CHAPTER THREE
3.6 Analysis of data
68 reduce the occurrence of bias and data skewing that such an act can create. On the flip side of the coin, the ease in exiting the survey before completion was a disadvantage of question pro as an online survey tool since this made the response rate very low. The customised link for this survey was accessible at http://PerceptionsGlassCeiling.questionpro.com.
To help respondents understand what the study was about, the study title and the aim of the study was explained on the consent page of the survey. Once the respondents agreed at their own discretion to participate in the survey respondents needed to click the „I agree‟ icon on the consent page to continue with the survey. If respondents did not want to participate in the survey, the option to exit the survey was provided. To increase the response rates, prior notice was given to respondents requesting their participation and reminder emails were sent to those who did not respond.
69 Once the data was collected, it was cleaned to eliminate outliers which are invalid data sets capable of skewing the results of the research (Lind et al., 2008). The cleaned data was then analysed using SPSS version 15 and presented as discussed below.
3.6.1 Data presentation
Presentation of data was done using descriptive and inferential statistics.
3.6.1.1 Descriptive statistics
Frequency tables and pie charts were used to present the demographic details of the respondents. The Likert scale responses from the perceptions section were also presented in the form of bar charts and frequency tables for ease in interpreting the sample responses.
Cross tabulations were done to find out how the demographic variables varied between themselves and other questionnaire variables to generate in depth information from the data and answer the research questions.
3.6.1.2 Inferential statistics
Pearson product correlations (r), cross tabulations and ANOVA were done in order to provide in depth information on the study sample and how their perceptions differed after which conclusions were drawn.
3.6.1.2.1 Cross tabulations
Cross tabulations are used when the researcher intends to gain in depth information about the sample and how the variables differ in this respect by using percentages to delineate the extent of the difference. (Pallant, 2007; Sekaran and Bougie, 2010) For example determining the percentage of respondents who agree/disagree on the one hand, and disagree/agree on the other hand with a variable under investigation.
3.6.1.2.2 Pearson product correlation coefficient (r)
Associations/relationships between variables were analysed using Pearson product correlation coefficients. The strength of the association between two variables may be positive or negative and is represented by the correlation coefficient (r) (Leech et al., 2008). A positive correlation means that an increase in one variable results to a corresponding increase in
70 another variable and vice versa. Such variables are said to be directly proportional. A negative correlation on the other hand results when an increase in one variable leads to a corresponding decrease in another variable meaning that the variables are inversely proportional to each other (Pallant, 2007).
The coefficient (r) may take any value between 1 (perfect positive correlation) to -1 (perfect negative correlation). An r value of 0 means that there exists no correlation between the two variables being compared (Pallant, 2007). According to Pallant (2007), guidelines to interpreting the r value are shown in Table 3.3.
Table 3.3 Guidelines to interpreting the correlation coefficient (r) Coefficient (r) value r interpretation
0 No association/correlation 0.1 to 0.29 + small/weak correlation -0.1 to 0.29 - small/weak correlation
0.3 to 0.49 + medium/moderate correlation -0.3 to 0.49 - medium/moderate correlation
0.5 to 1.0 + large/strong correlation -0.5 to 1.0 - large/strong correlation
Modified from: Pallant, J. 2007. SPSS Survival Manual: a step by step guide to data analysis using SPSS version 15. 3rd Ed. New York. Mc Graw Hill. Pp 132.
3.6.1.2.3 Regression analysis
The most common types of regression analyses that can be performed are simple and multiple regression analyses. A simple linear regression is done when one independent variable (x) causes variability in the dependent variable (y) (Lind et al., 2008). Multiple regressions are done when more than one independent variable (x) causes the variability in the dependent variable (y) (Sekaran and Bougie, 2010). According to Sekaran and Bougie (2010), regression is done when the researcher believes or postulates that that one independent variable affects a dependent variable. If a relationship exists, a regression equation termed the least squares function can be determined to express the strength of the linearity of the
71 variables (Lind et al., 2008). The goodness of fit of the least squares function is determined by obtaining the coefficient of determination (r²) which represents the extent to which the variability in the dependent variable is attributed to the independent variable (Lind et al., 2008). The closer r² is to 1 the greater the variability in the dependent variable (x) that is attributable to the independent variable (y) (Sekaran and Bougie, 2010). Simple linear regression analysis may be used in this study to determine the extent of the variation in reluctance to relocate or work experience (independent variables) that may be attributed to management level (dependent variable).
3.6.1.2.4 Analysis of variance (ANOVA)
According to Pallant (2007), the analysis of variance (ANOVA) compares the variance between different groups with the variability within each of the groups using an F-ratio. The larger the F-ratio, the more the variability between the groups being investigated that is attributable to the independent variable exists as compared to that within each of the groups referred to as the error term. For example in this study, ANOVA may be used to compare the perceptions that the respondents in first, middle or senior management have regarding barriers they consider as contributing to the glass ceiling.
Objective 6 which had an open ended question was analysed using the procedure from www.intelligentmeasurement.wordpress.com/2007/12/18/...http://