What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME) Session Overview and Introductions (5 minutes)
What is Data Quality? (5 minutes)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT Engage (Slide #1): Welcome people to the discussion and, if needed, have a quick round of introductions. Explain: (Slide #2): Review the objectives for the session. Tell the group that data quality is an ongoing conversation, but this is a starting point for thinking more deeply about how to make data quality a visible and intentional part of what we do. Ask for questions or concerns the group has about data quality. Engage: (Slide #3): Ask the group for words or a definition that comes to mind when they think of data quality. If you are using the guided notes, provide time for participants to answer this question on their own before leading a brief group discussion. After the group has had an opportunity to share and discuss some of the ideas, read the sample definitions of data quality below. Quality data are accurate depictions of the real word that are consistent across an enterprise, secure and accessible, delivered in a timely manner, and suitable for their intended applications (Redman, 2001). The state of completeness, consistency, timeliness and accuracy that makes data appropriate for a specific use (Government of British Columbia). Data quality institutionalizes a set of repeatable processes to continuously monitor data and improve data accuracy, completeness, timeliness and relevance (Holly Hyland and Lisa Elliott). Explain: (click to advance animation on slide) Three terms common to these definitions (and perhaps similar to the words and descriptions the group provided) are accuracy, utility, and timeliness. Review the definitions. Accuracy refers to whether the data collected and reported accurately denote the truth they represent. Utility can also mean validity. The data must supply the right information to answer the questions that are asked. Data must also be timely. This means that they must also be accessible to users at the correct time in order to provide information for decision-making. (click to advance animation on slide) Data quality is more than the sum of its parts, however. It is also a way of doing things. It has to be integrated with the philosophy and
MATERIALS Projector Laptop Slide Deck Guided Notes (optional)
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Dimensions (15 minutes)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT culture of program operations. Evaluate: If you are using the guided notes with the group, provide a minute for participants to respond to the question about comparing and contrasting their definition with the one on the slide. Engage (Slide #4): There are several components that affect the philosophy and culture of data quality. These are dimensions, factors, importance and roles. Tell participants they will spend time looking at each of these, beginning with dimensions. If you are using the guided notes with the participants, be sure to provide time for them to capture information about each dimension. Extend (Slide #5): These seven dimensions of data quality are taken from the National Center for Education Statistics (NCES) publication Traveling through Time: The Forum Guide to Longitudinal Data Systems, book three of four (http://nces.ed.gov/pubs2011/2011805.pdf). The term dimension is a synonym for feature or attribute. No single Data Quality dimension is complete by itself, and many times dimensions are overlapping. Three of the dimensions are ideas discussed with the definition of data quality (accuracy, utility, and timeliness), but others were suggested by the various definitions that were reviewed. These dimensions help us identify what we are trying to protect or improve with our data quality efforts. Explain (Slide #6): Accuracy refers to the “truth” of the data. An example of this truth is that a student’s primary language code should represent the native or first language spoken by the student. For this data to be accurate, it must be collected and reported according to published data definitions and codes. Depending upon the audience for this presentation, you can use one or more of the examples below to build understanding of accuracy. It is important to note that valid does not mean accurate. A language code in the primary language data element with a value of 44 is valid, but it is not accurate if the student’s primary language is Japanese (42) NOT Javanese (44). Think about accuracy like spell-check for a document. Spell-check can only tell you if the words are spelled correctly (valid), but it can’t tell you if the text makes sense
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Additional Background for Presenter http://nces.ed.gov/pubs 2011/2011805.pdf http://k12.wa.us/CEDAR S/default.aspx
