Category: Integrity

Adequate data management in research has become an increasingly important topic nowadays when the modern tools facilitate the gathering of information, and the pure amount of raw data can become overwhelming. Dealing with issues in data management requires accuracy, efficiency and compliance with certain guidelines and accepted standards, including the rules of ethics.

Maintaining the data integrity demands special effort on the part of the researcher. According to the guidelines published by Medicines and Healthcare products Regulatory Agency (MHRA), the universal, suitable for any type of research data, integrity requirements consist of the following points:

Establishing data criticality and inherent integrity risk & [with] overarching data governance system, which should include relevant policies and staff training in the importance of data integrity & the organizational (e.g. procedures) and technical (e.g. computer system access) controls applied to different areas of the quality systems.. [and] designing systems to assure data quality and integrity & in a way that encourages compliance with the principles of data integrity. (MHRA GMP Data Integrity Definitions and Guidance for Industry, 2015, p. 1-2)

The essential examples of such principles include consistency, legibility, completeness and relevance of the data generated in research, as well as its completeness throughout the time of the research. It is also important to keep in mind the risks, for example, human factor, software and hardware faults, conscious falsification of results, accidental errors, and changes in records or technology, which should be avoided by all means (McDowall, 2013, par. 2-9).

After collecting the data, the researcher may proceed to its interpretation however at that stage it may be difficult to remain unbiased. Focusing on the anticipated results of an experiment can affect the interpretation and even cast discredit on the research in whole. Ted Kaptchuk (2003) describes this effect in his article, claiming that the data-judgment interaction is rarely taken into account as there is no objective way to measure the subjective components of interpretation, while researchers tend to evaluate the evidence supporting their beliefs in a different way than the one that distinctly challenges such convictions (par. 2-3).

However not every research should be considered irrelevant due to the biased interpretation, Robert J. MacCoun (1998) argues that interpretation bias is a common phenomenon with a varied motivational, intentional, and purely cognitive determinants, however there is evidence suggesting that the magnitude of biases is often small and they are subtle; a key role the in identification of biased research interpretation and its sources have played the systematic empirical methods; finally, the biases are not all indefensible and the differing standards of proof are completely acceptable, provided that the researchers are self-conscious about standards and stances, and are explicit about them (p. 36).

Finally, for a researcher there always exists a moral barrier in a form of ethical guidelines. The transfer of research data to fellow researchers or academic institutions should be carried out with great care, and it should be considered whether or not such transfer is necessary.

An advice on data protection and transfer, published at the website of Cardiff University, states that the data should be secured during the transit; sharing the personal data with other organizations should be conducted under the Data Protection Act; access to personal data must be restricted to the minimum number of the research participants, and the issues with identifiability should be avoided as well (FAQs on Data Protection, n.d., par. 10-20). Another guideline sums it all up: However simple or complex your data set, think about what you might need to do to ensure that your management of the data respects the terms of your consent, and in particular, the confidentiality and anonymity that participants were promised (Data storage and data security, n.d., par. 14).

Reference List

Data storage and data security. (n.d.). Web.

FAQs on Data Protection and Writing a Research Protocol or Applying for Research Ethics Approval. (n.d.). Web.

Kaptchuk, T. J. (2003). Effect of Interpretive Bias on Research Evidence. BMJ: British Medical Journal, 326(7404), 14531455. Web.

MacCoun, R. J. (1998). Biases in the Interpretation and Use of Research Results. Annual Review of Psychology, 49, 259-287. Web.

McDowall, R. D. (2013). FDAs Focus on Laboratory Data Integrity  Part 1. Web.

MHRA GMP Data Integrity Definitions and Guidance for Industry. (2015). Web.

The main idea behind the relational database management system (RDBMS) is the concept of relationships that exist among objects or entities. A relationship is an association of entities based on how they interact. The following terms are used in relation to modeling a relational database model: Entity, Attributes, Primary Key, and Foreign key (Begg & Connoly, 2003). Using a RDBMS, three types of relationships can be defined among entities: one-to-one, one-to-many, and many-to-many. To be appropriate, relationships must observe some sort of data integrity. In this context, the following paragraphs describe the importance of data integrity.

