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What research and data analysis skills improve your grant administration practice?

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As a grant administrator, you are responsible for managing various aspects of the grant cycle, from proposal development to reporting and evaluation. To perform your role effectively, you need to have strong research and data analysis skills that can help you identify funding opportunities, design impactful projects, monitor progress, and demonstrate outcomes. In this article, we will explore some of the key research and data analysis skills that can improve your grant administration practice and how to develop them.

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Henri Kouam

Founder || Economist || Researcher ||
Ryan L.

Highways Development Management Officer at Buckinghamshire Council

1 Literature review

A literature review is a systematic and critical analysis of the existing knowledge and evidence on a specific topic or problem. It helps you to identify gaps, trends, challenges, and best practices in your field of interest and to justify the need and relevance of your proposed project. A literature review also helps you to align your project with the goals and priorities of the grantmaker and to establish your credibility and authority as a grant applicant. To conduct a literature review, you need to define your research question, search for relevant sources, evaluate their quality and reliability, synthesize and summarize their findings, and cite them properly.

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Ryan L.

Highways Development Management Officer at Buckinghamshire Council
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I believe that using an up-to-date plethora of academic papers to support relevant themes may also strengthen tue credibility of this research paper on grant administration principles. Let's say try as best as possible to cite from papers no older than 5 to 10 years from the timing of this paper.

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Zelalem Teklu Chala
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Grants could be already acquired or to be acquired in short or long term period. For those already acquired grants, they should be allocated and utilized according to their planned action plans. During utilization of grant funds, there could be price fluctuation or inflation of purchased goods/services. Therefore, grant administrators can be benefited from predictive data analytics. Knowing if price fluctuation or inflation is happening, can help administrators to timely adjust their budget and economize it accordingly. For funds to be acquired in short/ long term period, there could be a fluctuation in exchange rates. Predictive analysis for currency fluctuation can help administrator for a timely adjustment in this fund requests.

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2 Data collection

Data collection is the process of gathering and measuring information on variables of interest for your project. It helps you to establish a baseline, set targets, track progress, and evaluate results. Data collection can be quantitative or qualitative, depending on the type and purpose of your project. Quantitative data are numerical and can be measured, analyzed, and presented using statistics, graphs, and tables. Qualitative data are descriptive and can be explored, interpreted, and presented using narratives, themes, and quotes. To collect data, you need to define your indicators, select your methods and tools, design your instruments, sample your population, and ensure ethical and legal compliance.

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Ryan L.

Highways Development Management Officer at Buckinghamshire Council
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I believe for this paper a triangulation methodologies can be used to enrich the findings of such a research. It would be worth garnering information by both primary and secondary data collection schemes.

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3 Data analysis

Data analysis is the process of examining, organizing, and transforming data into meaningful information that can answer your research questions and support your project objectives. It helps you to identify patterns, trends, relationships, and differences in your data and to draw conclusions and recommendations based on evidence. Data analysis can be descriptive, inferential, or predictive, depending on the level and complexity of your data. Descriptive analysis summarizes and displays your data using statistics, charts, and tables. Inferential analysis tests hypotheses and estimates probabilities using statistical tests and models. Predictive analysis forecasts future outcomes and scenarios using machine learning and artificial intelligence. To analyze data, you need to choose your software, clean and prepare your data, select your techniques, perform your calculations, and interpret your results.

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Henri Kouam

Founder || Economist || Researcher ||
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Based on my new experience working with grants, I believe following your budget will ensure your grant is administered properly. Secondly, keep all receipts and proof of payment as this will help[ when drafting your grant report. Finally, create a system of accountability to prevent any form of fraud or corruption. For example, all receipts must be signed by the project manager and the accountant - to ensure greater accountability. Over time, this will build trust with your partners and stakeholders.

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4 Data visualization

Data visualization is the process of presenting your data in a visual and interactive way that can communicate your message and engage your audience. It helps you to highlight key findings, compare and contrast data, show relationships and correlations, and illustrate trends and changes over time. Data visualization can be static or dynamic, depending on the format and purpose of your presentation. Static visualization uses images, graphs, charts, and maps to display your data in a fixed and simple way. Dynamic visualization uses animations, dashboards, and stories to display your data in a flexible and interactive way. To visualize data, you need to select your tools, choose your type and style, design your layout, and add your annotations and labels.

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Sumitra P Potdar MD, MPH

Health Care Provider | Public Health Researcher
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The bridge between data analysts and other professionals is Data Visualization. The tech has recently been made a lot easier with the help of Tableau and Office 365 tools like Powerpoint and Excel charts. Tableau has the ability to show both static and dynamic visualizations of data, offset by the drawback i.e. quite pricey to subscribe yet one always create a tableau public account!

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5 Data quality

Data quality is the degree to which your data are accurate, complete, consistent, reliable, and valid for your project. It affects the credibility and usefulness of your data and the outcomes and impacts of your project. Data quality can be compromised by various factors, such as human errors, technical glitches, sampling biases, measurement errors, and data manipulation. To ensure data quality, you need to follow the data quality cycle, which consists of four steps: define, assess, improve, and monitor. Define your data quality standards and criteria, assess your data quality level and issues, improve your data quality by correcting and preventing errors, and monitor your data quality by tracking and reporting indicators.

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6 Data literacy

Data literacy is the ability to understand, interpret, and communicate data effectively and ethically. It is a crucial skill for grant administrators, as it enables you to make informed and evidence-based decisions, to demonstrate accountability and transparency, and to advocate for change and improvement. Data literacy also helps you to collaborate and communicate with other stakeholders, such as grantmakers, project partners, beneficiaries, and evaluators. To improve your data literacy, you need to develop your data mindset, knowledge, and skills, and to seek opportunities for learning and practice. You can also use online resources, such as courses, webinars, podcasts, and blogs, to enhance your data literacy.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What research and data analysis skills improve your grant administration practice?

1

2

3

4

5

6

7

1 Literature review

2 Data collection

3 Data analysis

4 Data visualization

5 Data quality

6 Data literacy

7 Here’s what else to consider

Grant Administration

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What research and data analysis skills improve your grant administration practice?

1

2

3

4

5

6

7

1 Literature review

2 Data collection

3 Data analysis

4 Data visualization

5 Data quality

6 Data literacy

7 Here’s what else to consider

Grant Administration

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