CHAPTER THREE

RESEARCH METHODOLOGY

3.0. Introduction

This chapter presents information on research design, area of study, study population, sampling Design sampling size, Data collection methods and instruments, Quality control methods, Data management and processing, Data analysis, Ethical considerations and finally limitations of the study.

3.1 Research Design

This section will focus on the research techniques which were adopted and used for the study with the aim of achieving the research objectives. A research design is defined as an overall plan for research undertaking and it provides the glue that holds the research project together.

The study used a case study design to investigate the effect of credit management on financial performance in Microfinance Support Centre, Mbale branch. The case study design will be used in that its findings will be based on the data collected from a geographic area of MSC which was used as a case study. The research will employed both qualitative and quantitative methods. The qualitative approach will be used in comprehending views obtained from respondents through questionnaire and interviews. The quantitative approach on the other hand will be used in computing data that will involve figures hence this will enable the use of percentages in data analysis.

3.2 Area of the study

The area of the study will be at Microfinance Support Centre, Mbale branch. Because it is strategically located and the researcher has easy access to the respondents. The area of the study refers to the anthropological or sociological research which is intended to gather and relate data on various aspects of a geographical region and its inhabitants, as natural resources, history, language, institutions, economic characteristics and Primary investigation into human ecology.

3.3 Study Population

The study population will consist of 60 respondents from the different departments which included finance (2), Human Resource Administration (5), loans officers (15), Information communication technology (02), Marketing and corporate Affairs (13), legal officer (10), customers (10) and internal audit (05). Therefore, the study population was60 out of which the sample size selection for the study was made.

3.4 Sampling procedures.

3.4.1 Sample size and Selection.

Sekaran, (2003) identified that, sampling is the process of choosing the research units of the target population, which are to be included in the study.  A sample size of 52 respondents will be selected out the population of the study population of 60 who will comprise of finance (5), Human Resource Administration (2), loans officers (15), Information communication technology (02), Marketing and corporate Affairs (13), legal officer (10), customers (10) and internal audit (05).  The sample size will be determined using Morgan and Krejcie table as given by Amin, (2005) (Appendix III)

Table 1: Showing category, population, sample size and sampling technique.

Source: Primary data (2021)

3.4.2 Sampling techniques.

The study will use both probabilistic and non-probabilistic techniques. This will include simple random and purposive sampling techniques.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group. Simple random sampling will be used for the study because it will consider a fair way of selecting a sample from a given population since every member is given equal opportunities of being selected.

Purposive sampling will be used for selecting heads of Human Resource Administration, finance and respondents from ICT departments.  This is preferred by the researcher because ;it  excludes people who are unsuitable for the  study  and remain  with  the most suitable candidates , it is  less time consuming, reduces  the costs for carrying out the sampling project,  the results of purposeful sampling are usually expected to be more accurate than those achieved with an alternative form of sampling.

3.5 Sources of data.

Two basic sources of data will be considered; both primary and secondary.

The study will use both primary and secondary data sources. The instruments are preferred for their greater convenience in the context of time, stability, uniformity and consistency.

The primary data refers to the data collected by the researcher herself. In order to achieve her objectives data as collected mainly using the structured questionnaires and interview guides from the respondents.

Secondary data refers to already available data set, this will be obtained from documents, journals, online data sets in order to supplement on the primary data collected by the questionnaires and the interview guides.

3.6 Data Collection Methods and Instruments

To collect a large quantity of data, the following data collection methods will be used: questionnaire, face-face interviews, and documentary review.

3.6.1 Data Collection Methods

Questionnaires:

A structured questionnaire will be used to collect data and in this study, questionnaires will be used to collect data from members and staff of MSC on issues surrounding credit management and financial performance. Questionnaires will be used because apart from being easier to administer, they more reliable and also easier to analyze (Amin, 2003). Questionnaires are often used to collect data from large samples because they are cheap to administer, free from bias of the researcher, provide adequate time for respondents to fill them (Kothari, 2006). Using an introductory letter from the MTAC, the researcher will deliver the blank questionnaire to the selected respondents in Micro finance Support Centre and provide an appropriate time for them to complete them and then she collected them.

