1. CARLOS ALBIZU UNIVERSITYSAN JUAN CAMPUS<br />MASTER SYLLABUS<br />RMIC-824: TECHNIQUES OF CORRELATION AND MULTIPLE REGRESSION<br />CREDITS: 3CONTACT HOURS: 45<br />COURSE DESCRIPTION<br />The main objective of this course is to familiarize the student with the correlation and regression techniques available for the treatment of research data. Topics included are: use and misuse of correlational analysis, correlation for nominal, ordinal and interval scales, multiple and partial correlation, statistical inference for correlation coefficients, simple and multiple regression analysis, stepwise regression, explained and residual variance and standard error of measurement.<br />PRE-REQUISITES<br />PSYF-568 – Inferential Statistics<br />COURSE OBJECTIVES<br />Enable the student to master the logic and interpretation of correlation coefficients in behavioral research, to master the logic and interpretation of regression analysis, to evaluate the advantages and disadvantages of correlation and regression analysis, to apply correlation and regression's procedures to research data, to apply inferential statistical procedures to correlation and regression coefficients, as well as to interpret results from such tests.<br />REQUIRED TEXT BOOKS<br />Gravetter, F.J., & Wallnau, L.B. (2008). Statistics for the behavioral sciences (8th ed.). Belmont, CA: Cengage Learning. ISBN-10: 0495602205; ISBN-13: 978-0495602200 <br />GIass, G. V. & Hopkins, K. D. (2008). Statistical methods in education and psychology (3rd ed.). Englewood-Cliffs, NJ: Prentice-Hall. ISBN-10: 0205673538; ISBN-13: 978-0205673537<br />Sánchez-Viera, J. (2004). Fundamentos del razonamiento estadístico (3rd ed.) República Dominicana: Universidad Carlos Albizu. ISBN-10: 1881724581<br />ITINERARY OF CLASS UNITS<br />Unit 1: Introduction<br />Unit 2: Behavioral research, correlation and regression<br />Unit 3: Measures of correlation for nominally scaled variables and their significance <br />Unit 4: Measures of correlation for ordinally scaled variables and their significance<br />Unit 5: Measures of correlation for interval/ratio scaled variables and their significance. Unit 6: Analysis of non-linear correlation.<br />Unit 7: Special cases for the correlation coefficient and their significance<br />Unit 8: Simple regression analysis<br />Unit 9: Regression analysis: Two predictors.<br />Unit 10: Regression analysis: Two predictors (continue)<br />Unit 11: Regression analysis: More than two predictors<br />Unit 12: Correlation and Covariance analysis<br />COURSE CONTACT HOURS <br />Professors who teach the course must divide the contact hours the following way:<br />Face-to-face time in the classroom must not be less than 40.0 hours (14 units, 2.5 hours per unit, 16 sessions).<br />For the remaining hours (≥ 10.0 hours), students will conduct research projects or homework outside the classroom. These projects or homework will include, but are not limited to application of different statistical formulas and interpretation of results.<br />METHODOLOGY<br />The professor who offers the course will select the specific methodology. These methodologies could include, but would no be limited to: conferences by the professor, group discussions of assigned readings, class research projects, student presentations, individual meetings with students and sub-groups in the classroom.<br />EDUCATIONAL TECHNIQUES<br /> <br />The professor who offers the course will select the specific educational techniques. These techniques could include, but would not limit to: debates, practical demonstrations, films/videos, simulations, slide shows and forums.<br />EVALUATION<br />The professor who offers the course will select the specific evaluation criteria. These methodologies could include, but are not limited to: terms papers, projects, literature reviews, exams, class presentations.<br />RESEARCH COMPETENCIES<br /> Application of different statistical formulas<br /> Interpretation of results.<br /> Testing hypothesis<br />ATTENDANCE POLICY<br />Class attendance is mandatory for all students. After two unexcused absences, the student will be dropped from the class, unless the professor recommends otherwise. When a student misses a class, he/she is responsible for the material presented in class. <br />AMERICANS WITH DISABILITIES ACT (ADA)<br />Students that need special accommodations should request them directly to the professor during the first week of class.<br />COURSE UNITS<br />UNIT I: INTRODUCTION<br />Upon successful completion of this unit, students will understand the relationship among the concepts commonly used in correlation and regression analysis, as well as the advantages and disadvantage of correlational studies in behavioral research.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Discuss the fundamental concepts of correlation and regression analysis.<br />Identify the limitations of correlation and regression analysis.<br />Discuss the logic behind correlation and regression analysis.<br />Discuss the consequences of violating the assumptions underlying the correlation and regression analysis.<br />ASSIGNED READINGS:<br />Sanchez, J. A. (2004)<br />Chapter 9 - Correlation and Simple lineal regression (Correlación y regression simple lineal).<br />UNIT 2: ANALYSIS OF VARIANCE: ONE FACTOR-FIXED EFFECT MODEL<br />Upon successful completion of this unit, students will understand the relative importance of correlation and regression techniques in behavioral research.