Syllabus and SAP Advanced Statistic - January 2014rev

MB 5202- Advanced StatisticsJanuary 2014

PROGRAM PASCA SARJANA Sekolah Bisnis dan Manajemen

MB 5202:

Advanced Statistics

Santi Novani, PhDShimaditya Nuraeni, MSM, M.Eng

MASTER OF SCIENCE IN MANAGEMENT SCHOOL OF BUSINESS AND MANAGEMENT

INSTITUT TEKNOLOGI BANDUNG2014

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1. Facilitators' Profile

1. Santi Novani, PhD

Santi joined the School of Business and Management (SBM) - ITB as a full-time tutor in 2009 and as a full time lecturer after she received a PhD from Tokyo Institute of Technology, Japan in 2013. Her master engineering degree is from Bandung Institute of Technology with a background Industrial engineering and management. Her experience as a lecturer in private university since 2000, as a research assistant and full time tutor at decision making and strategic negotiation of research interest group since 2005.

2. Shimaditya Nuraeni, MSM, M.Eng

Shima joined the School of Business and Management (SBM) - ITB as a full-time tutor after she received a Master of Engineering at Tokyo University of Science and Master of Science and Management at School of Business and Management, ITB.

2. Learning Outcomes

At the end of this course you will be able to:1) Apply basic concepts and theories of multivariate statistics to business and

management situations.2) Make effective research decisions regarding appropriate multivariate statistical

techniques in the analysis of data. 3) Perform the analysis using statistical software to help students effectively apply,

interpret, and evaluate different advanced statistical techniques. 4) Summarize and communicate the information obtained

The course begins with a brief presentation of multivariate techniques, their place among other statistical analysis methods. Applied statistics is designed to help students effectively apply, interpret, and evaluate different advanced statistical techniques.

Students will gain hands on experience through weekly presentation and several project assignments that will span the semester. These assignments will require students to learn how to draw statistical and substantive conclusions from the

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results of statistical analyses. Students will prepare written summaries of results using accepted discipline-specific writing guidelines that are common formats for journals in business/management disciplines.

3. Course Content

The purpose of this course is to introduce various topics in statistics (multivariate, time series etc) analysis and to provide some practical experience in their applications and interpretation. The statistical software (SPSS, Lisrel etc) package will be used throughout the course. This course covers basic statistical data analysis with an emphasis on multivariate data analysis for business, marketing research, consumer behavior, finance and related disciplines. This course is NOT intended to be a mathematical development of statistical techniques rather than intended to be a programming course on using statistical packages. In contrast, the course emphasizes the design of a multivariate research project, the choice of a multivariate method, the testing of the fundamental assumptions underlying various multivariate methods, the validation of a multivariate analysis, the important issues involved in evaluating the quality of a multivariate data analysis and interpretation of the results.

Topics include: Factor Analysis, Regression Analysis, Discriminant Analysis, Analysis of Variance, Conjoint Analysis, Cluster Analysis, Multidimensional Scaling, Structural Equation Modeling and Non Parametric Test.

4. Course structure:

The course will be interactive discussion groups rather than lecture based. We anticipate that students will gain hands on experience through weekly presentation and several project assignments after preparatory reading has been completed. The course will be taught in English.

Business scholars have made use of a broad range of methods and analytical strategies to address questions of interest. Because each approach to answering research questions involves trade offs, researchers have often found it necessary to employ a combination of analytical techniques to reach any firm conclusions. A major goal of this course is to facilitate decision making within these constraints.

We will discuss a variety of advanced statistical techniques. Throughout the semester, you will gain hands on experience through projects and learn how to draw statistical and substantive conclusions from results of analyses. You will be asked to

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prepare written summaries of results using either Academy of Management style or style guidelines for other journals in your field.

5. Course Organization and Teaching Methods

This course is designed for face-to-face delivery. It is divided into lecture sessions and student presentations. This course emphasizes active learning. You are expected to participate actively in both the lecture and presentation sessions. To prepare for active participation, you are expected, when asked, to read assigned materials before class; bring materials to class; join in any debates or discussions; and join any work groups that are formed in class.

