RESEARCH METHODOLOGY, DATA ANALYSIS AND INTERPRETATION
5.2 RESEARCH METHODOLOGY
5.2.2 Data gathering
This research gathers primary and secondary data.
5.2.2.1 Primary data
Data was collected among Protestant church leaders in the DR Congo, across ten out of eleven provinces. An authorisation letter was written by the church’s bishop for conducting the research among church leaders (see appendix).
5.2.2.2 Secondary data
The research makes use of some secondary data obtained from books, primary documents, and academic journals, as well as reports of national meetings held by the Protestant Church in the past years. Some pictures have been taken when conducting the research.
155 5.2.2.3 Target population
The target population consists of 95 church leaders of the 95 communities forming the Protestant Church in the DR Congo which, being an umbrella body, is usually called the ECC, or ‘Church of Christ of Congo’. The 95 communities are working independently from each other. The study focuses on the leaders of each community. In total, 83 church leaders were interviewed of whom 82 are males and 1 female. They hail from ten provinces out of a total of eleven, according to the past configuration of administrative provinces in the country. The eleventh province, Maniema province, has been omitted from the study, because accessing the region among security issues would have led to serious delays.
5.2.2.4 Sampling size technique
The participation criteria in this research consist in the simple random sampling to the population, aiming to include every church leader of all the communities. Each leader has the same probability of being selected at any stage of the selection process. This sampling technique guarantees an equal probability of selection. It ensures the researcher of a representative sample of the population (Turner, 2011:153). Considering the population size of 95 church leaders, the acceptable margin of error of .05 and an alpha value of .05 gets us to 77 as the minimum sample. Fifteen percent should be added to make up for respondents who might leave out some questions or choose not to answer them. Thus, a sample size of 88 is possible. Krejcie and Morgan (1970) in their table for determining sample sizes, indicate a sample of 76 as appropriate for a population of 95. Hence, the present researcher’s sample of 83 out of a population of 95 is representative.
5.2.2.5 Questionnaire
The questionnaire, among the most popular instruments in social research, is applied in the present study.
The questionnaire is made up of 23 questions divided into three sections. It is attached as an appendix to the research. The first section is concerned with demography, considers factors of possible influence on the study such as gender,
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experiences in the ministry, the location of communities, respondents’ positions in the church and in their communities. The second part deals with aspects of the investigation of the Protestant Church’s commitment to political issues. In the last part the feelings of participants when responding to the questionnaire are assessed. Each question is constructed in such a way that responses may be expected to help answering research questions and to clarify aspects of the study objectives. A professional statistician has analysed the questionnaire before it was distributed to the respondents.
Based on relevant discussions by scholars, the questionnaire uses five types of questions: a list of multiple choice, scale, ranking, complex grid or table, and open- ended questions (Blaxter, 2006: 181).
5.2.2.6 Ethical clearance
In accordance with academic rule, ethical clearance was sought and issued before conducting the field work.
5.2.2.7 Distributing the questionnaire
Participants received the questionnaire, prior to the date scheduled for interviews.
Data was recorded for analyses. In the case of those participants who were gathered in meetings in Kinshasa, for example of provincial and national committees, the questionnaire was distributed then and there, explained, and thereafter completed. The questionnaire was translated into French as the language that respondents are familiar with.
5.2.2.8 Data coding
The statistical tools employed in the present study consist in the Statistical Package for the Social Sciences version 24 (SPSS 24). Each question was coded in SPSS for the purpose of analysis and interpretation.
5.2.2.9 Data analysis
Data collected is analysed using both descriptive and inferential analysis methods.
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Descriptive analysis in quantitative research is based on the principle of a panellist’s ability to verbalise his or her perception of a product in a reliable manner.
The method embodies screening and training procedures, development and use of sensory language and the scoring of product in repeated trials to obtain a complete, quantitative description (Hootman, 1992:15). Many scholars qualify descriptive statistics as dealing with a set of data called ‘data set’. Descriptive statistics is used in understanding and summarizing the key numerical characteristics of the data set. The process is aimed at describing and summarizing the research data (Turner, 2011: 391). It is also defined as the systematic collection of quantitative information along lines specified by the rule of inductive logic (Moses, 2012:71) and looks at the mean, the standard deviation, the maximum and minimum values of data.
Inferential analysis consists in making inferences about populations based on the sample data (Turner, 2011:391). The two major parts of inferential analysis are estimation and hypothesis testing. The sample data is used to generalize the population. The use of random sampling is important in inferential statistic because the researcher cannot reach all of the population in which he is interested.
Inferential statistics allows the researcher to make warranted claims about the population parameters (Turner, 2011:425).
The analysis of variance (ANOVA) is one of the parametric statistics. This analysis is applied to determine the degree, the observed value, the significance level and the effect size (Tyler R. Harrison, 2002:55). It allows one to compare the mean score of a continuous variable between a number of groups, by testing the null hypothesis that several group means are equal to the population (Muijs, 2011:175).
5.2.2.10 Variable scale of measurement
The measurement is the act of measuring, conducted by assigning symbols or numbers to something according to a specific set of rules. (Turner, 2011:140).
According to Stevens, four scales of measurement can be distinguished. Three of these are used in this research, namely nominal, ordinal, and interval scales.
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Questionnaires and rating scales are commonly used to measure qualitative variables.
The nominal scale is a non-quantitative scale of measurement. It uses symbols such as words or numbers to classify the value. The research questionnaire uses this scale for example for gender which is either male or female. Svensson (2001:47)argues that the scaling of responses can vary from the dichotomous alternatives ’yes’ and ’no’ to a mark on a line, as in the visual analogue scale (VAS).
The ordinal scale is a rank-order scale. This scale is used in the present research to distinguish the degree of acceptance by respondents, from strong to weaker.
Some scholars are of the opinion that the median level and the quartiles or, in the case of small samples minimum and maximum (range), are appropriate measurements to describe the distribution of ordinal data. Bar charts point plots of VAS assessments and box and whisker plots are recommended for the graphical display of the distribution of ordinal data (Svensson, 2001:47).
The interval scale used in the present research serves to determine the difference between the equal adjacent points of interval. This study makes use of continuous variables.
Scholars suggest numerical labels as being commonly used for the recordings (Svensson, 2001:47). The study purpose, the properties of study groups and whether assessments or self- or observer-reported are important factors in the choice of research instruments. The structure of the instrument should be described, for example the dimensions of the variable, the number of items and the types of item responses. The joint frequency distribution of paired assessments could be presented in contingency tables or, in the case of VAS assessments, in scatter plots.