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Rights of Online Students

Two ideas that are discussed widely today are online learning and justice. When looking at these two ideas together it leads to the question of what rights do students have online?

Fortunately, one group has attempted to address this question. ProctorU has proposed a seven-point student bill of rights for online students. This bill of rights provides a framework for discussing this topic. It is not necessary to agree with every point that ProctorU makes. Instead, it is better to consider this as laying the groundwork for a deeper discussion. ProctorU’s framework covers such topics as teaching, academic dishonesty, privacy, and data collection. Below are the seven points.

Have your questions answered
Have your work presumed to be honest and accurate
Expect compliance with all privacy laws and policies
Review and understand policies protecting you and your work
Review and understand policies keeping others from disadvantaging you
Understand data collection, retention, and dissemination
Expect that data collection to be specific and limited

Taken from ProctorU. Student Bill of Rights for Remote and Digital Work. https://studenttestingrights.org/

We will look at the points below. Rather than examine each one individually we will group them on the following topics: teaching, academic dishonesty, and privacy.

Teaching

The first point of the Student Bill of Rights deals with asking questions. Answering questions is related to communication between students and teacher. In online learning, teachers must communicate quickly as this establishes presence in the online context.

Communication is a trait of excellent teaching both in the online context and in the traditional teaching format. Other examples of excellent teaching include providing clear, well-planned lessons, and reflection. In general, students have a right to teaching that does not cause confusion.

Students expect their teachers to provide excellence even if the students do not always show the same commitment. ProctorU has made the claim that answering questions is a right for online students which means that educators need to take notice.

Academic Dishonesty

Points 2, 4, and 5 of the Student Bill of Rights deal with ideas related to academic dishonesty. Academic dishonesty is a major headache in online teaching. Plagiarism and copy/paste, sharing work, etc. are just some of the problems found.

Point 2 of the student bill of rights implies that the work students do is done ethically. However, academic dishonesty is so common that this assumption seems misplaced. Despite this, the teacher is the one who needs to prove that academic dishonesty has taken place

Points 4 and 5 are related to procedural justice. Students have a right to review their school’s policies and know that they are not at a disadvantage compared to their peers. If students think a course is unfair they may justify academic dishonesty to make the course “fair.” To provide an extreme example, if a teacher demands that students memorize 500 words many students would consider that unreasonable and resort to cheating to pass the class.

Lastly, point 5 explains that students who cheat have an advantage over those who do not. Therefore, the teacher has a responsibility to maintain the fairness of the learning environment.

There are ways to reduce the temptation for students to cheat or commit other forms of academic dishonesty. For example, explaining the expectations of assessments is critical to helping students. When developing an online assessment a teacher should be aware of the course objectives, the type of assessment (formative or summative), the process or product focus of the assessment, and whether students will work alone or with others. It is also important to consider the technology needed for the assignment as students may not have the needed technology or lack the skill in using it

It is also critical that all directions are written down and available for the students to look at when needed. There are several things to consider after students complete an assessment. If the students did poorly, it is necessary to reteach the content. However, anyone who has taught online knows how difficult it is to change the content found in the learning management system. One way to address the need for reteaching online is to leave the original activities the same but add additional material for reviewing while also allowing students to retake a quiz or other minor assessment until mastery is achieved. Adding material is easier than rearranging in many circumstances. Providing these opportunities to fix past mistakes is one example of fairness and could discourage academic dishonesty

Privacy

Points 3, 6, and 7 all deal with privacy and data collection. Within the United States, several laws explain the school’s responsibility for data protection. For example, the Family Educational Rights and Privacy Act (FERPA) provides protection for students’ privacy concerning academic data, immunizations, and special needs, also known as personally identifiable information (PII). Upon turning 18, even parents must receive consent from their own child to access the child’s educational records. During the pandemic and the explosion in online learning, the Department of Education provides clarifications about FERPA.

Another law related to privacy is the Children’s Online Privacy Protection Act (COPPA). COPPA is a law in the United States that controls how websites gather data on minors under the age of 13 (Federal Trade Commission, 2013). For educators who may work at the K12 level and or get involved in private industry or non-profit work, it is important to be aware of this as online classes often point students to various websites, and an out-of-compliance website could cause problems.

The Teach Act defends the right of students to learn from copyrighted materials. The Teach Act explains that it is okay for schools to use copyrighted material in traditional or online teaching without prior permission. What this means, as an example, is that a teacher could upload a video and allow students to watch it. A law like this protects an online student’s right to learn.

There are also laws internationally outside the United States that protect a student’s privacy. For example, the EU General Data Protection Regulation (GDPR) provides tough restrictions on the use and sharing of data. What makes this law unique is that it applies not only in Europe but to any institution that has business or students in Europe.

Conclusion

Students have rights and teachers and institutions need to be aware of them. It is more than likely that laws on privacy will become more stringent as people look to protect themselves online.

Data Privacy Implementation Strategies

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Data privacy is a topic that many organizations are addressing. In this post, we will go through several steps that must be taken to implement a data privacy program.

Leadership Sponsor

As with any major initiative, data privacy is going to need the support of leadership. In particular, there will be a need for an advocate on the leadership team who will support the vision of improving data privacy. Who this person is will naturally vary from organization to organization.

The sponsor is not only an advocate but also serves as a medium of communication between the data privacy team and leadership. The sponsor serves as the eyes and ears for the privacy team to help them to avoid pitfalls is deal with concerns that are not shared directly from the leadership team to the privacy team.

Put Someone in Charge

Implementing any program or strategy requires that someone take the lead. Therefore, when it is time to develop a privacy approach someone needs to be in charge. The selection of the leader will naturally vary from one place to the other. The point is that the leadership sponsor needs someone they can talk to directly about the challenges and concerns that may be made at the leadership level.

Depending on the size of the project there might be more than one person identified as a leader. However, it is generally wiser to start small and scale as appropriate.

Examine the Data

Before any action can take place it is important to take an inventory of available data. Another name for this is the compiling of a data catalog. A privacy leader must know what data needs to be held private. Without this information, it is hard to ensure the quality.

Knowing the data works in combination with the policies and procedures that need to be made. For example, if the data includes personal information this will influence how privacy is maintained versus data that does not contain such information.

Compliance Expectations

Knowledge of the data is used concerning compliance expectations. For a corporation, the compliance standard might be GPDR. For other organizations, compliance might be determined by local laws or organizational standards.

Generally, a privacy team must provide evidence that they are implementing and or obeying compliance standards. Therefore, a team might have to document and archive how they comply with regulations in the event of a data breach and or audit.

Assess Risk

Assessing risk helps to inform the privacy team in terms of what sort of policy and or procedures to implement. Fortunately, it is not necessary to develop this risk assessment in a vacuum. There are risk assessment frameworks such as ISO 31000 or ISO 27005. Either of these frameworks or others can help you to determine the level of danger your data is potentially facing.

Create Policies and Procedures

Policies are broad guidelines based on the context in which it is being developed for. Most websites have some sort of privacy policy that explains how and what data is collected along with its purpose. Privacy policies can include an idea of the roles and responsibilities of the data privacy team as well.

Procedures are the steps that need to be taken to fulfill the policies that were created. In other words, data procedures provide step-by-step guidance of policies. For example, if the policy speaks about the importance of only certain people having access to data a procedure for this might be how to set up a password or to seek permission to access a particular database. Essentially, policies inspire procedures.

Controls

Controls are inspired by risk assessment. In this step, you are implementing ways to mitigate risk to data. For example, it might have been uncovered that sensitive data is too easy to access. The control for this example may be to move the data to more secure data or to ensure that the data is password protected.

The main point here is that all of these measures must be integrated and working together. The data catalog and knowledge of compliance inspire the policies and procedures which in turn helps with the development of controls

Training & Monitoring

Now that almost everything is in place it is time to train people on the new privacy rules. The training will be context specific but is critical for getting buy-in to the new system. Without the cooperation of the masses, there is no hope for the success of the program.

After training, the training is assessed through monitoring. Monitoring assesses how well the program is running. It deals with such challenges as whether people are obeying the new procedures that have been implemented. Monitoring also helps in providing feedback in terms of where there might be growth opportunities. No system is perfect and monitoring provides critical information to strengthen the program.

Conclusion

Data privacy can be improved in any organization. The ideas presented here provide information on how to start a data privacy program. Naturally, all of these steps may not work for each organization but many valuable ideas have been shared to support the protection of privacy.

Data Privacy

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A field closely related to data governance is data privacy. In this post, we will look at what data privacy is as well as principles that need to be kept in mind when trying to keep people’s data private.

Data Privacy

Privacy is a term that is difficult to define. For our purposes, data privacy is the amount of control a person has over personal information in terms of how this information is collected, managed, and stored. This definition gives the impression that people have little data privacy because we are so often compelled to share our information online.

Websites often require some surrendering of personally identifiable information (PII) such as name, address, phone number, etc while in the medical field, there is demand for personal health information (PHI). Sharing information about yourself can be frustrating for many but is the cost of doing business online. Naturally, once these various online companies have your data they must be sure to protect it.

Data security is not about collecting or managing data. Rather, data security is focused on the protection of data from unauthorized access. Securing data is critical to protect individuals and organizations from harm because of security breaches. For example, there can be serious financial repercussions if someone’s credit card number is stolen online.

Fair Information Practice Principles

With all the concerns regarding data privacy, it was natural that frameworks would be developed to help organizations with data privacy. One such framework is the Fair Information Practice Principles (FIPPs) developed by the Organization of Economic Development back in the early 1980s. Below are the eight principles in this framework.

Limits on data collections-Every organization need to determine the smallest amount of data they can connect while still maintaining success
Data quality-Data that is collected needs to be accurate and pertinent to the purposes of the organization.
Purpose determination-There must be a clear compelling reason to collect data.
Limits of use-Personal data must only be used for its intended purpose.
Security-Data must be protected
Transparency-People should know that their data is being collected
Individual participation-People whose data has been collected have the right to access their data, have it corrected, and or erased
Accountability-Whoever collects this data is responsible for adhering to the principles listed above

The principles shared above have been adopted by many organizations to provide a foundation on which they can develop their own data privacy policies and philosophy.

Conclusion

Data privacy is a major concern in the world today. Organizations whether online or offline continue to demand more information about their customers. As such, this implies that there must be safeguards in place to ensure the protection of this information.

Defense & Offense with Data

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Within the field of data governance, there are different ways of approaching data and the definition of truth. In this post, we will look at different approaches to data and also how truth can be defined with a data governance framework.

Defense

A defense approach to data is focused on controlling data. This can involve security and stringent governance of data through a highly centralized setting. In addition, the defensive data approach is concerned with minimizing risk and ensuring compliance with standards and expectations. Preventing theft and tracking the flow of data through an organization is also important.

When analytics are used they are used to detect fraud and unusual activity. How defensive an organization is depends on the field or industry. For example, banking and health care are highly defensive due to the type of data they gather.

Offense

An offensive approach to data is focused on developing insights with data. The goal is not to protect but to develop insights for decision-making. An offensive approach to data is characterized by flexibility and being focused on the customer. This style of approaching data is generally emphasizing a decentralized style of data governance.

Organizations that find themselves in highly competitive environments often are forced to become more offensive as they search for insights to maximize profits. How much offensive and defensive an organization needs does vary. However, in general, most if not all organizations start defensive and slowly become more offensive in nature.