6. Evaluation of Your Work

Course requirements:

Course requirements include: 1. Group project assignments/presentation 2. Identification of articles using techniques discussed in class; 3. Midterm exam; 4. Final paper (presentation+ submit).

Your course grade will be determined by grades on the following assignments: 15% Group project assignments/article critique presentation10% Quiz15% Participation & attendance30% Midterm exam30% Final Paper+presentation

Further information on each of these assignments will be discussed in class as the term progresses.

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Group project assignment/in-class presentation on one statistic topic Each student will be assigned a technique to learn in detail through student presentation on a statistical technique through journal article critique/ data was get from secondary or primary, by using Statistical software , with one or two other students. The small-group presentation will worth 10 % of your grade. The chosen journal article shall be a published article in a respected scholarly journal. It must include an analysis employing one of the statistical techniques covered in this course. We recommend you to use our list of paper in this syllabus, if you use your own choice of paper, you must inform the lecturer at least a week before the presentation.

QuizzesThere are at least 2 quizzes given over the semester. The quizzes are designed to enhance and reward the mastery of the material from the textbook and class sessions. The lowest quiz score is dropped. Quizzes usually emphasize recent lectures but quizzes can contain past material as well. There are no make-up quizzes.

Final PaperA research paper will include the development and testing of hypotheses using advanced (multivariate etc) statistics, using a class data set and the Statistics software program. Specific details on this assignment will be distributed in class. Students should plan on handing in both output and a complete, written analysis. The final paper should examine research questions or hypotheses to your discipline from a set of data. You must use one or more of the statistics (multivariate etc) techniques that we have covered in class to conduct your analysis. Results must be written in style guidelines in your discipline. MSM/ Management students will be required to submit their final paper to the international journal or international conference in their academic discipline.

In order to ensure you are on track with the paper at the end of the semester, you will be required to hand in an outline of what you plan to do before mid test exam. The outline should address the following:1. What data set will you use? How did you obtain access to this data? (Please

ensure all approvals to use data have been obtained prior to handing in your outline) What is your sample size? What variables are included in this data set?

2. What are the hypotheses or research questions you plan to answer? (Have at least 3 hypotheses or questions).

3. What statistical techniques will you use to test each hypothesis/answer each question? (You will need to ensure you meet assumptions associated with each technique).

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If you do not already have datasets to use from your own research projects, you can collaborate with your lecturer or other researcher who has several datasets which could be used for this project. We would expect that you would continue to do research on the paper for publication if there are interesting findings.

7. Course Policies

In this course:

1. ITB and SBM policies on Academic Conduct will be enforced at all times.

2. Submissions of assignments may be done through the lecturers’ room OR via email, as informed by the lecturers. Be sure to give complete and correct citations for any work quoted.

3. Attendance. As graduate students, I expect that you all will attend class and be engaged in learning. However, if you miss class, you are responsible for class material and announcements made in class including changes to the syllabus.

4. Class Disruptions. Please come to class on time, turn off your cell phones and pagers before class, and refrain from other activities that disrupt class.

5. Due Dates. Projects, article examples and the final paper must be handed in by the beginning of class on the day they are due. Anything that is handed in late will receive a reduction of the possible points if handed in within a day.

6. Collaborative Work. You are encouraged to work together to conduct data analysis for projects and prepare for exams. However, each student must independently write up the results of the data analysis for projects and hand it in with printouts from analyses. Students should complete exams and the final paper without assistance from other students.

7. Make Ups. The Mid-term exam and the Final exam are mandatory. If you miss one of these exams, your grade will be deducted. Make-up exams will not be allowed except under conditions of documented severe illness or emergency.

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Course Materials

Books1. J.F. Hair, R.E. Anderson, R.L. Tatham and William Black (2005) Multivariate Data

Analysis. 6th Edition. Prentice-Hall: N.J. (ISBN: 0138948585).This is required reading. It will be impossible to follow the course without this text.

