Analyzing & Interpreting Student Survey Results Data in Schools

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Many schools and educational organizations today are using student surveys to evaluate student perceptions, behaviors, and activities across a wide variety of topics such as bullying, substance abuse, school climate, and more.

However, successfully conducting a student survey doesn’t end once the responses have been collected. In fact, that’s when one of the most important parts of the process begins—the analysis and interpretation of results. In fact, accurately and effectively analyzing and interpreting the data and results from your student surveys is critical to achieving your school’s surveying goals.

As researchers at the National Center for Education Evaluation and Regional Assistance noted: “[g]enerally, schools collect enormous amounts of data on students’ attendance, behavior, and performance, as well as administrative data and perceptual data from surveys and focus groups. But when it comes to improving instruction and learning, it’s not the quantity of the data that counts, but how the information is used.”[1]

Conducting Manual Student Survey Data Analysis

 

If you are manually managing and analyzing your student survey data, the first step is inputting the raw data into a data file that allows for information analysis, such as an Excel spreadsheet or Access database.[2]

In order to accurately input the data, the questions on your survey must be split into distinct, quantifiable categories. The four primary question-and-answer-style survey questions are interval, ordinal, ratio, and categorical questions; each style of question requires a separate type of analysis.

  • • Interval questions involve a range of values with meaningful distances between them.[3] An example question might be: “How many times in the past month have you have seen a classmate being bullied?” with answer responses of “0 times, 1-5 times, 6-10, times, 11-15 times, 15 times or more.” Interval data is usually well-represented by a contingency table.
  • • Ordinal questions are posed on a Likert scale that asks “how much” or a similar qualitative data point.[4] An example question might be: “How much do you agree with the following statement: My teachers care about my success in the classroom,” with answer responses of “Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, Strongly Agree.” Ordinal data is best represented in a relative frequency or contingency table or graph.
  • • Ratio questions ask about precise measurements[5], which make them best for queries where a student can only have one definite response. An example question might be: “What grade are you currently in at school?” with response options of “6th grade, 7th grade, 8th grade, 9th grade.” Depending on the question, ratio data can be represented in a relative frequency table or a table including averages.
  • • Categorical questions, also sometimes called nominal questions, refer to different, non-quantifiable categories.[6] An example question might be: “What is your ethnicity/race?” with response options such as “Caucasian/White, Hispanic/Latino, Black/African American, Native American/American Indian, Asian/Pacific Islander, Other.” Categorical data is best represented using a relative frequency table or graph.

 

Conducting Technology or Program-Assisted Data Analysis

 

If you are using a specialized data analysis or surveying program, this program will likely handle these initial analysis steps for you. Once you input the raw data and indicate how you want it analyzed, the software can do the heavy lifting and provide you with the results.

Similarly, partnering with a proven surveying company that provides built-in analysis software can expedite this process even further. Once you have collected your raw student survey data, whether on paper or digitally, your school submits the data and the survey company handles all of the next steps, from data input to statistical analysis and creating a graphical representation of results. Some companies even offer electronic dashboards where educators can easily view, share, and analyze results.

Interpretation: Making Student Survey Data Work for Your School

 

Once you have completed an initial analysis of the raw data and it is in a more easily understandable form, such as a series of charts or graphs, the next step in the process is to interpret what the data is indicating.

“Interpreting results is a process of moving from data to insights (what do these numbers mean?) to judgments of fact (have we understood the data correctly?) then value (is this important to us?) and from there to action (what should we do?).”[7] These indications and interpretations are where large-scale conclusions can be drawn and trends can be recognized, ultimately leading to purposeful and impactful decision-making.

Establishing Benchmarks for Improvement

 

A primary goal of most student surveys is improvement, whether that refers to improvement of the school’s anti-bullying initiatives, improvement of the overall school climate, or something else entirely. But in order to show improvement, there must be a point from which the school can improve. This is referred to as a benchmark.

The data gathered from school surveys can indicate the presence of a problem (or the problem’s degree of magnitude) and establish a point-of-reference against which the results of future surveys can be measured once the problem is addressed.

Recognizing Trends and Implications through Longitudinal Analysis

 

Another way that survey data interpretations can benefit schools is by helping them recognize trends and the possible implications of those trends through longitudinal analyses.

For example, consider a situation in which a middle school surveys every eighth grade class for a period of five years and notices a distinct downward trend in positive perceptions of school climate. They could then dive deeper into the available data to see if there is a specific area of the school that consistently receives lower ratings, such as availability of adequate classroom supplies or teacher attitudes towards discipline. As a result of this data, the school could recognize a need for more textbooks or improved disciplinary resources and institute an action plan to address these needs.

Avoiding “Analysis Paralysis”

 

“Analysis paralysis” refers to a state in which data is collected, but not acted upon.[8] It is easy for schools and educators to “become tangled up in the endless work of collecting information” without moving to the next step of formulating a plan of action.[9] It is critical that decision-makers take steps to avoid becoming stagnant after investing time and effort to collect the raw data.

Getting Expert Help with Your School’s Survey Data

 

Clearly, when survey data is analyzed and interpreted accurately and effectively, it can be incredibly helpful to schools seeking to improve the educational environment for their students. “With appropriate analysis and interpretation of data, educators can make informed decisions that positively affect student outcomes.”[10]

However, it’s also arguable that analysis and interpretation are the most difficult parts of the surveying process. That’s where partnering with a proven and experienced school surveying company can help.

Pride Surveys takes the guesswork out of the surveying process. First, we provide your school with the scientifically reliable and valid surveys of your choice. Once the surveys are complete, we handle the input and preliminary analysis of the raw data and then provide decision-makers with an innovative electronic dashboard. Here, they have a comprehensive and easy-to-understand view of the school’s data, which enables them to begin the process of interpretation and action planning.

Browse the different types of student surveys we offer and find out why Pride is the best choice to help you survey your school. Questions? Give us a call at 800-279-6361 or fill out our quick online contact form.


[1]THamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.

[2]“Analyzing Survey Data.” Faculty Innovate: University of Texas. Retrieved from https://facultyinnovate.utexas.edu/sites/default/files/survey_-_analyze_data_detailed.pdf on November 10, 2016.

[3]Harrison, Chase. “Managing and Manipulating Survey Data: A Beginners Guide.” Harvard University: Department of Government. Program on Survey Research. Retrieved from http://psr.iq.harvard.edu/files/psr/files/ManagingSurveyData_0.pdf on November 10, 2016.

[4]Ibid.

[5]Ibid.

[6]Collie, Sarah L. & Rine, P. Jesse. “Survey Design: Getting the Results You Need.” Office of Process Simplification. University of Virginia. May 26, 2009. Retrieved from http://www.virginia.edu/processsimplification/resources/survey_design.pdf on November 10, 2016.

[7]“Understanding What Students Say: Interpreting My Voice Survey Results.” Quaglia Institute for Student Aspirations. Retrieved from http://opi.mt.gov/PDF/MBI/Voice/InterpretingSurveyResults.pdf on November 11, 2016.

[8]Ibid.

[9]Danielson, Charlotte. Teacher Leadership that Strengthens Professional Practice. Page 142. 2006. Association for Supervision and Curriculum Development. Alexandria, VA.

[10]Lewis, Dale, Madison-Harris, Robyn, Muoneke, Ada, and Times, Chris. “Using Data to Guide Instruction and Improve Student Learning.” SEDL Letter Volume XXII, Number 2, Linking Research and Practice. SEDL: American Institutes for Research. Retrieved from http://www.sedl.org/pubs/sedl-letter/v22n02/using-data.html on November 11, 2016.

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