Truth

Whether the approach to data is offensive or defensive it is important to determine what is the truth when it comes to data in an organization. Every organization needs a single source of truth (SSOT) for critical data. The SSOT is language used within data that is the same across an organization. For example, sometimes the same name can be entered in multiple different ways in an organization’s data. Take the company AT&T as an example it could be entered in some of the following ways

ATT

att

Att

AT and T

AT&T

Each of the examples above can be considered different and can lead to chaos when it is time to analyze data for insights. This is because redundant names can lead to redundant costs. For example, if AT&T was a vendor for our fictitious company there might be several different contracts with AT&T with several different divisions who all spell AT&T differently. To prevent this the SSOT will define the one way to code AT&T into the system and determine what it represents.

However, keeping the offensive approach to data in mind. There are times for the purpose of analysis that the SSOT can be modified. Doing this leads to what is called multiple versions of truth (MVOT). An example of MVOT is a department that classifies our example of AT&T different way from the SSOT. Accounting might see AT&T as a vendor while marketing might see AT&T as their internet provider, etc. Since everyone knows what the SSOT is they are aware when they make a MVOT for their distinct purpose.

Conclusion

Each organization needs to decide for themselves what approach to data they want to take. There is no right or wrong way to approach data it really depends on the situation. In addition, every organization needs to determine for itself how they will define truth and there is no single way to do this either. What organizations need to do is address these two topics in a way that is satisfying for them.

Juvenile Justice Programs, Practices, & Policies

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In this post, we will look at juvenile justice programs, practices, and policies. Each of these terms plays a different role within the juvenile justice system.

Program

A program in juvenile justice is a designed package that has clear procedures for delivery, has manuals and provides technical assistance. In addition, the outcome is commonly related to some sort of change such as recidivism. Two commonly implemented programs are multisystemic therapy and functional family therapy.

Another key aspect of a program within juvenile justice is recidivism. Programs are generally designed for specific use through the use of a logic model. A logic model is a visual depiction that shows the relationship among the resources, activities, outcomes, and outputs of a program. In other forms of social science research, the logic model is called a conceptual framework however these two concepts are not exactly the same. Logic models depict relationships while conceptual frameworks are a proposed theory that is attempting explain why certain outcomes take place.

Practice

PRactices in juvenile justice are not as clearly defined as programs are. In general, practices in juvenile justice are essentially programs that are more flexible in their application and use. For example, the design may not be as rigorous and the instructions made not be as detailed.

Due to their more flexible nature practices are often more general in nature and can thus be applied in different situations. To make things more confusing some programs are considered practices if they are more flexible than highly controlled programs. One common practice is the Treatment in Secure Corrections for Serious Juvenile Offenders.

Policies

Policies are regulations that apply to the general population. Policies generally lack empirical evidence for their usefulness in supporting youth. However, policies do provide guidance and structure which shows that they serve a different role than what is found with programs and practices.

Evaluating Programs and Practices

There are times when programs and practices are evaluated for their usefulness. Below are some commonly used ways to evaluate programs and practices.

One way to evaluate a program is the quality of the evidence or data. For example, randomized controlled experiments are considered the gold standard. Therefore, other methods of collecting data such as quasi-experiments and surveys will affect the perceived quality of a program.

A second criterion is looking at the quality and extensiveness of the research of the program. What this means is the quality and quantity of research that has assessed the value of a program. If a program has multiple studies that are a witness to its worthiness and these studies are of high quality it raises the value of this program.

A third criterion is the expected impact of a program. By expected impact, it is meant the effect size. The effect size is something that is extracted in the data analysis aspect of a study and helps to provide a number of the impact of a study. Programs with stronger effect sizes are seen as better.

Finally, another criterion for program/practice quality is the adoption rate. In other words, how many other people are using the program/practice? Tracking adoption is higher but there are program registries that have vetted programs and recommend them. Examples of program registries include crimesolutions and blueprints. Both of these registries have graded programs and provide links to studies about the programs.

Conclusion

Programs, practices, and policies all play a critical role in helping youth in the juvenile justice system. People who work within this system need to be aware of the meaning of these terms as well as how to judge good from bad programs.

Data Governance Methodology

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Data governance is becoming more and more common in today’s world. In this post, we will look at one commonly used process of implementing data governance. The steps are explained below.

Scope & Initiation

The first step in setting up a data governance system is to determine the scope of data governance. By scope, it is meant how deep and wide the program will be. In other words, you have to determine what will be governed and how thoroughly it will be governed.

It may surprise some that not all data is governed by data governance. For each organization, it will be different but generally, all organizations have data that is excluded from data governance. For example, some organizations will include emails under data governance while others will not. It depends on the situation and there is no single rule.

In addition, it is important to determine how thorough the governance will be. An example of this would be the tolerance for data quality issues. There are times were some data errors are permissible as long as they do not exceed a certain threshold but this also depends on the context

Assess

At the assessment stage, the purpose is to determine an organization’s ability to govern data and be governed by policies. Generally, there are three ways of assessing this and they are measuring the capacity to change, the culture of data use, and the ability to collaborate.

The capacity to change is self-explanatory and is a measure of an organization’s ability to accept new policies such as data governance policies. The data use culture is looking at how an organization uses data at that moment. Lastly, collaboration looks at how well people within the organization can work together. Collaboration is critical because data governance generally affects the entire organization and people from multiple departments must work together.

Vision

The vision is where terms are defined and steps going forward are set. For example, the organization needs to define what data governance is for them. In addition, requirements for doing data governance are also developed.

Vision setting is a theoretical experience and this is often boring for the more practical action-oriented individuals. However, setting the vision sets the tone for the rest of the project. Therefore, this must be planned and developed.

Align & Business Value

Aligning and business value is for determining the financial value of incorporating data governance into an organization and also refining how things will be measured. For profit-seeking organizations business value is critical. Most projects need to make or at least save money in this setting. For non-profit organizations, the motivation might be to increase efficiency or the ability to better serve stakeholders.

It’s not enough to talk about savings. Evidence must be provided for determining actual savings. This is where metrics come into play. There must be ways to measure the value of a data governance project. Again, how to do this will vary from place to place but it needs to be addressed.

Functional Design

Functional design is focused on the actual process of doing data governance. What will be done must be determined as well as established roles that support this process as well. Principles are often developed at this step and principles are similar to goals in terms of what is expected from implementing data governance. Following principles, the next thing that is developed are standards which are similar objectives in education in which you have some sort of measurable action.

Best practices often encourage data governance to be embedded within existing roles and responsibilities. In other words, setting up another department within an organization and calling it data governance is generally not considered the best way to make this happen.

Governing Framework Design

Once the plan has been developed it is time to find the people who will implement it. governing framework involves assigning processes to people and setting up the various roles associated with data governance. Generally. a lot of the aspects of data governance are being done at an organization but in a disjointed unaware way. Therefore, the main benefit here is not so much to give out more work but rather to make it clear who is already doing what and make sure they are aware of it.

Road Map

The road map step involves data governance going live. This is the point where data governance is integrated into the existing organization. Other things that are done at this step are designing metrics and reporting requirements. In other words, how good or bad does performance have to be on a standard and how will this be reported?

Change management is also addressed here and involves dealing with resistance and making sure that the scope and or goals of the project do not change. There are times when a project will wander from its original purpose which can be frustrating for people.

Rollout and Sustain

Roll out and sustain involves executing the plan and checking its effectiveness. Essentially, this step involves monitoring the data governance implementation and making corrections as necessary.

Conclusion

Data governance is a critical part of most organizations today. However, it can be tricky to figure out how to make this a part of an organization. The information above provides an example of how this could be done.

Types of Justice in the Classroom

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Justice can look many different ways. In this post, we will look at three different forms of justice procedural, substantive, and negotiated. In particular, we will look at how these different forms of justice work within the classroom.

Procedural Justice

Procedural justice means that the disciplinary power of the teacher is only used within the constraints of the policies and rules of the school. For example, most schools do not allow corporal punishment. What this means is that a teacher who makes the decision to spank a student has violated what is considered to be an acceptable process for discipline within that school.

Procedural justice also has to do with maintaining fairness. In other words, rules cannot be randomly enforced based on a teacher’s mood. When teachers are not consistent in the application and enforcement of rules it gives the appearance of unfairness and injustice to the students. When this happens it can trigger even more undesirable behavior from students.

However, everyone has their moments of inconsistencies, including teachers. Therefore, when a teacher makes a mistake in procedural justice it is wise to acknowledge the mistake and make efforts to correct the misstep. Doing this will help students to maintain faith in a system that when it makes mistakes it tries to correct them.

Substantive Justice

Substantive justice is the unequal impact enforcing rules has on different groups. A common example of substantive justice in the classroom is the disproportional amount of trouble males and minorities get into within the classroom.

Dealing with race and gender are both highly controversial topics. Therefore, teachers must be careful to be aware of these two demographic traits of their students. The perception of differences in justice due to substantive differences in demographic traits could lead to serious accusations and headaches.

Negotiated Justice

Negotiated justice is the process of how justice is discovered and carried out. A practical example would be a court trial. During the trial, the truth is sought so that justice can be delivered. In the classroom, there are many different ways in which teachers uncover what to do when it is time to administer justice.

For example, in some classes, a teacher will have both parties sit down and discuss what happened. In other classes, the students may be sent to the office to work out their disagreement. If the teacher witnessed what happened, there may be no questioning at all.

The ultimate point here is that a teacher needs to be aware of how they go about determining guilt and innocence in their classroom. At times, the emotions of teachers will overwhelm them and they may make just or unjust decisions without knowing how they made their decision. Naturally, we want to avoid unjust decisions but no matter what decision was made it is important to be aware of how the decision was developed.

Conclusion

Teachers must be careful with how they deal with justice in their classrooms. There is always a danger of being accused of oppression when you have power and authority over others. Awareness is at least one way that this problem can be avoided.

Views of Punishment

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Punishment is a part of juvenile justice. However, as with most ideas and concepts, there is disagreement over the role and function of punishment. In this post, we will look at common positions in relation to punishment.

Reductivist

The reductivist position on punishment views punishment as a means to prevent future crimes. This approach is based on a utilitarian position of causing the most happiness for the most people. By focusing on future crimes it is believed that preventing these crimes will bring the most harmony and happiness to people rather than looking at what has already happened.

There are several strategies that are used to support a reductivist approach. For example, the use of deterrence. Deterrence is the use of punishment to prevent crime by instilling fear. An example of deterrence would be capital punishment. Through hanging or public execution, the thought is that this will motivate others to be good. Other forms of deterrence that are used today would be boot camps which are meant to whip delinquent youths into shape and in some countries, corporal punishment such as caning is employed to maintain order.

Another manifestation of reductivism is reform-rehabilitation. Reform is meant to mean hard labor, such as working in a chain gang along with religious instruction. Rehabilitation involves treatment for some sort of vice that may have led to incarceration such as substance abuse, sex treatment, etc. The assumption is that there is something wrong with the prisoner that can be fixed through treatment. Again, the motivation behind reform and rehabilitation is to change the person for the benefit of society.

A final form of reductivism is incapacitation. Incapacitation is simply a strategy of keeping offenders locked up to protect the public. One way this was done was through the three strikes law used in parts of the United States. Once a person committed a third felony the sentencing could be 25 years to life.

Retributivist

The retributivist position looks to punish people for crimes already committed with no regard for the future. In other words, retributivists focus on the past while reductivists focus on the future. Punishment should restore equilibrium and focus on what is right to do rather than what is good to do (utilitarian position). The reason for this distinction is that right and wrong are more immovable than what makes people happy.

