Chapter Three Research Methodology
3.9 Data Analysis
According to Hair et.al. (2006) converting information from a questionnaire in order to be transferred into a data warehouse is referred to as the process of data preparation which is often a four step approach. The process begins with data validation, followed by editing and coding, and by data entry and data tabulations.
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3.9.1 Data Validation
This is the “process of determining, to the extent possible, whether a survey’s interviews or observations were conducted correctly and are free of fraud and bias” (Hair, et.al.;
2006:479). To ensure a certain degree of data validation, each respondent’s name, signature and the date was recorded. Whilst this information is not used for analysis, it does enable the validation process to be completed (Hair, et.al.; 2006).
3.9.2 Editing
“Editing is the process in which the interviews or survey instruments are checked for mistakes, errors and data omissions that may have occurred by either the interviewer or the respondent’s data collection activities” (Cooper & Schindler, 2006) Thus, data is edited to make certain of consistency across respondents and to locate omissions (Cooper &
Schindler, 1998). With regard to a survey using a questionnaire, editing helps to reduce errors, enhance legitability and clarify ambiguous responses. Hair et.al. (2006) asserted that the process of editing can assist the researcher in addressing several areas of concern such as:
• Asking the proper questions;
• Accurate recording of answers;
• Correct screening questions; and
• Accurate recording of close ended questions.
In this study, the data was edited by checking each questionnaire and ensuring it was correctly completed.
3.9.3 Coding
Coding refers to the activities of grouping and assigning values to various responses from a survey instrument (Cooper & Schindler, 2006: 493). According to Ghauri and Gronhaug (2002), coding can be viewed as some sort of classification which needs to be reliable and followed by rules. With regard to this study, the questionnaire was pre-coded which is essential for data analysis. Throughout the questionnaire, the numbers in parentheses indicate the data field where each coded response will be added on the data record. The researcher had also assigned precise numerical codes to each response throughout the questionnaire.
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3.9.4 Data Entry
Data entry is the procedure used to enter the data into the computer for subsequent data analysis (Hair, et.al.; 2006). Data entry involves tasks with the direct input of the coded data into specified software packages that allow the researcher to manipulate and transform raw data into useful data (Hair, et.al.; 2006). Data entry in this study was conducted by the researcher who ensured that the data entered was correct and error free.
3.10 Statistical Techniques
Various quantitative statistical techniques using the SPSS software were conducted in order to process data. The data was interpreted through frequency distribution and cross tabulations, as well as multiple regression, chi- square tests, factor analysis and cluster analysis. These outputs will be briefly explained:
• Frequency distributions- “Summary of how many times each possible raw response to a scale of question/setup was recorded by the total group of respondents” (Hair, Bush & Ortinau, 2006: 685).
• Cross tabulation- “Simultaneously treats two or more variables in the study, categorizing the number of respondents who have answered two or more questions consecutively” (Hair, et.al., 2006: 685).
• Multiple Regression- “A statistical technique which analyzes the linear relationships between a dependent variable and multiple independent variable by estimating coefficients for the equation for the straight line” (Hair, et.al., 2006: 689).
• Chi-Square statistic- “The standardized measurement of the observed difference squared between two frequency distributions that allows for the investigation of statistical significance in analysing frequency distribution data structures” (Hair, et.al.;
2006: 678).
• Factor Analysis- Is used to summarize the information contained in a large set of variables into a smaller number of subsets called factors” (Hair, et.al.; 2006: 591).
• Cluster analysis- “Is a multivariate interdependent technique whose primary objective is to classify objects into relatively homogeneous groups based on the set of variables considered” (Hair, et.al.; 2006: 599).
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3.11 Conclusion
This chapter outlined the research methodology used to conduct this research study. The problem statement revealed that the purpose of this study was to establish and examine the purchasing behaviour of consumers in the youth market with the view of allowing South African marketers to reap the benefits of utilizing social media as a marketing and communication tool. The sample of the study comprised of students from the University of Kwa-Zulu Natal who were selected using a non-probability sampling method, namely convenience sampling. The sample was made up of male and female respondents between the ages of 18-24 and represented the four major race groups in Durban. The sample size of the study was 150 respondents. Data was collected by making use of questionnaires which comprised primarily of closed ended questions and few open-ended questions. Quantitative data analysis was undertaken using SPSS software by interpreting the results of frequency distribution tests, cross tabulation, multiple regression analysis, factor analysis as well as cluster analysis. The results of the SPSS output is reported in the next chapter.
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