BMAC8030 Applied Statistics for Management Accounting and Business Research

 

Time:   Online course, videos and Zoom-meetings, 2 separate workshops Workshop #1: February 2–4, 2021

Workshop #2: March 16–18, 2021

Zoom-meetings 13:00–14:00 each day except last day

Level: PhD

ECTS credits: 7.5

Venue:   NTNU Business School

Teaching Language:   English

Pre-requisites:   Master's level course in statistics/econometrics/quantitative methods

Registration by email to:tor.g.jakobsen@ntnu.no (before December 1)

Students are also required to register at https://www.ntnu.edu/studies/researchcourses before February 1 2021.

 

Overview The objective of this course is to give the students an introduction to advanced econometric methods using Stata. In this course, we will focus on the practical application of the methods used in management accounting and business research using data relevant for the participants. Being able to write statistical research papers will help PhD-candidates in publishing their work in good academic journals. Statistics can also be applied in order to confirm results found in case studies.

 

 

Overview, cont. 

We will go through the basics of regression with a focus on diagnostics, before moving on to different ways of modeling when the OLS regression assumptions are breached. This course encompasses both cross-sectional, nested data, and time-series analysis. It differs from other courses in two main ways. First, the topics and examples used will be relevant for students within management accounting and business research. Second, the course will focus on applied statistics, thus enabling the candidates to produce a research paper within the framework of the

Students will get a repetition about the basics of regression analysis, before moving on to situations where we cannot apply ordinary least squares regression to different types of data. Then will go through different types of dependent variables, including dichotomous variables (logistic regression), variables with more than two categories (multinomial regression), and ordinal variables (ordered logistic regression). The next topic are data that breaches the assumption of independent units, that is, the data are nested. This includes hierarchical or multilevel data and panel data. The course will conclude with structural equation models.
This course will focus on statistical methods relevant for PhD-candidates in management accounting and business research. Research questions within these fields often require the analysis of data where the linear regression assumptions are breached, and where ways of handling nested- and time data are required.

 

Topics
-Linear regression and regression diagnostics
-Logit models, including ordered logit and multinomial regression
-Multilevel analysis
-Panel data analysis
-Structural equation modeling
The course aims to enable the students to:
1. Use Stata to perform statistical analyses relevant to their dissertation;
2. Be aware of the limitation of OLS regression;
3. Model data where OLS regression assumptions are breached;
4. How to deal with different types of dependent variables;
5. Write an independent statistical research paper;
6. Have knowledge on how to handle nested and time-series data;
7. Be able to write quantitative papers within the fields of management accounting or business research.

 

Learning outcomes
On completion of this module, students will be able to:
Knowledge:
 Demonstrate a knowledge of regression analysis and its limitations;
 To decide which type of regression analysis is appropriate for different types of dependent variables and data structures;
 To understand and use different types of statistical models, including different variants of logistic regression, multilevel modeling, panel data analysis, and time-series-cross-section data.
 Understand, interpret, and present statistical results;
Skills:
 Will be familiar with and able to use the statistical software Stata;
 Prepare data for use;
 Perform an independent statistical analysis;
 Write statistical method sections;
 Write quantitative research papers of good quality;

 

Activity and learning methods
The course consists of 2 separate workshops, which will comprise a mixture of formal presentations and lab work using example data. This will be presented through video lectures and computer labs. The focus of the workshops is to enable the students to be able to use what they learn in the course on their own data relevant for their respective doctoral theses. There will be an online meeting each day where the students can ask questions to the lecturer. We will also introduce the students to literature that will aid them in their further work on their statistical models.


Coursework requirements

The students will be required to hand in a 2–3 page research plan, consisting of their research plan (for the course paper), including research question/hypotheses, data to be used, and relevant variables. This should be handed in before the second workshop. It is expected (but not mandatory) that the students brings relevant data for the second workshop (if ready, they can also bring it to the first workshop). The students should use their own laptops and have the statistical program Stata installed on their computers. There is a requirement of at least 80 % attendance in the Zoom-meetings. The students are required to go through the relevant course material (PowerPoints, computer lectures, lecture videos) before each Zoom-meeting.

 

Grading
Pass/Fail: the grading for this course will be judged by faculty members on a pass/fail basis. Students will be assessed on their research paper. This paper should be delivered in the form of a theoretically based statistical analysis and have the form of an academic research paper (with extra weight given to the statistical part of the paper). Length should be 15 about pages. The students are required to use one (or more) of the methods presented in the course. There will be no formal examination for this course.