The three fundamental types of data integrity are entity integrity, referential integrity, and domain integrity. Entity integrity is a form of data integrity that requires every table in a relational database to have a unique column or columns that ensure that each row is uniquely identified. The unique column or columns is known as the primary key, thus no duplication of rows within a given table is acceptable. In essence, a primary key should be unique but not null (Gilfillan 2000). To ensure that there is validity and accuracy of data, a database designer should be able to select the correct primary key that reflects the requirements of the database in regard to information search, updating, and deletion of the stored data. For instance, when designing a school database, tables such as the STUDENT table and COURSES table may be included in the database. Thus, to ensure the accuracy of the entities, student number in the STUDENT table can act as the primary key, while course number in the COURSES table may act as the primary key. Both student number and course number uniquely identify other information in their respective tables hence entity integrity.

Another type of data integrity is the referential integrity. These are rules that are followed to preserve the defined relationships between tables when records are added or deleted. Referential integrity is typically enforced with a primary key and a foreign key combination. To link a table with another table, these two keys must exist. A foreign key is a key in one table that must match a primary key in another table to establish a relationship. Moreover, referential integrity requires that the matching fields must have the same data type, field properties, and same data. Secondly, both tables must belong to the same database. Lastly, a record cannot be deleted or updated from the primary table if matching records exist in a related table (Watson 2005). An example of referential integrity may exist in a Company database in which we have the EMPLOYEE table and the DEPARTMENT table. To connect EMPLOYEE table to DEPARTMENT table, Employee number field acts as a primary key and Department number field act as a foreign key, thus ensuring consistent relationships. Enforcing referential integrity is typically provided by the database management system (Navathe, 2000).

Lastly, domain integrity defines the required features of a specific column in a table. Every column has a certain domain that stems from the specified data type. Domain integrity is characterized by the data type, the NULL value recognition, the allowable values, and the default value. For instance, if we define the attribute of UnitNumber, of a STUDENT entity, as an integer, the value of every occurrence of such element must be a number and an integer. If we specify that the value should be positive, then a negative figure is not allowed. Again, having it as mandatory value in the column shows that the column is specified as NOT NULL (Begg & Connoly, 2003). In essence, each attribute should be specified with the following domain information: Data Type, Length, Date Format, Range, Constraints, Null Support, and Default Value. Domain integrity ensures consistency of data, validity of data, and identification of data.

Therefore, entity integrity, referential integrity, and domain integrity are important in relational databases because they ensure that data is valid and consistent, and thus relationships are set. Secondly, they reduce the redundancy of data since records can only be entered once. Thirdly, searching records is much faster (Gilfillan Ian, 2000). For instance, having Product number as a primary key in a Products table ensures that records are stored once and searches become faster. On the other hand, having supplier number as a foreign key in the same table enable a relationship to be set between the Product table and the Supplier table, hence increasing searches and lessening design difficulties.

References

Begg, C., & Connoly, T., 2003, Database Systems, 3rd Ed., Pearson Education Limited, Delhi.

Gilfillan, I., 2002, Introduction to Relational Databases, Web.

Navathe, S., 2000, Fundamentals of Database Systems, 3rd Ed., Addison Wesley, USA.

Watson, R., 2005, Data management: databases and organizations, 5th Ed., John Wiley, New York.

Introduction

In this scenario, its important to realize that data integrity and confidentiality is achieved by ensuring that only the authorized person(s) gain access to the data and that during transmission, storage and retrieval data originality is maintained.

That is to say no alterations can be made whatsoever during these processes. We also know that the store employees are not authorized to access data sent for storage by their clients. This essay tries to discuss how this can be achieved from a CIOs viewpoint.

Secure transmission and storage

Analyzing the requirements for this to be achieved we realize that, data should be illegible during transmission to the store and back to the customer since it is wired over a public network, (Merkle 1978); also the store staff should not be able to read it.

