IGR’s core mission is to bridge the gap between knowledge and policy, and to do this requires credible data that can be shared with policymakers, donors, partners, and citizens. Data integrity is of the utmost importance to IGR, its staff, and reputation. Trusted data collection and generation is at the core of IGR’s work. Generating trusted data by all partners, even if they disagree with the intent and use of the data requires all IGR staff to be guided by several core institutional values and set of ethics.
These values are:
Patience and Perseverance
Conducting research is a time-consuming and often costly process. The amount of time needed to conduct quality research typically takes more time than what an individual or organization generally wants to allocate, but because of poor infrastructure throughout Sierra Leone, conducting research can take time. In addition, getting access to key informants can be challenging if there is not a personal connection with the individual. Patience and perseverance are the two most important traits that a researcher must have in Sierra Leone.
Good Research Design
The research design for any research is a well-devised plan for data collection in an empirical research project. Good research designs often occur with direct input from stakeholders and partners. Input from local communities and organizations working in specific areas is always taken into consideration when designing programs and projects at IGR.
Case Study or Deep Dive
A case study or deep dive typically focuses on one unit. The case study or deep dive allows IGR to drill down and really explore what are some of the central issues or processes that are influencing a particular issue. This type of research provide deeps understanding and analysis. Often the limitations of a case study or deep dive is the lack of generalizability of the findings to other areas or institutional settings.
Large-N vs. Small-N research
The N simply refers to the number of units that are being assessed. Large-N research is typically 4 or more cases and can be limitless, in theory. Small-N studies are typically 2 to 3 units. So, for example, IGR could conduct an assessment on universal health insurance schemes in Sierra Leone, Ghana and Senegal. This would be considered a small-N study, whereas a study on internet usage in all 48 sub-Saharan African states would be considered large-N. The benefits of a large-N study is that by studying an issue within a bunch of units or cases is that trends can be identified to identify similarities and also differences between units or cases. Typically, large-N studies are quantitative and expressed with statistics. Small-N research however can look at similar issues between units or cases that can be either quantitative or qualitative in form but be unable to highlight some of the trends found in large-N studies.
Sampling
Samples can vary depending upon the nature of the research and geographic location where the research will be conducted. For national level surveys approximately 1,200 individuals are needed to provide a representative cross-section of respondents that are 18 years of age and above. IGR only speaks to individuals 18 and above. For research in Freetown for example, with an approximate population of 1.5 million residents, IGR can sample as few as 400 to as many as 800 people to provide a representative cross-section of society with a reliable margin of error.
Weighting
Weighting data is a statistical technique used to balance out survey data to improve the accuracy of findings. A few of the reasons why data can be weighted is the unequal probability of selection during the survey sampling stage. For example, surveys often occur in unequal environments like a city and a rural area. The survey data can be weighted in favour of those in a rural area to compensate for fewer respondents due to less population and also the remote nature of the area. The second reason for weighting of data is due to the issue of nonresponse. Often times people do not want to take part in a particular survey for a number of reasons. One could be that they are not familiar with the issue(s), another can be that they find the issue too sensitive and don’t want to talk about it, or people might simply not want to take part in any survey because they don’t know the individuals asking the question(s). Data are often weighted to address some of these concerns.
Margin of Error
The MOE is often expressed as a percentage that tells the audience the level of sampling error in the results of a survey. The larger the MOE the greater the likelihood that the survey results are seen as less reliable. A very strong MOE is typically 3%, and the highest MOE that IGR will accept in any research is 5%, again due to the unreliability or questioning of the findings. Decisions around an acceptable MOE are often related to financial concerns. To decrease the MOE requires IGR to increase its sample size and speak to more respondents, which inevitably is more time consuming and costly. However, depending upon the nature of the research and a client’s interest, a 5% MOE can be more than adequate. There are always trade-offs in conducting surveys.
Electronic data capture
All of IGR’s work is conducted with up-to-date smartphones and. tablets. In addition, software that captures geo-location and time-stamp at the time of interview is always utilized to aid in quality assurance.
Well-trained and seasoned data collectors
IGR works with a body of data collectors over several years. These are first-class degree holders that have worked on several IGR projects and are regularly trained and re-trained on how to properly conduct research. IGR always tries to ensure that gender parity is observed on every assignment with a mixture of women and men representing every region of the country.
Desk Review
A desk review or understanding the literature is often the first step in conducting research and proposing policy reforms. Academics, non-governmental organizations (NGOs), donors, multilateral organizations, and policy-based think tanks like IGR often produce data-driven analyses and reports on particular issues. These pieces of literature become useful in looking at gaps or limitations of particular approaches or ways of thinking about issues.
Interviews
IGR regularly conducts key-informant interviews (KIIs) with key individuals often in positions of authority that can provide information on critical issues or institutional processes. Interviews can be either in a formal or informal setting and can be conducted in as little as 5 minutes and can last as long as 1 hour depending upon the nature of the issue as well as the respondent’s time and schedule.
Focus Group Discussions
FGDs are typically informal discussions that involve 6 to 8 respondents (you don’t want too many respondents in one setting) that allow a researcher to gather a large amount of information from respondents of similar backgrounds or experiences together to discuss a specific topic of interest. It is a form of qualitative research where questions are asked about their perceptions attitudes, beliefs, opinion or ideas. In focus group discussions, participants are free to talk with other group members. The FGD is led by a moderator (interviewer) in a loosely structured discussion of various topics of interest.
Survey (Questionnaire)
Surveys are a useful tool to gauge the views of citizens on issues. At IGR all surveys are randomly conducted with electronic capture and geo-location and time-stamp tracking so that data accuracy, credibility and reliability can be ensured. Random survey sampling is conducted to ensure that each and every Sierra Leonean has an equal chance of being selected.
Telephone-based polling
Telephone-based survey research is challenging in Sierra Leone. User access to a cell phone tower, reliable electricity to charge one’s cell phone, not always predictable household work patterns, and an irregularly-updated national registry of all cell phone users are just some of the issues that IGR has to grapple with. Telephonic polling has a lot of potential in Sierra Leone to get quick snapshots of citizen’s views, but it will continue to evolve as infrastructure improves and households gain greater access to electricity and affordable cell phone service.
One can think of data analysis as a set of tools that helps one interpret and tell a story. Data analysis is either quantitative or qualitative in nature. Mixed methods analysis utilizes both qualitative and quantitative data to tell a story.
Qualitative Analysis
Qualitative data cannot be objectively measured or counted. It is data that expresses the interpretive qualities of an item or process. Qualitative data can come in a variety of formats, including words, audio and video clips. Analysing data qualitatively introduces some element of subjectivity into the process. Analysing qualitative data can come in the form of interpreting textual data and the precise meaning of words, body language, an individual’s appearance, and more importantly the impact of policy on individuals and local communities. Interpreting qualitative data requires time, skill, and attention to detail.
Quantitative Analysis
Quantitative data is a set of information that is expressed in numerical form of that represents a given reality. Quantitative analysis uses mathematics and statistical modelling to understand particular phenomena and represents a given reality within a specific time frame. It is primarily conducted using specialised software packages like SPSS, STATA and Excel, and is often described by researchers as data that is objectively analysed via some level of measurement. Measurement can come in the form of whole numbers and percentages and is often used to highlight trends over a given period of time.