Honesty is a component of moral character that connotes positive and virtuous attributes, such as integrity, truthfulness, and openness — including clarity of conduct, along with the absence of lying, cheating, theft, etc. Honesty also involves being reliable, trustworthy, loyal, fair, and sincere

Collect, use and present your data accurately. Be clear about how your M&E processes work and be honest about any limitations to your work.
should not present data that you know is inaccurate. However, it also means that you will need to constantly check the accuracy of your data. In Module 3, you will learn about data quality assessments, one technique for ensuring that your data is accurate.
Be honest about these limitations with donors, beneficiaries or anyone else who might be interested in your work. Ideally, share these limitations before your project starts, so that people do not develop unrealistic expectations.

honest provides the true picture of the data at hand without any selfish interest. this should be done in such a way that whether there is a benefit or not, when it comes to information delivery; it should not be controlled by what should be gained if one answered in a certain way. it should be known that any unhonest information can bring damage to the image the person and the company or organisation

How can they ,donor side, know/measure the presented data has honesty?

Am enjoying what am learning so far

Interesting,
Simply not lying is not enough. True. Accuracy in data collection, conclusion, sharing of limitations and conflict of interest in your presentation. I just hoped I could get more...

We need to make sure that the data we present is accurate and be honest about the limitations of the M& E strategies.

It is very hard to maintain the values you have while analyzing data. However, honestly makes the data reliable. hence we should focus on what is valid and reliable as we want smooth and authentic output.

The most important thing always to put at the back of your mind when gathering data in HONESTY, because that will be the basis in which people will accept your research or the analysis of your data.

The most important thing always to put at the back of your mind when gathering data in HONESTY, because that will be the basis in which people will accept your research or the analysis of your data.

Absolutely, true.

honest is a principle for the data, which it's important to led the monitoring and evaluation be in the correct and truth process

Quite insightful, its quite interesting to note that "honesty"as a principle is quite broad in scope

M&E means measuring the truth about what we are trying to learn about. Therefore, we need to make sure to collect the data accurately and in order to come close to accuracy we need to constantly check it, after collecting the data we need to represent it accurately as well, we should be clear about the findings and not too strongly word it if we are not 100 % of the results. If, for whatever reasons, we had limitations on data gathering, we need to share it with the donors, stakeholders etc. Conflict of interest should be out of our M&E process.

Honesty is an important ethical principle that cannot be overemphasized time and again..

Honest as another ethical principle for Monitoring and Evaluation professionals as it ensure that the data collected is honestly and transparently collected, managed, analyzed and presented.

HONESTY-THE DATA YOU COLLECT SHOULD BE A REFLECTION OF WHAT IS HAPPENING ON THE GROUND.ACCURATE DATA WILL HELP MAKE INFORMED DECISIONS

In summary, the principle of honesty requires us to ensure that

• the data we present is accurate
• the findings of our M&E are accurately represented
• the limitations of our M&E strategy are known;
• And that any conflicts of interest regarding the project are publicly known.

Honesty in all sectors of professionalism is important. IN order to present accurate data, honesty has to be applied.
Being explicit with information is also a good thing.

It is obvious that often times you are honest i your every day life either at home, work or community which is moral, however, the standard of that is even higher for M&E professionals than it is for other people this is because data from M&E processes are expected to be as close as possible to truth. ways to ensure that your is abiding to the honesty principle includes:
Ensure the date you present is accurate
Ensure that findings from your M&E are accurately presented
Share the limitations of your M&E strategies
Share any areas your or organization may have conflict of interest

Honesty is indeed one of the ethical principles that must be truly applied in all the M&E processes. this makes decisions to be made using the data to be real and honest decisions as well.

Honesty is very key as a M&L professional. This is enhanced through ensuring that the data you present is accurate, ensuring that findings from your M&E are accurately represented, share the limitations of your M&E strategies, share any areas where you might have a personal or professional interest in a certain outcome.

Conflict of interest is a regular bias that can be encountered in data presentation and analysis. Most of time organization are motivated by political benefits

Any donation for data collection, analysis or presentation should be recorded in the organizations data base.
Should be used in the organization development and all members should be taken into concedration

Honesty: When we collect the data, we must honest to communities.

Honesty is one key aspect in data that must be observed. This is because of the purple of data, which is mainly for informing decision making.

conflict of interest truly should be shared because some people go into a project to get certain results which may not be in tandem with whats obtainable thereby making them alter data and data looses credibility.