In this case we employ cryptography which is a mechanism for encoding character strings using some predetermined keys and encryption algorithm, ( Curry, 2001). The sender encrypts the message changing it to a form known as a ciphertext and the receiver on the other hand decrypts it to obtain the original message.

In our case, the receiver should not be able to read the message, therefore we use double encryption. First, the CIO should come up with a set of two keys, a public key PuKs and a private key PrKs. PuKs should be made known to all the customers, while PrKs should be kept secret only known to the staff in charge of data receipt and transmission.

Each customer also comes up with a set of two keys public, PuKc which is given out and private PrKc which is only known to the customer. He also comes up with another key PKc only known to him. The sender in this case, the customer encrypts the data using an algorithm and the key PKc.

This ensures confidentiality of data since only the owner has got the key so only him can read this data. However, the receiver, the store in this case needs to verify that the message received is the original one sent by the customer it has not been tampered with on the way.

Since the store employee receiving it cannot read it, the originality of sender can be verified by use of a digital signature. Both parties agree on some verification code known as the digital signature, (Rivest, Shamir and Adleman, 1978).

When the customer is sending his data, he encrypts it with PKc, generates a hash function, (Niven and Zuckerman, 1972) of the original verification code and attaches it to ciphertext, C1 obtained and encrypts the combination a second time using his private key PrKc to get ciphertext, C2 and sends it to the store.

When data is received at the store, the receiver decrypts it with the key, PuKc to verify the signature and if it matches the originally agreed code, then the data is original from the customer. The signature is removed from the message and the C1 is stored. This ensures data integrity. This method is made secure by the fact that any changes made to the data, will translate to a change in the digital signature, (Calabrese, 2004)

When the customer requires his data, the original ciphertext from the customer is retrieved from storage. Then the store employee in charge appends his digital signature to the ciphertext and encrypts it with the stores private key, PrKs.

He then sends it over to the customer who decrypts the message with the stores public key, PuKs which he already has to verify the sender. If the signature matches the original code, then the ciphertext is original from the store. He then detaches the signature from the ciphertext and decrypts the ciphertext with his private key PKc; the one he used to encrypt.

Secure email exchange

In the store, the staffs agree on an encryption method to secure their email exchange. Each member comes up with a set of two keys a public and a private key. Each of them gives out his public key to all the others but keeps his private key secret, (Curry, 2000)When party A wants to send a confidential email to party B, he encrypts the message with party Bs public key and sends the ciphertext to party B.

On receipt, party B decrypts the message with his private key and reads it, (Potter, 1977). To verify the originator and integrity of emails, the sender appends his digital signature to the message, the signature in our case could be the senders name for example but he encrypts it with his private key and the receiver can decrypt it using the senders public key which he already has, (Diffie, Hellman, 1976)

Secure backup transmission

During backup data transmission, StoreItRite employee in charge should combine all ciphertexts received from their customers during that day and encrypt it using a key only known to them, which is their private key PK3. The ciphertext, C3 is then sent over the leased line as one packet hence cutting on cost.

Conclusion

In conclusion we can say that encryption is the most appropriate way to ensure data security during transmission over a network and also during storage. Digital signatures enhance this security by providing a way of verifying that the data received is original from the sender.

No changes have been made along the way since it is only the sender who has his private key which was used to encrypt the signature. Double encryption technique enhances this integrity check by ensuring that any changes made to the data translate to a change in the digital signature.

References

Calabrese T. (2004) Information Security Intelligence: Cryptographic Principles & Applications. Delmar Learning. New York. Clifton Park.

Curry Ian, (2000), Key Update and the complete story on the Need for Two Key Pairs. Version 1.2. Canada. Entrust Technologies.

& (2001). An Introduction to Cryptography and Digital Signatures. Canada. Entrust Technologies.

Diffie W. and Hellman M. (1976) New Directions in Cryptography, IEEE Transactions on Information Theory 22(6). 645-650. Web.

Merkle, R. (1978). Secure communications over an insecure channel. Communications of the ACM, 21(2):120126.

Niven, I., and Zuckerman, H.S. (1972). An Introduction to the Theory of Numbers. New York. Wiley.