The main strategy for retribution is just deserts. Just deserts are a way of punishing people for the crimes they have committed and doing no more. As such, there is no support for three-strike laws, deterrence, or other methods among people who have a retributivist perspective.

Conclusion

The point is not to state that one of these positions is superior to the other. Rather, the goal was to explain these two different positions to inform the reader about them. There are times and circumstances in which one of the positions would be a better position than the other.

Terms Related to Data Storage

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There are several different terms used when referring to data within an organization that can become confusing for people who are not experts in this field. In this post, we will look at various terms that are often misused in the field of data management.

Database

Databases are for structured data which is data that has rows and columns. Among the many benefits of using a database over an Excel spreadsheet is that databases can hold almost limitless amounts of data. In addition, databases can have multiple users querying and inputting data at the same time which is not possible with a spreadsheet.

Data Warehouse

A data warehouse is a computer system designed to store and analyze large amounts of data for an organization. The data for a data warehouse can come from various areas within the organization. Since the data comes from many different places it also helps to integrate data for the purpose of analysis which is valuable for decision-making and insights.

Data warehouses take pressure off databases by providing another location for data. However, because of their size, often over 100 GB, data warehouses are hard to change once they are up and running. Therefore, great care is needed when developing and using this tool.

Data Marts

Data marts are similar to data warehouses with the main difference being the scope. Like data warehouses, data marts are also databases. However, data marts are focused on one subject or department whereas data warehouses gather data from all over an organization. For example, a school might have a data warehouse for all student data while it has a data mart that only holds student classes and grades.

Since they have a focus on a given subject, data marts are generally smaller than data warehouses at less than 100 GB. The rationale of a data mart is that analytic teams can focus when trying to develop insights rather than searching through a larger data warehouse.

Data Lake

Data lakes are also similar to data warehouses. Just like a data warehouse data lakes contain data from all over the organization from many sources. Data lakes are also generally larger than 100 GB. One of the main differences is that data lakes contain structured and unstructured data. Unstructured data is data that does not fit into rows and columns. Examples can include video data, social media, and images.

Another purpose for a data lake is to have a place for keeping data that may not have a specific purpose yet. Another to think of this is to consider a data lake as a historical repository of data. Due to their multipurpose nature, data lakes are often less complex in comparison to data warehouses.

Conclusion

All of the various data products discussed here work together to give an organization access to its data. It is important to understand these different terms because it is common for people to use them interchangeably to the confusion of everyone involved. With consistent terminology, everyone can be on the same page when it comes to delivering value through using data.

Types of Data Quality Rules

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Data quality rules are for protecting data from errors. In this post, we will learn about different data quality rules. In addition, we will look at tools used in connection with data quality rules.

Detective

Detective rules monitor data after it has already moved through a pipeline and is being used by the organization. Detective rules are generally used when the issues that are being detected are not causing a major problem when the issue cannot be solved quickly, and when a limited number of records are affected.

Of course, all of the criteria listed above are relative. In other words, it is up to the organization to determine what thresholds are needed for a data quality rule to be considered a detective rule.

An example of a detective data quality rule may be a student information table that is missing a student’s uniform size. Such information is useful but probably not worthy enough to stop the data from moving to others for use.

Preventative

Preventive data quality rules stop data in the pipeline when issues are found. Preventive rules are used when the data is too important to allow errors, when the problem is easy to fix, and or when the issue is affecting a large number of records. Again, all of these criteria are relative to the organization.

An example of a violation of a data quality prevention rule would be a student records table missing student ID numbers. Generally, such information is needed to identify students and make joins between tables. Therefore, such a problem would need to be fixed immediately.

Thresholds & Anomaly detection

There are several tools for implementing detection and prevention data quality rules. Among the choices are the setting of thresholds and the use of anomaly detection.

Thresholds are actions that are triggered after a certain number of errors occurred. It is totally up to the organization to determine how to set up their thresholds. Common levels include no action, warning, alert, and prevention. Each level must have a minimum number of errors that must occur for this information to be passed on to the user or IT.

To make things more complicated you can tie threshold levels to detective and preventive rules. For example, if a dataset has 5% missing data it might only flag it as a warning threshold. However, if the missing data jumps to 10% it might now be a violation of a preventative rule as the violation has reached the prevention level.

Anomaly detection can be used to find outliers. Unusual records can be flagged for review. For example, a university has an active student who was born in 1920. Such a birthdate is highly unusual and the system should flag it as an outlier by the rule. After reviewing, IT can decide if it is necessary to edit the record. Again, anomaly detection can be used to detect or prevent data errors and can have thresholds set to them as well.

Conclusion

Data quality rules can be developed to monitor the state of data within a system. Once the rules are developed it is important to determine if they are detective or preventative. The main reason for this is that the type of rule affects the urgency with which the problem needs to be addressed.

Data Profile

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One aspect of the data governance experience is data profiling. In this post we will look at what a data profile is, an example of a simple data profile, and the development of rules that are related to the data profile.

Definition

Data profiling is the process of running descriptive statistics on a dataset to develop insights about the data and field dependencies. Some questions there are commonly asked when performing a data profile includes.

How many observations are in the data set?
What are the min and max values of a column(s)?
How many observations have a particular column populated with a value (missing vs non-missing data)?
When one column is populated what other columns are populated?

Data profiling helps you to confirm what you know and do not know about your data. This knowledge will help you to determine issues with your data quality and to develop rules to assess data quality.

Student Records Table

StudentID	StudentFirstName	StudentLastName	StudentBirthDate	StudentClassLevel
1001	Maria	Smith	04/04/2000	Senior
1002		Chang	09/12/2004	Junior
1003	Francisco	Brown		Junior
1004	Matthew	Peter	01/01/2005	Freshman
1005	Martin		02/05/2002	Sophmore

The first column from the left is the student id. Looking at this column we can see that there are five records with data. That this column is numeric with 4 characters. The minimum value is 1001 and the max value is 1005.

The next two columns are first name and last name. Both of these columns are string text with a min character length of 5 and a max length of 7 for first name and 5 for last name. For both columns, 80% of the records are populated with a value. In addition, 60% of the records have a first name and a last name.

The fourth column is the birthdate. This column has populated records 80% of the time and all rows follow a MM/DD/YYYY format. The minimum value is 04/04/2000 and the max value is 01/01/2005. 40% of the rows have a first name, last name, and birthdate.

Lastly, 100% of the class-level column is populated with values. 20% of the values are senior, 40% are junior, 20% are sophomore, and 20% are freshman.

Developing Data Quality Rules

From the insights derived from the data profile, we can now develop some rules to ensure quality. With any analysis or insight the actual rules will vary from place to place based on needs and context but below are some examples for demonstration purposes.

All StudentID values must be 4 numeric characters
The Student ID values must be populated
All StudentFirstName values must be 1-10 characters in length
All StudentLastName values must be 1-10 characters in length
All StudentBirhdate values must be in MM/DD/YYYY format
All StudentClassLevel values must be Freshman, Sophomore,, Junior, or Senior

Conclusion

A data profile can be much more in-depth than the example presented here. However, if you have hundreds of tables and dozens of databases this can be quite a labor-intensive experience. There is software available to help with this but a discussion of that will have to wait for the future.

Data Quality

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Bad data leads to bad decisions. However, the question is how can you know if your data is bad. One answer to this question is the use of data quality metrics. In this post, we will look at a definition of data quality as well as metrics of data quality

Definition

Data quality is a measure of the degree that data is appropriate for its intended purpose. In other words, it is the context in which the data is used that determines if it is of high quality. For example, knowing email addresses may be appropriate in one instance but inappropriate in another instance.

When data is determined to be of high quality it helps to encourage trust in the data. Developing this trust is critical for decision-makers to have confidence in the actions they choose to take based on the data that they have. Therefore data quality is of critical importance for an organization and below are several measures of data quality.

Measuring Data Quality

Completeness is a measure of the degree to which expected columns (variables) and rows (observations) are present. There are times when data can be incomplete due to missing data and or missing variables. There can also be data that is partially completed which means that data is present in some columns but not others. There are various tools for finding this type of missing data in whatever language you are using.

Validity is a measure of how appropriate the data is in comparison to what the data is supposed to represent. For example, if there is a column in a dataset that measures the class level of high school students using Freshman, Sophmore, Junior, and Senior. Data would e invalid if it use the numerical values for the grade levels such as 9, 10, 11, and 12. This is only invalid because of the context and the assumptions that are brought to the data quality test.

Uniqueness is a measure of duplicate values. Normally, duplicate values happen along rows in structured data which indicates that the same observation appears twice or more. However, it is possible to have duplicate columns or variables in a dataset. Having duplicate variables can cause confusion and erroneous conclusions in statistical models such as regression.

Consistency is a measure of whether data is the same across all instances. For example, there are times when a dataset is refreshed overnight or whenever. The expectation is that the data should be mostly the same except for the new values. A consistency check would assess this. There are also times when thresholds are put in place such that the data can be a little different based on the parameters that are set.

Timeliness is the availability of the data. For example, if data is supposed to be ready by midnight any data that comes after this time fails the timeliness criteria. Data has to be ready when it is supposed to be. This is critical for real-time applications in which people or applications are waiting for data.

Accuracy is the correctness of the data. The main challenge of this is that there is an assumption that the ground truth is known to make the comparison. If a ground truth is available the data is compared to the truth to determine the accuracy.

Conclusion

The metrics shared here are for helping the analyst to determine the quality of their data. For each of these metrics, there are practical ways to assess them using a variety of tools. With this knowledge, you can be sure of the quality of your data.

Data Governance Solutions

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Data governance is good at indicating various problems an organization may have with data. However, finding problems doesn’t help as much as finding solutions does. This post will look at several different data governance solutions that deal with different problems.

Business Glossary

The business glossary contains standard descriptions and definitions. It also can contain business terms or discipline-specific terminology. One of the main benefits of developing a business glossary is creating a common vocabulary within the organization.

Many if not all businesses and fields of study have several different terms that mean the same thing. In addition, people can be careless with terminology, to the confusion of outsiders. Lastly, sometimes a local organization will have its own unique terminology. No matter the case the business dictionary helps everyone within an organization to communicate with one another.

An example of a term in a business dictionary might be how a school defines a student ID number. The dictionary explains what the student ID number is and provides uses of the ID number within the school.

Data Dictionary

The data dictionary provides technical information. Some of the information in the data dictionary can include the location of data, relationships between tables, values, and usage of data. One benefit of the data dictionary is that it promotes consistency and transparency concerning data.

Returning to our student ID number example, a data dictionary would share where the student ID number is stored and the characteristics of this column such as the ID number being 7 digits. For a categorical variable, the data dictionary may explain what values are contained within the variable such as male and female for gender.

Data Catalog

A data catalog is a tool for metadata management. It provides an organized inventory of data within the organization. Benefits of a data catalog include improving efficiency and transparency, quick locating of data, collaboration, and data sharing.

An example of a data catalog would be a document that contains the metadata about several different data warehouses or sources within an organization. If a data analyst is trying to figure out where data on student ID numbers are stored they may start with the data catalog to determine where this data is. The data dictionary will explain the characteristics of the student ID column. Sometimes the data dictionary and catalog can be one document if tracking the data in an organization is not too complicated. The point is that the distinction between these solutions is not obvious and is really up to the organization.

Automated Data Lineage

Data lineage describes how data moves within an organization from production to transformation and finally to loading. Tracking this process is really complicated and time-consuming and many organizations have turned to software to complete this.

