# RMTD 404 Introduction to Linear Models

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1 RMTD 404 Introduction to Linear Models Instructor: Ken A., Assistant Professor Phone: (312) Office: Lewis Towers, Room 1037 Office hour: By appointment Course Content This course introduces students to techniques of data analysis and statistical inference commonly used in educational and psychological research. The major topics are simple/multiple regression, simple analysis of variance (one-way ANOVA), and factorial analysis of variance (two-way ANOVA) followed by multiple comparisons, and analysis of covariance (ANCOVA). Knowledge of basic algebra is required, as is an understanding of the fundamental principles of descriptive statistics and hypothesis testing (as taught, for example, in RMTD 404/421 or equivalent). Knowledge of calculus is not required. Technological Knowledge and Skills Students will analyze the NELS:88 (National Education Longitudinal Study of 1988) and other example data sets, using SPSS. This NELS:88 data set is one of the largest and most important datasets collected by the U.S. government. It includes extensive measurements of students beliefs, aspirations, attitudes, and background, as well as related information from teachers, parents, and schools. Required text Howell, D.C. (2013). Statistical Methods for Psychology (8 th ed.). Belmont, CA: Wadsworth, Cengage Learning. Recommended Text (Only need one The first option is online and free; the second option is for those who prefer to have a physical book as a resource) 1) Howell, D.C. (2011). Fundamental Statistics for the Behavioral Sciences (7th ed.). ual/spsslongermanual.html OR 2) Field, A. (2013). Discovering statistics using SPSS (4 th ed.). Thousand Oaks, CA: SAGE Publications. Course Objectives By the end of the course the student should have demonstrated the ability to: 1.! identify continuous and discrete (or categorical) variables as either dependent or independent, and choose appropriate statistical procedures for their analysis;

2 2.! describe relationships between predictor variables and a continuous outcome variable; 3.! formulate one- and two-factor between-groups analysis-of-variance models, estimate their parameters, and test hypotheses about those parameters; including a.! identify situations in which it is correct to apply, b.! identify the assumptions underlying parametric ANOVA, c.! differentiate between fixed and random effects models, d.! identify situations in which various transformations are appropriate for ANOVA, e.! compute and interpret effect size indicators for the analysis of variance. 4.! design and implement tests of specific a priori and post hoc contrasts in the context of analysis of variance models; specifically a.! identify and differentiate between comparison and familywise error rates, b.! describe when a priori and post hoc comparisons are appropriate, c.! compute planned and post hoc comparison procedures, and d.! identify the various post hoc comparison procedures (e.g., Tukey s HSD and Scheffe test), and conditions wherein each procedure is appropriate. 5.! calculate point estimates, confidence intervals and hypothesis tests for regression slopes; 6.! delineate assumptions of linear statistical models and examine data to evaluate their conformity to those assumptions; 7.! formulate and interpret multiple regression models appropriate for various research problems and interpret computer output relevant to those models; 8.! think about the regression analysis in matrix form; 9.! recognize similarities and differences between regression and analysis-of-variance models; 10.!understand the roles of error terms in linear models; 11.!write coherent summaries and interpretations of data analyzed by the above procedures. Evaluation Grades will be based on points accumulated on the homework and examinations. There will be 100 total possible points, distributed as follows: Homework assignments 50% Midterm exam (scheduled time only) 20% Final exam (scheduled time only) 20% Quizzes 10% The grade ranges in terms of percentages are: = A = B = C and below = F = A = B = C = B = C 2

4 can be found at: df Dispositions Each course in the School of Education focuses on one or more professional dispositions. Students are offered opportunities to receive feedback on their dispositional growth in the areas of professionalism, fairness and/or the belief that all students can learn. The expected behaviors for the specific dispositions for this class and the evaluation rubric are listed in the end of this syllabus. IDEA Objectives IDEA is an evaluation system that our School uses to assess whether a class reaches its major goals by the end of the semester. The essential objectives for this course are: 1. Gaining factual knowledge (terminology, classifications, methods, trends) 2. Learning fundamental principles, generalizations, or theories 3. Learning to apply course material (to improve thinking, problem solving, and decisions) 4. Developing specific skills, competencies, and points of view needed by professionals in the field most closely related to this course SOE ACADEMIC POLICIES Diversity The School of Education is committed to diversity including but not limited to race, gender, sexual orientation, social class, ethnicity, and ability. Through this course, students will learn how to interpret and critique fundamental research methods used in the social sciences. The course is designed to provide students with the knowledge necessary to evaluate research concerning the social dimensions mentioned above. Addendum You are encouraged to visit the following website which provides information related to academic honesty, accessibility, the SOE conceptual framework, ethics reporting, and electronic communication policies: 4

5 Tentative schedule Week Date Topic Readings Slides Due 1 8/24 Review of correlation and begin simple linear regression 2 8/31 Simple linear regression/multiple linear regression I Ch. 9 1 Ch /7 Labor Day: No Class 4 9/14 Multiple linear regression I: Continuous predictors 5 9/21 Multiple linear regression II: Categorical and continuous predictors Ch HW1 Ch /28 Multiple linear regression III: Interactions Ch /5 Fall Break: No Class 8 10/12 Multiple linear regression III: Interactions (cont d) Ch. 7 5 HW2 9 10/19 Exam /26 Simple analysis of variance (ANOVA) Ch /2 Multiple comparisons I Ch /9 Multiple comparisons II Ch HW /16 Factorial analysis of variance I Ch /23 Factorial analysis of variance II Ch /30 Analysis of covariance (ANCOVA) Ch. 11 HW /7 Exam 2 5

6 Evaluation of Disposition in RMTD 404 Rubric Area Target Acceptable Unacceptable Systematic Inquiry IL- LUC-DISP.1 Candidate communicates effectively and appropriately with faculty and peers. Candidate is working on communicating effectively and appropriately with faculty and peers. Candidate is unable to communicate effectively and appropriately with faculty and peers. Responsibilities for General and Public Welfare IL-LUC-DISP.1 Candidate s written work is appropriate and effective for the course. Candidate s written work is sometimes appropriate and effective for the course. Candidate s written work is inappropriate and ineffective for the course. Timeliness IL-LUC- DISP.1 Candidate is able to meet all deadlines. Candidate is sometimes able to meet all deadlines. Candidate is unable to meet all deadlines. AccountabilityIL-LUC- DISP.1 Candidate attends all classes and fulfills all professional obligations. attends classes and fulfills professional obligations. Candidate s attendance to class is inconsistent and is unable to fulfill all professional obligations. Collegiality IL-LUC- DISP.1 Candidate is able to work with peers. respects the viewpoints Candidate has difficulty respecting the viewpoints Integrity/HonestyIL- LUC-DISP.2 Candidate respects the viewpoints respects the viewpoints Candidate has difficulty respecting the viewpoints Interpersonal Integrity/HonestyIL- LUC-DISP.2 Candidate recognizes potential conflicts and handles them appropriately. recognizes potential conflicts and handles them appropriately. Candidate has difficulty recognizing potential conflicts and handling them appropriately. Academic Integrity/HonestyIL- LUC-DISP.2 Candidates appropriately represent procedures, data, and findings attempting to prevent misuse of their results. Candidates represent procedures, data, and findings in a manner that is likely to allow the misuse of their results. Candidates misrepresent procedures, data, and findings. There is minimal attempt to prevent misuse of their results. 6

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