Potter, R.J., (1977) Electronic mail Science. Federal Register 40, PP 84-86.

Rivest, Shamir, Adleman (1978). A method for obtaining digital signatures and public-key cryptosystems; Communications of the ACM, 21(2) PP120-126.

Adequate data management in research has become an increasingly important topic nowadays when the modern tools facilitate the gathering of information, and the pure amount of raw data can become overwhelming. Dealing with issues in data management requires accuracy, efficiency and compliance with certain guidelines and accepted standards, including the rules of ethics.

Maintaining the data integrity demands special effort on the part of the researcher. According to the guidelines published by Medicines and Healthcare products Regulatory Agency (MHRA), the universal, suitable for any type of research data, integrity requirements consist of the following points:

Establishing data criticality and inherent integrity risk & [with] overarching data governance system, which should include relevant policies and staff training in the importance of data integrity & the organizational (e.g. procedures) and technical (e.g. computer system access) controls applied to different areas of the quality systems.. [and] designing systems to assure data quality and integrity & in a way that encourages compliance with the principles of data integrity. (MHRA GMP Data Integrity Definitions and Guidance for Industry, 2015, p. 1-2)

The essential examples of such principles include consistency, legibility, completeness and relevance of the data generated in research, as well as its completeness throughout the time of the research. It is also important to keep in mind the risks, for example, human factor, software and hardware faults, conscious falsification of results, accidental errors, and changes in records or technology, which should be avoided by all means (McDowall, 2013, par. 2-9).

After collecting the data, the researcher may proceed to its interpretation however at that stage it may be difficult to remain unbiased. Focusing on the anticipated results of an experiment can affect the interpretation and even cast discredit on the research in whole. Ted Kaptchuk (2003) describes this effect in his article, claiming that the data-judgment interaction is rarely taken into account as there is no objective way to measure the subjective components of interpretation, while researchers tend to evaluate the evidence supporting their beliefs in a different way than the one that distinctly challenges such convictions (par. 2-3).

However not every research should be considered irrelevant due to the biased interpretation, Robert J. MacCoun (1998) argues that interpretation bias is a common phenomenon with a varied motivational, intentional, and purely cognitive determinants, however there is evidence suggesting that the magnitude of biases is often small and they are subtle; a key role the in identification of biased research interpretation and its sources have played the systematic empirical methods; finally, the biases are not all indefensible and the differing standards of proof are completely acceptable, provided that the researchers are self-conscious about standards and stances, and are explicit about them (p. 36).

Finally, for a researcher there always exists a moral barrier in a form of ethical guidelines. The transfer of research data to fellow researchers or academic institutions should be carried out with great care, and it should be considered whether or not such transfer is necessary.

An advice on data protection and transfer, published at the website of Cardiff University, states that the data should be secured during the transit; sharing the personal data with other organizations should be conducted under the Data Protection Act; access to personal data must be restricted to the minimum number of the research participants, and the issues with identifiability should be avoided as well (FAQs on Data Protection, n.d., par. 10-20). Another guideline sums it all up: However simple or complex your data set, think about what you might need to do to ensure that your management of the data respects the terms of your consent, and in particular, the confidentiality and anonymity that participants were promised (Data storage and data security, n.d., par. 14).

Reference List

Data storage and data security. (n.d.). Web.

FAQs on Data Protection and Writing a Research Protocol or Applying for Research Ethics Approval. (n.d.). Web.

Kaptchuk, T. J. (2003). Effect of Interpretive Bias on Research Evidence. BMJ: British Medical Journal, 326(7404), 14531455. Web.

MacCoun, R. J. (1998). Biases in the Interpretation and Use of Research Results. Annual Review of Psychology, 49, 259-287. Web.

McDowall, R. D. (2013). FDAs Focus on Laboratory Data Integrity  Part 1. Web.

MHRA GMP Data Integrity Definitions and Guidance for Industry. (2015). Web.