2. Aczel, Amir D. (1999) Complete Business Statistics. Boston: Irwin McGraw-Hill. (ISBN: 0072893028).

3. Campbell, S. (1999) Statistics You Can’t Trust: A Friendly Guide to Clear Thinking About Statistics in Everyday Life. Parker, CO: Think Twice Publishing.

4. Eldredge, D. L. (2002) A Microsoft® Excel Companion for Business Statistics (2nd ed.). Cincinnati, OH: South-Western College Publishing.

5. Moore, D. S. (2001) Statistics: Concepts and Controversies (5th ed.). New York: W. H. Freeman and Company.

6. Zikmund, W. G. (2003) Business Research Methods (7th ed.). Cincinnati, OH: South-Western College Publishing.

Recommended list of papers

1. Course Overview

Overview of Multivariate Statistics Hair et al., Chapters 1 Big Things Have Small Beginnings: An Assortment of 'Minor' Methodological

Misunderstandings. By: Cortina, Jose M. Journal of Management, 2002, Vol. 28 Issue 3, p339, 24p.

The Role of Sampling in Strategic Management Research on Performance: A Two- Study Analysis. By: Short, Jeremy C.; Ketchen, Jr., David J.; Palmer, Timothy B.. Journal of Management, 2002, Vol. 28 Issue 3, p363, 23p.

Research Methodology in Management: Current Practices, Trends, and Implications for Future Research. By: Scandura, Terri A.; Williams, Ethlyn A. Academy of Management Journal, Dec2000, Vol. 43 Issue 6, p1248-1264.

Entrepreneurship Research in AMJ: What Has Been Published, and What Might the Future Hold? By: Ireland, R. Duane; Reutzel, Christopher R.; Webb, Justin W. Academy of Management Journal, Aug2005, Vol. 48 Issue 4, p556-564.

2. Examine Data

Hair et al., Chapters 2:Data Cleaning and Multivariate Techniques Roth, P.L., & Switzer, F.S. 1995. A monte carlo analysis of missing data techniques

in a HRM setting. Journal of Management, 21: 1003-1023.Page 7 of 16


Roth, P. L., Switzer, F. S., III, & Switzer, D. 1999. Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques. Organizational Research Methods, 2: 211-232.

Assumsion: Normality, Heteroscedasticity, Linearity Outlier, Missing Data Level of Measurement: Nominal, Ordinal, Interval, Ratio

3. Dependence Technique: Multiple regression, interpretation, assumptions, diagnostics, and model testing

Hair, et al, Chapter 4 St. John, C. H. & Roth, P. L. 1999. The impact of cross-validation adjustments on

estimates of effect size in business policy and strategy research. Organizational Research Methods, 2: 157-174.

Multinational Companies and the Natural Environment: Determinants of Global Environmental Policy Standardization. By: Christmann, Petra. Academy of Management Journal, Oct2004, Vol. 47 Issue 5, p747-760.

Technical and strategic human resources management effectiveness as determinants of firm performance By: Huselid, Mark A.; Jackson, Susan E.; Schuler, Randall S.. Academy of Management Journal, Feb97, Vol. 40 Issue 1, p171, 18p.

An Application of Multiple Regression Analysis to the Greek Beer Market Author(s): K. E. Kioulafas Source: The Journal of the Operational Research Society, Vol. 36, No. 8 (Aug., 1985), pp. 689- 696

4. Dependence Technique: Discriminant Analysis

Hair, et al., Chapter 5 Is Dunning's Eclectic Framework Descriptive or Normative? By: Brouthers, Lance

Eliot; Brouthers, Keith D.; Werner, Steve. Journal of International Business Studies, 1999 4th Quarter, Vol. 30 Issue 4, p831- 844.

Australian and Japanese Value Stereotypes: A Two Country Study . By: Soutar, Geoffrey N.; Grainger, Richard; Hedges, Pamela. Journal of International Business Studies, 1999 1st Quarter, Vol. 30 Issue 1, p203, 14p.

Housing Market Segmentation and Housing Careers: A Discriminant Analysis of Brisbane”Simon Huston.

