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The idea that I found most interesting and had not initially thought of is the need to disclose relationships and potential conflicts of interest with your studies. While I knew that it is unethical to outright lie in your data to support a desired conclusion, such as the example of the pharmaceutical company and lying about the outcomes of the test. However, I had not considered the need to disclose a potential conflict of interest even when the data presented is 100% accurate. I now understand the need to disclose potential conflicts of interest, as hiding them from the public is still an unethical practice. Additionally, I thought the need to ensure that your conclusions are accurately reflected by your data. When presented with data such as that in the example given, it can be easy to see how someone might misinterpreted the data and give an inaccurate conclusion such as counseling programs help lower adolescent pregnancy rates. That is why it is important to consider what your data actually shows, in this case a correlation between the two. Correlation does not equate to causation.

It is crucial to ensure data consistency before any presentation and to be well-informed about the process of data collection, transformation, and usage.

Honesty is very needed in how we get and present actual data that also influence the quality and information we convey to other relevant parties.

There is indeed often a conflict of interest regarding being honest when dealing with program data. This is often so, when M&E is constantly pressured by implementing teams or programs teams that are constantly needing to achieve or are trying to show that they have achieved the project goals.
It then is important to ensure that honesty of paramount importance when presenting data, more especially while working a project team that is abscessed with results either by hook or crook. As M&E, we really ought to give the proper directions so that ethics are adhered to and enlighten teams on how biased and dishonest data could potentially affect or harm the populations we are trying to help.

We have to present the data that are truth. If we present the fake data then it may lead to failure of the project. In other word it can misguided the outcome and impact

I saw the importance of ethics in the data collection phase

I saw the importance of ethics in the data collection phase

without data we cannot do anything however data can cause many legal issues when used incorrectly and also cause embrassment to individual, when using data that is confidential ensure that the data is no persimable to unathorised persons. we apply honesty and integrity .

Honesty issues occur all the time especially during reporting of project outcomes and findings. in Many cases, project are not as planned. Donors have rigid contracts with organizations, and organizations either want to remain working, or do not want to be considered incompetent end up reporting something very far from the actual.

It all has to start from how a program is planned, and all programs must give room for modifications where there is need. Without all these, people will be forced to report false data.

Well for me, I take accurate data, and report what I have found.

The implication is that we have to replan

Honesty is a very important part of data collection, analysis and presentation, it can affect the aims and objectives of the program.

Honesty is a very important part of data collection, analysis and presentation, it can affect the aims and objectives of the program.

It's very important to honest with data, however i don't understand the example given with the pharmacy company, why publishing that the drug is effective if the date is accurate, could be unethical

When we collect data ensure that every piece of information fill correctly and honestly no fraud and false information filled , this is also ensures that if anyone one told you something personal, and do not want to fill any fill because he share his personal thoughts with you by faith ensure that if you see any new or something interesting views or something touches your mind or heart please consider it if you make interest in any of family or other things collect in your data.it may also consider that your M&E is concise and everyone can understand easily

it's not enough to till the truth the circumstances of data collection and the personnel involved should be clarified for more transparency

its very important for you as a project manager to inform your participants what they are participating

if you are not honest you cannot work any people

Collect use and present your data accurately.
You understand that lying is morally wrong and can couse damage.
It's significantly to don't present the data that you know it's in accurate.

Provide skilled professional service.
You should have to ensure the cultural and technical skills to carry out M&E process.
Like
Mapping stake holder needs
Storing data securely
Designing surveys

Honesty- It is a simple concept but encompasses a few sub concepts such as: data should be accurate, the date should be represented accurately, limitations of strategy should be communicated clearly and share personal and professional outcomes vividly.

collect use and present your data accurately.
you understand that lying is morally wrong and can couse damage.
ensure that data your data your present is accurate

Collect use and present your data accurately
You understand that lying is morally wrong and can couse damage.
Ensure that the data your present is accurate.

Honesty and integrity are the integral component of M&E

Transparency and honesty is very important in communicating data.

the team need to honesty to present in data

I am totally reflecting on why most organisations offer consultants some contracts to carrry out some market studies or surveys on their behalf. To curb conflict of interest from them

honesty is not credible data, which reflects reality

It is important to be honest, it will safe lots of stress and damages.

