5 8 1 Data entry
CHAPTER 6 PRELIMINARY ANALYSIS OF DATA
6 . 1
INTRODUCTION
The purpose of this chapter is to report the preliminary analyses done on questionnaire data to prepare the data for further analysis, which will follow in Chapter Seven. The statistical packages and data analysis techniques employed in this study were introduced in Chapter Five. This chapter presents the results from the preliminary analysis, which includes the reliability and validity testing of the measurement instrument. The preliminary stages of data analysis started with the transformation of data into a format that is useful for proposition testing. Then, the nature of the data was investigated to ensure that the assumptions underlying parametric analysis were met. These tests are presented in the first part of this section. Thereafter, the reliability and validity of the measurement instrument are explained.
6 . 2
DATA TRANSFORMATION
The measurement instrument consisted of 1 23 items (see Appendix A). These items had to be transformed into a format that would be useful for data analysis. This section summarises the transformation of the data that was explained in Chapter Five and captured in Table 5 .2. Except for the transformation of demographical items, which is explained first, scales were transformed in one of two ways. Some scales were summed to provide one new variable: an index. Other scales were factorised to provide more than one new variable: factors.
Demographical variables were derived or calculated from the data obtained in Section A of the instrument. The age of the firm and length of service of the manager in years were recorded and analysed as ratio variables. Industry life cycle was recorded and analysed as a nominal variable. Firm size in full time equivalent (FTE) employees was calculated as follows: (number of full time employees) + [(number of part time employees * average number of weekly hours)/40]. Size (FTEs), age and length of
service were categorised for certain tests, such as ANOVA as follows: one to five, six to 1 0, 1 1 to 20, 2 1 to 30, 3 1 to 50, and 5 1 and more. These variables were named 'size
category', <age category' and 'length of service category' . Industry sector was recorded and analysed as a nominal variable. However, to facilitate interpretation these 2 1 ANZSIC categories were collapsed into four new categories, namely services, manufacturing, construction and property, and retail and wholesale for certain tests such as ANOV A. This variable was named ' industry categories' .
Four indices were calculated. The first performance index 'perception of performance'
was calculated by mUltiplying the ' importance' and 'satisfaction' columns of a list of performance variables and adding them (Sections F 1 and F2). The use of thi s method has been supported by many researchers (e.g. Covin & Slevin, 1 989; N aman & Slevin,
1 993). It was, however; decided to also run a number of statistical procedures with alternative ways of calculating this performance index, to ensure that this method o f calculating perception o f performance with not adversely affect the results o f this thesis. All the tests that include perception of performance were repeated by ( 1 ) only adding the satisfaction scores to provide an index, and (2) by weighting the satisfaction scores of each firm with the average of the importance scores across firms. The results of the tests ran with both these alternative indices were in each case more significant (in the same direction) than the tests ran with the index as employed in this thesis. Another two tests were employed as a final check. The correlations for the importance and satisfaction scores of each indicator showed that the scores for all the indicators, except sales and cash (one and three), were correlated at the 0.0 1 level of significance - indicating that firms generally rated indicators similar for importance and satisfaction. Furthermore, the Cronbach alpha for the products of the importance and satisfaction scores was 0.9 1 and when the weights were applied it was 0.82. Collectively these results show that the calculation of the index used in this thesis is more conservative than alternative methods. EO, organicity and the second performance index ('compared performance') were calculated by adding the items in Sections B, C2 and F 3 respectively. The E O and organicity indices were subsequently divided into two categories (high and low each) by using the means as the split point. Perception o f performance was divided into three categories (high, medium and low) by using the means. Firms that scored ten above or below the mean were categorised as 'medium performers' ( 1 30 to 1 50). Firms with less than 1 30 were termed ' low performers' and those with more that 1 50 'high performers' . All these calculations were explained i n detail in Chapter Five.
A number of factors were also calculated. The environment scale (Section C l ) was factorised into 'hostile' , 'dynamic' and 'stability' factors. The strategy scale (Section D) consists of three factors, namely 'differentiation', ' cost-leadership', and lack of breadth (' focus'). Because the last factor measures breadth but is used to represent the opposite (focus), most of the tests containing this factor had negative signs (e.g. Table E. 1 ). The last set of factors was derived from the strategy-making scale (Section E) and is named simplistic, adaptive, intrapreneurial and participative strategy-making modes. The extraction of factors is explained at a later stage in this chapter. Once the items that make up a specific factor were decided, they were added and divided by the number of items, for example, three items made up the adaptive mode of strategy-making. The three items were added and divided by three to give a number out of five (the number of choices on the Likert scale) which could be compared to other factors from the same scale. The scales for modes of strategy-making were also combined into sets of two, three and four modes to inspect the combined effect of these modes by, for instance, adding the scales for two modes and dividing by two.
These variables, indices and factors are used for statistical testing in the next chapter and are first inspected for normality, linearity and homoscedasticity.