM MATH ACHIEVEMENT ANALYSES iii Acknowledgements I offer my thanks to personnel from the Allegany County Public School system: to William AuMiller, Superintendent of Allegany County Public Schools (retired), for approving my request to conduct this research project; to Don Knotts, Supervisor of Math (retired), Nil Grove, Chief Technology Officer for discussing the project and its feasibility with me, and to Steve Milburn, PC Analyst, for retrieving the data that was used in the research. Without your help, this project would not have been possible. Thank you, Ann Crabtree, Program Assistant (retired) for the Technology, Learning & Culture Department, for your service to, encouragement of, and watchful eye over me and other doctoral students at West Virginia University. Your presence in the department was heartening. Thank you, Char Allen, Academic Advisor, for your guidance on necessary graduate student processes and procedures. Thank you, Gayle Neldon; thank you, Allison Swan Dagen; and thank you, Ugur Kale for your unhesitating willingness to join my committee late in the game. Thank you for the bolstering encouragement of your “yes” as I climbed my last mountain in this journey. I appreciate your gifts to me—your time and your feedback; they have contributed to this work. Thank you, Neal Shambaugh, chair of my committee, for your time, feedback, and administrative contributions. Moreover, thank you for fostering growth in me throughout my doctoral studies: by encouraging me to see the big picture, through thoughtful feedback on my assignments and projects that often provoked deeper thinking and inspired me to keep going, by modeling excellence in teaching and in the scaffolding of projects for your students, by encouraging creativity, and by taking risks in the classroom. Thank you for flinging open new gates of exploration and qualitative research to me. Running head: COMPARISON OF MLR & MLM MATH ACHIEVEMENT ANALYSES iv Sebastian Diaz, thank you for the many hours you have given to me as my dissertation advisor—introducing me to multilevel modeling; planning, discussing and mapping out the research with me; teaching me about data preparation and the ins and outs of SPSS; reviewing my work; and providing input and feedback throughout this long process. Thank you for seeing me as a colleague—listening to and valuing my opinions, collaborating in brain-storming sessions, taking time to discuss topics beyond this study. Thank you for challenging me, in your classroom and through this study. In both contexts, you compelled me to think beyond the immediate issues at hand; you broadened the scope of my thinking. Thank you for caring: your question of “How’s life?”, which you pose before “How’s your dissertation coming?” or “How’s work going?” speaks loudly to me of your regard for and treatment of others as human beings. I am ever so grateful for what you have taught me about life, work, statistics, and research. Dear One, Gerry Wojnar, thank You for standing by me; thank You for your endless patience as I rambled on about dissertation ideas or issues until my path became clear; thank You for your concern and empathy during the times I felt over-burdened by life/work/dissertation; and thank You for seeing me through to the end of this arduous journey. Many, many thanks to You, Dear One. Running head: COMPARISON OF MLR & MLM MATH ACHIEVEMENT ANALYSES v Dedication To my entire family, with special dedication to my husband, Gerry; and to my children: Larry, Belinda, Daniel, Rachel, and Dunel. I am blessed and inspired by the diversity of your individualities, your beauties, your perspectives, your many talents; by your courage in the face of adversities, by our collective wisdom and our individual and collective strength; by your exuberance for life, your passion, your humor, your humanity, your growth, your desire to make a difference; by your complexity, your passions, and your friendship. I love you. Running head: COMPARISON OF MLR & MLM MATH ACHIEVEMENT ANALYSES vi

Implications of Interactions Among Society, Education and Technology: A Comparison of Multiple Linear Regression and Multilevel Modeling in Mathematics Achievement Analyses