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT (accurate). The best up front tool for data accuracy is a “single, exhaustive data dictionary.” The data dictionary must be published, understood, and used. This is the definitive source for data elements that will include data definitions, formats, codes lists, formats for each type of data and restrictions on values or ranges such as GPA are five characters including the decimal point and valid values are 0.000 to 4.000). For CEDARS this resource is the CEDARS Data Manual and Appendices. The most recent version of these documents is available at http://k12.wa.us/CEDARS/default.aspx. Another way to improve accuracy is to use well-designed data collection (intake) forms. For example, an enrollment form that lists the race and ethnicity codes next to the parent selection check box will assist the data entry staff to enter the correct code and not require the use of a data manual or look up table each time data is entered from the enrollment form. Other forms that are used for data entry can also help to promote accuracy. A withdraw checklist form could have the correct codes for school, district and program withdrawal provided along with a simple explanation of the withdraw reason. This will assist the staff completing the form as well as the staff responsible for the data entry to be accurate. Application developers can also help increase data accuracy by automating some quality control. Validations that prevent obviously incorrect data promote accuracy. For instance the Student Information System (SIS) may prevent an entry of the letter “O” for the number “0”. These edit checks will prevent some errors. A validation cannot always exclude inaccurate data. We have to remember that valid is not synonymous with accurate. Grade 11 is a valid, however if our fingers slip as we enter the information for an incoming 1st grade student as grade “11”, there may not be a validation that rejects the grade 11 based on the school or birth date of the student. Checking our work and running some simple reports from the SIS uncover this type of data entry error. (Slide #7): Inconsistent or incomplete data are not useful for comprehensive data reporting or analysis. Data can be considered complete even if some elements are not available or are “allowed null.” A data requirement may be required, conditional, or
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT optional. If your audience is CEDARS staff, you may want to review the descriptions below. Required data elements are those that must be provided for every student. They are often the demographics that define or describe cohorts of students. The word cohort in demographics or statistics defines a group with some characteristic in common. "The cohort of people aged 30 to 39 . . . were more conservative" (American Demographics). These data are required in order to answer many types of data analysis questions. For example, every student must be identified as either male or female, have a grade level, and have a birth date. Validations in a system often enforce completeness. If a required data element is missing from a data entry screen or from a submission, the validation may stop the process at that point or reject the submission. It is important to note that data cannot be considered complete simply because no validation errors occurred. Data entry staff must know what data is expected. Some data elements are conditional, meaning that they may only be required in some cases. For example, In the CEDARS District Student File every student record must have a grade level (required), however the Expected Grad Year is conditional. The condition is “Data are required if the student is in grades 9-12.” A data element is optional when there are no conditions defined for that field. For example, in CEDARS, a Social Security Number is optional. (Slide #8): Data consistency is related to data reliability. It requires that documentation about data elements be written clearly. If the data dictionary leaves room for different interpretation by the staff creating and using the data, this will lead to problems with data consistency. Use the examples below, if needed, to develop understanding for your audience. For consistency, everyone who uses the data dictionary needs to understand and apply the codes in the same way. An example of consistency is the withdrawal codes that represent the specific reasons students leave a school or district. Every instance of a withdrawal code must represent the same or similar set of circumstances in order to be consistent. For example, If School A codes a student
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT who simply quits attending classes as D0 (Other – dropped out, but unknown reason) and School B codes the same set of circumstances as U1– Unknown, this is an inconsistent representation of a similar set of circumstances. The truth is that a student who simply ceases to attend classes is not a drop out. If the school has no knowledge of why the student is no longer attending, the code should be U1. Calculated data must also be consistent. A GPA calculation should be computed the same way if it is done more than once. GPAs must be reported in a standard format. Internal consistency is also required. For example, a student cannot have one District ID in the District Student file and another District ID in the School Student file. Validations will often enforce the consistency of the data. Consistency of data submitted to the state does not prohibit schools or districts from maintaining some data codes or elements that are meaningful to them that are not part of the state data dictionary. These data may simply not be submitted to the state or may be transformed (mapped) to state defined data elements when submitted. For example, a SIS may have a code that signifies a student is “withdrawn” at the end of the school year such as END. This code is not meaningful or consistent with the withdraw codes published by the state. This code should not be submitted to the state as a withdrawal. A district may define a grade level that is meaningful to the district