HCA Method Seminars
HCA is launching a series of one-day method seminars targeted individuals working with corporate data. Compared to other method seminars, our starting point is in the kind of data companies have, the way the data are organized, and the goals for which the data need to be analyzed. Hence, the seminars will provide the participants with very applied for in-house analytics.
Seminars are independent of each other and can therefore be taken separately. Seminars will be taught by faculty from Copenhagen Business School as a mix between lecture-style classes and supervised workshops/exercises.
Below you will find an overview of seminars. If you are interested in participating in a seminar or your organization would like to host a seminar for employees with our faculty, please contact: firstname.lastname@example.org
Working with Organizational Survey Data
The objective of this seminar is to cover the main steps you will need to follow in order to analyze survey data. We will explore the techniques used in weighting sample surveys, including methods for adjusting for nonresponse and alternative techniques for imputing values for missing items. You will also learn how to apply standard descriptive statistics and regression analysis to survey data. Finally we will cover the basics of record linkage and statistical matching. The course will feature both frontal lectures and interactive exercises using Excel.
Outline (changes may occur):
8.30-9.00: Registration and breakfast
9.00-12.00: Analyzing Survey data
13.00-16.00: Analyzing Survey data + workshop/exercise
- Types of data
- Representativeness – testing, problems and possible solutions
- Useful descriptive statistics for survey data and graphical representation
a. Frequency tables and histograms
b. Median and range
c. Mean and standard deviation
- Establishing relationships between variables
b. Linear regression
c. Binary logistic regression (TBC)
d. Multinomial and Ordinal Logistic regression (TBC)
Afternoon: Analysis of real survey data (through a guided procedure) and reporting (in pairs) - 2 hours preparation, 1 hour presentations and debriefing
Instructor: Valentina Tartari
This seminar is intended for anyone working with data reporting and analytics, who wants to start using panel data in their work. The seminar gives an overview on panel data analysis methods. Panel data are collected over multiple time periods. Traditional regression analysis methods, like ordinary least squares, do not make use of the full information contained in the longitudinal data, and lead, thus, to biased estimates. The course will first introduce the typical panel data structure, show common problems related to panel data, and introduces common panel data regression models (fixed and random effect analyses). All methods will be applied using the statistical software Stata. Experience with statistical software is a great advantage but not required, as this will be introduced during the seminar.
Instructor: Wolfgang Sofka
In this course attendants will learn about foundations of statistics, in particular: descriptive statistics (i.e. summary statistics), hypothesis testing (T-Test, Proportional Test, differences in groups’ distributions, etc.), goodness of fit (simple linear regression). Attendants will apply statistical concepts to real data and touch typical data analyst challenges and ways out.
Instructor: Francesco Di Lorenzo
To achieve individual and organizational goals, decision-makers must access and mobilize knowledge and information across individuals, groups, and organizations. While human capital ("what" we know) certainly influences our potential to achieve goals, an equally if not more important variable is our social capital ("who" we know). In other words, the strategic management of social relationships is critical to business success. This seminar focuses on how social structures shape incentives and behaviors, presenting opportunities and constraints to individuals and organizations. The seminar includes an applied part where the participants will learn how to use the UCINET software to analyze social network data, and characterize individuals’ positioning in their networks.