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Discuss the role of correlational and regressional analysis in behavioral research.<br />Identify the differences between correlational and experimental studies.<br />Discuss the difference between correlation and causation.<br />Offer research examples of correlation and regression analysis.<br />ASSIGNED READINGS:<br />Sánchez, J.A. (2004)<br />Chapter 9 - Correlation and Simple lineal regression (Correlación y regression simple lineal).<br />Gravetter, F. J. & Wallnau, L. B. (2008)<br />Chapter 16: Correlation and Regression<br />UNIT 3: MEASURES OF CORRELATION FOR NOMINAL SCALED VARIABLES AND <br /> THEIR SIGNIFICANCE<br />Upon successful completion of this unit, students will understand the commonly used<br />Coefficients of correlation for normally scaled variables, as well as of the statistical<br />procedures for testing their significance.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Identify the most frequently employed coefficients of correlation for nominally scaled variables.<br />Discuss the assumptions underlying nominally scaled variables coefficients of correlation.<br />Calculate coefficients of correlation for nominal data (Tables 2x2).<br />Calculate coefficients of correlation for nominal data (Tables greater than <br />2x2).<br />Know how to interpret coefficients of correlation for nominally scaled variables.<br />Identify and apply tests of significance for nominally scaled variables correlation coefficients.<br />Know how to interpret results of tests significance.<br />ASSIGNED READINGS: <br />Sánchez, J.A. (2004)<br />Chapter 9- Correlation and Simple lineal regression (Correlación y regression simple lineal).<br />Chapter 16-Hypothesis testing for Correlation coefficients (Pruebas de hipótesis para coeficientes de correlación <br />Gravetter, F. J. & Wallnau, L. B. (2008)<br />Chapter 8: Introduction to Hypothesis testing<br />UNIT 4: MEASURES OF CORRELATION FOR ORDINALLY SCALED VARIABLES<br />Upon successful completion of this unit, students will understand the commonly used coefficients of correlation for ordinally scaled variables, as well as the statistical procedures for testing their significance.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit, students will be able to:<br />Identify the most frequently used coefficients of correlation for ordinally scaled variables.<br />Discuss the assumptions underlying ordinally scaled variables coefficients of correlation.<br />Calculate coefficients of correlation for ordinally scaled variables.<br />Know how to interpret coefficients of correlation for ordinally scaled variables<br />Know how to interpret results of tests of significance for ordinally scaled <br /> variables.<br />Know how to interpret results from tests of significance.<br />ASSIGNED READINGS:<br />Sánchez, J.A. (2004) <br />Chapter 9- Correlation and Simple lineal regression (Correlación y regression simple lineal).<br />Gravetter, F. J. & Wallnau, L. B. (2008)<br />Chapter 8: Introduction to Hypothesis testing<br />Chapter 16: Correlation and Regression<br />UNIT 5: MEASURES OF CORRELATION FOR INTERVAL/RATIO SCALED <br /> VARIABLES<br />Upon successful completion of this unit, students will understand the Pearson's product moment correlation coefficient, as well as of the statistical procedures for testing its significance.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Discuss the assumption underlying the Pearson's product-moment coefficient of correlation.<br />Calculate the Pearson's product-moment coefficient of correlation.<br />Know how to interpret Pearson's product-moment coefficient of correlation.<br />Identify and apply tests of significance to Pearson's product-moment coefficient of correlation.<br />Know how to interpret results from tests of significance. Assigned Readings:<br />ASSIGNED READINGS:<br />Sánchez, J.A. (2004) <br />Chapter 11- The Normal curve: Theory and applications (La curva normal: Teoría y aplicaciones).<br />Gravetter, F. J. & Wallnau, L. B. (2008)<br />Chapter 16: Correlation and Regression <br />UNIT 6: ANALYSIS OF NON-LINEAR CORRELATION<br />Upon successful completion of this unit, students will understand<br />the correlation coefficient Eta as well as the statistical procedures for testing its significance.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit, students will be able to: <br />Discuss the assumptions underlying the coefficient Eta. <br />Calculate Eta for given sets of data.<br />Calculate the F test for the significance of Eta. <br />Know how to interpret Eta coefficients and F ratios<br />ASSIGNED READINGS:<br />Glass, G.V. and Hopkins, K. D. (2008). <br />Chapter 8: Linear and Multiple Regression: Inferences among correlation coefficients.<br />UNIT 7: SPECIAL CASES FOR THE CORRELATION COEFFICIENT AND THEIR <br /> SIGNIFICANCE<br />Upon successful completion of this unit students will understand special cases of the correlation coefficient, as well as the statistical procedures for testing their significance.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Identify special cases of the correlation coefficient as apply to behavioral <br /> research.<br />Discuss the assumptions underlying special cases of the coefficient of <br /> correlation.