such as KA for Kindergarten AM and KP for Kindergarten PM. Because these grade levels codes are not part of the state data dictionary; to be consistent, these grade levels must be transformed when submitted to K2 – attends half day. The district may have written guidance that defines these data elements to support consistency internally. (Slide #9): Data elements are valid when they provide the right information to answer questions. For example, we may report that we have enrolled a fourth grade male student named Charles. Although Charles may like to be called Charlie, Chuck or Chas, we know that the dimension of consistency demands we report the truth that his name is Charles every time we collect and report data about him. In order to provide the correct information for these mandated data collection we need clear documentation. That brings us back once again to the data dictionary, the single source that unifies the
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT understanding of these data. The data dictionary becomes “the documented clear data standard that supports the utility or validity of collected data.” Data collections will continue to be refined as the systems mature and users at every level realize the value (utility) of those data. (Slide #10): A data submission may provide demographic information about students that can be considered high quality data by many measures. The data may be accurate and useful, but they must also be accessible to users at the correct time in order to provide information for decision-making. In an organization with a culture of data quality, all data handlers will support the practice of getting the data right the first time and keeping the data accurate throughout the information life cycle. As the truth about any data changes, that will be reported in the next data submission. Use the examples below, if needed, to develop understanding about timeliness. For example, if Free and Reduced Lunch data is not in good shape by the time the list of high-poverty schools is pulled for National Board grants, potential grant recipients might lose their opportunity to get grants. Other data are also time sensitive. A student’s LRE (Least Restrictive Environment) code for special education is not static. As new evaluations are done, this code may change. The LRE code is required for the annual Special Education Count. This report is collected for a student’s status as of November 1. To collect and report this information accurately (note the overlapping dimensions), it must be reported in time. There are numerous data requests received at OSPI and processed each month from various data collections. Some of these data requests are information requests for grants or scholarships that will directly support the schools, students or staff. It will be important to have the most accurate information about students and schools whenever these data are pulled. (Slide #11): Security protects data. User roles and permissions need to be carefully defined and implemented in order to protect data while granting the correct role based accessibility. Data can be changed unintentionally by someone who is not familiar with the application. Make sure passwords are strong, not shared or loaned to another
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT person. Passwords should be memorized and changed frequently. Data must also be protected while they are moving. Student level data should be transferred securely (encrypted). Today’s information portability although convenient, increases the risk of unauthorized access to private educational information. Laptops, flash drives and the many hand held devices available require that schools and districts define the necessary precautions to protect student and staff privacy. For further information, access the NCES curriculum on security and confidentiality. You can download the lesson plan and materials at http://nces.ed.gov/pubs2007/curriculum/ls_security.asp (Slide #12): Data quality results from data use. This makes accessibility critical to quality. If staff at any level views data collection and reporting simply as chores to perform for an authority, they may lose incentive to protect data quality. Though data must be secured to protect privacy and prevent tampering, it must also be accessible to authorized users. Data should be used at every level to provide an incentive for data quality. Many student information systems provide pre-defined reports for day to day operations, as well as the ability to customize reports to answer specific questions at the school of district level. Examples of these reports may include counts of students by class for a room utilization report, or attendance and grade book data to populate the reports parents access for their students. Administrators or school boards may ask for reports such as number of students enrolled in specific courses, or grade point average for a sports team. If data handlers see that the data are used for calculations, accountability, or public relations, they have a greater incentive to ensure that the data are of high quality. The state also provides reports and information for a variety of uses. One example is the district report cards on the OSPI website which draw data from several sources. This accountability report lets schools and districts check their own status as well as allows district administrators to see how their schools compare to similar schools and districts in the state.
