As indicated above, Likert scale was used for the purpose of the primary data collection meaning that the data analysis conforms to the method of data analysis obtainable in this context. Though this is a survey research, it is not designed to delve into the core area of statistics. It shall use tables, column charts, bar charts and simple percentage to analyse the data collected for the purpose of interpretation. Below is brief explanation on the data preparation, analysis and presentation involved in this study.
The process of preparing data before they are analysed went beyond initial stretch of imagination. In course of trying to establish how best the data can be prepared before they are taken for analysis, several findings came to the fore. First is the need to categorise the data. Second, data cleaning. Third, is to assign code to the data before they are ready for analysis. And fourth is capturing the data into an SPSS file for computer generated analysis. Another important aspect is the presentation of the data.
4.8.1 Data Categorisation
Gray (2014) observes that data categorisation is a crucial aspect of the research process attributing this relevance to the fact that the use of statistical test for data analysis is a function of the kind of data gathered. He opines that it is important to place the data into category at the initial stage before the data analysis process begins.He further distinguishes between what he called ‘categorical’ data and ‘quantifiable data’. According to him the categorical data are arranged into set or group or grade (p555). While those classified into a group or set are called ordinal data those placed into certain kind of grades are called nominal data because they cannot be numerically quantified. The quantifiable data, as Gray argues, are more exact because they can be numerically computed. Gray concludes that the quantifiable data can further be classified into two namely interval and ratio. This study evolved the categorisation approach indicated above since it targets ordinal data. Gray (op cit) points out that the ordinal data uses ordinal scale for questions that tend to determine the quality of a phenomenon ranging from ‘strongly agree’ ‘agree’ ‘to disagree’(p556).
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4.8.2 Data cleaning
Data cleaning was carried out in this study. This is a process of making data analysis more correctly (Gray, 2014). Gray (2014) believes that it is only ‘clean data’ that can make data entry into the computer more correctly. He suggests that to achieve correct entry of data into the computer, data can be entered by different people separately, though which he considered to be cost intensive. He however recommends use of ‘frequency analysis on a column of data’ (p558). The implication is that any number that is falsely entered can easily be detected. His last recommendation is that research subjects should be closely monitored in the process of entering data into the research instrument to ensure they go through the question thoroughly. The idea is to avoid the incident of wrong data entry. This study uses frequency analysis for entry data collected from the fieldwork. 4.8.3 Data coding
Data coding is the process of assigning number to data (Frankfort-Nachmias and Nachmias, 2002). The essence of assigning number is to be able to identify such data hence Gray (2014) calls it ‘Identification number’ ID. Frankfort-Nachmias and Nachmias insist that the coding should be uniform in the analysis of a set of data that are same. They suggest that information on the implication of the code should be written in a book called codebook35. Pallant (2010) believes that the codebook fulfills two major objectives namely first, to describe and label all the variables that are strategic to the study, and second, to assign number to every response in the questionnaire. Describing and labeling of variables which is also known as naming follow certain rules. Gray suggests that in naming all variable must be unique, begin with a letter instead of number, should consists of symbols nor command related words, no blank spaces, not ended with full stop and should not contain more than 64 characters. Gray argues that when the coding is successfully accomplished, it becomes easy to collate the data. He cautions against taking the code for raw data. Certain rules are believed to be helpful in the coding process. Frankfort-Nachmias and Nachmias spell out some rules. They maintain that for coding to be meaningful, the number assigned should be easily known without much conscious effort. This implies that low score should be assigned to low variable while high score or numbers are assigned to high variable. That is numbering should be sequential starting with 0 or 1 as the case may be and progress to higher numbers may be 10 or more. While a low variable is assigned to 1, high variable is assigned to 10 especially in measuring
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the degree of variation between negative and positive in terms of using the Likert scale of strongly disagree as 1 and strongly agree assigned 10 depending on the length of the continuum. Frankfort- Nachmias and Nachmias conclude that coding rule should therefore relate coding to the research theory, it should be mutually exclusive, every category should be adequately complete and should be to detail in the sense of anchoring on the research question. In this study the data were coded before they were analysed.
4.8.4 Data analysis using SPSS
Several methods of data analysis are available for data analysis. However, since this study uses survey approach to the collection of data, it is only appropriate for it to rely on method of data analysis that is suitable to the nature of this research. Gray (2014) maintains that frequency distribution is the most appropriate and popular approach to analysis of survey data. He further admits that the frequency36 distribution is typical of Likert scale. Frankfort-Nachmias and Nachmias (2002) agree that frequency distribution should be designed for data analysis once data coding is done. They add that frequency distribution alone does not offer meaningful interpretation of data. But beyond frequency distribution design, percentage distribution should be created. According to Frankfort-Nachmias and Nachmias, percentage distribution assists for drawing comparison between different variables. They further stress that to draw comparison, frequencies can be converted to percentages or proportion.37 Likert scale provides the inspiration for the framing of the questionnaire suggesting why the data analysis is descriptive which involves presentation of data graphically.
Statistical package for social science, SPSS provides the interface for entry and analysis of data collected. Gray (2010) maintains that before variables are entered into SPSS, it should be evident that they are harmonious. To be harmonious implies that the variables are described in line with the data they are to be consisted of. To achieve this, Gray points out that variables can be classified into numeric, string and date.
Numeric variables according to Gray are number related. This suggests that every variable that has to do with numeracy when entered into SPSS, spontaneously collect at one point. He argues that numeric variable includes Binary variables which represent two types of variables such as ‘female
36 Frequency in this context means the number of occurrence in a particular group or unit.
37 Frankfort-Nachmias and Nachmias (2002) believe percentage or proportion assist in displaying the comparative thickness of each category in distribution.
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or male,’ ‘yes or no’(p544). He insists that to calculate them, numbers are assigned to them in order to ascertain the actual part of the whole each occupies.
String is the value of a variable that is not number related for the purpose of calculation. Gray (2010) argues that though string can be used for numbers such as telephone numbers or post codes, they cannot be calculated and result will be of no significance that is meaningless if attempt is made to calculate them. Date is a variable that consists of time value. According to Gray, it includes calendar which appears in ‘date ‘or clock which appears in ‘time format’
Once data have been given labels and value, it will be ready for entry into the SPSS in the computer. This study is subject to the data preparation, labelling and assignment of value as indicated above in course of analysing data collected from the field.
4.8.5 Data Presentation
Two types of statistical presentations of data appear to be available in the social sciences. Gray (2014) agrees that descriptive statistics and inferential statistics exist but they differ somewhat. According to him, while the descriptive statistics describe the data, inferential statistics go further to infer from the description given. He adds that descriptive statistics use graphs to describe but he cautions that the type of graph used is naturally a function of the type of data presented. This implies that use of graphs to describe in descriptive statistics will depend on whether the data is nominal, ordinal, interval or ration.
Black (1999) offers a picture of what is appropriate application in graphical representation of data in descriptive statistics. His analysis suggests that bar chart is more suitable for data related to nominal and ordinal. While he believes that pie chart is suitable for only nominal, histogram is more suitable for both interval and ratio. He concludes by pointing out that frequency distribution is more suitable for interval and ratio. For the purpose of data analysis in this study, the researcher used frequency distribution, percentage distribution, bar chart, column chart, line chart and other relevant graphs to present data analysed.