<br />Calculate special cases to the coefficient of correlation.<br />Identify and apply tests of significance for special cases of the coefficient of correlation.<br />Familiarize with reading and interpreting results from special cases of the coefficient of correlation and from tests of significance.<br />ASSIGNED READINGS:<br />Glass, G.V. and Hopkins, K. D. (2008)<br />Chapter 8: Linear and Multiple Regression: Inferences among correlation coefficients.<br />UNIT 8: SIMPLE REGRESSION ANALYSIS<br />Upon successful completion of this unit, students will understand the logic and underlying assumptions behind one predictor regression analysis, as well as the statistical procedures for estimating Y from X.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Discuss the purpose, logic and underlying assumptions of one predictor regression analysis.<br />Identify the computational formulas for one predictor regression analysis.<br />Conduct one predictor regression analysis for given data.<br />Know how to interpret results from one predictor regression analysis (regression coefficients; regression line; error of estimate; etcetera).<br />ASSIGNED READINGS:<br />Sánchez, J.A. (2004) <br />Chapter 9- Correlation and Simple lineal regression (Correlación y regression simple lineal).<br />UNIT 9 & 10: REGRESSION ANALYSIS: TWO PREDICTORS<br />Upon a successful completion of this unit, students will understand the logic and underlying assumptions behind two predictor's regression analysis, as well as of the statistical procedures involved.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />Discuss the purpose, logic and underlying assumptions behind two predictor's regression analysis.<br />Identify the computational formulas for two predictor regression analysis.<br />Conduct two predictors regression analysis for given data.<br />Know how to interpret results from two predictors of regression analysis (b, B <br /> coefficients, partial and multiple correlation: etcetera).<br />ASSIGNED READINGS:<br />Glass, G.V. and Hopkins, K. D. (2008)<br />Chapter 8: Linear and Multiple Regression: Inferences among correlation coefficients.<br />UNIT 11: CORRELATION ANALYSIS: MORE THAN TWO PREDICTORS<br />Upon successful completion of this unit, students will understand the multiple regression analysis with three or more predictors; logic and underlying assumptions of stepwise regression analysis; statistical procedures for tests of significance for regression coefficients; etc.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students will be able to:<br />I. Discuss the purpose, logic and underlying assumptions behind multiple <br /> regression analysis with three or more predictors.<br />Discuss the stepwise regression procedure for multiple regression analysis.<br />Conduct multiple regression analysis with three predictors.<br />Conduct tests of significance for regression coefficients.<br />Know how to interpret results from three or more predictors regression analysis (b, B, error of estimate, partial and multiple correlation, explained variance; etcetera)<br />ASSIGNED READINGS:<br />Glass, G.V. and Hopkins, K. D. (2008)<br />Chapter 8: Linear and Multiple Regression: Inferences among correlation coefficients.<br />UNIT 12: CORRELATION AND COVARIANCE ANALYSIS<br />Upon successful completion of this unit, students will understand correlation and covariance analysis in behavioral research; purpose, logic and underlying assumptions.<br />LEARNING OBJECTIVES:<br />Upon successful completion of this unit students should be able to:<br />Discuss the purpose, logic and underlying assumptions of covariance <br /> analysis.<br />Identify the computational formulas for covariance analysis.<br />Conduct covariance analysis for give data.<br />Know how to interpret results from covariance analysis. Assigned Readings:<br />ASSIGNED READINGS:<br />Glass, G.V. and Hopkins, K. D. (2008)<br />Chapter 20: An Introduction to the Analysis of Covariance <br />REFERENCES<br />Champion, D. J. (1981). Basic statistics for social research (2nd ed.) New York: McMillan.<br />Edwards, L.A. (1984). An introduction to liner regression and correlation (2nd ed.). New York: Freeman.<br />Fraenkel, J.R. & WalIen, N.E. (2003). How to design and evaluate research in education (5th ed.). New York: McGraw Hill.<br />GIass, G. V. & Hopkins, K. D. (2008). Statistical methods in education and psychology (3rd ed.). Englewood-Cliffs, NJ: Prentice-Hall. <br />Gravetter, F.J., & Wallnau, L.B. (2008). Statistics for the behavioral sciences (8th ed.). Belmont, CA: Cengage Learning. <br />Hernández-Sampieri, R., Fernández-Collado, C. & Baptista-Lucio, P. (2006). Metodología de la investigación (4th Ed.). México: McGraw Hill.<br />Kerlinger, F.N. & Lee, H. B. (2002). Investigación del comportamiento: Métodos de investigación en ciencias sociales. (Pineda, L.E., Mora, I., Diez, C.B. & Vadillo, G. Trads.) México: McGraw Hill. (Trabajo original publicado en 1986).<br />Mertens, D.M. (1998). Research methods in education and psychology: Integrating diversity with quantitative & qualitative approaches. Thousand Oaks, CA: Sage Publications, Inc. <br />Sánchez-Viera, J. (2004). Fundamentos del razonamiento estadístico (3rd ed.) República Dominicana: Universidad Carlos Albizu. <br />Revised by: Juan A. Nogueras, Ph.D. (August, 2008)<br />Sean K. Sayers Montalvo, Ph.D. (March, 2010)<br />
Be the first to comment