The primary benefit of tracking data lineage is increasing the trust and accuracy of the data. If there are any problems in the pipeline, data lineage can help to determine where the errors are creeping into the pipeline.

Data Protection, Privacy, QUailty

Data protection is about securing the data so that it is not tampered with in an unauthorized manner. An example of data protection would be implementing access capabilities such as user roles and passwords.

Data privacy is related to protection and involves making sure that information is restricted to authorized personnel. Thus, this also requires the use of logins and passwords. In addition, classifying the privacy level of data can also help in protecting it. For example, salaries are generally highly confidential while employee work phone numbers are probably not.

Data quality involves checking the health of the accuracy and consistency of the data. Tools for completing this task can include creating KPIs and metrics to measure data quality, developing policies and standards that defined what is good data quality as determined by the organization, and developing reports that share the current quality of data.

Conclusion

The purpose of data governance is to support an organization in maintaining data that is an asset to the organization. In order for data to be an asset it must be maintained so that the insights and decisions that are made from the data are as accurate and clear as possible. The tools described in this post provide some of the ways in which data can be protected within an organization.

Total Data Quality

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Total data quality as its name implies is a framework for improving the state of data that is used for research and reporting purposes. The dimensions that are used to assess the quality of data are measurement and representation

Measurement

Measurement is focused on the values gathered on the variable(s) of interest. When assessing measurement researchers are concerned with.

Construct-The construct is the definition of the variable of interest. For example, income is can be defined as a person’s gross yearly salary in dollars. However, salary can also be defined as per month or as the net after taxes to show how this construct can be defined differently. The construct validity must also be determined to ensure that it is measuring what it claims to measure.
Field-This is the place where data is collected and how it is collected. For example, our income variable can be collected from students or working adults. Where the data comes from affects the quality of the data concerning the research problem and questions. If the research questions are focused on student income then collecting income data from students ensures quality. In addition, how the data is encoded matters. All student incomes need to be in the same currency in order to make sense for comparision
Data Values-This refers to the tools and procedures for preparing the data for analysis to ensure high-quality values within the data. Such challenges addressed are dealing with missing data, data entry errors, duplications, assumptions for various analytical approaches, and or issues between variables such as high correlations.

Representation

Representation looks at determining if the data collected comes from the population of interest. Several concerns need to be addressed when dealing with representation.

Target population- The target population is potential participants in the study. The limitation here is determining the access of the target population. For example, studies involving children can be difficult because of ethical concerns over data collection with children. These ethical concerns limit access at times.
Data sources- Data sources are avenues for obtaining data. It can relate to a location such as a school or to a group of people such as students among other definitions. Once access is established it is necessary to specifically determine where the data will come from.
Missing data-Missing data isn’t just looking at what data is not complete in a dataset. Missing data is also about looking at who was left out of the data collection process. For example, if the target population is women then women should be represented in the data. In addition, missing data can also look at who is represented in the data but should not be. For example, if women are the target population then there should not be any men in the dataset.

Where measurement and representation meet is at the data analysis part of a research project. If the measurement and representation are bad it is already apparent that the data analysis will not yield useful insights. However, if the measurement and representation are perfect but the analysis is poor then you are still left without useful insights.

Conclusion

Measurement and representation are key components of data quality. Researchers need to be aware of these ideas to ensure that they are providing useful results to whatever stakeholders are involved in a study.

Data Types

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There are many different ways that data can be organized and classified. In this post, we will look at data as it is classified by purpose. Essentially, data can be gathered for non-research or research purposes. Data collected for non-research purposes is called gathered data and data collected for research purposes is called designed data.

Gathered Data

Gathered data is data that is obtained from sources that were not developed with the intention of conducting research specifically. Examples of gathered data would be data found in social media such as Twitter or YouTube and data that is scraped from a website. In each of those examples, data was collected but not necessarily for an empirical theory testing purpose.

Gathered data is also collected in many ways beyond websites. Other modes of data collection could be sensors such as traffic light cameras, transactions such as those at a store, and wearables such as those used during exercise.

Just because the data was not collected for research purposes does not mean that it cannot be used for this purpose. Gathered data is frequently used to support research as it can be analyzed and insights developed from it. The challenge is that the gathered data may not directly address whatever research questions a researcher may have which necessitates using this data as a proxy for a construct or rephrasing research questions to align with what the gathered data can answer. Gathered data is also referred to as big data or organic data.

Designed data

Designed data is data that was developed and collected for a specific research purpose. Often this data is collected from people or establishments for answering scientifically designed research questions. A common way of collecting this form of data is the use of a survey and these surveys can be conducted in-person, online, and or over the phone. These forms of data collection are in contrast to gathered data which collects data passively and without human interaction. This leads to an important distinction in that gathered data is probably strictly quantitative because of its impersonal nature while designed data can be quantitative and or qualitative in nature because it is possible to have a human element in the collection process.

When a researcher wants designed data they will go through the process of conducting research which often includes developing a problem, purpose, research questions, and methodology. All of these steps are commonly involved in conducting research in general. The data that is collected for design purposes is then used to address the research questions of the study.

The purpose of this process is to ensure that the data collected will answer the specific questions the researcher has in mind. In other words, designed data is designed to answer specific research questions while gathered can hopefully answer some questions.

Conclusion

Understanding what data was collected for is beneficial for researchers because it helps them to be aware of the strengths and weaknesses the data may have based on its purpose. Neither gathered nor designed data is superior to the other. Rather, the difference is in what was the inspiration for collecting the data.

Data Governance Office

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The data governance office or team are the leaders in dealing with data within an organization. This team is comprised of several members such as

Chief Data Officer
Data Governance Lead
Data Governance Consultant
Data Quality Analyst

We will look at each of these below. It also needs to be mentioned that a person might be assigned several of these roles which are particularly true in a smaller organization. In addition, it is possible that several people might fulfill one of these roles in a much larger organization as well.

Chief Data Officer

The chief data officer is responsible for shaping the overall data strategy at an organization. The chief data officer also promotes a data-driven culture and pushes for change within the organization. A person in this position also needs to understand the data needs of the organization in order to further the vision of the institution or company.

The role of the chief data officer encompasses all of the other roles that will be discussed. The chief data officer is essentially the leader of the data team and provides help with governance consulting, quality, and analytics. However, the primary role of this position is to see the big picture for big data and to guide the organization in this regard, which implies that technical skills are beneficial but leadership and change promotion is more critical. In sum, this is a challenging position that requires a large amount of experience

Data Governance Lead

The data governance leads primary responsibilities to involve defining policies and data governance frameworks. While the chief data officer is more of an evangelist or promoter of data governance the data governance lead is focused on the actual implementation of change and guiding the organization in this process.

Essentially, the data governance lead is in charge of the day-to-day operation of the data governance team. While the chief data officer may be the dreamer the data governance lead is a steady hand behind the push for change.

Data Governance Consultant

The data governance consultant is the subject matter expert in data governance. Their role is to know all the details of data governance in the general field and even better if they know how to make data governance happen in a particular discipline. For example, a data governance consultant who knows how to make data governance happen within the context of a university in particular.

The data governance consultant supports the data governance lead with implementation. In addition, the consultant is a go-between for the larger organization and IT. Serving as a go-between implies that the consultant is able to effectively communicate with both parties on a technical level with IT and in a layman’s matter with the larger organization. The synergy between IT and the larger organization can be challenging because of communications issues due to vastly different backgrounds and it is the consultant’s responsibility to bridge this gap.

Data Quality Analyst

The data quality analyst’s job is as the name implies to ensure quality data. One way of determining data quality is to develop rules for data entry. For example, a rule for data quality is that marital status can only be single, married, divorced, or widowed. This rule restricts any other option that people may want. When this rule is supported it is an example of high quilty within this context.

A data quality analyst also performs troubleshooting or root cause investigations. If something funny is going on in the data such as duplicates, it is the data quality analyst’s job to determine what is causing the problems and to find a solution. Lastly, a data quality analyst is also responsible for statistical work. This can include statistical work that is associated with the work of a data analyst and or statistical work that monitors the use of data and the quality of data within the organization.

Conclusion

The data governance team plays a critical role in supporting the organization with reliable and clean data that can be trusted to make actionable insights. Even though this is a tremendous challenge it is an important function in an organization.

Roles in Data Governance

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Working with data is a team event. Different people are involved in different stages of the data process. The roles described below are roles commonly involved in data governance. The general order below is the common order in which these individuals will work with data. However, life is not always linear and different people may jump in at different times. In addition, one person might have more than one role when working with data in the governance process.

Data Owners

Data owners are responsible for the infrastructure such as the database in which data is stored for consumption and use. Data owners are also in charge of the allocation of resources related to the data. Data owners also play a critical role in developing standard operating procedures and compliance with these standards.

Data Producers

Once the database or whatever tool is used for the data the next role involved is the data producer. Data producers are responsible for creating data. The creation of data can happen through such processes as data entry or data collection. Data producers may also support quality control and general problem-solving of issues related to data. To make it simple the producer uses the system that the owner developed for the data.

Data Engineers

Data engineers are responsible for pipeline development which is moving data from one place to the other for various purposes. Data engineers deal with storage optimization and distribution. Data engineers also support the automation of various tasks. Essentially, engineers move around the data that producers create.

Data Custodians

Data custodians are the keepers and protectors of data. They focus on using the storage created by the data owner and the delivery of data like the data engineer. The difference is that the data custodian sends data to the people after them in this process such as stewards and analysts.

Data custodians also make sure to secure and back up the data. Lastly, data custodians are often responsible for network management.

Data Stewards

Data stewards work on defining and organizing data. These tasks might involve working with metadata in particular. Data students also serve as gatekeepers to the data which involves keeping track of who is using and accessing the data. Lastly, data stewards help consumers (analysts and scientists) find the data that they may need to complete a project.

Data Analysts

Data analysts as the name implies analyze the data. Their job can involve statistical modeling of data to make a historical analysis of what happened in the past. Data analysts are also responsible for cleaning data for analysis. In addition, data analysts are primarily responsible for data visualization and storytelling development of data. Dashboards and reports are also frequently developed by the data analyst.

Data Scientists

The role of a data scientist is highly similar to data analyst. The main difference is that data scientists use data to predict the future while data analysts use data to explain the past. In addition, data scientists serve as research designers to acquire additional data for the goals of a project. Lastly, data scientists do advance statistical work involving at times machine learning, artificial intelligence, and data mining.

Conclusion

The roles mentioned above all play a critical role in supporting data within an organization. When everybody plays their part well organizations can have much more confidence in the decisions they make based on the data that they have.

Data Governance Framework Types and Principles

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When it is time to develop data governance policies the first thing to consider is how the team views data governance. In this post, we will look at various data governance frameworks and principles to keep in mind when employing a data governance framework.

Top-Down

The top-down framework involves a small group of data providers. These data providers serve as gatekeepers for data that is used in the institution. Whatever data is used is controlled centrally in this framework.

One obvious benefit of this approach is that with a small group of people in charge, decision-making should be fast and relatively efficient. In addition, if something does go wrong it should be easy to trace the source of the problem. However, a top-down approach only works in situations that have small amounts of data or end users. When the amount of data becomes too large the small team will struggle to support users which indicates that this approach is hard to scale. Lastly, people may resent having to abide by rules that are handed down from above.