Interviews

Face to face interviews will be held to collect data from the staff at MSC in order to collect in-depth data on credit management and financial performance. The researcher will arrang to meet the respondents in these three categories and hold face to face interviews with them. During the process, a set of questions will be asked and their responses will be written down by the researcher. At the end of the interview process, the researcher will go over what had been captured and ensured that no useful information is left out. 

3.6.2 Data Collection Instruments

Questionnaire.

As an instrument, a 5 point-Likert scale questionnaire will be used. It will comprise of both open ended and close ended questions. This serves to the respondents majorly on appointment who will  then be required to tick against the provided questions or fill in where necessary. This instrument is good because it has limited choice for respondents there by limiting assumptions and falsifications by respondents.

Interview Guide

As an instrument, an interview schedule will be used to collect data from the top SMT, the top management team of the organization. This is because these respondents are ever busy that they are unable to take off time to answer a questionnaire. The interview schedule will contain a set of questions that will be followed while interviewing the respondents to avoid going off topic. This instrument is good because it minimizes differences between interview responses since the guiding questions are pre-determined and interviewees are subjected to the same environments (Canals, 2017).

3.7 Quality Control Methods

The instruments of data collection in this study will be assessed in terms of validity and reliability to ensure dependability of the results of the study.

3.7.1 Validity

Validity of the instrument will be determined by computing the Content Validity Index after rating of the items by the supervisor. The researcher will request the supervisor to rate the items in the data collection instruments as Very Relevant (VR), Relevant (R), Somewhat Relevant (SWR) or Not Relevant (NR). From the rating, the researcher will use the formula below to compute the Content Validity Index (CVI), which will be an indicator of the level of validity of the instrument.

Formula used:    CVI     = VR + R  

K;           Where VR is for Very Relevant, R for Relevant and K is for total number of items in the instrument.

From the supervisor’s rating, 10 items are rated very relevant; 13 are rated relevant, 5 will be rated somewhat relevant and 3 as not relevant. This will be out of the 37 items in the instrument. By substitution in the formula above;

CVI     = 10 + 13 = 23   =   0.793

29            29

The value of Content Validity Index (CVI) obtained will be interpreted using the George and Mallery (2003) scale. Since the value of CVI obtained was 0.793, which is above 0.7, it indicated good reliability (George and Mallery, 2003).

3.7.2 Reliability

Reliability refers to consistency in delivering results. To ascertain reliability, the researcher will pre-test the research instrument on a reasonable number of staffs within MSC, which may not be used in the final data collection process. After pre-testing, the Chronbach’s Alpha formula will be used to compute the reliability coefficient which is an indicator of the level of reliability of the instrument (George and Mallery, 2003). A reliability coefficient value of 0.7 will be obtained indicating acceptable reliability (George and Mallery, 2003).

3.8 Measurement of variables

The dependent variable of the study; financial performance will be measured by Profitability, cash flow and liquidity position.  The independent variables of the study credit management will be measured by client appraisal techniques, credit risk control tools and collection policies. A questionnaire with 5 point rating scale as per Likert scale ranging from strongly agree (1) to strongly disagree (5) of Munene, (2000) local measure  will be used to measure respondent’s evaluation by asking them the degree of agreement with statements. The measurement scale of 1 up to five on every statement simply measures the strength of the respondents’ opinion on the particular statement. If the respondent ticks it implied that one strongly agrees with the statement, 2 = agrees, 3 = not sure in other words one does not take any side on the statement, 4 = disagrees and 5 = strongly disagrees with the statement under discussion.

3.9 Analysis of Data

Data collected was first cleaned and scrutinized for any missing values before actual analysis was done.

3.9.1 Quantitative Data

Quantitative data collected was centered into the Statistical Package for Social Scientists (SPSS) computer software. The software Will commanded to generate descriptive statistics such as frequencies and percentages. Then a correlation analysis will ran using the SPSS software to establish the effect of the Credit Management on financial performance in Microfinance Support Centre, Mbale branch.

