Data analysis is the process of bringing order, structure and meaning to the mass of collected information (Mugenda & Mugenda, 2003). Again, they present two approaches to data analysis namely quantitative and qualitative. In this study the procedure for data analysis were guided by research questions and related issues in the literature reviewed, hence data were analyzed through both quantitative and
qualitative approaches. Types of data which were obtained from questionnaire through five Point Likert-scales and were measured as nominal, ordinal and interval depending on the nature of the research question and the data collected. Fife-Schaw (2012) suggested that nominal scale could be useful to measure categorical data such as sex, age, occupation and working experience. In this study, the nominal scale was used to measure the variable related to respondent’s experiences in the service. The purpose of asking these questions was to determine the background information of respondents in relation to decision making in managing students’ discipline in government secondary schools in Tanzania.
Boone and Boone as cited in Khan (2013) suggest the techniques for analyzing Likert scales data. According to them, Likert numbers assigned to Likert-type items express a "greater than" relationship; however, how much greater is not implied. Due to aforementioned conditions, Likert-type items fall into the ordinal measurement scale.
3.12.1 Quantitative Data Analysis
Descriptive statistics recommended for ordinal measurement scale items include a mode or median for central tendency and frequencies and percentages for variability Moore (2010) also presents analysis techniques for the Likert scale that if you have a series of individual questions that have Likert response options for your participants analyze them as Likert-type items by using modes, medians, frequencies and percentages. However, Moore suggested that the technique could be useful when created by calculating a composite score (sum or mean) from four or more type
Likert-type items combined together therefore, the composite score for the Likert scales should be analyzed at the interval measurement scale and descriptive statistics recommended included mean for central tendency and standard deviations for variability (Nachmias & Nachmias, 2003).
The means and standard deviations were aimed at showing the average and the degree to which a response of individual respondents on decision making on managing students’ discipline in government secondary schools vary from the mean. The higher means denoted acceptance perceptions and most frequently occurring on a particular observation and while lower meant to signify rejection perceptions occurring on observation on the effects of decisions made by School Board decisions on students’ discipline to the school climate. For the case of standard deviation, the smaller the standard deviation suggests that respondents were in more agreement with one another than would be the case with a large standard deviation (Kasomo, 2007). The quantitative approach was descriptive statistics whereby the simple frequencies, percentages, means and standard deviation were used as statistical analysis techniques to answer four research questions.
Therefore, regarding the aforementioned characteristics in this study, the data collected by means of questionnaire involved cleaning the data collected using questionnaires by checking for any incompleteness, inconsistencies and mistakes. In addition, data was processed for actual analysis by editing, coding and entering it into the computer using the Statistical Package for Social Science (SPSS Version 17) and excel spread sheet (Mugenda & Mugenda, 2003).The data was made ready at
univariate level for descriptive presentation using frequency tables, percentages, means and standard deviations. Finally, it was the task of interpretation in relation to questions. Generally, this was organized in such a way that it establishes the effects of school boards decision making on managing students discipline in government secondary schools in Lake Zone Victoria in Tanzania.
3.12.2 Qualitative Data Analysis
On the other hand, data obtained through interview guide and documents were analyzed and presented through Constant Comparative Method (CCM). Maykut and Morehouse (1994) define Constant Comparative method of analyzing qualitative data as a process which combines inductive category coding with a simultaneous comparison of all units of meaning obtained. The work of Boeije et.al (2002) outlined various strategies of constant comparison method such as comparison within a single interview; comparison between interviews within the same group that is persons who share the same experience; comparison of interviews from groups with different perspectives but involved with the subject under study; comparison of pairs of an interview with two partners belonging to a couple and finally comparisons of interviews with several couples.
Therefore, the comparison of interviews from groups with different perspectives but involved with the subject under study was employed in analyzing data from interviews and documents. This analysis technique was involved because of triangulating data sources aimed at completing the picture about effects of the School Board decision making on managing students discipline in government secondary
schools through enriched information. According to Kimchi, Polivka and Stevenson (1991), it involves a series of steps such as collecting data from the field; identifying key issues or activities in the data that would be a focus of categories; writing about the formed categories with an intention to describe all existing issues and constantly looking for new incidents; working with the data in an attempt to develop a model to capture social processes and relationships.
In this study, the researcher followed specific steps in analyzing the data obtained by means of interview guide and documents. These specific steps were: noted all field notes, transcribed verbatim all taped interviews, coded all types of data, unitized data into units which had specific meaning and themes, coded each theme, coded each piece of data to indicate source, indicated in a word or phrase each unit’s meaning, wrote each unit’s meaning under notation on data source, cut apart identified units of data, reread data in notebook, looked for recurring themes that emerged from interviews, listed on a sheet of paper recurring themes and combined themes to form conclusions. Following these techniques, the qualitative data were presented based on narrative and quote forms and incorporated within a particular presentation. These, along with quantitative data, formed the basis of discussion regarding the reviewed literature.