The main idea behind the relational database management system (RDBMS) is the concept of relationships that exist among objects or entities. A relationship is an association of entities based on how they interact. The following terms are used in relation to modeling a relational database model: Entity, Attributes, Primary Key, and Foreign key (Begg & Connoly, 2003). Using a RDBMS, three types of relationships can be defined among entities: one-to-one, one-to-many, and many-to-many. To be appropriate, relationships must observe some sort of data integrity. In this context, the following paragraphs describe the importance of data integrity.

The three fundamental types of data integrity are entity integrity, referential integrity, and domain integrity. Entity integrity is a form of data integrity that requires every table in a relational database to have a unique column or columns that ensure that each row is uniquely identified. The unique column or columns is known as the primary key, thus no duplication of rows within a given table is acceptable. In essence, a primary key should be unique but not null (Gilfillan 2000). To ensure that there is validity and accuracy of data, a database designer should be able to select the correct primary key that reflects the requirements of the database in regard to information search, updating, and deletion of the stored data. For instance, when designing a school database, tables such as the STUDENT table and COURSES table may be included in the database. Thus, to ensure the accuracy of the entities, student number in the STUDENT table can act as the primary key, while course number in the COURSES table may act as the primary key. Both student number and course number uniquely identify other information in their respective tables hence entity integrity.

Another type of data integrity is the referential integrity. These are rules that are followed to preserve the defined relationships between tables when records are added or deleted. Referential integrity is typically enforced with a primary key and a foreign key combination. To link a table with another table, these two keys must exist. A foreign key is a key in one table that must match a primary key in another table to establish a relationship. Moreover, referential integrity requires that the matching fields must have the same data type, field properties, and same data. Secondly, both tables must belong to the same database. Lastly, a record cannot be deleted or updated from the primary table if matching records exist in a related table (Watson 2005). An example of referential integrity may exist in a Company database in which we have the EMPLOYEE table and the DEPARTMENT table. To connect EMPLOYEE table to DEPARTMENT table, Employee number field acts as a primary key and Department number field act as a foreign key, thus ensuring consistent relationships. Enforcing referential integrity is typically provided by the database management system (Navathe, 2000).

Lastly, domain integrity defines the required features of a specific column in a table. Every column has a certain domain that stems from the specified data type. Domain integrity is characterized by the data type, the NULL value recognition, the allowable values, and the default value. For instance, if we define the attribute of UnitNumber, of a STUDENT entity, as an integer, the value of every occurrence of such element must be a number and an integer. If we specify that the value should be positive, then a negative figure is not allowed. Again, having it as mandatory value in the column shows that the column is specified as NOT NULL (Begg & Connoly, 2003). In essence, each attribute should be specified with the following domain information: Data Type, Length, Date Format, Range, Constraints, Null Support, and Default Value. Domain integrity ensures consistency of data, validity of data, and identification of data.

Therefore, entity integrity, referential integrity, and domain integrity are important in relational databases because they ensure that data is valid and consistent, and thus relationships are set. Secondly, they reduce the redundancy of data since records can only be entered once. Thirdly, searching records is much faster (Gilfillan Ian, 2000). For instance, having Product number as a primary key in a Products table ensures that records are stored once and searches become faster. On the other hand, having supplier number as a foreign key in the same table enable a relationship to be set between the Product table and the Supplier table, hence increasing searches and lessening design difficulties.

References

Begg, C., & Connoly, T., 2003, Database Systems, 3rd Ed., Pearson Education Limited, Delhi.

Gilfillan, I., 2002, Introduction to Relational Databases, Web.

Navathe, S., 2000, Fundamentals of Database Systems, 3rd Ed., Addison Wesley, USA.

Watson, R., 2005, Data management: databases and organizations, 5th Ed., John Wiley, New York.

Introduction

While conducting a research study, it is important to uphold integrity by ensuring that the results are credible, valid, and reliable to the target parties (Patton, 2002). In addition, the research study must involve ethical practices which ensure that the interactions with respondents are professional (Schram, 2006). In this light, the aspects should be used in parts of the research including sampling, collecting, and analyzing data (Shank, 2006). Therefore, this paper will focus on the practices that could be embraced in the envisioned research to ensure the viability and reliability of the results.

