dissertation in pdf-format - Aalto-yliopisto

34 T. Heimonen and M. Virtanen
The second question was “what are the problems connected with different methods of
analysing factors that distinguish high growth and HS SMEs and how does the selection
of different analytical techniques affect the results?” In Appendix 3, we present
characteristics of the statistical methods used. These characteristics include modelling
criteria and assumptions, evaluation criteria for models and challenges in interpretation.
In all the models – as in all the statistical model analysis – the specification of the model
is the basic problem. Model specification affects the validity of the results and in that
sense it is of the utmost importance for the robustness of the analysis. Operationalisation
and measurement have an impact on reliability of the analysis. Selection of variables
(Appendix 3) has an important role from the perspective of both validity and reliability.
Skewed distributions may cause the most severe problems when using discriminant or
LRA if cross validation is not used. In DA the overall classification without cross
validation was 78% but correct classification of HGS was only 15%, whereas with cross
validation the rates for both were 64%. In LRA without cross validation the overall
classification rate was 80% but the correct classification was only 27% compared to 65%
with cross-validation. Thus we can conclude that ignorance of a priori probabilities will
reduce the performance of discriminant and logistic regression models and the results will
be adversely affected.
How could the robustness of the data, and methods used in the analysis be improved?
In this paper we have explained the refinement of data and compared the results of
different analyses. It can be concluded that, before using certain methods for analysing
data, one should carefully investigate the characteristics of the data and be aware of the
assumptions and aims of the selected methods of analysis. Moreover, it is appropriate to
exploit statistical procedures that take account of the skewed data characteristics.
This paper is inspired by our experiences of analysing growth, success and the
development of growing and successful businesses in a regional context. In the course of
the research process we have learned that there are more myths than realities in the world
of HG and HS businesses. These myths are driven by the unconscious application of
widely respected statistical methods to problems and contexts where their use is not
necessarily appropriate. The same or even better results could be achieved by simple
analysis of distributions and the application of univariate statistics. However, careful
analysis of data and its refinement may improve our understanding of the parallel growth
and success of businesses.
Low and McMillan’s (1988) advice to select a proper framework and define the
purpose of any analysis should be remembered. The purpose and framework of the study
affects the methodological opportunities and selection of analysis. We have to
acknowledge that the chosen framework in this study has not enhanced our aim to exploit
a variety of methods, but we have learned through experience – through trial and error.
This has been very valuable and we wish to share the experience to save our peers’ and
students’ time and expenses.
The limitation of this study was the use of cross-sectional analysis. Even if we have
longitudinal data the selection of methods and the time span of the data do not support the
use of longitudinal data. Siegel et al. (1993) stated that: “A longitudinal study that
follows companies through defined stages of growth and focuses on the characteristics
that set companies apart at different stages in their life cycle would greatly contribute to
our ability to predict winners and losers at their inception”. Bygrave (2006) demands
more field research, including descriptive and in-depth longitudinal case studies.