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Factors (10 minutes)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT MATERIALS Evaluate: If you are using the guided notes with the group, provide time for participants to complete their notes. Be sure to clarify any elements with the group. Engage (Slide #13): The dimensions of data quality provide a basis to talk about the Additional Background for factors that affect data quality. We need to recognize these characteristics of data Presenter: quality in order to know what we are trying to protect or improve when we talk about http://nces.ed.gov/pubs 2005/2005801.pdf the factors that affect data quality. A factor actively contributes to an accomplishment, result, or process with regards to data quality. They are processes that help establish a culture of data quality. The factors identified on the slide come from the NCES publication, Forum Guide to Building a Culture of Quality Data: A School & District Resource (http://nces.ed.gov/pubs2005/2005801.pdf). If you are using the guided notes, provide time for participants to record some examples of each of the factors throughout the discussion. Explain (Slide #14): Schools are bound by district policies as well as state guidelines and federal regulations. Schools and districts have many policies that affect data; examples of these are policies about grading, attendance and high school graduation requirements. Schools must adhere to regulatory policies in order to ensure students receive the services they need, protect the students’ rights, and protect the expenditure of public funds. Because school administrators are ethically obligated to report data as accurately as possible, they must work with their school board and staff to develop effective policies that support quality data. Districts should have policies that protect the data. For example, a policy that defines access to student records. The policy and procedure will likely describe the types of records and who can have access to them. This addresses at least two of the dimensions of data quality: security and accessibility. Districts may also have policies and procedures about attendance. These policies will usually define if an absence is excused or unexcused, what constitutes a tardy, what documentation and verification is required. This type of policy speaks to the dimension of consistency – every school governed by the policy must apply the guidance and rules consistently. 8
What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT Districts also have policies that describe and define instruction. A policy on English as a second language may cite the legal reference and give an overview of the program. The procedure often has more nuts and bolts information that would support data quality such as registration procedure, evaluations and how credit will be assigned. Many of the dimensions could be covered in such a policy. Timeliness is cited in the regulations for when screening should take place, utility is included in the guidance on how to ask the right questions about previous course work for assigning credit and accuracy is implied in the collection of the information about language spoken at home and other factors that qualify students for this program. FERPA (Family Education Rights and Privacy Act) and HIPPA (Health Insurance Portability and Accountability Act) laws are examples of regulations that protect individual privacy. These regulations define to whom specific data elements in specific collections can be disclosed. Formally adopted School Board policies may not have enough detail about specific data quality issues to guide the data entry process. This leads to the next factor, Standards and Guidelines. (Slide #15): A school or district program may choose to adopt standards and guidelines that are specific to their work. These standards can define the benchmarks that will assist in measuring data quality successes and misses such as less than 3% missing data, same day response to a help desk call, or 48 hours to respond to information request. The guidelines should be developed to give process to the standards. A guideline can define how to correct for missing data. For instance, a guideline for missing ethnic codes could define who is responsible for follow up, whether the contact is by phone or in writing, or define the escalation process for obtaining the data. This guideline speaks to the dimension of completeness. Guidelines can also be a resource navigation tool defining where to look for clarity on definitions to ensure data accuracy. Program staff are essentially the experts on data quality because they have a deeper understanding of and history with the information. Data Quality starts at the source; a culture of data quality is present from the
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT bottom to the top of an organization. Those closest to the work and responsible for correcting data errors often have very useful advice about ensuring data accuracy. Data entry staff are often responsible for entering the data that supports and documents data about students across several programs. Collaboration with program staff can help ensure that submitting data from one program should not be very different from submitting data for another program. The data entry staff can help describe the guidelines, procedures and business rules that regulate these data activities. The guidelines may take the form of a checklist of required data elements necessary for any data transaction such as requiring an SSID and District ID on every data request to ensure the correct student data is being updated. The collaborative effort for developing standards and guidelines will lead to a better understanding and appreciation for the data at all levels. This appreciation of data may increase the use or accessibility of data. Lot of overlapping dimensions once again. Districts may maintain document libraries with specific guidance for data staff as well as offering Training and Professional Development – the next factor. (Slide #16): The quality of data used to run our schools is directly related