Data Integrity

In a previous discussion on research orientation, it was stated that a sample could be collected by the random purposive method. In this light, the random purposeful technique ensures that the sample is not collected in a biased manner (Sheu & Wei, 2009). This approach also ensures that it does not favor the realization of results that direct towards predefined hypotheses. Instead, the results should be obtained in a naturalistic manner. On the other hand, the purposive design ensures that the people involved in the research are relevant to the study (Stein & Springer, 2008). Otherwise, interviewing people without the relevant information about the research purpose would lead to a lack of credibility in the data obtained. Since random purposive sampling is a combination of the two methods, it ensures that the collection is random and purposeful (Trochim & Donnelly, 2008). Therefore, this will lead to a collection of relevant, credible, and valid data.

Flexible questionnaires are designed in a manner that allows modification (Shibata & Ikeda, 2008). This flexibility ensures that the questions could be manipulated to accommodate unexpected issues or remove irrelevant themes (Trochim & Donnelly, 2008). While storing these documents, they must be stored in a manner that does not compromise the integrity of results. To avoid inconvenience, the inquiries are put on shelves under population segments. In this research, there will be three segmentations relating to NGO representatives, government officials, and migrant workers. The respective questionnaires will be stored concerning the three groups to avoid confusion. They will be kept under the care of a person who can ensure that the data is not altered. They should be kept in a safe place that is free from theft or destruction. During data collection, it is important to ensure that the respondents feel free when giving information. The confidentiality of information must be assured to encourage people on giving much information. For example, posing personal questions should be avoided since they threaten the privacy of respondents.

Ethical Issues

Various ethical issues could arise when sampling, collecting and analyzing data. One of these ethical issues is a lack of professionalism. For example, the data collectors could be tempted to choose the samples without following the right procedures that reduce workload. In addition, they could be tempted to fill in the questionnaires rather than collect the actual data. It is also possible for them to provide results without analyzing. Lack of professionalism and informality can lead to profound ethical issues which compromise data integrity. As a result, the researchers should be trained before taking their respective roles. This will help in maintaining the right standards.

Conclusion

The research study must be ethical and credible. In this light, various activities on research sampling, data collection, and data analysis must be conducted with integrity and an ethical approach. Otherwise, they could compromise the integrity of the research.

References

Patton, M. (2002). Qualitative research & evaluation methods Thousand Oaks, CA: Sage Publications.

Schram, T. (2006). Conceptualizing and proposing qualitative research (2nd ed.). Upper Saddle River, N.J.: Pearson Merrill Prentice Hall.

Shank, G. (2006). Qualitative research: A personal skills approach. Upper Saddle River, N.J.: Pearson Merrill Prentice Hall.

Sheu, S., & Wei, I. (2009). Using Snowball Sampling Method With Nurses To Understand Medication Administration Errors. Journal of Clinical Nursing, 18(4), 559-569.

Shibata, Y., & Ikeda, H. (2008). Questionnaire Survey Of Physical Properties Of Urea Preparations. Nishi Nihon Hifuka, 70(6), 634-638.

Stein, H., & Springer, M. (2008). Comparison Of Two Sampling Methods For Biomonitoring Using Aquatic Macroinvertebrates In The Dos Novillos River, Costa Rica. Ecological Engineering, 34(4), 267-275.

Trochim, W., & Donnelly, J. (2008). The research methods knowledge base. Mason, OH: Cengage Learning.

Integrity is a virtue as explored by different scholars in different disciplines. When applied in ethics the term implies honesty and accuracy that is sometimes called truthfulness. Integrity represents consistency in ones conscience. Issues that may be consistent include personal actions, attributes, principles, values, and patterns of behavior.

In these issues, accountability becomes the benchmark of measuring integrity when discrepancy occurs. A person acting in a consistent way with regard to the above actions will most likely exhibit quality in his or her character and will therefore be said to have integrity.

Objects can also be associated with integrity. The meaning derived when integrity is viewed in this perspective is purity and how intact a thing is. Examples of such objects include a computerized database. This is said to be pure if an error or a virus has not corrupted it. An undeveloped land can be said to have integrity if it has not been development. Musical work that flows rhythmically with all instruments well  organized, rhyming well with the lyrics is said to be pure and hence holds integrity.

