Difference between revisions of "Primary Data Collection"

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* <code>[[iefieldkit]]</code> is a Stata package that aids '''primary data collection'''. It currently supports three major components of this process: [[Iefieldkit#Before Data Collection|testing survey instruments]]; [[Iefieldkit#During Data Collection|survey completion]]; and [[Data Cleaning|data-cleaning]] and [[Iefieldkit#After Data Collection|survey harmonization]].
 
* <code>[[iefieldkit]]</code> is a Stata package that aids '''primary data collection'''. It currently supports three major components of this process: [[Iefieldkit#Before Data Collection|testing survey instruments]]; [[Iefieldkit#During Data Collection|survey completion]]; and [[Data Cleaning|data-cleaning]] and [[Iefieldkit#After Data Collection|survey harmonization]].
  
== Guidelines ==
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== Overview ==
While impact evaluations often benefit from [[Secondary Data Sources|secondary sources of data]] like administrative data, census data, or household data, these  sources may not always be available. In such cases, researchers need to collect data directly through a series of [[Questionnaire Design|well-designed]] interviews and [[Field Surveys|surveys]]. The process of collecting primary data requires a great deal of foresight, [[Field Management|planning]] and coordination.  
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While impact evaluations often benefit from [[Secondary Data Sources|secondary sources of data]] like [[Administrative and Monitoring Data|administrative data]], census data, or household data, these  sources may not always be available. In such cases, [[Impact Evaluation Teams|research teams]] need to collect data directly using well-designed [[Computer-Assisted Personal Interviews (CAPI)|interviews]] and [[Field Surveys|surveys]]. The process of '''primary data collection''' consists of several steps, from [[Questionnaire Design|questionnaire development]], to [[Enumerator Training|enumerator training]]. Each of these steps require detailed [[Field Management|planning]], and coordination among the members of the '''research team'''. Given below are the key components of '''primary data collection'''.  
Listed below are the crucial steps involved the in [[Preparing for Field Data Collection | preparation and collection]] of primary data.
 
 
 
 
=== Acquire approval from human subjects ===
 
=== Acquire approval from human subjects ===
 
There are strict rules about [[Human Subjects Approval | acquiring approval from human subjects]]. Researchers must understand the [[Research Ethics|ethics]] and rules for [[Data Security|security of sensitive data]], and should use proper tools for [[Encryption | encryption]] and [[De-identification | de-identification]] of [[Personally Identifiable Information_(PII)|personally identifiable information (PII)]].
 
There are strict rules about [[Human Subjects Approval | acquiring approval from human subjects]]. Researchers must understand the [[Research Ethics|ethics]] and rules for [[Data Security|security of sensitive data]], and should use proper tools for [[Encryption | encryption]] and [[De-identification | de-identification]] of [[Personally Identifiable Information_(PII)|personally identifiable information (PII)]].

Revision as of 21:53, 25 May 2020

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.

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Overview

While 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, research teams need to collect data directly using well-designed interviews and surveys. The process of primary data collection consists of several steps, from questionnaire development, to enumerator training. Each of these steps require detailed planning, and coordination among the members of the research team. Given below are the key components of primary data collection.

Acquire approval from human subjects

There are strict rules about acquiring approval from human subjects. Researchers must understand the ethics and rules for security of sensitive data, and should use proper tools for encryption and de-identification of personally identifiable information (PII).

Compile the survey budget

Researchers must prepare a survey budget before procuring a survey firm. This step allows researchers to calculate expected costs of conducting a study, and compare these with the proposals of firms that submit an expression of interest (EOI).

Determine relevant parameters of a study

After agreeing upon a budget, researchers then decide upon factors like the adequate sampling frame (which is a list of individuals or units in a population from which a sample can be drawn), sample size, and statistical power based on which they can then randomize treatment.

Procure a survey firm

The next step is to procure a survey firm after issuing detailed terms of reference (TOR), and performing due diligence among local research firm options.

Carry out a pre-pilot

The first stage of the survey pilot, the pre-pilot involves two things: piloting content and piloting protocols. Clear protocols allow researchers to ensure that field collection is carried out consistently across teams and/or regions, and ensure that published research is reproducible.

Refine and review the survey design

The first stage of the survey pilot allows researchers to develop a design for the instrument. The researchers then conduct the second stage of the survey pilot, called content-focused pilot, to review and refine the structure of the instrument.

Translate the survey instrument

After the content-focused pilot, the research firm translates the instrument into all local languages. This step helps to ensure that the survey can be taken by more people, therefore making the study more effective.

Program the instrument

After obtaining IRB approval, researchers program the questionnaire. This step makes it easier to share surveys that rely on methods like Computer-Assisted Personal Interviews (CAPI) or Computer-Assisted Field Entry (CAFE).
Also refer to SurveyCTO coding practices to learn more about programming surveys.

Train enumerators and monitor data quality

After validating the programming of the questionnaire, the researchers train enumerators and monitor data quality to generate a final draft of the instrument. Monitoring can be done in the form of back checks, high frequency checks, as well as other methods.

Maintain an organized data folder

DIME Analytics has created a Stata command, iefolder. Part of the DIME Analytics Stata packageietoolkit , it helps increase project efficiency, and reduces the risk of error in a study.

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