5. Dependence Technique: Regression: Qualitative, Truncated, and Categorical Dependent Variables: Logistic, Multinomial Logistic, & Tobit

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http://en.wikipedia.org/wiki/Heteroscedasticity


Hair, et al. Chapter 5 Knowledge Sharing in Organizations: Multiple Networks, Multiple Phases . By:

Hansen, Morten T.; Mors, Marie Louise; Løvås, Bjørn. Academy of Management Journal, Oct2005, Vol. 48 Issue 5, p776-793.

Competition Within and Between Networks: The Contingent Effect of Competitive Embeddedness on Alliance Formation. By: Gimeno, Javier. Academy of Management Journal, Dec2004, Vol. 47 Issue 6, p820-842.

The Effects of Discontinuous Change on Latent Errors in Organizations: The Moderating Role of Risk. By: Ramanujam, Rangaraj. Academy of Management Journal, Oct2003, Vol. 46 Issue 5, p608-617, 10p.

6. Dependence Technique: Linear Modeling of Multiple Outcome Variables: MANOVA & MANCOVA

Hair, et al, Chapter 6 Examining the Human Resource Architecture: The Relationships Among Human

Capital, Employment, and Human Resource Configurations. By: Lepak, David P.; Snell, Scott A.. Journal of Management, 2002, Vol. 28 Issue 4, p517-543, 27p.

Downsizing in Privatized Firms In Russia, Ukraine, and Belarus . By: Filatotchev, Igor; Buck, Trevor; Zhukov, Vladimir. Academy of Management Journal, Jun2000, Vol. 43 Issue 3, p286-305.

7. Dependence Technique: Conjoint Analysis

Hair, et al, Chapter 7 Credit card development strategies for the youth market: The use of conjoint

analysis, International Journal of Bank Marketing, ISSN: 0265-2323 Preferential segmentation of restaurant attributes through conjoint analysis ,

International Journal of Contemporary Hospitality Management, ISSN: 0959-6119 The Quantification of Decision Support Benefits within the Context of Value

Analysis Author(s): Arthur Money, David Tromp, Trevor Wegner Source: MIS Quarterly, Vol. 12, No. 2 (Jun., 1988), pp. 223-236 Published by: Management Information Systems Research Center, University of Minnesota

8. Dependence Technique: Structural Equations Modeling, Confirmatory Factor Analysis, Equivalence of Measures, and Structural Modeling

Hair, et al, Chapters 10, 11, & 12Page 9 of 16

http://www.emeraldinsight.com/journals.htm?articleid=854856&show=abstract

http://www.emeraldinsight.com/journals.htm?articleid=854856&show=abstract


The Effects of Centrifugal and Centripetal Forces on Product Development Speed And Quality: How Does Problem Solving Matter? By: Atuahene-Gima, Kwaku. Academy of Management Journal, Jun2003, Vol. 46 Issue 3, p359, 15p.

Safeguarding Investments in Asymmetric Interorganizational Relationships: Theory And Evidence. By: Subramani, Mani R.; Venkatraman, N.. Academy of Management Journal, Feb2003, Vol. 46 Issue 1, p46-62, 17p.

Harris, M. M. & Schaubrock, J. 1990. Confirmatory modeling in organization behavior/human resource management: Issues and applications. Journal of Management, 16: 337-360.

Riordan, C.M., & Vandenburg, R.J. 1994. A central question in cross cultural research: Do employees of different cultures interpret work related measures in an equivalent manner? Journal of Management, 20: 643-671.

Williams, L.J., Edwards, J.R., & Vandenburg, R.J. 2003. Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29: 903-936.

Bagozzi, P. P. & Yi, Y. 1988. On the evaluation of structural equation models. Academy of Marketing Science, 16: 74-94.

Cortina, J. M., Chen, G., & Dunlap, W. P. 2001. Testing interaction effects in LISREL: Examination and illustration of available procedures. Organizational Research Methods, 4: 324-360.

Hall, R. J., Snell, A. F., & Foust, M. S. 1999. Item parceling strategies in SEM: Investigating the subtle effects of unmodeled secondary constructs. Organizational Research Methods, 2: 233-256.

