Operations

Quality Assurance

IRIM performs several internal quality checks and controls at every stage of the research project. The aim of employing different types of internal checks is to ensure the best quality of data collection and to allow for timely and appropriate intervention if an issue is identified.  

Our realistically developed implementation plans, operational budgets, risk management policy and mitigation plans give our clients the assurance that they will receive a quality service in an effective and timely manner.

IRIM has the following structure to perform regular internal controls at both organisational and project level:

  • It is the team leader and project manager who are responsible for conducting internal process monitoring through highlighting the strengths and weaknesses in project implementation and enabling the responsible personnel to deal with problems, improving performance, building on successes and adapting to changing circumstances.
  • The Operations Manager - performs standard internal process quality checks and controls at every stage of each project and on a daily basis.
  • The Deputy Director - is responsible for the consistency of quality of project outputs/products - data, study reports, presentations, and workshops. 

Data quality check

One of the main experiences of IRIM in implementing projects is its focus on “data quality management”. IRIM has a Data Quality Unit which performs internal quality assurance and controls at every stage of the project and on a daily basis. The internal checks ensure the best quality of data collection and to allow for timely and appropriate intervention if a project is not adhering to the required quality.

Data quality control is multidimensional, and involves data management, quality control and assurance, storage and archiving. The main data quality measuring criteria are shown below:

  • Completeness.More than 98 percent of all questions asked of respondents are complete and consistent with other responses.
  • Validity.Data validity is the correctness and reasonableness of data.
  • Accuracy.The accuracy of the collected information to reflect exactly what respondents have said is verified as part of the data quality monitoring.
  • Consistency.This involves identifying discrepancies among responses provided by the respondent.
  • Timeliness.Timeliness will measure the time from when data is collected and entered to when it is available for reporting to the Client.

In addition to assuring internal quality monitoring, IRIM offers data quality services to external organisations.