 

Required readings
Mehmetoglu, Mehmet & Tor G. Jakobsen (2017) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage.
Menard, Scott (2002) Applied Logistic Regression Analysis, 2nd ed. Thousand Oakes, CA: Sage.

 

Required readings which will be distributed
Baum, Christopher B. (2006) An Introduction to Modern Econometrics using Stata. College Station, TX: Stata Press. Chapter 9. (28 p.).
Hamilton, Lawrence C. (1992) “Regression Criticism” in Regression with Graphics: A Second Course in Applied Statistics. Belmont, CA: Duxbury Press: 109–144 (36 p.).
Hilbe, Joseph M. (2009) Logistic Regression Models. Boca Raton: CRC Press. Chapter 10–11 (57 p.).
Petersen, Trond (2004) “Analyzing Panel Data: Fixed- and Random-Effect Models” in Melissa Hardy & Alan Bryman (eds.) Handbook of Data Analysis. Oxford: Oxford University Press: 331–345 (15 p.).

 

WORKSHOP 1: 
Regression analysis, diagnostics, logistic regression, and multilevel analysis 
Date:   February 2–4 (3 days), 2021 
Venue:   NTNU Business School 
Faculty:   Professor Tor Georg Jakobsen 

Topics: 

  • A guide to Stata (1/3 day) 
  • Research and statistics (1/3 day) 
  • Linear regression (1 day) 
  • Regression diagnostics (1/3 day) 
  • Logistic models (2/3 day) 
  • Ordered logit and multinomial regression (1/3 day) 

Readings 
OLS regression and diagnostics 
Hamilton, Lawrence C. (1992) “Regression Criticism” in Regression with Graphics: A Second Course in Applied Statistics. Belmont, CA: Duxbury Press: 109–144 (36 p.). 

Mehmetoglu, Mehmet & Tor G. Jakobsen (2016) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 1, 2, 3, 4, 5, 6, 7. 

Logistic modeling 
Hilbe, Joseph M. (2009) Logistic Regression Models. Boca Raton: CRC Press. Chapter 10–11 (57 p.). 

Mehmetoglu, Mehmet & Tor G. Jakobsen (2016) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 8. 

Menard, Scott (2002) Applied Logistic Regression Analysis, 2nd ed. Thousand Oakes, CA: Sage. (111 p.)
 

 

WORKSHOP 2:
Multilevel analysis, instrumental variables, and panel data
Date:   March 16–18, 2021 (3 days)
Venue:   Trondheim Business School
Faculty:   Professor Tor Georg Jakobsen & Mehmet Mehmetoglu
Topics:

  • Multilevel modeling (1 day)
  •  Panel data analysis and TSCS data (1 day)
  •  Dummy variables and interaction effects (1/2 day)
  • Structural equation modeling (1/2 day)

Readings

Panel data
Baum, Christopher B. (2006) An Introduction to Modern Econometrics using Stata. College Station, TX: Stata Press. Chapter 9. (28 p.).

Mehmetoglu, Mehmet & Tor G. Jakobsen (2017) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 10.

Petersen, Trond (2004) “Analyzing Panel Data: Fixed- and Random-Effect Models” in Melissa Hardy & Alan Bryman (eds.) Handbook of Data Analysis. Oxford: Oxford University Press: 331–345 (15 p.).


Multilevel analysis
Mehmetoglu, Mehmet & Tor G. Jakobsen (2017) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 9.


Dummy variables and interaction effects
Mehmetoglu, Mehmet & Tor G. Jakobsen (2017) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 5 & 6.


Structural equation modeling
Mehmetoglu, Mehmet & Tor G. Jakobsen (2017) Applied Statistics using Stata: A Guide for the Social Sciences. London: Sage: ch. 12.

 

Short Biographies of Faculty Members:


Tor Georg Jakobsen
Jakobsen is Professor of Political Science at NTNU Business School; formerly postdoctoral researcher at the Department of Sociology and Political Science at the Norwegian University of Science and Technology. His areas of interest include political behavior, peace studies, and quantitative methods. He has published peer reviewed articles in Norwegian and international journals using a wide range of statistical methods, and is also co-author of Applied Statistics using Stata (Sage, 2017).

 


Mehmet Mehmetoglu
Mehmetoglu is a professor of Research Methods in the Department of Psychology at NTNU. His research interests include consumer psychology, evolutionary psychology, and statistical methods. He has co/publications in about 30 different refereed international journals, among which, Scandinavian Journal of Psychology, Personality and Individual Differences, and Evolutionary Psychological Science.

Tags: Economics
Published Oct. 20, 2020 12:49 PM - Last modified Oct. 20, 2020 1:33 PM