Applications of structural equation modeling in marketing and consumer research: A review, Hans Baumgartner , Christian Homburg.

Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling Author(s): Scott B. MacKenzie Source: The Journal of Consumer Research, Vol. 28, No. 1 (Jun., 2001), pp. 159-166

9. Interdependence Technique: Factor Analysis

Hair et al., Chapter 3 Conway J. M., & Huffcutt A.I. 2003. A review and evaluation of exploratory factor

analysis practices in organizational research. Organizational Research Methods, 6: 147-168.

Hurley, A. E., Scandura, T. A., Schriesheim, C. A., Brannick, M. T., Seers, A., Vandenberg, R. J., & Williams, L. J. 1997. Exploratory and confirmatory factor analysis: Guidelines, issues, and alterations. Journal of Organizational Behavior, 18: 667-683.

A Consumer Values Orientation for Materialism and Its Measurement: Scale Development and Validation Author(s): Marsha L. Richins and Scott Dawson Source: The Journal of Consumer Research, Vol. 19, No. 3 (Dec., 1992), pp. 303-316

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10. Interdependence Technique: Cluster Analysis

Hair, et al, Chapter 8 The application of cluster analysis in strategic management research: an analysis

nad critique, David J. Ketchen, Strategic management Journal Vol 17, 441-458. International Human Resource Strategy and Its Determinants: The Case of

Subsidiaries in Taiwan. By: Hannon, John M.; Ing-Chung Huang; Bih-Shiaw Jaw. Journal of International Business Studies, 1995, Vol. 26 Issue 3, p531-554.

Cluster Analysis in Marketing Research: Review and Suggestions for Application, Girish Punj and David W. Stewart, Journal of Marketing Research, Vol. 20, No. 2 (May, 1983), pp. 134-148.

Cluster Analysis in Test Market Selection Author(s): Paul E. Green, Ronald E. Frank, Patrick J. Robinson Source: Management Science, Vol. 13, No. 8, Series B, Managerial (Apr., 1967), pp. B387-B400.

11. Interdependence Technique: Multidimensional Scaling

A Typology of Deviant Workplace Behaviors: A Multidimensional Scaling Study Author(s): Sandra L. Robinson and Rebecca J. Bennett Source: The Academy of Management Journal, Vol. 38, No. 2 (Apr., 1995), pp. 555-572

12. Non Parametric Tests

Exports, international investment, and plant performance: evidence from a non- parametric test, Sourafel Girma, Holger Go¨rg, Eric Strobld.

Non-Parametric Tests of Consumer Behaviour , Hal R. Varian, The Review of Economic Studies, Vol. 50, No. 1 (Jan., 1983), pp. 99-110.

Application of Non-parametric Analysis Technique amongst Postgraduate Education Research: A Survey of South African Universities ; Anass BAYAGA and Liile Lerato LEKENA.

A Survey Report on Non-Parametric Hypothesis Testing Including Kruskal-Wallis ANOVA and Kolmogorov–Smirnov Goodness-Fit-Test, Vishwa Nath Maurya International Journal of Information Technology & Operations Management Vol. 1, No. 2, May 2013, PP: 29-40, ISSN: 2328 -8531.

Course Plan

This course is a dynamic process, subject to change. You are responsible for maintaining awareness of changes in class scheduling if you have missed class.

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Wk Day Activity Topic Materials Capaian Belajar Fac

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1 Wed, 22 January 2014

Lecturing & discussion

Introduction. Course Syllabus and Course policy

Explain what multivariate analysis is and when its application is appropriate.

Determine which multivariate technique is appropriate for a specific research problem.

SN


Overview of Multivariate Statistical Techniques

Hair et al. (Chapter 1)

Discussion from Student (Group)

Student discuss what technique they decide (project)

SN

2 Wed, 29 January 2014


Examine Data Hair et al. (Chapter 2)

• Test the data for the assumptions underlying most multivariate techniques.

• Determine the best method of data transformation given a specific problem.