It is important to be honest, it will safe lots of stress and damages.

Limitations of data collection and use must take into consideration the area of interests of key stakeholders and the participants of the study.

I love the statement that says If your are not sure of raw data do not use, but what of the case that is external that enter the data

we have to be responsible and positive with what we have collected or our coleections

Good insight, very helpful.

I always see conflict of interest to mean a different thing, but today I have been enlighten that it means doing things honestly, representating the data as it is without any favour of whether this is your donor or an affiliate organization.

Honesty, speaking and acting truthfully, is more than not lying, deceiving, stealing, or cheating.

Honesty is a core ethical value / principle for the M&E professionals to maintain the reliability and accuracy of the data. Another point is the transparency should be maintain in the situation where the conflict of interest arises. The limitations of the M&E strategies also need to be mentioned.

Honesty, i must say is lacking nowadays. whenever we plan to present data, though we know what that means, we always try to manipulate it if it doesn't bring favorable answers.
so prepare yourself to tell the truth, always tell what we could have done better.

Be honest.

Presenting accurate data makes things easier for someone who is analysis the data because if the data is inaccurate then M&E specialist will have to take extreme measures and that might damage the reputation of the organization. The data should be clear and honest .

Honesty is equally a very important principle as it reminds data collectors on the need to present data accurately and as M&E practitioners we are reminded to be honest about any limitations in our work.

Embracing ethical behavior in Monitoring and Evaluation (M&E) practices offers numerous benefits. It builds trust and credibility among stakeholders, supporting ongoing support for projects. Accurate data representation empowers informed decision-making, ensuring actions align with intended outcomes. Acknowledging limitations fosters realistic expectations and a nuanced understanding among stakeholders. Ethical integrity, including conflict of interest disclosure, guards against bias and enhances trust. Ethical M&E practices also facilitate learning and adaptation, leading to more effective interventions. In sum, ethical M&E practices not only protect an organization's reputation but also contribute to credible, impactful, and sustainable initiatives.

at times the conflict of loyalty can happen in religion, affecting a relative or choosing between tasks that affect you differently. here you choose a MIN

Before collecting any data it is useful to stop and assess the situation, to make sure that money and time is
not wasted. The basic principles of data collection include keeping things as simple as possible; planning
the entire process of data selection, collection, analysis and use from the start; and ensuring that any data
collected is valid, reliable and credible. It is also important that honesty is considered to achieve the previous ideas properly.

Before collecting any data it is useful to stop and assess the situation, to make sure that money and time is
not wasted. The basic principles of data collection include keeping things as simple as possible; planning
the entire process of data selection, collection, analysis and use from the start; and ensuring that any data
collected is valid, reliable and credible. It is also important that honesty is considered to achieve the previous ideas properly.

Honesty is very important..In this case the data collector might give false information to the donor and it might work out for them and eventually succeeding them but later on the truth still finds its way when the donors get the real truth about them,or get to know them personally and find something different they wil back out
Hence damaging the name of the pharmaceutical company
So honesty is important at all cost even in data collection.

Useful insights. Mostly because such honesty is likely to build or strengthen the trust with various stakeholders and vice-versa.

I think upholding the "honest principle" fosters credibility, integrity, and trustworthiness in the M&E process, ultimately contributing to more effective and ethical development work.

Honesty is an important principle in M&E. Ensure that you address any questionable M&E practices during data collection and analysis whether due to negligence or mistakes by M&E staff. Correct any questionable M&E practices even if it means collecting additional data. This probably will ensure that data collected is accurate and is a representation of the target population.

This truly gives clarity on how honesty is important. The data collected should be able to overrule personal prejudices and be presented in the most accurate and unbiased way.

Evaluators must ensure the honesty and integrity of the entire evaluation process

Reporting realistic data and reporting data to satisfy donor's expectations are important issues that needs utmost honesty.

Formulating your needs as questions or hypotheses helps ensure that you are gathering the data you should be gathering and that you are thinking about the possible gaps in the data. Of course, the questions you ask should evolve as you look at the data.

It's a five-step framework to analyze data. The five steps are: 1) Identify business questions, 2) Collect and store data, 3) Clean and prepare data, 4) Analyze data, and 5) Visualize and communicate data.

Wow am learning good stuff!