Honesty is the best way to go in M&E, ensuring the audience your data is being presented to, have a holistic context with which to interpret the data.

I have observed that many organisations represent wrong information/findings in the sense that it's they are generised. This is usually committed as a way to entice donors or stakeholders. With this all the stakeholders need to be alert and establish checks that would satisfy whether the title ry present whats on the ground

Honesty in data collection and presentation is very important. I look at a situation where my organization working to get the rural folk to earn more from their dairy ventures actually provide me with information that informs a gap in the market. This is one that i can benefit from hugely but again ethically wrong not to disclose this. There is a lot that still needs to be done to handle this successfully

Honesty uprightness · honorableness · honor · integrity · morals · morality · ethics · principle · (high) principles · nobility · righteousness · rectitude · right-mindedness · upstandingness · virtue · goodness · probity · worthiness · high-mindedness · justness · fairness · incorruptibility · truthfulness · truth · veracity · trustworthiness · reliability · conscientiousness · scrupulousness · reputability · dependability · loyalty · faithfulness · fidelity · sincerity · candor · frankness · directness · forthrightness · openness · straightforwardness · plainness · genuineness · bluntness.

honesty means that clear and real picture should be presented. Transparency should be adhere. and risk need to be identify at early stage and more plans need to be developed to respond to the early alarming situations.

This is very important aspect in M&E (Honesty) the indicators we are choosing should be Honesty and presented well

The key take away if the need to ensure that what is told to participants as the purpose, data required and intended use be truthful to avoid issues around incorrect information due to dishonesty.

Honestly can not be over-emphasized. This is one of the first things I learned about research.

Honesty should be the important aspects during the presenting the data of organization

Truth wins the world. In M&E data tools and processes must accurate to ensure quality data collection.

The need for Honesty in data analysis can not be overemphasized. It has become pertinent to ensure that data reported is done with utmost honesty this is because a dishonest data will lead to an inaccurate evaluation that could affect the very people interventions are intended to help.

Every researcher should thrive to collect and present accurate data regardless of their own personal hindrances. They opt to know that a very wide society depends on their research and findings foe education, good management and governance and even health researches.

For me, honesty in data is a major thing to consider. because it show and defines the integrity of your data and results at the end of the day. The data will not be misleading and the out come can be reliable anywhere.
Honesty pay always in the end.

Hi.everyone.good course

Data collection to data use .importants to archaving

Honesty is a phenomenon that entails dessimination of accurate and validated data with no conflict of interest.

Honesty will give accurate data and will bring determined change in the results

The ethical principle of honesty is very crucial in data collection, management,analysis, use and presentation. M&E personnel should as a matter of responsibility ensure complete compliance to all aspect honesty principles.

My take home from "Honesty" is that honesty for M & E Professionals is not optional

Integrity is key in data, ensure accurate data is collected and presented. Identity your limitations.

This is true.

Sharing the honesty part of what the data will be used and ensuring the data quality, helps create a balance and give a clear indication in the communities or among participants how the information shared or collected can be of importance to there communities or lives.

Honesty is simply collecting, using and presenting your data accurately hence, requires one to be open and clear about how his/her monitoring and evaluation processes work and further provide some of the limitations to his or her M&E work.
However, the standard of honesty is higher for M&E professionals than it is for other people. Honesty is all that matters in obtaining the best and good quality i their work.
Data accuracy basically rely on the honesty virtue and if dishonesty is encouraged they will be obtain wrong information leading to poor quality work.

Any organization that becomes lifted up due to honesty as a principle of ethical behaviour is resulted when data is collected,manipulated and analyzed before presentation to the donors and even to the individuals.The information from the organization must be clearly reliable and efficient for a smooth use when placed for a reason,

It means that the researcher or the person evaluating the samples
Should have some responsibility for the data collected and not comply with the organization's directives
If it causes problems for society?

Of course, accuracy is required in collecting data, and this is a trust on the researcher and on organizations

But I think that state-funded research seeks to polish the image of governments, especially in the countries of the Middle East

Honesty principle in M&E is crucial and should be seriously considered

Absolutely

Good observation

I have seen in the past an organization working on the HIV project that falsified its data and this caused the project to stop for this NGO because the donor no longer has confidence and its data is no longer reliable or credible.

As an old adage states-- "Honesty is the best policy."
This reflects that an honest team is a successful team because it tries its best to work for excellence by recognizing its flaws and improving its functionalities for the benefit of the society.

Being honest is being true to oneself.

