Bryman (2016) holds that data analysis is a body of scientific methods that help to describe facts, detect patterns, develop explanations and test the hypothesis. It is the analysis of the data that enables the understanding of the research outcomes (Makanyeza & Dzvuke, 2015). The following section presents how the empirical results were analysed.
5.9.1 Quantitative data analysis
The quantitative data underwent statistical analysis. Statistical analysis involves the process of computing certain indices or measures and searching for a pattern of relationships that exists among the groups of data (Kothari, 2009). The current study employed both descriptive and inferential statistics to analyse the collected data (Bhattacherjee, 2012). The following sections discuss the statistical methods adopted in this study.
5.9.1.1 Descriptive analysis
The data was analysed descriptively and presented in the form of figures, tables, graphs, pie charts, and percentages. The descriptive analysis involved the examination of frequencies, mean, and standard deviation (Saunders et al., 2016; Bhattacherjee, 2012). Frequency tables are presented in Chapter Six for items illustrating their counts, percentages, mean and standard deviation. Mean (M) scores, also called the arithmetic mean are the most common measure of central tendency (Zikmund et al., 2013).
The standard deviation (SD) was used to illustrate the average distance of the scores from the mean values (Zikmund et al., 2010). A high SD implied that study participants’ responses varied to a great extent on particular aspects. Therefore, descriptive statistics helped summarise, organise, and reduce large amounts of data (Macmillan & Schumacher, 2010).
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The study adopted used correlation as a statistical technique to analyse the connection between study variables. The appropriateness of this technique in this study lies in the fact that it allowed the researcher to describe the direction and degree of linear relationships (Iacobucci & Churchill, 2010; Graham, 2009). Furthermore, the correlation coefficient is a statistical measure of the strength of a monotonic association between paired data (Pallant, 2010).
Correlation coefficients range from -1 to +1, with zero indicating absolutely no connection between variables under study (Saunders et al., 2016). According to Zikmund et al. (2010), -1 and +1 represents a perfect correlation between two variables. Saunders et al. (2016) note that the further the coefficient is from zero, the stronger the connection between variables.
5.9.1.3 Regression analysis
The study used regression analysis as a statistical test. Regression analysis was also used to describe the strength and direction of the relationship between the predictor variable and the outcome variable (Graham, 2009). This technique was most suitable as the study was also aimed at investigating the relationship between one independent variable and many dependent variables (for instance one strategy and short term financial performance and long term financial performance) (Saunders et al., 2016).
According to Zikmund et al. (2010), when a researcher investigates the relationship between the independent variable (predictor variable) and the dependent variable (outcome variable), this is called simple regression analysis. However, when two or more predictor variables are examined to determine which ones are good predictors of the outcome variables, the analysis is called multiple regression. The researcher used both multiple and single regression.
5.9.1.4 Analysis of Variance (ANOVA)
According to Hair et al. (2014), ANOVA is used to compare two, and also more than two, different groups and conditions. The study used ANOVA to identify differences among the manufacturing SMEs with different approaches to strategy making. The meaning of the F-ratio and P-value were discussed to interpret the result of the ANOVA.
According to Sekeran and Bougie (2013), the F-ratio is the ratio between groups' estimates of variance (the differences between groups) and within groups' estimates of variance (general variability of respondents within the groups). The ratio gives a measure of how much variance can be attributed to the different treatments (for example, approaches to strategy formulation) versus the variance expected from random sampling. The probability of getting the F-ratio by chance alone is given by the p-value
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(Hair et al., 2014). However, the p-value needs to be less than 0.05 for the F-ratio to be regarded as significant (Sekeran & Bougie, 2016).
5.9.1.5 Hypothesis testing
Hypotheses were tested using multiple regression analysis in SPSS Version 23. Field (2012) defines multiple regression as an extension of linear regression in which a dependent variable is predicted by numerous variables that are in linearity. The multiple regression method has the power to remove insignificant and uninformative predictors to formulate a better and representative model. In this study, the 5% level of significance was taken as the level of decision criteria whereby the null hypothesis was rejected if the p-value was less than 0.05 and accepted if otherwise.
5.9.2 Analysis of data from in-depth interviews
Data collected in the last stage of the study, from the semi-structured interviews were analysed using inductive content analysis. Inductive analysis means that the patterns, and themes, came from the data;
they emerge out of data rather than being decided prior to data collection and analysis. (Patton, 1987 cited in Dana & Dana, 2005). That which is referred to as semiotic technique of analysis (common in sociology) is based on the perspective of the community studied (Dana & Dana, 2005). Satu and Kyngäs (2007) view content analysis as a method of analysing written, verbal, or visual communication messages. Krippendorff (2012) concurs and adds that content analysis is a qualitative data analysis procedure used to analyse data within specific contexts because of the meanings attributed by the participants. Content analysis is a widely used qualitative research technique (Hsieh & Shannon, 2005);
used to analyse data within specific contexts because of the meanings attributed by the participants (Krippendorff, 2012).
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5.9.3 Summary of data analysis for the study
Table 5.6 summarises how data analysis methods were applied to address the research objectives.
TABLE 5.6: RESEARCH OBJECTIVES AND METHODS OF DATA ANALYSIS Research objective Data sources Data analysis methods RO1. To determine the extent
to which manufacturing SMEs in Harare, Zimbabwe have adopted strategy formulation.
1. Questionnaire survey.
2. In-depth interviews.
Descriptive statistics
Content analysis
RO2. To identify the strategy formulation approaches employed by manufacturing SMEs in Harare.
1. Questionnaire survey.
2. In-depth interviews.
Descriptive statistics
Content analysis
Analysis of Variance
RO3. To determine how strategy formulation influences the financial performance of manufacturing SMEs in Harare.
1. Questionnaire survey.
2. In-depth interviews.
Descriptive statistics
Content analysis
Analysis of Variance
Correlation analysis
Regression analysis
Hypothesis testing RO4. To investigate the
relationship between the strategy formulation approaches and financial performance of manufacturing SMEs in Harare.
1. Questionnaire survey 2. In-depth interviews.
Descriptive statistics
Content analysis
Analysis of Variance
Correlation analysis
Regression analysis
Hypothesis testing RO5. To determine the effect of
business strategies on the financial performance of the manufacturing SMEs in Harare, Zimbabwe.
1. Questionnaire survey.
2. In-depth interviews.
Descriptive statistics
Content analysis
Analysis of Variance
Correlation analysis
Regression analysis
Hypothesis testing Source: Own compilation (2020)