Other disciplines that also evaluate the concept of integrity include politics, philosophy; that covers medicine and action, the mind that covers consciousness and cognition among others. These areas of study can be broadly regarded as artistic, professional, and intellectual forms of integrity. Among these forms, philosophy deals with the general characters. Philosophers seek to explore this by checking broad characteristics of ones life.

In such analysis, the integrity of a person is viewed in two perspectives. The first perspective relates to how one relates with oneself. The other one deals with how a person relates to the world around him. Philosophers state that relating with oneself directly affects how one relates with the world. Evaluations of such values as commitment to a persons principles are very important when we want to separate concepts in integrity.

This culminates in the analysis of etiquette. Integrity is the bond that holds norms of integrity and unity together. Conflicts can arise from among the several types of integrity. The conflicts are always related to the interchangeable application of skills and virtues of integrity. This means that the skills of development in one type of integrity can easily be applied in the other type. A good example is that the skills that keep up integrity among intellectuals can be applied in conflict resolution.

Integrity can be tested. This is done either subjectively or objectively. In the former method, internal consistency and accountability of human behavior is used as a benchmark. The later is a scientific method. The method applied will depend on how people will trust the result. In subjective testing, integrity relies on the social constructs. They are all human subject to mans cause and effect. The scientific system only serves to reinforce the subjective system that of course is usually value based.

This method involves among others, preparation of hypothesis, and assumption of particular behavior reacting to a particular way. If the results match the hypothesis then it is concluded that integrity exists. If the results do not match the hypothesis then the behavior under study will be said to lacking integrity. Trust in this method is the founded on independent analysis. This method is applied in statistics, mathematics, and physics.

As a general concept, honesty is a moral quality, which consists of the fact that a person does not deceive others and oneself, does not hide ones motives, and does not break promises. I believe that it is important for an honest person that ones actions are legal comply with generally accepted rules, and do not violate other peoples rights. A manager can set a good example and encourage open and honest interaction within the team and in relation to clients. An honest attitude requires high levels of trust (Ridge, 2015). The key is creating a trustworthy work environment where each person feels recognized and respected, and honesty is valued rather than being defended or even punished.

Suppose a leader sets an example of hypocritical behavior, and employees see that the manager accepts this type of behavior. In that case, they will behave similarly when entering such an environment. The next time the leader asks for the correct documents to be provided to the reviewers, the employee will believe that the reports must be dressed up to comply with the requirements fully (Ridge, 2015). On the one hand, there is a specific atmosphere of understanding and even support in the team that such a leader has formed. On the other hand, the situation may get out of control, and the manager will receive a similar attitude in his/her direction.

The leader seeks to show the total effectiveness of the department, so they introduce permitted imitation. It can lead to the fact that when signing the annual effectivity evaluation, the manager may sign an appraisal that was accidentally not signed by the employee. First, it will affect the actual productivity of employees; secondly, it opposes legitimate activities and can lead to severe consequences. In addition, if a manager is not interested in how an employee achieved specific results, it means they do not care how honestly the employee did it (Ridge, 2015). However, to create a healthy atmosphere in the team, all employees must realize that honesty and ethics are essential principles of the company in which they work. Proper behavior needs to be shaped from the beginning, as it will be difficult to stop such practices in the future.

Reference

Ridge, R. A. (2015). Putting the I in integrity. Nursing Management, 46(4), 5254.

Ecological Integrity

The ecological integrity is one of the concepts currently discussed. In fact, the term ecological integrity means biodiversity typical of the location and ability of nature to sustain this diversity.

In other words, human activity can damage the health of ecological system by destroying the diversity which includes different species of fauna and flora that contribute to the development of the ecosystem and its maintenance in the way it was done for a long period of time. Health of nature is the main concept that should be protected in order to sustain natural biodiversity and preserve biological balance on the planet.

Human System

The diversity of natural resources varies depending on the location and resources typical for this or that region. In this respect, natural resources are developed and distributed to different countries all over the world to make sure that needs of all people in natural resources are met.