Honesty is a fundamental ethical principle that emphasizes truthfulness, sincerity, and straightforwardness in one's actions and communication. It involves being genuine and transparent, upholding the truth even when faced with difficult situations. In ethical contexts, honesty forms the basis of trust between individuals, organizations, and societies. Upholding honesty promotes integrity, fosters credibility, and strengthens relationships. It also plays a vital role in decision-making processes, ensuring that choices are made based on accurate information. Overall, honesty is essential for maintaining ethical standards and promoting a fair and just society

Honesty was, is and will always be the best policy no matter the circumstances. That said, some organizations are sometimes tempted or actually blow their numbers or mis-report findings in order to magnify their achievements and gain favour with donors, partners or other stakeholders. With regular recording of progress honestly, with time organic progression will be evident.

All personnel must be committed to telling the truth in all forms of communication and in all actions. This includes never purposely telling partial truths, selectively omitting information, making misrepresentations or overstatements.
Honesty, speaking and acting truthfully, is more than not lying, deceiving, stealing, or cheating. It entails showing respect towards others and having integrity and self-awareness.

The principle of honesty implies a general prohibition against falsifying, fabricating, or misrepresenting data, results, or other types of information pertaining to scientific publication. Honesty applies to a variety of other aspects of research, such as grant proposals, peer review, personnel actions, accounting and finance, expert testimony, informed consent, media relations, and public education. As mentioned previously, honesty plays a key role in the search for knowledge and in promoting cooperation and trust among researchers. Few scientists or scholars dispute the importance of honesty and most people understand what it means to fabricate or falsify information pertaining to research. However, a few points of clarification will be helpful.

It i important to be honest while presenting data

there is need to have accurate collected and presented

Ensuring honesty in Monitoring and Evaluation (M&E) involves accuracy in data presentation, accurate representation of findings, transparency about limitations, and disclosure of any potential conflicts of interest to maintain ethical standards.

The data must not be used for publication or disclosure of its confidentiality to determine goals

Aa an M&E specialist it is essential that you ensure program teams are abiding by the honesty principle throughout the project phase. Some of the guiding steps towards achieving honesty include.

• Cross checking data regularly to ensure that it is accurate
• Analyzing data and confirm that the findings are accurately presented
• Share with all the stakeholders the limitations of your M&E strategy
• Share with all the stakeholders any of the findings that you might have a conflict of interest in a certain outcome of your project

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a conflict of interest may be the hardest thing to show, but as I am an antrhopologist I'm quite ussed to sharing everything so that the research is as accurate as possible

I learned the importance of honesty during data collection of M&E and the principles you should follow to keep your data ethical.

Monitoring: Collecting project information regularly to measure the progress of your project or activity. This helps to track performance over time and to make informed decisions about the effectiveness of projects and the efficient use of resources.

Evaluation: Evaluation measures how well the project activities have achieved the project’s objectives and how much changes in outcomes can be directly linked to a project’s interventions.

Honesty is an important ethical principle in data collection, it insures that data is collected and presented correctly.

Be honest in all those things you are doing, be clear with the pupils and everything will be good

ce module cadre avec mes attentes

In All efforts, ensure to always observe ethical issues, identify them and work with your team members to apply the principles.

The principle of honesty in monitoring and evaluation (M&E) is paramount for maintaining the integrity and credibility of the data collected. Here are some key points for discussion regarding honesty in M&E:

Accuracy in Data Presentation:

Discuss the importance of presenting accurate data and the potential consequences of presenting misleading or incorrect information.
Share experiences or examples where inaccurate data presentation could have serious implications.
Representation of Findings:

Explore the idea that even well-collected data can be misrepresented in the interpretation phase.
Discuss ways to ensure that findings accurately represent the data without making unwarranted causal claims.
Sharing Limitations:

Consider the challenges and limitations inherent in M&E processes and how organizations can be transparent about them.
Share examples of how acknowledging limitations upfront can manage expectations and build trust with stakeholders.
Conflict of Interest:

Discuss the concept of a conflict of interest in the context of M&E and how it can compromise the honesty of data.
Explore strategies to identify and address potential conflicts of interest within an organization.
Mitigating Ethical Issues:

Brainstorm ways to mitigate ethical concerns related to honesty in M&E, such as establishing clear ethical guidelines and review processes.
Discuss the role of ethics committees or external reviewers in ensuring the honesty of M&E processes.
Balancing Transparency and Positivity:

Examine the balance between being transparent about limitations and maintaining a positive narrative about the impact of programs.
Discuss strategies for framing data in a way that is both honest and constructive.
Learning from Mistakes:

Share stories or examples where organizations faced challenges related to honesty in M&E and discuss the lessons learned.
Emphasize the importance of a learning culture that acknowledges mistakes and continuously improves M&E practices.
Stakeholder Communication:

Explore effective ways to communicate with stakeholders about the honesty principle, ensuring they understand the complexities of data collection and interpretation.
Discuss the role of clear and accessible communication in building trust with diverse stakeholders.
Real-world Applications:

Encourage participants to share their experiences with honesty in M&E and how they navigated ethical considerations in their specific contexts.
Discuss any ethical dilemmas participants have faced and how those were addressed.
Continuous Improvement:

Highlight the iterative nature of M&E and how continuous improvement processes contribute to the honesty and reliability of data over time.
Discuss mechanisms for incorporating feedback and making adjustments to data collection and reporting processes.

Ensuring honesty in monitoring and evaluation (M&E) practices is crucial for maintaining credibility and ethical conduct. Here are some key strategies to abide by the honesty principle:

Accuracy of Data:

Regularly check the accuracy of your data through techniques like data quality assessments.
Avoid presenting data that is known to be inaccurate, and ensure that the data accurately reflects the information collected.
Accurate Representation of Findings:

Represent M&E findings accurately without overemphasizing or making conclusions that cannot be proven.
Clearly communicate the level of certainty associated with your conclusions to avoid misrepresentation.
Example: Instead of claiming causation, present correlations and acknowledge alternative explanations for the observed data.

Transparency about Limitations:

Acknowledge the limitations of your M&E strategies, such as resource constraints or incomplete data.
Communicate these limitations transparently to donors, beneficiaries, and stakeholders to set realistic expectations.
Example: If your survey size is limited, disclose this information to provide context to the data.

Disclosure of Conflicts of Interest:

Identify and disclose any potential conflicts of interest that might influence the interpretation of data.
Be transparent about relationships with donors, stakeholders, or entities that could benefit from certain outcomes.
Example: If an organization receives funding from a pharmaceutical company, disclose this information when presenting data related to that company's products.

Honesty in M&E process is an important principle. Making sure of the accuracy of presented data always leads to reveal the desired truth. On the other hand accurate data has to be presented in accurate way so that chances of data representation can be avoided and outcomes will be more reliable and authentic. Misinterpretation or interpretation of data with out evidence will misguide the impact of the project. Similarly identifying and sharing the weakness of data while representation also helps to maintain the honesty in M&E process. It provides the stakeholder to be specific while analyzing the outcome of the project. Sharing the clear relation between the donor and project implementation while sharing the findings and conclusion from the collected data can create more trust among all stakeholders and also maintains the honesty.

intersting

intersting

Honesty helps in developing good attributes like kindness, discipline, truthfulness, moral integrity and more

The principle of honesty implies a general prohibition against falsifying, fabricating, or misrepresenting data, results, or other types of information. Honesty applies to a variety of aspects, such as grant proposals, peer review, personnel actions, accounting and finance, expert testimony, informed consent, media relations, and public education. Honesty plays a key role in the search for knowledge and in promoting cooperation and trust among all parties involved in a project.

Honesty in M & E entails demanding accuracy, transparency, and objectivity throughout the process. Accurate data, free from manipulation or bias, is crucial for informing effective development interventions. Transparency in methodology and analysis builds trust and allows for verification, while objectivity in reporting guarantees reliable insights that reflect reality.
Imagine a scenarios rural Kenyan teachers inflating student test scores and Malian farmers under-reporting crop yields. Honesty compels us to address these challenges with tailored solutions. In Kenya, independent monitoring, re-tests, and anonymous reporting channels can combat score inflation while satellite imagery, field validation, and community-driven data collection can tackle under-reporting by Malian farmers.
Honesty as an ethical principle is not just about technical accuracy; but about building trust and ensuring data reflects reality. By actively promoting accuracy, objectivity, and openness, M&E practitioners can guide equitable and impactful development in their project.

