Advanced Statistics

Learning outcomes

1.       Able to develop new knowledge, technology and or art in the field of science or professional practice through research to produce creative, original and tested work through advanced statistical courses;

2.       Able to solve design, technology, and or art problems in their field of expertise through inter, multidisciplinary and Transdisciplinary approaches through advanced statistical courses;

3.       Able to manage and lead research and development that produces creative, original and tested work that is beneficial for human benefit and able to get national and international recognition through advanced statistical courses;

4.       Able to manage, lead and develop research and development that is beneficial for the science of basic education and the benefit of mankind, and able to get national recognition Internationally.


Subject aims/Content

1.       Generating new knowledge analyzing multivariate, creativity of new technologies in deleting and transforming data so that the resulting factor analysis and regression.

2.       Providing solutions in efforts to solve problems of science, technology, and or art in their areas of expertise through inter, multidisciplinary, Transdisciplinary approaches on canonical correlation, conjoint analysis;

3.       Generate new knowledge from its professional practice through research on discriminable analysis, logistic regression, manova, cluster analysis, multidimension scaling, correspondent analysis, structural equation model (SEM) to produce creative, original, and tested work;

4.       Produce research and development that is beneficial for human benefit

5.       Gaining national and international recognition in his profession continuously in the field of basic education by conducting research as a reflective and evaluative action.


Teaching methods

1.       Lecture

2.       Discussion

3.       Question and answer

4.       Case study

5.       Paper

6.       Report

7.       Mid Term Exam

8.       Final Exam



·         Byrne, B.M. (1998) Structural Equation Modeling with LISREL, PRELIS and SIMPLIS: Basic Concepts, Applications, and Programming. New Jersey: Lawrence Erlbaum Associates Inc.

·         Hair, Joseph F.J., Black, W.C., Babin, B.J., Anderson, R.E. (2010) Multivariate Data Analysis, New Jersey: Pearson Education Inc.

·         Johnson, R.A., Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. New Jersey: Pearson Education Inc.

·         Montgomery, D.C. (1991) Design and Analysis of Experiments. New York: John Wiley & Sons.

·         Suyono (2015) Regression Analysis forResearch. Yogyakarta: Deepublish.

·         Kerlinger, Fred Nichols and Howard Bing Lee. (2000). Foundation of Behavioral Research. New York: Harcout College Publishers.

·         Creswell, John W. (2010). Qualitative Inquiry & Research Design: Qualitative, Quantitative and Mixed Approach. Yogjakarta: Student Library.

·         Donald Ary,Lucy Checer Jacobs, Chris Sorensen, Asghar Razavieh. (2006) Introduction to Research in Education. Belmont: Wadsworth.

·         Martyn Denscombe. (2010). The Good Research Guide For small-scale social research projects. New York: McGraw-Hill Education

·         Robert E. Stakes. (2010). Qualitative Research. New York: The Guilford Press.

·         Shaughnessy, John J., (2015). Research Methods in Psychology,New York: McGraw-Hill.

·         Putrawan, I Made (2019) Hypothesis Testing in Researches, 3rd Printing, Bandung: Alfabeta.

·         Kline, Rex B. (2016) Principles and Practice of Structural Equation Modeling, New York: Guilford Press. Journal of Sports Science and Medicine. ISSN : 13032968.

·'s Journal of the International Society of Sports Nutrition. ISSN : 15502783.


·         Sumargo, Bagus. Konsep Dasar Structural Equation Modeling. Program Studi Statistika Fak MIPA. Jakarta : UNJ.





· (Framework theory Quantitative research)

· ( Developing a Quantitative Research Plan: Choosing a Research Design)

· (The Research Process: Topic Selection)

· (Developing (Types of Variables: Dependent, Independent, Moderating, Mediating & Control Variable Experiment)

· (Quasi Experiment)

· (Developing a Quantitative Research Plan: Research Questions)

· (Two-Way ANOVA – Overview)

· (One-Way MANOVA – Overview)

· (Treatment Fidelity/ Integrity (The Quantitative Research Proposal Series)

· (Hypotheses (The Quantitative Research Proposal Series)

· (Number of Null Hypotheses (The Quantitative Research Proposal Series)

· ( Overview of Quantitative Research Methods)

· ( Basic Quantitative Research Overview)


· (Types of Variables: Dependent, Independent, Moderating, Mediating & Control Variable)

· ( What are Dependent and Independent Variables?)

· ( Research Questions Hypothesis and Variables)

· (Quasi Experiment)

· (Quasi Experiment) (Quasi Experiment)

· (internal and external validity) (2 factorial design)

· ( interaction) ( design two variables and interaction)