What if the honesty would mean harm to the reputation of leaders, who are could be responsible for what the organisation advocates against?

Honest is positive character which makes you trusted to the people and accept the result of the data you collect

Data collected should not cause any harm to people who were directly involved or participated in the collection of data, this includes the stakeholders. This means, as M&E we should consider the experience of the people we are collected data from. This module emphasized that, we should honor the dignity, well being and self worth of the individuals. Therefore, in this scenario the data released cannot be used by the politicians because it will harm the people who participated in gathering the data. This data will not serve the purpose, because the politicians will view it based on the history of the participants. So, in this case the data is suppose to be kept anonymous and confidential, to protect the participants dignity, and their wellbeing.

It is evident that honesty is a key, therefore, we need to be transparent on the data is collected, and limitations should be discussed upfront. This includes consent of the people participating, to ensure confidentiality. This means, we should discuss the processes to be used to collect data, the manner in which the data will be analyzed and managed and presented.

i think its very interesting

i think its very interesting

Of course transparency and honesty is important to any study, but it is valuable to stress its importance in M&E. The advice of sharing the limitation of your M&E strategies was helpful because I feel as if most people would not share their limitations; it may downplay the work they had did. But in fact it can strengthen your work because it shows the truth. The truth being that data collection won't be perfect so erasing that expectation can allow for people to get more in-depth data analysis.

Honestly, is key in everything we do. It builds trust and credibility. Therefore, we should avoid dishonestly in M&E

ACCURACY OF THE DATA
I am data capturer by profession for 5 years. I have seen that data is not discarded, it is stored. Even if the data is old, it still influences the current decisions. There are many stakeholders that depend to the data. High profile people use data for procurement and making decisions. Good data quality provides a certain level of confidence to all who depend on that specific data. If data quality is good, the users will be able to produce better outputs. This increases business efficiency and lowers the risk in outcomes.

ENSURE THAT FINDINGS FROM YOUR M&E ARE CORRECTLY REPRESENTED:
Correction data interpretation is essential. A company’s planning, forecasting, budgeting, business intelligence, and all such activities are based on the accuracy of the data. If the data is irrelevant or incorrect, it can disrupt the entire working system of the business. It's important to interpret data as is not to exaggerate and threaten listeners or build high hopes. It is ideal to stay in the context of the numbers not to negative or too anxious about the future.

SHARE LIMITATIONS OF M&E STRATEGIES
Reporting tools should be renewed and updated due to the work on the ground that keeps on changing. The reporting tool announces what is happening on the ground. Data can never be 100% accurate, but it can be more or less accurate depending on how close it adheres to reality. The closer that data sticks to reality, the higher its accuracy will be. Data accuracy refers to the degree to which data reflects what really happened in real life during a specific time period.

Understanding the people

It is imperative to know the kind of people that are used as a target to collect data from so that the people that use it can have confidence to use it. The target group should be analyzed in terms of the level of education that the target group has. Knowing the education level will help the data collectors to be aware of which method should be used to collect data. Failure to analyze the target group will result in irregularities in data collection.

Observations

Taking tone of the habits of people will make a huge impact in effective data collection. The customs tendencies and habits of people are very vital to bear in mind. Other cultures forbid for a stranger to come in someone's house and stand in the yard. A stranger should sit or squat if no mat or chair is available at the time. Getting acquainted about culture of the people is very vital, this will enable a smooth access to them.

An organization is providing counseling services to girls. They collect and present some data about the effects of their is like my organization because people don't think about the others.

Honesty is the practice of being honest and showing a consistent and uncompromising adherence to strong moral and ethical principles and values. In ethics, honesty is regarded as truthfulness or accuracy of one's actions.
For an organization or party tp collect data accurately, they should be clear about how their M&E processes work and be honest about any limitations to their work. For a arty to abide by the honesty principle, it should allow strictly the following; ensure the data you present is accurate, ensure their findings from M&E are accurately presented, share the limitations of their M&E, share their areas of conflict of interest

Honesty is the practice of being honest and showing a consistent and uncompromising adherence to strong moral and ethical principles and values. In ethics, honesty is regarded as truthfulness or accuracy of one's actions. The topic of honesty has been educative as it has exposed me to what I dint knew majorly concerning integrity. For one to be more successful they must adhere to the honesty principle.

Honesty as an ethical principle
As a monitoring and evaluation officer, you should ensure that you collect,use and presents your data accurately
You should ensure that findings from your monitoring and evaluation are accurately represented

Honesty as an ethical principle states that,M&E should collect,use and present data accurately .
Findings from M&E should be accurately represented

Honesty is important in data collection, analysis and management to avoid bias.