to the expertise of staff who enter that data, as well as those who use the data. Training in specific SIS and data entry mechanics are essential and ongoing. In addition to the internal district communication, it is often helpful for data personnel to exchange ideas and expertise with peers in other districts. This peer training can be done through user group meetings, conferences, or scheduled discussions. A professional development program that is effective will also train staff to understand why data are so important. Professional development and ongoing communication are essential to understanding the importance of data. All staff need to know why data is collected and how it affects decisions. They must understand that data relates to the money the school and district receive. Data may be used for school and district decisions both in the near and long term that have significant impact on the resources or defining the services the district may provide. The data may also be used to change the instructional plan for an individual
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT student. An effective professional development program can link the entry of data to reports created and instructional programs. For example, administrators, teachers and other staff may need to know the relationship of enrollment data to appropriate course scheduling and facility usage, timely reporting of student attendance to the ability to notify parents of absentees, accurate and timely entry of grades to the ability to produce transcripts, and free and reduced price lunch data to schools’ eligibility for funded nutrition programs or Title 1 services. In a culture of data quality, administrators can demonstrate their commitment when they encourage staff to attend professional development and training sessions, allow staff to discuss findings and new directions at staff meetings or in written communication, hold discussions that include all staff members so that everyone understands the importance of the data, and involve those responsible for data entry and security in discussions about needed changes in procedures. (Slide #17) Technology that supports the data needs of the people who use the technology is central to a culture of data quality. Technology that streamlines data entry and reporting are indispensable to data quality. One way that technology supports data quality is through validations. Validation procedures implemented through technology are crucial to keeping poor quality data out of data collections at the onset. Validations often enforce many of the dimensions of data quality. For instance, validations can reject records that are incomplete or inconsistent. Collaboration is evident again when IT staff, program staff and data entry staff communicate to make the technology the most effective tool for data quality. School district staff doesn’t usually manage the hardware or software that supports the data entry. Data quality can depend on how user-friendly the systems are. When the data entry screens are confusing, use different names for data elements or do not flow well, there is a greater risk of data entry errors. As users of the data entry technology, school personnel do have a role to play in making the systems effective tools for quality data entry. For example, data entry staff may suggest data entry screen designs that work more efficiently. Data entry needs to drive the
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT development of technology, not the other way around. Data entry staff may also request support for broader tools such as Excel or database software. These systems may streamline some reporting or data quality checks users can access quickly. Software vendors are your partners. The vendors serve you by developing software applications for collecting student information and educational data that are userfriendly systems. Vendors have a stake in their customers’ ability to perform their jobs effectively. (Slide #18): A culture of quality data includes support and resource allocation that value the data entry process. Support for data quality can take many different shapes in a school or district. It may be providing additional support during peak data entry times. It may be re-organization of duties to provide adequate time for the day to day data entry activities. It can take the form of frequent opportunities for school and district administrators to touch base with the data entry staff. It also takes the form of providing training and professional development that will make the data entry tasks more efficient. All seven of the data quality dimensions are reinforced when the data entry staff are supported. School offices are the hub of many activities that are vital such as greeting parents and students, answering phones and bandaging wounded knees. In the midst of all this activity school administrators need to meet the more concrete needs for data entry activities. These needs may include access to the correct technology, adequate desk and storage space and the ability to keep confidential information secure. With competing and often diminishing resources, it is important that quality data be prioritized. An ideal data entry environment is one that values the activity as important to the success of school or district. (Slide #19): Calendars help to plan support for data that is reported in a useful timeline. There are peak data requirements for all schools and programs. A calendar will note these deadlines and crunch times and provide an opportunity to plan for meeting the requirements. Everyone who works with the data should be involved in developing the
