Before data can be analysed, it must first be edited and coded. Data needs to be analysed in order to make it meaningful and understandable. This section discusses the editing, coding and analysis of data.
4.5.1 Editing and coding
Data collected from respondents is known as raw data. All inconsistencies and incompleteness need to be removed from the raw data. This is done by editing the raw data (Kumar, 2013: 255).
The editing and coding of the study began with the researcher’s identifying any incomplete or inaccurately answered questions in the questionnaire and discarding them from the sample. Question 2.11 and 3.7 were answered incorrectly by the respondents, therefore, only 13 of the 17 responses were analysed.
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The questionnaire was self-coded in that each answer on the questionnaire was assigned a number, except in instances where the respondent selected the “Other” option or was asked an open-ended question. In these sections, the researcher grouped the answers of the respondents into categories and assigned each category a number. Codes were created for section A, question 1.5, where the respondents were asked to state what “Other” highest educational qualification they had; for question 1.6, where the respondents were asked to specify the department in which they worked; and for question 1.7, where the respondents were asked to provide the researcher with their job title. In section C, codes were created for question 3.9, where respondents had the option of indicating that they had acquired a managerial skill from a place other than what was listed on the questionnaire, and for question 3.10, where the respondents were asked to list any additional managerial skills not listed in the questionnaire. In section D, codes were created for question 4.9, where respondents were asked to list the skills that they believe females possess that males do not; for question 4.11, where respondents were asked to identify the skills that they believed that female managers excel in, and lastly, for question 4.13, where respondents were required to identify the skills that they believed to be lacking in the construction and engineering industry of NMB.
4.5.2 Data analysis
Data can be analysed using either descriptive statistics or inferential statistics. Both methods of analysis are discussed in the section below.
a) Descriptive statistics
Descriptive statistics summarise or define the situation based on the data collected. They report the facts without making comparisons or investigating relationships between variables (Israel, 2008: xv).
b) Inferential statistics
Inferential statistics draw assumptions and interpretations about the population based on the data obtained from the sample. Inferential statistics allow the
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researcher to draw conclusions about the nature of the entire population based on the data collected from a small group of the population (Israel, 2008: xvi).
Once primary data was collected, it was analysed using appropriate statistical methods. Descriptive statistics were used to analyse the data to determine the skills profile of female managers in the construction and engineering industry of NMB, as reflected in the collected data.
4.6 SUMMARY
This chapter presented the research design and methodology used in the study. A social survey design was used because data was collected by questionnaire on more than one case at a single point in time to collect quantitative data with more than two variables. The quantitative research approach was used in order to solve the main problem of this study.
A non-probability sampling method was used for this study. The type of non- probability sampling that was used for this study is purposive sampling. This method seemed most appropriate as self-completion questionnaires were used to gather data from female managers, and the researcher investigated only the skills exhibited by them. The Master Builders’ Association was contacted in order to identify respondents in construction. Respondents in engineering were identified using snowball sampling.
A questionnaire was developed based on the management skills identified in the literature study conducted. This questionnaire included four sections that focused on the biographical data of the respondents; their perception of the generic skills needed to manage an organisation; the skills needed my construction and engineering managers; and females as managers and leaders. The types of questions used to collect data from respondents included category questions, open- ended questions and rating or scaled questions.
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Primary data was collected through e-mail distribution of a cover letter requesting the respondent to complete the attached questionnaire. The data was then loaded on an Excel data base for further use and analysis.
Once primary data was collected, it was analysed using appropriate statistical methods. Descriptive statistics were used to analyse the data to determine the skills’ profile of female managers in the construction and engineering industry of NMB, as reflected in the collected data.
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CHAPTER FIVE
RESEARCH RESULTS
5.1 INTRODUCTION
The previous chapter focused on the research design and methodology that were used in the study. This chapter presents the findings obtained from the statistical analysis of the questionnaire and the description of the sample.
This chapter answers the following sub-problem of the study: what is the skills profile of female managers in the construction and engineering sector of NMB?
This chapter consists of a number of tables and graphs formulated to display the results of the statistical analysis which comprised a discussion of the descriptive statistics used to analyse the data.