# BMAC8030 - Applied Statistics for Business and Social Science Research

Lessons are not given in the academic year 2023/2024

### Course content

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 business research and other social sciences 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.

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 economics and other fields within social science. Second, the course will focus on applied statistics, thus enabling the candidates to produce a research paper within the framework of the course.

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 and also SEM (note that the topics can vary from time to time).

This course will focus on statistical methods relevant for PhD candidates in the social sciences. 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.

### Learning outcome

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-crosssection 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.

### Learning methods and activities

The course consists of two separate workshops, which will comprise a mixture of formal presentations and lab work using both example data and the students' own data. 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. We will also introduce the students to literature that will aid them in their further work on their statistical models.

### Further on evaluation

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 bring their own laptops and have the statistical program Stata installed on their computers. There is a requirement of at least 80 % attendance.

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 about 15 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.

The course is reserved for PhD students. Contact us at this email address: kontakt@hhs.ntnu.no if you want to take the course.

### Required previous knowledge

The students must be registered on a PhD programme.

TBA

### Credit reductions

Course code Reduction From To
BMAC6030 7.5 AUTUMN 2018
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

No

Language of instruction: English

Location: Trondheim

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