Primary data collection is the process of gathering data through surveys, interviews, or experiments. A typical example of primary data is household surveys. In this form of data collection, researchers can personally ensure that primary data meets the standards of quality, availability, statistical power and sampling required for a particular research question. With globally increasing access to specialized survey tools, survey firms, and field manuals, primary data has become the dominant source for empirical inquiry in development economics Show
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OverviewWhile impact evaluations often benefit from secondary sources of data like administrative data, census data, or household data, these sources may not always be available. In such cases, the research team will need to collect data directly using well-designed interviews and surveys, and the research team typically owns the data that it collects. However, even then, the research team must keep in mind certain ethical concerns related to owning and handling sensitive, or personally identifiable information (PII). Before moving on to the discussion of concerns about ownership and handling, however, it is important to understand the process of collecting primary data. The process of primary data collection consists of several steps, from questionnaire development, to enumerator training. Each of these steps are listed below, and require detailed planning, and coordination among the members of the research team. Develop QuestionnaireThe first step of primary data collection is to design a survey instrument (or questionnaire). It is important to remember that drafting a questionnaire from scratch can be a time-consuming process, so the research team should try to use existing resources as far as possible. While developing the questionnaire, keep the following things in mind:
Pilot QuestionnaireSurvey pilot is the process of carrying out interviews and tests on different components of a survey, including content and protocols. A good pilot provides the research team with important feedback before they start the process of data collection. This feedback can help the research team review and improve instrument design, translations, as well as survey protocols related to interview scheduling, sampling, and geo data. A pilot has three stages - pre-pilot, content-focused pilot, and data-focused pilot. Typically, the pilot is carried out before hiring a survey firm. The research team must draft a clear timeline for the pilot, and allocate enough time for each component of the pilot. DIME Analytics has also created the following checklists to assist researchers and enumerators in preparing for, and implementing a pilot:
Pilot Recruitment StrategyBesides testing content and protocols, it is also important for the research team to pilot recruitment strategy before starting data collection. This is especially important in the following cases:
One of the ways to test the recruitment strategy is to test 3 different recruitment strategies, say, A, B, and C. The research team can then finalize the strategy that has the highest take-up rates. Another method is identifying the ideal incentives which can ensure higher participation by the eligible population. After finalizing a recruitment strategy, the research team can move on to drafting the terms of reference (TOR) for the data collection. TOR: Create Budget and Plan FieldworkAfter finalizing the survey instrument and recruitment strategy, the research team must prepare a detailed terms of reference (TOR) for hiring a survey firm. The terms of reference (TOR) define the structure of the project, as well as the responsibilities of the survey firm. While preparing the TOR, the research team must create a survey budget, and plan the fieldwork. Create budgetThe research team should calculate standard, as well as project-specific costs, and prepare a survey budget. In this stage, the research team should also consider what sample size it can afford for the data collection. This allows the research team to calculate expected costs of conducting a study, and compare these with the proposals of survey firms that respond to the terms of reference (TOR). Plan fieldworkIt is also important to plan fieldwork in advance to give potential survey firms an idea of the responsibilities and tasks involved in the data collection. For the field coordinators (FCs), this includes deciding number of interviews each enumerator will conduct in a day, number of field teams, modes of transport, and keeping extra buffer time for possible delays. Similarly, for the survey firm, this involves defining basic parameters like sample size, sampling strategy, timeline, etc. Contract Survey FirmAfter the research team finalizes and issues the terms of reference (TOR), multiple survey firms can express interest in signing a contract with the research team. The research team will then select one of these survey firms, and sign a contract with the selected survey firm. This completes the process of procuring a survey firm. After signing the contract, the research team and the survey firm should agree on the parameters defined in the terms of reference (TOR), the survey timeline, and discuss possible scenarios and common issues that might arise during data collection. One such issue that the research team and the survey firm must discuss in detail is the data quality assurance plan. Data Quality Assurance PlanThe research team must draft a data quality assurance plan, and share it with everyone in the research team, as well as the survey firm before starting with data collection. A data quality assurance plan considers everything that could go wrong ahead of time, and makes a plan to resolve these issues. Some of the issues that can affect data quality include errors in programming or translation, attrition (or dropping out of respondents during a survey, and faulty tablets used during computer-assisted personal interviews (CAPI), among others. A comprehensive data quality assurance plan has 3 major components for each of the following stages - before, during, and after data collection. Before data collectionBefore data collection, the research team can include the following in the data quality assurance plan:
During data collectionDuring data collection, the research team can include the following in the data quality assurance plan:
After data collectionAfter data collection ends, the survey firm usually provides a final field report. This report can be used to improve data quality in the last stage of the data collection process. It can provide qualitative information to the research team about everything that could not been captured by the survey instrument, such as :
Obtain Ethical ApprovalMembers of the research team must ensure that they protect the rights of all human subjects in a study, including the right to privacy. In this context, all living individuals whose sensitive or personally identifiable information (PII) is contained in the data collected by the research team are considered human subjects. In this step, the research team must consider issues like IRB approvals, informed consent, and data security. IRB approvalsThe research team must obtain IRB approvals for studies that use personally identifiable information. Institutional review boards (IRBs) are organizations that review and monitor research studies ensure the welfare of human subjects. In addition to IRB approvals, the research team should also obtain approvals from local institutions in the location of the study. This will ensure that the study complies with local regulations, and does not violate any laws in that area, particular with respect to the right to privacy. Informed consentBefore involving any individual in a research study, the research team must obtain informed consent from each individual. This means that the research team must clearly mention all possible risks and benefits from participating in a study, either for the survey pilot or as a respondent in the actual data collection. Data securityThe research team must understand the ethics and rules for data security, and should use proper tools for encryption and de-identification of personally identifiable information (PII). Data security also means ensuring that members of the research team who are not listed by the IRB can not access any confidential data. Data can be confidential for multiple reasons, but the most common reason is that it contains personally identifiable information (PII). Other reasons include that the data was shared under a data usage license that requires the data to be kept confidential. Train EnumeratorsFinally, the research team must plan and conduct comprehensive enumerator training. Enumerator training is usually a joint effort between the research team and the survey firm. The content and structure of the training can be divided into the following sections: ObjectivesThe purpose of the training should be to ensure that all field staff know all the survey protocols. Also ensure that enumerators understand all questions in the survey instrument. They should also be comfortable with using tablets used in CAPI, or paper forms used for PAPI. Finally, ensure that all field staff know and understand their duties. PlanningIn terms of planning, the survey firm should coordinate with the research team on logistics, such as deciding a venue for the training, printing field manuals, questionnaires, and the agenda for each session. It is a good idea to recruit enumerators and experienced trainers in advance. The field coordinator (FC) should finalize the field manual, update the training manual, and also make sure the trainers are aware of the objectives and the context of the study. Finally, prepare quizzes for the assessments, and plan practice interviews for enumerators. ComponentsGenerally, the survey firm leads the training, while the field coordinator (FC) monitors the sessions. The training should explain the context and content of the questionnaire, methods used for data collection, sample selection, and protocols. It is also important to anticipate potential issues that enumerators may face, and train them on how to handle these issues. AssessmentConduct assessments and quizzes to select enumerators for the actual data collection. Select enumerators based on scores on these quizzes, observations of the field coordinator (FC) and supervisors, communication skills, and familiarity with the survey instrument. Always train more enumerators than you need for the data collection. Provide regular feedback during the training to ensure transparency. Tips and ideasFinally, follow best practices in training. Examples of these practices include taking notes during the sessions, recording training sessions so that enumerators can watch them again later, and regular practice interviews. Click here for pages that link to this topic. Additional Resources
What is the primary instrument in data collection?For such research, the primary instrument for data collection is typically a survey/ questionnaire, that the interviewer or researcher use to direct their questions during interviews and to gather information.
In what research is the researcher the primary instrument for collection and analysis of data?The qualitative researcher is the primary instrument for data collection and analysis. Data are mediated through this human instrument, rather than through inventories, questionnaires, or machines. Qualitative research involves fieldwork.
What are the example methods of primary data collection?Basic types of primary data collection include online, offline, and self-collection. Offline primary data collection includes offline surveys, interviews, offline quizzes, delphi technique, focus groups and observations. The Delphi Technique is a survey method that uses a panel of experts to make decisions.
What is primary instrument in research?According to Ary (2010), the primary instrument is an instrument collected by researchers specifically as the primary instrument for data collection in qualitative research. It means, researcher use himself as the main instrument in this research.
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