dissertation in pdf-format - Aalto-yliopisto

14 T. Heimonen and M. Virtanen
we used and the results of our estimations. Finally the conclusions, implications and
limitations of the study are presented.
2 Research outline and research questions
In our previous papers we have used both regression analysis (RA) (Virtanen and
Heimonen, 2007) and logistic regression (Heimonen and Virtanen, 2007) when analysing
almost the same data as that discussed herein. Moreover, in Virtanen and Heimonen
(2007) we used correlation analysis to study the connection between growth and success.
The inspiration for the current paper was the fact that, when using this kind of data, one is
faced with problems associated with the research design and objectives, the meaning of
outliers, and the applicability of the research methods. Thus in the process of learning
about appropriate techniques the data should be refined to eliminate biases and to create a
proper platform for a robust analysis of firms exhibiting high performance. For example,
when studying growth and success simultaneously, the distribution will be highly
skewed. Thus one should take into account a priori probabilities when conducting logistic
regression or discriminant analysis (DA). In this paper, we focus on simultaneous growth
and success. A lot of the discussion relating to performance studies has been devoted to
definitions and measurement of different concepts, i.e., growth and success. Growth,
even if it includes several dimensions (Davidsson and Wiklund, 2000; Delmar, 1997;
Delmar et al., 2003), is easier to define than success since it is difficult to find an
objective measure of success. In this study success means financial success, which
includes profitability, solvency and liquidity.
The purpose of this paper is to answer the following questions:
1 What are the factors that explain simultaneous growth and success in different
statistical models?
2 What are the problems connected with different methods of analysing HG and HS
SMEs?
3 How does the selection of different analytical techniques affect the results?
4 How could we improve the robustness of the data and the methods used in the
analysis?
The subsidiary question that underlies the different methodological problems is: What
kind of impact does the ignorance of a priori probabilities have on the results of the
analysis?
In order to understand the research questions we would like to point out the dilemmas
associated with using different methods for the data analysis. When RA is used properly
we must assume that the data and error terms are normally distributed and that variances
are equal. We can manipulate these features of the data by scaling of the variables, using
logarithmic (or other) transformations and by the use of dichotomic variables. But when,
for example, we change a continuous variable to a dichotomic variable we lose
information and the interpretation of the coefficient is made more difficult.
The problem of explaining simultaneous fast growth and high success demands the
use of a dichotomic variable as a dependent variable. This calls for the use of either DA
or maximum likelihood methods when analysing the data. When we use logistic