Surveys and Data Analysis
- Prior to class, create a survey in Google Forms. Post the link to the survey in Google Classroom for students to access. Survey questions should reflect both categorical and numerical data (i.e. gender for categorical, age for numerical).
- Tell the students you would like to learn more about them, and would like for them to take a quick survey to help you gain more insight into who we are as a class. What makes us unique? What makes us the same?
- Have students take the survey, and share data by projecting the "Summary of Responses" to the screen.
- Engage in discussion on what the students notice about the data.
2 Direct Instruction
- Have students use Padlet to brainstorm other examples of categorical verses numerical data.
- Following discussion on the differences between the two types of data, look back at the data you gathered from the students. Choose a piece of numerical data (i.e. age, number of siblings) and model analyzing the data by solving for the mean, median, and mode.
- Engage in a class discussion about what the three different mearures of central tendency might tell us, and which one we think is best for describing what is "typical" in our class.
- Model creating a bio on the homepage of your own Weebly site comparing yourself to the data you collected from the class (What makes you unique? What makes us all the same?).
3 Guided Practice
- Students work in table groups to create an original survey to administer to their classmates. Their survey should contain both numerical and categorical data.
- Students post the link to their survey in Google Classroom and groups are allowed time to take each other's surveys.
- As a team, students analyze and discuss the results of the survey, calculating for mean, median, and mode when appropriate.
4 Independent Practice
- Students compare themself to the results of the survey data, and design a personal profile on their Weebly site. What makes them unique from the class? What are common characteristics or interests that they share with their classmates?
- Students present their homepages to the class, sharing what they discovered about themself in relation to the class data.
- The teacher will monitor a side conversation on Todaysmeet, where students will be able to ask each other additional questions either about the results of their data, or about the unique facts they learned about each other.
- The lesson concludes with Kahoot for a game-based formative assessment. The teacher will design a Kahoot in advance with questions that ask students to distinguish between categorical and numerical data, and to solve for mean, median, and mode and determine when each is appropriate as a measure of central tendency.
Key Standards Supported
Statistics And Probability
Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. For example, “How old am I?” is not a statistical question, but “How old are the students in my school?” is a statistical question because one anticipates variability in students’ ages.
Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.
Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.
Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
Summarize numerical data sets in relation to their context, such as by:
Reporting the number of observations.
Describing the nature of the attribute under investigation, including how it was measured and its units of measurement.
Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.
Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.