Bottom-Up

The bottom-up approach to data governance is the mirror opposite of the top-down approach. Where top-down involves a handful of decision-makers bottom-up focus is on a democratic style of data leadership. Bottom-up is scaleable due to everyone being involved in the process while top-down does not scale well. Generally, controls and restrictions on data are put in place after the raw data is shared rather than before when the bottom-up approach is used.

Like all approaches to data governance, there are concerns with the bottom-up approach. For example, it becomes harder to control the data when people are allowed to use raw data that has not been prepared for use. In addition, because of the democratic nature of the bottom-up approach, there is also an increased risk of security concerns because of the increased freedom people have.

Collaborative

The collaborative approach is a mix of top-down and bottom-up ideas on data governance. This approach is flexible and balanced while placing an emphasis on collaboration. The collaboration can be among stakeholders or between the gatekeepers and the users of data.

One main concern with this approach is that it can become messy and difficult to execute if principles and goals are not clearly defined. There it is important to spend a large amount of time in planning when choosing this approach.

Principles

Regardless of which framework you pick when beginning data governance. There are also several terms you need to be familiar with to help you be successful. For example, integrity involves maintaining open lines of communication and the sharing of problems so that an atmosphere of trust is maintained or developed.

It is also important to determine ownership for the purpose of governance and decision-making. Determining ownership also helps to find gaps in accountability and responsibility for data.

Leaders in data governance must also be aware of change and risk management. Change management is tools and process for communicating new strategies and policies related to data governance. Change management helps with ensuring a smooth transition from one state of equilibrium to another. Risk management is tools related to auditing and developing interventions for non-compliance.

A final concept to be aware of is strategic alignment. The goals and purpose of data governance must align with the goals of the organization that data governance is supporting. For example, a school will have a strict stance on protecting student privacy. Therefore, data governance needs to reflect this and support strict privacy policies

Conclusion

Frameworks provide a foundation on which your team can shape their policies for data governance. Each framework has its strengths and weaknesses but the point is to be aware of the basic ways that you can at least begin the process of forming policies and strategies for governing data at an organization.

Data Governance Framework

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In this post we will look at a defining data governance framework. We will also look a the key components that are a part of a data governance framework.

Defined

A data governance framework is the how or the plan for governing the data within an organization. The term data governance determines what needs to be governed or controlled while the data governance framework is the actual plan for controlling the data.

Common Components

There are several common components of a data governance plan and they include the following.

Strategy
Policies
Processes
Coordination
Monitoring/communication
Data literacy/culture

Strategy involves determining how data can be used to solve problems. This may seem pointless but certain data can be used to solve certain problems. For example, customers’ addresses in California might not be appropriate for determining revenue generated in Texas. When data is looked at strategically it helps to ensure that it is viewed as an asset in many cases by those who use it.

Policies help to guide such things as decision-making and expectations concerning data. In addition, policies also help with determining responsibilities and tasks related to data management. One example of policy in action is the development of standards which are rules for best practices in order to meet a policy. A policy may be something like protecting privacy. A standard to meet this policy would be to ensure that data is encrypted and password protected.

Process and technology involve steps for monitoring the quality of data. Other topics related to process can include dealing with metadata and data management. The proper process mainly helps with efficiency in the organization.

Coordination involves the processes of working together. Coordination can involve defining the roles and responsibilities for a complex process that requires collaboration with data. In other words, coordination is developed when multiple parties are involved with a complex task.

Progress monitoring involves the development of KPIs to make sure that the performance expectations are measured and adhered to. Progress monitoring can also involve issues related to privacy, quality, and compliance. An example of progress monitoring may be requiring everyone to change their password every 90 days. At the end of the 90 days, the system will automatically make the user create a new password.

Lastly, data literacy and culture involve training and developing the skill of analyzing and or communicating data to people and others within the organization of use or consumption data. Naturally, this is an ongoing process and how it works depends on who is involved.

Conclusion

A framework is a plan for achieving a particular goal or vision. As organizations work with data, they must be diligent in making sure that the data that is used is trustworthy and protected. A data governance framework is one way in which these goals can be attained.

Data Governance Benefits

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Data governance is a critical part of many organizations today. In this post, we will look at some of the commonly found benefits of incorporating data governance into an organization.

Improved Data Quality

In theory, when data governance is implemented within an organization there should be a corresponding improvement in data quality. What is meant by improved data quality is better accuracy, consistency, and integrity. In addition, data quality can also include the completeness of the data and ensuring that the data is timely.

When data quality is high it allows end users to have greater trust in the analysis and conclusions that can be made from the data. Improved trust can also lead to an increase in confidence we sharing and or defending the decision-making process.

Risk Reduction

Data governance can also reduce risk. There are often laws that organizations have to follow concerning data governance. Common laws often include laws about privacy. When data governance is implemented and carefully enforced it can help in complying with laws and thus lower the risk of breaking laws and or facing legal consequences.

The typical organization probably does not want to deal with legal matters. As such, it is in most if not all organizations’ benefit to comply with laws through data governance. The process of abiding by laws also provides a good example to stakeholders and creates a culture of transparency.

Improved Decision-Making

Decisions are only as good as the information that they are based upon. If data is bad then it puts at risk the making of bad decisions. There is an idiom common in the data world which states “garbage in garbage out.” Therefore, it is critical that the data accurately represents what it is supposed to represent.

As mentioned earlier, good data leads to good decisions and increase confidence. It also helps with improving understanding of the context in which the data came from.

Improved Processes

Data governance can also improve various processes. For example, roles relating to data have to be clearly defined. In addition, various tasks that need to be completed must also be stipulated and clarified. Whenever steps like these are taken it can improve the speed at which things are done.

In addition, improving processes can also reduce errors. Since people know what their role is and what they need to do it is easier to spot and prevent mistakes as the data moves to the various parties that are using it.

Customer service

Data governance is also beneficial for customer service or dealing with stakeholders. When requests are made by customers or stakeholders, accurate data is critical for addressing their questions. In addition, there are situations in which customers or stakeholders can access the data themselves. For example, most customers can at least access their own personal information on a shopping website such as Amazon.

If data is not properly cared for users cannot access it or have their questions answered. This is frustrating no matter what field or industry one is working for. Therefore, data governance is important in enhancing the experience of customers and people who work in the institution

Profit Up

A natural outcome of the various points mentioned above is increased profit or decreased expenses depending on the revenue model. When efficiency goes up and or customer satisfaction goes up there is often an increase in revenue.

What can be inferred from this is that data governance is not just a set of ideas to avoid headaches but a tool that can be employed to enhance profitability in many situations.

Conclusion

Data governance is beneficial in many more ways than mentioned here. For our purposes, data governance can allow an organization to focus on making cost-efficient, sound decisions by ensuring the quality and accuracy of the data involved in the process of making conclusions.

Interaction with Plotly in R

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The video below provides an explanation of how to create dropdown and check boxes for visuals using Plotly in R.

Influences and Approaches of Data Governance

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Data governance has been around for a while. As a result of this, there have been various trends and challenges that have influenced this field. in this post, we will look at several laws that have had an impact on data governance along with various concepts that have been developed to address common concerns.

Laws

Several laws have played a critical role in influencing data governance both in the USA and internationally. For example, the Sarbanes-Oxley (SOX) Act was enacted in 2002. The SOX act was created in reaction to various accounting scandals at the time and large corporations. Among some of the requirements of this law are setting standards for financial and corporate reporting and the need for executives to verify or attest that the financial information is correct. Naturally, this requires data governance to make sure that the data is appropriate so that these requirements can be met.

There are also several laws related to privacy in particular. Focusing again on the USA there is the Health Insurance Portability and Accountability (HIPAA) which requires institutions in the medical field to protect patient data. For leaders in data, they must develop data governance policies that protect medical information.

In the state of California, there is the California Consumers Protection Act (CCPA) which allows California residents more control over how their personal data is handled by companies. The CCPA is focused much more on the collection and selling of personal data as this has become a lucrative industry in the data world.

At the international level, there is the General Data Protection Regulation (GDPR). The GDPR is a privacy law that applies to anybody who lives in the EU. What this implies is that a company in another part of the world that has customers in the EU must abide by this law as well. As such, this is one example of a local law related to data governance that can have a global impact.

Various Concepts that Support Data Governance

Data governance was around much earlier than the laws described above. However, several different concepts and strategies were developed to address transparency and privacy as explained below.

Data classification and retention deals with the level of confidentiality of the data and policies for data destruction. For example, social security numbers is a form of data that is highly confidential while the types of shoes a store sells would probably not be considered private. In addition, some data is not meant to be kept forever. For example, consumers may request their information be removed from a website such as credit card numbers. In such a situation there must be a way for this data to be removed permanently from the system.

Data management is focused on consistency and transparency. There must be a master copy of data to serve as a backup and for checking the accuracy of other copies. In addition, there must be some form of data reference management to identify and map datasets through some general identification such as zip code or state.

Lastly, metadata management deals with data that describes the data. By providing this information it is possible to search and catalog data

Conclusion

Data governance will continue to be influenced by the laws and context of the world. With new challenges will be new ways to satisfy the concerns of both lawmakers and the general public.

Animation with Plotly in R

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It is possible to animate your visualizations in R using Plotly. The video below provides some tips for how to make animations a reality in your visualizations.

Early Views on Criminology

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In this post, we will look at some late 19th and early 20th-century views on criminology. In particular, we will look at the functionalist perspective and the Chicago School.

Functionalist Perspective

Emile Durkheim (1858-1917) was a major contributor to the functionalist perspective of criminology. This approach looks at crime in terms of the values and mores of society. For example, in most societies, certain values are preferred over others such as beliefs about roles in a family or about music and respect. In a similar line of thought, values of justice are preferred over values that encourage crime. What this means is that what is good is preferred over what is bad as defined by a group of people.

Deviance, as defined as breaking the rules and values of a society, serves an important function of defining what is right and wrong. Deviant behavior is an example of what is not acceptable and by seeing this negative behavior good is defined. For example, most cultures believe stealing is wrong and this behavior defines that asking for something and or giving something willingly is good.

Another tenet of the functionalist perspective is that fighting deviance helps to strengthen the cohesion and unity of a society. There are many examples of horrendous crime happening that galvanizes a community to pass laws to fight such abhorrent behavior. In other words, after something terrible happens society will rise up to make sure it never happens again and this only happens because deviant behavior had taken place.

On the flip side, if deviance is tolerated long enough it can help to establish no social norms. Many ideas that are supported and championed today were at one time or another considered deviant behavior. Views on reproductive rights, sexuality, and gender roles have faced tremendous pressure to change. Other proponents of behaviors that were once considered deviant have rallied to press their views into the forefront and place people who do not share their views on the defensive.

Chicago School and Crime

Another view of criminology that was developed around the same time as Durkheim’s work is the Chicago School perspective. This view was developed and encouraged by Robert Ezra Park (1964-1944). What was truly unique about this approach was its focus specifically on city life and crime that happens there.

Park explained that crime is worst in cities because of the structure of society. Cities encourage a higher degree of anonymity which can convince people they can get away with something without damaging any of their relationships. In addition, cities are more tolerant of diversity and thus deviance.

Park also shared the idea that crime can be found in certain areas of a city. This idea is based on how cities were designed. Certain parts of town were zoned in certain ways and industrial areas often will have more crime than suburban areas.

Crime is also considered a learned behavior. This idea was surprising for its time because many during Park’s era believed that people had a genetic predisposition to crime. For Park to place the blame on learning from others was a unique view.

Lastly, Park believe that agencies were the best defense against crime. For example, developing and funding government departments that deal with criminal behavior may be supported be Park.