Natural gas and oil are the most important and widely applied resources used in different fields such as manufacturing, pharmaceutical industry, cosmetics, and other areas. Wood is used for making paper, furniture, building houses, and a great variety of other fields. So, I use water, air, gas, oil, wood, and other resources in my everyday life.

Environmental Degradation

The impact of human activity on the environment is mostly negative whereas many people and corporations try to reduce the gas emissions and pollution of water, soil, and air. In other words, the degradation of natural environment is the expected outcome of the use of natural resources without thinking about the results of it and its impact on ecosystems that become less and less sustainable.

Social Issues

The use of resources should be discussed on the local, regional, national, and international levels instead of criticizing every individual separately. However, the changes that can be introduced to reduce the negative impact of human activities on ecosystem and biodiversity should start with a personal donation of every individual. In this respect, I can say that buying devices that can be easily recycled and reducing the use of energy inefficient technologies would be my first steps in changing my impact on society from using resources. Moreover, encouraging others to reduce the negative impact on ecosystem could be a good contribution as well.

Sustainability Goals

The difference can be made with the help of environmentally safe products, resources, and technologies. Moreover, it is important to remember that people can improve the ecological sustainability by stopping destruction of forests and impact on ocean floor. In other words, every part of the ecosystem consists of the species that are native to the location and the resources such as water sources or forests that are natural setting for these species. So, the main sustainability goal concerns maintaining the ecosystem so that it was not damaged and could remain biologically diverse during a long period of time.

The Timeline

The timeline necessary to sustain the biodiversity includes more than a decade because the impact of human activity should be neutralized and certain improvement should be made in the economic and industrial fields to ensure that people would not pollute the ecosystem over and over. However, some changes such as distribution of energy efficient devices that can be recycled is one of the effective steps to sustainable ecological health.

Introduction

The current economy has grown to be a knowledge-based one and as a result, most people are taking their academics seriously, going to greater heights than before. More people are now enrolling for degrees in universities and other tertiary colleges. The education system and teaching methods have been improved over time. On the other hand, there are some very bad academic crimes that students or tutors commit which could compromise the integrity of academics by giving out dishonest results.

Qualities of a University Graduate in American Context

It has been argued that more people are being released into the job market in the US and as such the reputation of the academic institutions is facing lots of challenges. This has increased competition for employment with other people who are not graduates. Nonetheless, graduates have some unique qualities. On average, graduates are usually very intelligent people and very competitive at workplaces. This is basically because of the simple fact that the more knowledge a person gets the smarter he/she becomes. Graduates are also trained to critically think and reason on their own thus making them very good problems solvers.

Having attended the university, they face a lot of life experience challenges and from this, graduates have a broader perspective of life and work skills and are also more prepared to face challenges since the curricula have provisions like internships which exposed them to real work situations after they complete their studies.

Most People in Society Believe That College Graduates Are Prepared To Become Leaders

Many people believe that graduates are prepared to become better leaders in their specific job lines. This is attributed to the fact that there has been increased competition in the job market leading to an economy that is knowledge-depended. The higher education system has hence developed a flexible, personalized, and convenient approach that teaches students key transferable skills. These changes are inspired by the globalization phenomenon and the advances in technology. These skills are acquired in universities and be very critical in the employability agenda and leadership development.

This connection between society and education has seen graduates learn enterprising skills, improve responsibility, attain effective communication, become computer literate and develop brilliant problems solving skills. These qualities in the current economy can stand cultural forces thus making these graduates better prepared to be leaders in their respective fields of work.

Changes in Values and Attributes of Colleges Students

There have been a lot of changes from the past generation to the current one in terms of the essentials that college students are expected to have upon completion of a college education. Some of these include computer literacy, leadership skills, self-responsibility, better communication skills, and specialized knowledge. These changes are very advantageous to the job market. This is because employers can find very competent workers hence assuring greater output at work. With improved communication and computer skills (internet included), the workplace is likely to be very productive. Wider knowledge and competition inspire innovation and creativity as well.