This is the most crucial part in M&E. How can you balance between funding and pure honestness?

From my perspective its very important for data collectors to avoid collecting cooked data or fake data because it can be come a challenge if a doner was to go on the ground to sack information from the targeted participants or beneficiaries which is different with the report who was sent to the doner .so data collectors are expected to be honest with the way they collect data

Honest is key for M&E. Without it, the result of evaluation will be unreliable.

#honesty is the highway of success also anti lie

honesty the best gift your can give

this module honesty teaches us to avoid lying while presenting information from the field and avoid self interests while conducting data collection

Honesty goes a long way. It builds character and makes one more viable. It is not always the easy thing to do and in some cases may even call for going against te the grain. Even then, the rewards of honesty are great in that you do not havr to keep looking over your shouder wondering which lie has caught up with you.
I would take honesty any given time.

Interesting synonyms. They help bring out the extent of what entails honesty.
Thank you.

It may even still be possible to stage manage the ground to apeal to the donor. The challenge comes about living up to expectation. The dishonesty sets a standard that cannot be proven, or even replicated. At some point the lie catches up with you and once discredited it is hard to recover, if at all.
It is not worth the trouble lying, you would rather not takle that role.

I agree that sharing limitations does indeed stregthen outcomes. It manages expectations and adds validity to the outcomes. Without which, it distorts what would have been a good outcome by making the results seem doctored. The falsehood cuts across, as the persons implementing are fooled into what is not true. Sharing limitations also allows for opportunity that they may be covered at a later point to get a more accurate outlook.

honesty in data collection process is a two-way concern, first from researcher point of view to keep your promises about confidentiality and safety of the individual involves in data collection procedures, secondly from interviewee perspective specially when it comes to electronic interview so levels of honesty is very low

Ensuring that data is honestly collected needs transparency from the m$e team.
Accuracy of data should always be maintained as well so that biased data won't be collected.

Honesty is an important ethical principle in data collection. It ensures that data is collected and presented correctly. M&E professionals are expected to exercise high standards of honesty. Honesty need to be applied in data collection, management, analysis and presentation. There are a number of ways to ensure that one is abiding to honesty principle;

1. It is important to ensure that the presented data is accurate.
   2.It is important to declare conflict of interest if they exist.
2. Findings should be accurately presented.
3. It is important to share the limitations of the project before it starts so as to avoid unrealistic expectations.

For an organization or party to collect data accurately, they should be clear about how their M&E processes work and be honest about any limitations to their work. For a arty to abide by the honesty principle, it should allow strictly the following; ensure the data you present is accurate, ensure their findings from M&E are accurately presented, share the limitations of their M&E, share their areas of conflict of interest

In what manner or approach or form, do I disclose the conflict of interest between my organisation and donor during data presentation?

in this module it actually brings out how accurate we should be while presenting our data.
additionally honesty is needed while sharing the limitations of your M&E strategy.
still, sharing the areas where you might have a personal and professional interest in a certain outcome that is the conflict of interest is highly recommended in this module.

the standard of honesty is even higher for M&E professionals than it is for other people.

The data collection process should be honest and through sharing any anticipated challenges in executing the task both to the donors, beneficiaries or any person interested in your work.
Issues of conflict of interest should be declared. The reporting of study finding should not be strongly worded or inaccurate.

La honnêteté te fera que les autre te respecte.

It is indeed very important to be always honest when collecting and presenting data because it promotes cooperation and trust among researchers and organizations as well.

its reall true for if we need to change the world with information from our data then honesty is paramount

Collect, use and present your data accurately. Be clear about how your M&E processes work and be honest about any limitations to your work.

Collect, use and present your data accurately. Be clear about how your M&E processes work and be honest about any limitations to your work.

Sharing data to potential Funders about a students neighborhood and address and staying that these families income is poor or poverty level because the neighborhood is a predominantly African American neighborhood

Honesty, in this part of the module has been referred to as the demonstration of utmost faith when reporting the outcome of collected data. Avoiding conditions that will defined as a conflict of interest is very important. Also, showing the limitations or hindrances that influenced the scope of the collected data is also another important matter. Data should be collected and reported in the most correct possible manner.

In this principle, you need to be sure that data collected is accurate. Failure to be accuracy may result in damage of data.

To avoid inacurate data, make sure that you share your M&E strategies to donors before the start of the project .