I love the mention of grant proposals, peer review, every little thing matters

i like this module 1: Honesty. it has broaden my understanding concerning data presentation and how import it is to be truthful as a professional M&E officer

Honestly, discharging your M&E duties goes a long way in determining outcomes. A little misinterpretation, a change in the facts or misinformation can alter not only the outcome but can also cause significant damage to the project, participants, community etc.

Honesty as an ethical has taught me many things which initially I never new or had little understanding on their application. In short, data should not be used to please people through lying but it should be collected as such. been dishonest can result in fake decisions and the donors or any other interested could be at pain if they discovered that they were been cheated upon and risk losing funding. It is also important to acknowledge any challenges that were observed during collection of data and always these are usually there to avoid been so over confident with the results. It is import to avoid too strong worded conclusions that might give over confidence but better present in a manner that is just clear unlike trying to exaggerate the findings

To ensure that Monitoring and Evaluation (M&E) practices cause no harm to participants, stakeholders, or other people, and to address the concerns raised in your scenario, you can follow these specific steps:

1. User-Centric Data Collection:
   Design data collection processes to be user-friendly, minimizing stress, confusion, and time demands on participants.
   Use simple and intuitive tools and methods, avoiding unnecessary complexity.
2. Informed Consent:
   Prioritize obtaining informed consent from participants, ensuring they understand the purpose, risks, and benefits of data collection.
   Clearly communicate the details of participation and provide opportunities for questions.
3. Legal and Ethical Compliance:
   Familiarize yourself with relevant laws and regulations governing data collection, especially when working with vulnerable populations.
   Ensure strict adherence to ethical guidelines and obtain necessary approvals from ethics committees.
4. Anonymity and Confidentiality:
   Keep participant data anonymous whenever possible to protect privacy.
   Establish and uphold confidentiality agreements, limiting access to those with explicit permission.
   Regularly review and update security measures to prevent data breaches.
5. Transparency in Data Handling:
   Clearly communicate to participants how their data will be handled and stored.
   Be transparent about the measures taken to ensure data security and confidentiality.
6. Risk Assessment:
   Conduct a thorough risk assessment to identify potential harms associated with data release.
   Develop strategies to mitigate risks and protect participants from harm.
7. Equity Considerations:
   Assess potential impacts on existing social inequities and work to avoid reinforcing stereotypes.
   Consider how data may be used to exacerbate inequalities and take steps to mitigate these risks.
8. Stakeholder Engagement:
   Engage with stakeholders, including participants, throughout the M&E process to gather input and address concerns.
   Consider the perspectives of all involved parties to ensure a comprehensive understanding of potential impacts.
9. Regular Ethical Reviews:
   Conduct periodic ethical reviews of your M&E practices to identify and address any emerging concerns.
   Seek external input, such as ethical review boards or community representatives, to enhance objectivity.
10. Strategic Communication:
    Develop a communication strategy to responsibly share findings and insights from the M&E process.
    Provide context and emphasize the limitations of the data to avoid misinterpretation.
11. Advocacy for Responsible Data Use:
    Advocate for responsible data use among stakeholders and the broader community.
    Highlight the importance of interpreting data accurately and avoiding misuse that could cause harm.
    By integrating these considerations into your M&E practices, you can minimize the potential for harm to participants, stakeholders, and other individuals involved in the data collection process. It's crucial to foster a culture of ethical awareness and continuous improvement to ensure the responsible and beneficial use of collected data.

Being honesty is very important when it come to data.

In the context of Monitoring and Evaluation (M&E), honesty refers to the ethical and transparent communication of information related to the performance, results, and impact of a project, program, or intervention. This principle is crucial for maintaining the credibility and integrity of the M&E process.

As M&E professionals, we all know that when we claim that the data is standard. In my opinion, maybe there are many ways to respond, but the most important sign for standard data to reliability.

To ensure that my team is abiding by the honesty principle I have to make sure that the data I present is accurate, that findings from my M&E are accurately represented. I should as well be honest about limitations with donors, beneficiaries or anyone else who might be interested in my work. I will also have to share any areas where I might have a personal or professional interest in a certain outcome (a conflict of interest).

Hello,
I have a question.
if our honesty shows negative aspect of our project how we can share with donor

Honesty brings peace of mind, Honesty helps one to gain trust from people that surround you.