3.9.2 Qualitative Data

The qualitative data will be analyzed for content and language used by thorough transcribing of recorded interviews looking out for similarities and differences to identify themes and develop categories according to the objectives. Data cleaning, editing and coding of the items in the questionnaire will be employed to cross check and interpret qualitative data and generate theoretical relations for making conclusions. The interplay between the findings solicited by both quantitative and qualitative data will enable the researcher to draw conclusions and subsequently make recommendations.

3.10 Ethical Considerations

The researcher will respect anonymity of the respondents by ensuring confidentiality of the respondents and the data provided. This will be done through assurance that the information they provided will be purely for academic purposes and that their identity would not be disclosed to anyone. This will be highlighted in the introductory part of the questionnaire and before the interview sessions. Lastly but not least, objectivity will be considered during report writing to avoid personal bias.

REFERENCES

Books

ABEDI, S. (2000): Highway to Success, Credit Management Journal, and http:// leathers inters.

. New Jersey: Prentice Hall. Balduino,

W.F. (2000). Risk Is In. [On-line]. Available http://www.dnb.com(22/10/07).Com

ARNOLD, G. (2003). Corporate Financial Management

BINKS, M.R. AND ENNEW, C.T. (1992).Information asymmetries and the provision of finance to small firms: International Small Business Journal

BINKS, M., AND ENNEW, T. (1996). Financing small firms, small business and entrepreneur, 2^nd edition.

BINKS, M., ANDENNEW, T. (1997). Small business and relationship banking: the impact of participative behavior, entrepreneurship: Theory and practice vol. 21, No.4 pp 83-92.Ed Macmillan.

BRIGHAM, E.F., GAPENSKI, L.C. AND DAVE’S, P.R. (1999). Intermediate Financial Management. Florida: The Dryden press.

CGAP (2009) [Online]. Measuring results of micro finance Institutions Available http://www.gap.org

CHRISTEN, P., E. RHYNE, R. C. VOGEL, AND C. MCKEAN (1995), “Maximizing the Outreach of Microenterprise Finance: An analysis of Successful Micro finance programs “,

EDWARD. B (1993) Credit Management (6^thEd.)  http://www.gowerpublishing.com

EDWARDS, P. &TURNBULL (1994). Finance for small and medium sized enterprises.

KREJCIE and MORGAN, 1970. Determining Sample Size for Research Activities.

https://home.kku.ac.th/sompong/guest_speaker/KrejcieandMorgan_article

MYERS, C. & BREALEY, R. (2003). Principles of Corporate Finance. New York: McGraw- Hill.

HITT, E. HOSKISSON, A. JOHNSON, D. (1996). The Market for Corporate Control and Firm Innovation

Journals

DEAKINS, D., HUSSEIN, G. (1999).Risk assessment with asymmetric Information: International Journal of Bank Marketing.

NELSON, L. (2002). Solving Credit Problem. Retrieved on 21 July 2015 from http://www.cfo.com

Reports

EPPY, I. (2005) Perceived Information Asymmetry, Bank lending Approaches and Bank           Credit Accessibility by SMES in Uganda Makerere University.

TURYAHEBWA, A (2013) Financial Performance in the Selected Microfinance Institutions In Uganda (unpublished master’s thesis) Kampala International University,West campus,

SHEILAH, A.L. (2011) Lending Methodologies and Loan losses and Default in a Microfinance Deposit Taking Institutions in Uganda; a research report presented to the Makerere University Uganda.

OWINO, M. (2012) Effect of the Lending Policies on the Levels of Non-performing Loans (NPLs) on Commercial Banks of Kenya.

DALLAMI, K. & GUIGALE, M. (2009) Reflection to Credit policy in developing Countries Policy.

DHAKAL, S. (2011), „Risk management in SACCO‟s, Econometric Analysis‟. Second Edition Macmillan. London.

NAGARAJAN, M. (2011), “Credit risk management practices for microfinance institutions in Mozambique”. Unpublished MBA project-University of Maputo.