Conclusion

The two views on criminology shared in this post provide insights into how researchers in the past viewed crime and its factors. Naturally, no single theory explains everything about a phenomenon. However, examining different theories helps a reader to understand the field of their studies.

Making Scatterplots Using Plotly in R

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Making scatterplots is a part of the data analyst’s life. The video below shows how a scatterplot can be created using ploty in the R environment.

Making Bar Graphs with Plotly in R VIDEO

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The video below provides examples of how to make bar graphs using plotly in R.

Early Forms of Criminology

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In this post, we will look at some of the first schools of thought on crime. The two schools, in particular, we will look at are classical and positivist criminology. Both of these schools of thought are still found in varying degrees in the modern era.

Classical Criminology

The classicist school of criminology dates back to the 18th century. Major influencers of this school of thought include Cesare Becarria and Jeremey Bentham. Classicism is also based heavily on ideas from the Enlightenment. For example, there is an assumption in Classicism that people are rational and free-willed and weigh the risk and rewards of actions. For criminologists, the assumption of rational thought implies that criminals go through a decision-making process before committing a crime. Therefore, if the deterrents are strong enough people will not choose to break the laws.

Social contract theory was another tenet of the classical school. Social contract theory states that people come together to make society work. In other words, people make agreements among themselves to abide by certain rules which implies that there is some form of a deterrent if people do not follow the rules.

A closely related idea to social contract theory is utilitarianism. Utilitarianism states that whatever is useful to most people should be implemented. Within the context of criminology, this means that laws that benefit the most people should be adopted and enforced.

Secularism is another critical component of classicism. Essentially, secularism within the context of criminology is based on the idea that man should make laws rather than God or the church. In other words, secularism seeks to push religious morals out of the criminal justice system. All forms of divine revelation and God’s law should not remove and reason should be the mechanism for right and wrong.

In terms of punishment, classic criminologists wanted to move from barbaric to more rational forms of punishment. During the 18th century, people were hanged, drawn and quartered, burned alive, tortured, mutilated, etc. For significant and even small crimes depending on their social rank. Classicists want to move to other forms of punishment such as imprisonment. Classists want the punishment to match the crime along with a measure of humanity in the method of correction.

Criticism of classicism includes the assumption that people are mostly rational in their decision-making process. People are often driven by emotions which is generally ignored in classicism. In addition, classicism absolves society from any role individuals play in breaking laws because it is assumed that society is fair and justice which is often not true.

Positivist Criminology

The positivist school of criminology originated in the 19th century and was a reaction to the classicist school. Major proponents of this school included Cesare Lomroso, Enrici Ferri, and Francis Galton. The supporters of positivism use a scientific approach to addressing crime and its motives. Whereas classicism blamed the individual positivism would blame a person’s genetic makeup and or society as a whole.

Positivism has a deterministic view of crime based on the physical characteristics of an individual. For example, studies were done that would determine criminality by body type, the shape of the skull, and even the chromosomal makeup of people such as how many y chromosomes a person had. Men with XYY chromosomal were deemed more dangerous than individuals with only the more commonly found XY chromosome.

Since there was an emphasis on the appearance of the individual. It was commonly believed that criminals were different from society. Criminals are driven into crime outside of their own control. This implies a reduction in harsher sentences because people are generally not normal as they are committing crimes.

Positivism has its own problems. The traits found in criminals that are claimed to cause them to commit crimes are commonly found in the general public. In addition, it is difficult to establish causation just because a group of people all share similar traits and behavior. Lastly, positivism removes self-agency and the freedom of the individual to choose to do good or evil.

Conclusion

Understanding the origins of different ideas in a discipline can help an individual to appreciate the source of diversity of thought that is found in different places. Classicism and positivism serve different purposes in criminology. Each approach plays a critical role in shaping criminology in the world today.

Brief History of Policing and Juveniles

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This post will provide a brief overview of policing as found in England and the USA. There will also be a link to juvenile justice.

Early forms of Policing

Policing has changed a great deal over time in England. Some of the earliest forms of policing was the pledge system which took place before the Norman conquest. In the pledge system, neighbors would work together to protect each other’s property from thieves and other criminal threats. In addition, the pledge system was also supposed to deal with problems among neighbors which implies that people were expected to police themselves.

Naturally, the pledge system has its limitations. Its strength was dependent on the strength of the community which may not have been enough to deal with serious threats. In addition, the pledge system could be politically messy as self-polic9ing would be based on who had power and or charisma rather than right and wrong. However, the pledge system did allow for a great deal of autonomy for those involved.

The next system of policing that moved through England was the watch system. The watch system was designed for urban and densely populated areas. Men were organized in their parish to patrol their communities at night. Eventually, England turned away from citizen-led policing to professionals. The professionalization of law enforcement led to the development of such positions as the constable for policing and the justice of the peace for judicial matters.

With the continued development of the Industrial Revolution further development of law enforcement was also needed. The need for better policing led to the creation of the bobbies. However, this next step in crime fighting was not successful in stopping crime and was often influenced by money and politics.

United States

In the US the sheriff was the equivalent of a constable or bobby. But in the mid-19th century, police departments were already being established. During this time juvenile offenders were treated just like adults if they were accused of committing a crime. With time reforms ended this practice but law enforcement professionals continued to share concerns about the lack of treatment of young people.

In many departments today, the police will have either a juvenile officer or a unit that focuses on dealing with youths. There is also an emphasis in some jurisdictions on community policing which involves reducing people’s fear of law enforcement through decentralization of decision-making and community involvement.

When dealing with juveniles there is a great deal of discretion in how to handle each child’s situation. The focus is always on treatment rather than punishment in many places. What this means is that the police officer can choose or not choose to “arrest” a juvenile in many different situations. The same idea applies to the probation officer and prosecutor who determine whether or not to pursue the arrest for punishment or disposition and this extends to the judge as well. The flexibility of discretion has led to accusations of unfairness and even racism.

Another problem with discretion is the inconsistencies in how police approach youth. There have been court cases over such matters as search and seizure, Miranda rights, habeas corpus, and other technical legal matters because law enforcement did not carefully consider the constitutional rights of children because it has often been assumed that children do not have these rights.

Conclusion

Policing has seen a great deal of growth and development over the years. Despite the flaws in the system law enforcement is still dedicated to helping keep people safe. Evidence for this can be seen in the reforms that have been made to maintain the trust of the communities in which they serve.

Child Savers

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During the 19th century in the United States that was a huge influx of people into urban areas in search of jobs and other opportunities. With this change in how people lived, there was a change in the family as well. Before the growth of urban centers, parents and children were all together in a rural setting in a farm-like community and this helped to monitor and control a child’s behavior. Now, parents often would have to leave their children unattended for long periods while they worked. Children who are left unattended tend to get into trouble. Over time and with years of neglect continuing some of these youth became delinquents. Once children began to turn to crime the local government began to step in and try and deal with this problem.

One solution that was tried was developed by a group of juvenile reformers from the Child Saving movement. In this post, we will look at the history and beliefs of the members of this movement.

Child Saving Movement

The Child Saving Movement wanted the government to monitor and control the activities of wayward youth. As mentioned before, this used to be a responsibility of the family but there was a breakdown in the family as a result of living in the new conditions of dense city life.

To help delinquent youths proponents of the Child Saving movement developed the House of Refuge, which was an early form of a reform school. Wayward youth were sent here for status offenses to major crimes. Before this youth were often sent to adult prisons for the offenses they committed.

The House of Refuge was funded primarily privately. However, the irony of the House of Refuge is that funding came from the state of New York, and this funding involved taxes collected from bars, theaters, and even circuses. In other words, venues that contributed to delinquency were used to reform students who were delinquents.

The House of Refuge was opened in 1825 with only 6 youths. Within the first ten years, the facility would serve 1600 youths. Both boys and girls were housed at the facility. Boys were taught blue-collar skills such as skills found in woodshop. Female residents were taught skills related to the home such as cooking and sewing.

The original location of the House of Refuge was in the city. However, with time the facility was moved to a rural location. In all, the House of Refuge would last about 100 years well into the early 20th century. Among the main criticism of this approach was that the facility was trying to play the role of the parent through the use of strict discipline and long work hours.

Conclusion

There are strengths and weaknesses to all forms of reform for young people who become delinquents. There is always something wrong and something that is done well by most movements. What all reform movements have in common is a desire to help young people and to make society safer. Sometimes it might be better to focus on this rather than on the failures of various movements such as the Child Savers.

School Failure and Delinquency

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School failure is a big problem in education today. In addition, school failure has also been examined in the context of delinquency. In this post, we will look at school failure and delinquency.

Definition

School failure is the opposite of school success. Essentially, school failure is the inability of a student to have academic success in the classroom. The concern with school failure in this post is that it is often associated with delinquent behavior. In other words, kids who struggle academically are often kids who are struggling behaviorally as well.

There are several ways in which school failure and delinquency are related. For example, school failure may cause delinquency. As a young person becomes frustrated with their school they may choose to become delinquent to win praise and acceptance that they are unable to achieve through academics.

Another view of school failure and delinquency focuses on the emotional aspect. For some kids, school failure causes emotional distress and this may cause delinquency. As the student loses control of their emotions they make poor decisions in terms of behavior.

A final view on school failure point to a cause that is above school failure and delinquency. From this view, the real problem is socioeconomic issues such as poverty, family issues, or some other disruption to the student’s life. These instabilities make it difficult for the youth to function in an academic setting like a school.

Other Variables Associated with School Failure

There are also several other variables and concepts that have been linked with school failure. Tracking involves placing students in classes based on ability or test scores. For example, all of the high achievers are in one class and the struggling kids are in a different class. Such an approach is highly detrimental to the weaker students because they know when they are being tracked and this can lead to self-esteem issues and indifference.

Some students truly do not like school and or their teachers. Alienation is defined as the inability to see the relevance or importance of schools. Education pays dividends over time. For people who have a shorter vision of themselves, spending years in school learning various facts and figures is meaningless. Delinquent behavior provides the immediate results that young people often want.

The consequences of school failure can be tremendous. Dropouts make less money than peers who finish K12 and people who go to college. Furthermore, dropouts are often trapped in the lowest-earning jobs that are available. Therefore, it is not in a child’s best interest to fail in school.

Conclusion

School failure is a sign that a child needs help. The cause could be one that was explained here or a different one. Either way, teachers need to be willing to support students who are struggling academically as this can lead to delinquency.

General Theory of Crime and the Classroom

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The General Theory of Crime (GTC) was developed by Gottfredson and Hirschi. This theory attempts to explain criminal or delinquent behavior in terms of criminal offender traits, criminal opportunity, and the act of committing a crime. In this post, we will look at each of these components in terms of how they help to explain delinquent behavior within the context of teaching.

Criminal Offender

The ideas associated with criminal offenders focus on personality and social connections. Insights into personality play an important part in the GTC framework. Impulse control is one main component of GTC. Impulsive people are often more short-sighted and prone to risk-taking. What this means is that they may be more prone to delinquent behavior because they lack the foresight of the consequences of their actions. Impulsive behavior is also something teachers struggle with when dealing with children who frequently make poor choices due to impulsive actions.

Another component that is highly related to impulsiveness is self-control. In other words, individuals who struggle to control themselves may be more likely to commit a crime. The authors of GTC explain that low self-control can be caused by poor management and or criminal parent(s), lack of supervision, and or self-centeredness. Again, the ideas here are similar to the causes of misbehavior in the classroom. It appears that excellent parenting is a primary tool for alleviating delinquent behavior in the classroom.