SN

Practice by using SPSS

3 Wed, 5 February 2014


Dependence Technique: Multiple Regression


• Determine when regression analysis is the appropriate statistical tool in analyzing a problem.

• Interpret the results of regression.

SN

Student presentation & Discussion

Paper on Multiple Regression

An Application of Multiple Regression Analysis to the Greek Beer Market Author(s): K. E. Kioulafas Source

Practice by using SPSS on Multiple Regression

SN



Dependence Technique: Discriminant Analysis


Identify the major issues relating to types of variables used and sample size required in the application of discriminant analysis.

Interpret the results of discriminant analysis.

SN


Paper on Discriminant Analysis

Housing Market Segmentation and Housing Careers: A DiscriminantAnalysis of Brisbane”

SPSS Practice Discriminant Analysis SN


Guess Lecture Statistical Learning MS


SPSS practice Dependence Technique: Logistic Regression


• Understand the strengths and weaknesses of

SD

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logistic regression compared to discriminant analysis and multiple regressions.

• Interpret the results of a logistic regression analysis,

• Interpret interaction results when more than one independent variable is used in MANOVA.

SPSS practice Dependence Technique: MANOVA


SD

Quiz (review on multivariate techniques (multiple regression, discriminant, MANOVA, logistic regression) and Discussion on Students’ project

7 Wed, 5 March 2014


Dependence Technique: Conjoint Analysis


Know the guidelines for selecting the variables to be examined by conjoint analysis.

Explain the managerial uses of conjoint analysis.

SN


Paper on Conjoint Analysis

The Quantification of Decision Support Benefits within the Context of Value Analysis

SPSS Practice Conjoint Analysis SD8 Wed, 12

March 2014

Mid-term Exam

9 Wed, 19 March 2014


Dependence Technique: SEM

Hair et al. (Chapter 8 )

• Understand the distinguishing characteristics of SEM.

• Distinguish between variables and constructs.

• Understand SEM and how it can be thought of as a combination of familiar multivariate techniques.

SN


Structural Equation Modelling (SEM)

pportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling

Lisrel Practice SEM SD10 Wed, 26

March 2014

Guess Lecture

11 Wed, 2 April 2014


Interdependence Technique: Factor Analysis


• Differentiate factor analysis techniques from other multivariate techniques.

• Distinguish between exploratory and confirmatory uses of factor analytic techniques.

SN


Paper on Factor Analysis

A Consumer Values Orientation for Materialism and Its Measurement: Scale Development

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and Validation

Practice by using SPSS on Factor Analysis

SD

12 Wed, 9 April 2014(Libur)

Pengganti 11 April 2014


Interdependence Technique: Cluster Analysis

Hair et al. (Chapter 8 )

Define cluster analysis, its roles and its limitations.

Identify the research questions addressed by cluster analysis.

Understand the differences between hierarchical and nonhierarchical clustering techniques.

SN


Paper on Cluster Analysis

Cluster Analysis in Test Market Selection

Practice by using SPSS on Cluster Analysis

Cluster Analysis SD

13 Wed, 16 April 2014

Lecturing and discussion

Interdependence Technique: Multidimensional Scaling


• Define multidimensional scaling and describe how it is performed.

• Understand the differences between similarity data and preference data.

• Understand how to create a perceptual map.

SN


Paper on Multidimensional Scaling

A Typology of Deviant Workplace Behaviors: A Multidimensional Scaling Study

SPSS practice Multidimensional Scaling

SD

14 Wed, 23 April 2014

Lecturing and DiscussionQuiz (review on interdependence technique)

Non-parametric Tests • Differentiate the use of parametric and non parametric test

SN


Non-parametric Tests A Survey Report on Non-Parametric Hypothesis Testing Including Kruskal-Wallis ANOVA and Kolmogorov–Smirnov Goodness-Fit-Test

SPSS practice Non-parametric Tests SD

15 Wed, 30 April 2014

Presentation on Student Project for Final paper

SN

16 Wed, 7 May 2014

Final Paper (submit) and ext presentation if necessary

Final paper due date

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Documents

Syllabus and SAP Advanced Statistic - January 2014rev