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Importance (10 minutes)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT calendar. Communication about a program office’s need for data is important for the data entry staff. It is also important for the program office to know the amount of effort required from the data entry staff to meet data demands with time for procedures to check the data quality and resolve errors. When staff are involved and informed, their understanding of the process will enable them to perform at a higher level of competency. The data entry and reporting calendar is also useful for the technology staff. Knowing when to expect greater demand on the systems is useful information for planning. For instance server maintenance should be scheduled before or after the peak times. There may be a greater demand at the help desk during peak times. The calendar should include monthly templates that include report due dates, data entry validation days (including time needed to query and make corrections), designated non work days, testing days or conflicts with other school events, and that identify the staff responsible for implementation. Involving the program staff and the IT staff at the school and district level to develop the calendars may help reduce redundant data requests, identify overlapping dates, and increase understanding the reasons for data collections. Engage (Slide #20): Importance is really the “So What?” of data quality. Good data quality as a function of data-informed decision-making processes leads to effective decisions, improved instruction, and more. If you are using the guided notes, provide time for participants to record some benefits for establishing a strong culture of data quality, as well as some consequences of not doing so. Have participants share some ideas and use these as you discuss the next two slides. Explain (Slide #21): Data are the facts you work with – such as grade level or ethnicity. Information includes the data with some context and allows the user to see relations between data. Information/knowledge is useful when the users start to see and understand patterns in the information to give predictability and reliability. Data driven decisions impact schools and districts for program funding at every level. The United States Department of Education and OSPI rely on data to set policy and guide funding. Applications for grants available to state education
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT departments, districts and schools often have data intensive components. Program and instructional staff require data on students and courses to support decisions. Districts rely on data to for decisions on curriculum, programs, staffing, and facility use to name a just a few. The importance of reliable data for these decisions must be emphasized as a fundamental obligation for all data handlers. There is a growing awareness that effective teaching, efficient schools and quality data are linked. Requests for information derived from data about students and schools come in from a variety of stakeholders on a regular basis. As the collection of education data grows the questions asked to support data driven decisions are increasingly complex, pulling data from multiple data collections. (Slide #22): If data are not reliable they provide little guidance or perhaps even misguidance to anyone accessing the data. It is much easier and less frustrating to enter data accurately the first time. For instance, it is much harder for a district to correct bad Free and Reduced Lunch data several months or a year after the fact. Correction that relies of pulling data from prior months takes more time and is more prone to error. Another example is that a district that is not monitoring their data may end up with assessment booklets for withdrawn students. As part of the data entry process there should be some auditing tasks that ensure the data are correct. This can be as simple as checking your own work or running appropriate edit reports. Extend (Slide #23): All strategies to improve a process must start with an assessment of the current processes. The objective of the self-assessment is to determine the current status of data quality at the school or district level. The self-assessment should point out strengths and weaknesses of the existing systems. Once completed and studied the self-assessments can be useful as tools to determine next steps to increase the data quality at all levels. The self-assessment worksheets provided by the National Center for Education Statistics address each of the factors affecting quality data in this presentation. It asks the participants to rate the school or district by answering several questions about each area. You can download the worksheets here http://nces.ed.gov/pubs2007/curriculum/ls_ass_instruction.asp.
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What is data quality ? Presenter Notes Session Objectives
ACTIVITY (TIME) Roles (5 minutes)
Closure (10+ minutes)
Define data quality. Describe a culture and philosophy that support data quality. Identify strategies for increasing data quality.
CONTENT Explain (Slide #24): Data quality is highest when everyone in the organization is engaged with this effort. (Slide #25): The roles each of us play in building the Culture of Quality Data are examined in some detail on tip sheets available from the National Center for Education Statistics. You can download the sheets here: http://nces.ed.gov/forum/pub_2005801.asp. Evaluate: If you are using the guided notes, provide time for participants to record some examples of roles and how note which dimensions and factors might be most important for those. Explain (Slide #26): Remind everyone that additional resources are available on the OSPI Data Quality web site (http://www.k12.wa.us/CEDARS/dataquality/). There are also materials for specific data issues available from the NCES (http://nces.ed.gov/). Extend (Slide #27): Move into the discussion portion of the program. Where does data quality play a role in your program? What are the implications for low and high data quality for your program? How do you communicate with the field to support high data quality? Evaluate: If you are using the quiz, provide copies to participants to complete. Otherwise, consider using an exit slip or other evaluation tool to collect feedback on the presentation.
MATERIALS Download data quality tip sheets for those roles represented by participants: http://nces.ed.gov/foru m/pub_2005801.asp
Quiz (optional) Quiz Key (optional)
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