Another main cause of low self-control is a weak or a lack of social bonds between a youth and friends and family. When youths are attached and committed to the people around them it discourages poor behavior because people generally don’t want to hurt the people they are close to. The same idea applies in the classroom and is one reason why developing relationships with students and parents is often emphasized. Students with strong relationships often do not want to misbehave.

Criminal Opportunity

Criminal opportunity is essentially the openings that a delinquent finds to commit a crime. Often, an individual is more likely to commit a crime when there is a lack of supervision and there are easy targets or potential victims around. Naturally, this is what happens in the classroom when students are always looking for opportunities when the teacher is not looking so they can make poor choices.

What this implies is that students need large amounts of attention to maintain proper behavior. Sadly, many students and delinquents come from homes that lack the one-on-one attention they need.

Criminal Act

The criminal act is self-explanatory. Examples of criminal acts can be any action that breaks the law. Unlike adults, there are additional offenses that youths can commit and these are status offenses. Status offenses are acts that are legal for adults but illegal for minors such as smoking and drinking.

All of these criminal acts are the result of a combination of impulse control, self-control, social relationships, and opportunity in the context of GTC.

Conclusion

Of course, no single theory can explain everything about any behavior. There are differences in behavior based on race, gender, and other factors when it comes to crime. Despite this, the GTC provides a simplistic overview into what motives criminal behavior.

Developmental Theory

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Developmental theory was developed by Sheldon and Eleanor Glueck back in the 1930s. With this theory, the authors try to explain the dynamics, in terms of the activity level, of a youth in delinquent behaviors. In other words, developmental theory explains delinquent behavior over time rather than why a youth is misbehaving at a given moment. We will look at the concepts of this theory along with its application in the educational setting.

Tenets

One of the core tenets of developmental theory is the idea that chaos leads to delinquency. When there is a breakdown in the home through divorce or there is death or loss of a job, any of these adverse childhood experiences can lead to delinquency. Even something as mundane as moving can lead to poor behavioral choices. All of the examples above are examples of problem behavior syndrome which is a term used to describe negative events in a young person’s life.

Naturally, this can spill over into the classroom when students are having negative experiences in their personal life it can lead to behavioral problems in school. One danger to be aware of is antisocial behavior. The danger with antisocial behavior is that it is of link with persistent delinquent behavior.

There are also life experiences that can reduce delinquent behavior. Often these are events associated with maturity such as marriage, having children, starting a career, etc. Essentially what happens is that as the youth matures and becomes invested in something or someone the payoff for delinquent behavior is not worth the risk. Another way to view this is that these life experiences are associated with maturity and impulsive behavior naturally declines with age.

Paths to Delinquency

Developmental theory also addresses various pathways to delinquency. There are at least three and they are authority conflict, covert, and overt. Authority conflict is when a youth defies and avoids authority. Most teachers have dealt with students who simply refuse to comply and or avoid dealing with the teacher altogether. In such instances, the youth is heading towards delinquency through conflict with authority.

The covert pathway involves passive-aggressive behavior that is delinquent in nature. By covert, it includes behavior in which the authority and or the victim is unaware of what happened. For example, a student steals something without the other person being aware. With time, covert actions can lead to more serious offenses like breaking into homes.

The overt pathway is minor aggressions that eventually become violent. For example, a youth starts out by pushing other students which could one day lead to assault.

Types of Delinquents

The overall pattern of delinquency of youth varies. Life course persisters start young and continue into adulthood. Adolescent-limited defenders start young and stop once they mature. Other kids do not begin to misbehave until their teen years and they either stop once they mature or continue with poor behavioral choices.

Teachers have also experienced all of these various types of delinquents. Some kids start young while others do not. Some kids stop while others do not. The age at which a youth begins delinquency and whether or not they stop is at least partially related to the tenets that were discussed above.

Conclusion

As educators, we need to be aware of problem behavior syndrome and the negative experiences that kids are having outside the classroom because this may partially explain their behavior in class. In addition, we also need to be aware of how long the child has been delinquent as this can provide insight into what to do to help them.

Differential Association Theory and the Classroom

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Differential association theory was Developed by Edwin Sutherland in the 1930s. This particular theory is a part of the social learning school in which youth learn criminal behavior through interacting with others. A youth’s behavior is based on what they see as right and wrong as determined by the actions of others. In his context the focus was delinquency but some of these ideas apply in the classroom as well.

Sutherland explains his theory through several principles. We will look at each of these core principles and connect them to the classroom when appropriate

Principles of Differential Association Theory

Behavior is Learned

Negative behavior such as behaviors associated with criminal activities are learned just as any other behavior is learned. What this implies is that nobody is naturally a criminal but instead is a victim primarily of what they learn. In terms of nature vs nurture, this principle falls squarely in the camp of nurture.

Sutherland’s principle is not without merit. In most classrooms, students are often imitating the behavior of each other and even the teacher. When this happens it can be beneficial when the example provided is positive but can be detrimental when the example is negative.

Learning Happens through Interaction

People learn through interacting with others. What this means is that behavior, such as delinquency, cannot be learned in isolation it takes the support of others. Of course, this denies a person the ability to learn on their own in a self-directed manner. As individuals are socialized they acquire the ability to obey or break rules and laws.

In the classroom, it is common for students to teach each other things that may not be positive in nature. Youths can acquire questionable abilities through poor peer interactions. Therefore, teachers have to remain aware of the students who provide a poor example to others.

Learning is Personal

The closer a youth is to an individual the stronger the influence that person has on a youth’s delinquent behavior. In other words, a corrupt best friend will have more of an influence on a youth’s behavior for worse than a stranger who is an upstanding citizen. The level of intimacy in a relationship is a predictor of delinquent behavior.

In the classroom, children learn from children but they learn best from friends. What this means for the teacher is that they want students to have strong relationships with kids who act appropriately rather than with kids who are having behavioral issues.

Criminal Techniques Require Development

Delinquents are often mentored by a more experienced offender. For example, a youth would need someone to show them how to steal a car or how to sell drugs profitably. In addition, youths need to be socialized into how to deal with the police when interactions occur.

Bad students must also develop skills through mentoring or acceptance. Mentoring can involve how to steal or bully other students without getting caught. Acceptance can involve performing various disruptive behaviors to solicit laughs from other students while in the classroom. For students who need feedback isolation can stifle this process.

Rule Perception and a Child’s Perception

Rule perception is how others view rules. Some people are conscious of rules while others have no respect for rules and have an open disdain for them. For youth, seeing contrasting views on rules can be confusing and lead to internal conflict. The reason for this conflict is that young people haven’t formed their own posirion on following rules and are trying to decide whether or not to follow and take rules seriously.

Children in the classroom face a similar dilemma. They see the teacher stressing obedience, that some of the good kids obey, but that the other kids do not. The final decision a child makes during this conflict can be based on family values and or which peer group the child values more than the other.

Differential Association Varies

Submitting to authority can also depend on the dosage of the relationships a youth has. The dosage of a relationship can be measured in terms of duration, frequency, and intensity. Duration is the length of a relationship. In other words, a long-term friend has more influence on youth than a new friend. Frequency is a measure of how often the youths interact. Someone who sees the youth every day has more influence than someone who sees the youth once a year. Intensity is a measure of the amount of respect the youth has toward the influence. An example would be that a parent has more influence than a friend in most situations.

The same concept mentioned in the previous paragraph applies in the classroom. Long-term friends, who see each other frequently, and with the most respect will shape the behavior of a student the most. Relationships are critical to the formation of positive or negative behaviors.

Lastly, Sutherland claims that good and poor behavior does not have the same source. The only reason for delinquent behavior is what the youth has learned. Being rich or poor doesn’t matter. What really matters is what the youth has learned over time.

Conclusion

Sutherland’s work is highly influential in explaining delinquent behavior. His work provides one viewpoint on the way youth’s go down the wrong path. For teachers, watching the relationships students develop may be key to addressing challenges in the classroom.

Deterrence and the Classroom

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Deterrence theory is a theory found within criminology that states that policies that encourage fear, high risk, and punishment will discourage delinquent behavior. Without knowing it, many teachers support this view in their classroom management philosophy.

In this post, we will look at deterrence theory as defined in criminal justice while providing applications of this approach in the classroom with teachers. For our purpose, there is general deterrence, which is the heart of deterrence and then there are several variations of general deterrence.

General Deterrence

General deterrence believes that harsh punishment will reduce crime in society or poor behavior in the classroom. Examples of general deterrence would be mandatory sentences, three-strike laws, etc. In schools, it is common to see zero-tolerance policies for specific behaviors such as drug use.

Despite the best efforts of advocates of general deterrence crime and poor behavior persists. This is due in part to the underlying assumption that delinquents and students are rational individuals who weigh the pros and cons of their actions before doing them. Frequently this is not the case students and people frequently do not think things throw before doing them and this is especially the case when emotions are involved or substance abuse.

In addition, people are often convinced that the odds of getting caught are low and thus they can get away with it. Statistical this is correct as the majority of crimes go unsolved. However, in the context of education, it is generally hard to get away with misbehavior because there are usually only 30-40 suspects when something happens in the classroom.

Lastly, general deterrence when it is working well can overwhelm the system as more and more people are arrested and or placed in a facility. As people are caught it simply strains the system rather than stops crime. In the school, if enough students are breaking strict rules it can strain the administration as they try to process all the kids who are causing problems. This simply moves the chaos from the classroom to the office.

Variations of Deterrence

There are several variations of the implementation of deterrence as well. Specific deterrence focuses on making punishment so horrible that the offenders change their behavior. As already mentioned this often does not work and can harden the youth to resist. In addition, if offenders or bad students are labeled because of their mistakes they may commit themselves to live up to the label or reputation that they have now.

Incapacitation is an implementation strategy that focuses on incarcerating delinquents. The thought is that if the youth is locked up they cannot terrorize the community. In schools, this strategy might manifest itself through suspending and or expelling rowdy students. Within the context of juvenile justice, this approach often does not work due to restrictions on resources and the problem that youth who are locked up are often corrupted within the facilities. For schools, students who are removed simply fall behind academically, and when this happens simply will continue to disrupt the learning experience.

Lastly, situational crime prevention involves removing opportunities and increasing the risk of committing a crime. For example, many homes now have cameras which naturally discourage crime because of the threat of being caught. Within schools, the use of cameras has become ubiquitous as well. This approach leads to the protection of potential victims, increases the effort to get away with delinquency, and prevents any excuses because of the silent witness of video recording.

Conclusion

Deterrence is a view that promotes a tough approach to dealing with disobedience. As with any approach, this style works in some cases and not in others. Since there is no single solution to the problem of delinquency deterrence should be viewed as one of many tools that can be used.

Physiological and Neurological Theories on Delinquency

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This post will look at physical and mental reasons for delinquent behavior. The ideas presented here can be useful for teachers who also deal with youths who participate in delinquent behavior.

Biosocial

The biosocial theory of crime is in many ways a spin on the nature vs nurture debate. Supporters of biosocial theory believe that a combination of personality (nature) and environment (nurture) influence delinquent behavior. Such a premise seems reasonable as people have natural talent but they also develop skills and traits based on the environment in which they grow up as a child and beyond. Teachers are aware of this because of the classroom environment that they establish as they train children.