If you are involved in Dishonesty when collecting or analysis data you can loose trust from partners, Donors, in short from every stakeholder

Honefd

From the previous lesson on the importance of "Do No Harm" during M&E data collection, I have learned several key points. First and foremost, I now understand the ethical considerations and responsibilities that M&E practitioners have towards the well-being and safety of participants. Respecting their rights, maintaining confidentiality, and obtaining informed consent are paramount.

I have also realized the significance of avoiding harm and unintended consequences during data collection processes. By proactively identifying and mitigating risks, practitioners can ensure that the data collection process does not cause adverse effects, stigmatization, or negative outcomes for participants.

Trust and cooperation emerged as vital components of successful data collection. Building trust with participants by adhering to "Do No Harm" principles fosters cooperation and willingness to engage. This, in turn, enhances the quality and reliability of the collected data.

The lesson has also highlighted the importance of valid and reliable data. By prioritizing the well-being of participants and creating a safe environment, M&E practitioners can minimize biases, encourage honest and accurate responses, and reduce the potential for data manipulation or coercion. This ultimately contributes to meaningful analysis and decision-making.

Furthermore, I have learned that the principle of "Do No Harm" has long-term implications for the impact and sustainability of development initiatives. By upholding ethical standards and protecting participants, M&E practitioners contribute to positive change and prevent unintended negative consequences.

Honesty is very essential in M&E. You should always ensure that the data you present is accurate and the findings are well presented.

Honesty is an essential aspect of effective monitoring and evaluation (M&E) processes. It is crucial to collect and present data accurately, without any manipulation or bias. By being honest in our approach, we can ensure transparency and trustworthiness in our findings.

Furthermore, it is important to be honest about the functioning of our M&E processes. Clearly explaining how data is collected, analyzed, and utilized allows stakeholders to understand the methodology and make informed interpretations of the results.

Honesty also entails acknowledging any limitations in our work. No M&E system is perfect, and it is important to openly discuss and address the constraints, such as resource limitations, data quality issues, or methodological challenges. By acknowledging these limitations, we can provide a realistic and unbiased assessment of our findings.

Honestly really is key even on day to day communication. As an M & E professional there should be no room for intentional lies, we need to ensure that we do our best to present the best

Very true, even though it doesn't meet the donors expectation it should be communicated with honesty.

The scenario on Honesty brings me back to serving humanity, specifically the refugee population. Believe me, being a refugee, one has high expectation. There are issues pertinent to conflict of interest more and more.
One could be supervising the Livelihood Program for refugees. As the organization provides inputs/supplies to the refugee, the Programmer has a confidant who accomplishes all of his missions. On that note, he takes portion of the inputs/supplies and divert them to his personal livelihood project through his confidant. He realizes that these inputs continue to yield more results.

Unfortunately, these inputs aren't very effective at the project site, as the refugees requested a revisit. Instead of the Programmer considering the opinions of refugees, he continues to order these particular inputs to boast his mini-project. This is a complete conflict of interest and dishonesty at high esteem. Enriching oneself, while the actual beneficiaries perish is dishonesty.

Inaccurate Data (Participant Tracking Form) > the confidant is normally marked present during sessions; though he's at the Program Staff's farm working.
Increase Orders for Unwanted Livelihood inputs > the Program Staff will continuously deceive management to purchase these inputs.
His conclusions will be biased and favorable only to him.

The accuracy of the data should be captured in the findings or rather the conclusions. Also, we need to share the limitations of the M&E study to the donors and also the stake holders to avoid the conflict of interest.

The accuracy of the data should be captured in the findings or rather the conclusions. Also, we need to share the limitations of the M&E study to the donors and also the stake holders to avoid the conflict of interest.

This is very right but now M& E have budget constraints, you might not be able to go an extra mile to collect more data. However, it is always good to verify the nature of the data collected.

Honesty in M&E can be generalized to mean the accuracy of the data. This means that the data collected should be made accurate in whichever means possible and this will translate to accurate findings. Again, it is always paramount to share project limitations with the donor or any other stakeholder with high consideration of conflict of interest effect.

It is very paramount to ensure that integrity leads all research processes.

Information should be very original and relevant to make sure research objectives are achieved.