Chapter 5 Research Methodology
5.7 The statistical analysis of the questionnaire data
parts and requests the respondent to provide ideas on how they would act in the government role to improve the rate of start-ups and the rate of growth for SMEs in South Africa. These are effectively selective interventions that they believe are necessary to improve the start-up and growth rate of SMEs.
Question 25 requests the respondent to provide ideas on how they would act in the government role to improve the overall economic environment in South Africa. These are effectively functional interventions that they believe are necessary to improve the overall economic situation in South Africa.
Finally in Part 2, Questions 26 through 34 measure the impact of functional interventions made by government. Each question reviews the impact on the business of different functional interventions.
While Questions 18 through 23 address the impact of selective interventions, Questions 26 through 34 measure the impact of functional interventions. Ideally this should allow us to measure which of the types of intervention had the largest impact.
in the context of the applicability of the questionnaire in achieving the research goals of the study. They will advise whether the expected output from each question will be within the parameters the researcher expects to receive. Their feedback will be used to modify the questionnaire where appropriate and necessary (Zikmund, 2003:302; Baker 2002:105).
However, the questionnaire must also be reliable, or expressed differently, it must supply consistent results (Meadows 2003:563).
This reliability would normally be tested by applying the same instrument at different times in order to assess whether or not the same results are achieved on both occasions. As this is not a longitudinal study, the instrument will only be used once. Therefore, the reliability of the instrument needs to be tested with Cronbach’s coefficient alpha (Churchill & Iacobucci 2002:416). In order for the instrument to be deemed reliable, an alpha score in excess of 0.7 must be achieved (Pallant 2003:85; Thirkell & Dau 1998:821).
On completion and acceptance of the face validity of the questionnaire, the data collection process can continue.
The purpose of analysing data using statistical techniques is to extract additional information from data, intelligently prepare reports, and to assess the validity of statistical findings. Statistics can be a useful tool in converting masses of data into meaningful information.
The idea is to convert common sense into mathematical formulae and principles so that they can be replicated in a similar situation elsewhere (Wegner, 2001:3).
Basic summary statistical analyses will be completed on the primary data, including cross tabulations, means and standard deviations,
where applicable. ANOVA testing will also be completed on relevant variables, if appropriate, to assess what the variances are. This will allow for the review of the primary data in a variety of different methodologies in order to see what data is statistically significant within the primary data.
There are a number of different criteria related to skills, education, knowledge, and a host of other criteria, all of which are known to have some contributory influence, to varying degrees, on the success of entrepreneurs. In order to assess whether a relationship exists, and the nature of that relationship, between the dependent variable and the multiple independent variables, a multiple regression test is executed. Then, in order to assess the strength of the relationship of these many factors on the success of the entrepreneurs, the primary data will be analysed using correlation testing (Wegner 2001:303).
A cross tabulation would also be completed, followed by multiple linear regression and multi-collinearity testing. These tests would be applied to data collected relating to SME success factors, as there are a number of these factors, and multi-collinearity could result in confusion when interpreting the results. Multi-collinearity is a problem associated with not being able to separate the effects of two or more independent variables on the dependent variable and occurs when executing multiple regression testing. If the independent variables are significantly alike or interdependent, it becomes impossible to determine which of the independent variables accounts for variance in the dependent variable. As a rule of thumb, the problem primarily occurs when a number of independent variables are more highly correlated with each other than they are with the dependent variable.
The Durbin-Watson test will be executed to ensure that autocorrelation has not occurred within the multiple regression
testing. Finally, a residual analysis of the errors in the multiple regression test will be executed. All these tests are intended to ensure that the results of the multiple regression tests are accurate, dependable and can be used to make predictions (Wisniewski 1997:351).
In the case of this study, hypothesis testing would appear to be another appropriate test to use. Hypotheses, the first step, have been formulated for this research study. A two-sided hypothesis test will be conducted on each hypothesis as the second step in this process.
The next step will determine what the area of acceptance is for the hypothesis being tested (Welman, Kruger 2001:215). Care will be taken to avoid Type I and Type II errors in testing. The significance level must be pre-decided in order to calculate the area of acceptance, and, for the purpose of this study, a significance level of 0.05 will be used (Welman, Kruger 2001:215). The Chi Square test will be done to measure the significant differences between observed distribution of data among categories, and the expected distribution of data among categories and the expected distribution based on the null hypothesis (Cooper & Schindler 2001:499). The Chi Square test will assess if the difference between statistically expected and actual scores is caused by chance, or if it is statistically significant (Welman, Kruger 2001:203; Cooper & Schindler 2001:486). Furthermore, the emphasis in Chi Square tests is on establishing whether a random variable follows patterns of outcomes in the population (Wegner, 2001:248).