Concerning nurture, genetics have been studied to understand delinquent behavior. Researchers have found strong relationships between parents and children, twins, and siblings in terms of associating with delinquent behaviors. In other words, crime often runs in the family. Of course, there are many instances of people choosing to take their life in a different direction from the example that was set by relatives. Again, many teachers have seen good kids come from bad families and vice versa. Often it seems there is no way to know how a child will turn out when you meet them.

Several factors are related to biosocial theory in terms of the nurture aspects. For example, there have been biochemical arguments made that state that chemical imbalances brought on by a poor diet can contribute to delinquent behavior. These imbalances can cause hormonal issues as well and this can be critically important due to the natural hormonal changes of teenagers. However, it must be mentioned that many people suffer from biochemical imbalances and never commit crimes. Therefore, there must be something else going on to attribute delinquent behavior to.

Many of the ideas related to diet are also related to Maslow’s Hierarchy of Needs. If students are not getting enough to eat then studying is almost impossible. The need to provide for these biochemical concerns is perhaps one reason for the school meal programs that are ubiquitous today.

Other Theories

One other theory of delinquent behavior is arousal theory. This theory essentially states that some youth commit crimes for thrill-seeking reasons. In other words, they commit crimes for fun. Teenagers often are seeking new experiences as they push the boundaries of what they’re capable of. In addition, this would also help to explain why kids in stable, comfortable homes act out and make poor choices. Most young people want to have fun and the thrill of crime is one avenue for them to achieve this.

Another theory is tied to evolution. The evolutionary theory of delinquency states that impulsive men are often more successful in reproducing with multiple women due in part to their impulsive and often aggressive behavior. As such, these traits of aggression and unpredictability are passed on to the next generation and this process is repeated. Over time, this leads to a population becoming more and more willing to committ acts of violence and crime

The evolutionary theory is interesting but seems difficult to prove conclusively as there may be no known way to test this experimentally. In many ways, this is another variation of focusing on nature as the cause of delinquent behavior

Conclusion

The takeaway for teachers is that there are multiple reasons why a youth may become a delinquent. In addition, given the multitude of explanations, it appears that there is not a great deal of agreement on what motivates youth to participate in delinquent acts.

Student Behavioral Self-Modification VIDEO

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Behavioral self-modification is about empowering students to understand what their problems are and allowing them to solve them. In the video below, we will look at how to guide students through the process of self-management.

Focal Concerns and the Classroom

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Focal concerns is a theory developed by Walter Miller in the late 1950s that tries to explain cultural deviance among criminals and delinquents. Miller’s work was focused specifically on cultural deviance among the poor. In his work, he found several cultural values of the poor that he believes contribute to violent and illegal behavior. Below is a list of the focal concerns he found

Trouble
Toughness
Smartness
Excitement
Fate
Autonomy

These concerns may not only explain criminal behavior but may be useful in understanding misbehavior in the classroom as well.

Trouble

According to Miller, among the lower class, the ability to cause and deal with trouble is an important value or focal concern. Drug use, alcohol abuse, and promiscuity are all ways of making trouble. Dealing with trouble is also valued. For example, being able to handle one’s self in a physical altercation or deceive someone are ways of garnering respect.

Within the classroom, there are always students who pride themselves on causing trouble. TO frustrate the teacher and other students is a form of prestige and pride for the problem student. Therefore, what a teacher needs to do is remove the honor of causing trouble for a student who is seeking the prestige of causing trouble. For example, a teacher who does not get upset when a student’s poor behavior is denying that student the prestige of angering the teacher. In the field of behavioral psychology, this is called extinction.

TOughness

Toughness is focused on physical prowess and emotional control. In general, this focal concern is focused on males. Male members of the lower class who can attain physical strength along with some degree of stoicism garner more respect than males who cannot do this.

The same idea applies in the classroom. At least on the surface, many disruptive male students want to show how strong and un-feminine they are. A student might demonstrate their toughness by how they respond to discipline. By laughing or appearing in different the student is exerting indifference and perhaps flippancy in the face of extreme punishment.

Smartness

Smartness relates to knowing how to survive on the streets. This is more of a passive-aggressive skill in that it is defined as being able to outsmart or out-con an individual rather than face them head-on in a confrontation. For example, a delinquent might be tough but stupid, and vice versa. Smartness is a skill of deception rather than raw power.

Smartness in the classroom might be a student who is skilled at getting away with something or who knows how to do the minimum while still getting the grades they want. A student with smartness is annoying to a teacher, especially one who wants to teach the student a lesson.

Excitement

Excitement, according to Miller, is a desire for fun. Excitement is not a focal concern of just the lower class but perhaps of people in general. It seems that everybody wants to have a good time. Perhaps the difference might be in how the lower class has fun compared to other socioeconomic groups.

All kids want to have fun but the difference for the disruptive student is how much fun they want to have. Often difficult students have a higher need for fun compared to other students which are why they are causing problems in the classroom. The problems and fights with the teacher are fun for some students.

Fate

Fate is a superstitious view of the world. For example, some are lucky and some are not. There is no rhyme or reason to the world in terms of success. There is little concern for fairness in this supposed fundamental belief of criminals and the poor.

Fate is an idea that students of users when they get caught doing something inappropriate. They will blame their downfall not on the evil act they committed but on the fact that the teacher got lucky when they caught the,. This is a way of excusing poor behavior as acceptable.

Autonomy

Autonomy is not independence from bad luck but rather an independence from those with authority. Autonomy essentially means not having to obey anybody especially individuals outside of the local hierarchy. Therefore, resisting the police is a way to protect one’s autonomy

In the classroom, students are frequently concerned with having some sort of autonomy. When students break rules it is because he or she wants to do something instead of following rules. One strategy to deal with this is to use reverse psychology on the student and respect their autonomy. Autonomy is not bad but misguided. Giving the student choice for good and bad behavior is a way to encourage cooperation while still respecting the need for autonomy.

Conclusion

Miller’s works are considered outdated today. This is because of the push and desire for racial and gender equality and that poverty cannot be attributed to socioeconomic status. Whatever the case focal concerns provide another avenue for encouraging childhood disobedience.

Types of Planning

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Planning is a critical part of the educational process. Teachers plan every day what they will do. Administrators might actually do more planning than the teachers at times. Due to the nature of their position, leaders need to make many different types of plans to guide their institutions. In this post, we will look at some of the different types of plans that are used by institutions.

Hierarchical Plans

Hierarchical plans are plans that have levels to them. There are several types of hierarchical plans. Some of the hierarchical plans include strategic administrative and operating plans. Each of these plans serves a specific purpose within an institution.

Strategic plans explain the general position of the school in terms of mission and vision. The strategic plan may also include a philosophy statement of what the institution is about. This is perhaps the highest level at which planning can take place. In addition, most accreditation agencies expect some sort of mission and vision statement along with evidence of how these statements are communicated to shareholders.

The administrative plan is for determining the allocation of resources within an institution. Another way to see this is the administrative plan explains how resources are distributed for the achievement of the mission and vision statement of the strategic plan. The purpose behind this is that resources must be shared to achieve the mission statement of the institution and thus the strategic plan guides the administrative plan which is focused on implementation.

Lastly, the operating plan deals with the day-to-day running of the institution. After the vision is set, and the resources are distributed, the operating plan uses the resources daily. This can include salaries, lesson plan development, grade submission, activities for students, etc.

Frequency of use plans

There are of course other plans besides the hierarchical plans mentioned above. Another type is frequency of use plans. These are plans that are referred to often in the day-to-day of the institution. Standing plans include the rules, policies, and procedures of an organization. Policies guide decision-making and guide behavior. Examples can include policies and professional development which are often not rigid and can be negotiated with the school or committee in charge of this process.

Rules are stricter than policies and remove the interpretation that can happen under policies. For example, it might be a rule that teachers can only spend a certain amount of money on travel per year. Lastly, Procedures specify steps to take to complete a task, such as logging into the institution’s email system.

Some plans might only be used once. These can include budgets that are used once a year and then updated. Other examples can be plans for a project which has a specific start and end date. Once the project is over the plan will probably not be reused again.

Other PLans

There are also several other miscellaneous plans. Time-frame plans are plans based on the duration of the plan. Short plans are less than a year and an example would be most lesson planning. Medium plans last up to five years and are generally institutional-level plans to meet accreditation expectations. Lastly, long-term plans are over five years in length and are generally long-term development plans for an institution.

Plans can also be focused within a specific scope of the institution. There could be specific plans for various departments within a school. In addition, plans might only involve specific stakeholders. For example, there might be plans that only affect teachers or only affects students. Lastly, there are also contingency plans which are plans that usually deal with emergencies such as fires or natural disasters.

Conclusion

Planning is always going to be a major responsibility of institutions as they look for ways to support their stakeholders. The examples shared here are probably plans that many have made before but may not know the exact terminology involved. Therefore, hopefully, what was shared here is insightful.

Overview of Intro to a Research Paper VIDEO

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Writing a research paper is an extremely challenging experience. The beginning in particular is perhaps the most difficult part as it is unclear what to do. The video below provides an overview of the different components of the introduction of a research paper.

Historical Ways of Supporting Children

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Today there is a huge industry that looks to support children from unfortunate backgrounds. These can be kids who come from broken homes, have learning disabilities, and or from a generally poor background. Whatever the case, these problems have been around in one way or another for a long time. In this post, we will look at how such unfortunate were supported in the past.

Middle Ages

During the Middle Ages, families in Europe were primarily patriarchal in nature. The father had a great degree of authority over his family. Among the poor, most children had to endure harsh discipline and no real sense of childhood. As soon as possible a child was expected to work and help the family. Boys would learn blue-collar skills such as farming or blacksmith while girls would learn domestic skills such as cooking and caring for children.

Among the wealthy things were slightly better. Wealthy children received a superior education being able to study such things as the classics and Latin. Boys of the upper class would focus on warfare while girls would continue to develop domestic skills. One thing the rich had in common with the poor was harsh discipline.

Things Begin to Change

During the Enlightenment, there are some changes to the structure of the family. The extended family gave way to the nuclear family. Schools become more common and even higher education becomes something that the middle class can take advantage of.

Various thought leaders (could philosophers in those days) began to share new views on child-rearing. Rousseau, Lock, and Voltaire all spoke of “childhood” as a unique part of life and how there should be more leniency in disciplining children. The ideas of childhood being a separate part of life and the need for different methods of disciplining children would influence reforms in juvenile justice.

Supporting Unfortunate Children

During this same time of the Enlightenment, there were several efforts to support poor or disadvantaged children. England had poor laws which allowed a family to care for a neglected child and teach them a trade. The neglected child had no choice and had to work for this family. An entire industry sprang up to identify children who were neglected. Naturally, there were times when this system was abused by the family and even by the children at times.

Another similar way of supporting children was apprenticeships. It’s hard to tell the difference between poor laws and apprenticeships. The main difference may be that apprenticeships were available to anybody and not just poor children.

Both poor laws and apprenticeships were used in Europe and the USA. Such a system helps to keep kids off the street and gives them a skill by which they can support themselves and maybe a family one day. Eventually, this system of supporting young people would give way as many master craftsmen were put out of business by the rise of factories which negated the need for an apprentice. Children could skip this process and go straight to the factories to work and this is what happen for several decades before laws were passed to require school attendance.

Conclusion

Children will always make mistakes and challenge authority. However, the blatant disrespect of today was not found in the past. The harsh discipline that children experienced during the Middle Ages helped to temper disrespectful behavior. Of course, children were still found to commit crimes and hurt each other but the contempt for authority was not as strong as is found today.

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Student Records Table

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