Survey of Statistical Methods

• Demonstrate skills in data analysis and evaluate which techniques/methods to apply in their own research
• Understand survey construction and administration process.
• Understand the measurement of the variables and key issues related to measurement theory.
• Relate classes of research problems to suitable techniques for data analysis.
• Assess the assumptions underlying each statistical technique.
• Evaluate the outcomes of different statistical techniques.
• Understand the practical dimensions of the statistical procedures and analyze these in relation to their own research.
• Explain data structures typically relevant for business management and administration research.
• Apply a statistical software (SPSS/ STATA).

This course provides an overview of multivariate data analysis methods for research in the area of business management and administration. The course will present the procedures for applying basic multivariate statistical techniques and provide hands-on experience with many of those techniques.

The primary objectives for this course are (1) to provide the student with the knowledge and confidence to properly identify appropriate data analysis tools for testing theories that are relevant to their field of research and (2) to equip the student with the background needed to develop deeper knowledge of techniques that are not explicitly covered in this class, or are not covered in sufficient detail for their particular research projects. The major topics and techniques introduced in this course are:

1. Introduction

1. Philosophy of Science and research method
2. The research process, research strategies
3. Hypothesis testing
4. Descriptive statistics, correlation, covariance
5. Independent sample t-test, paired sample t-test, Chi-square, ANOVA

1. Administering questionnaires

1. Types of questionnaire
2. Guidelines for survey design
3. Principle of measurement
4. International dimensions of surveys
5. Managerial implications and ethics

1. Measurement of variables: Operational definition

1. How variables are measures
2. Operational definitions
3. International dimensions
4. Goodness of measures (validity and reliability)

1. Factor analysis
   1. Exploratory factor analysis
   2. Confirmatory factor analysis

1. Regression

1. Bivariate and multivariate regression
2. Moderated and mediated regression
3. Hierarchical regression
4. Dummy variable regression
5. Logit and Probit Regression

1. Time series analysis
   1. Stationarity, autocorrelation, co-integration
   2. ARIMA
   3. Vector Autoregression (VAR)
2. Panel data models
   1. Fixed effect
   2. Random effect

Statistical packages
Statistical software SPSS/STATA will be used in the lab session

Exam requirements: Approved individual assignment.

Individual assignment. Students will be provided with an open ended business problem. Students will utilise the learnings from the lectures and labs and find solutions to the business problem. The assignment will involve following activities: collecting data, analysing data using various techniques, and preparing a research report.

The final exam is divided into two parts. Take home exam in groups of two counts 50% of the final grade and a portfolio assessment consisting of different quizzes and exercises in class counts 50% of the final grade. Furthermore, it is compulsory that students attend 75% of the classroom quizzes and exercises. 

The grading is Pass/Fail, where pass equals the letter grad B or better. For the group exam, the same grade will be given to the whole group.