Data Visualization: Bringing Data to Life in an Introductory Statistics Course

Figures & data

Table 1 Principles of graphic and analytical design.

Principles of graphic design (Cairo 2015)	Principles of analytical design (Tufte 1982)
· It is easier to identify objects by color than by shape.	· Prevent your audience from asking “so, what?” by showing comparisons, contrasts, and differences.
· When possible, use direct labels instead of a legend.	· Guide the audience in their exploration of the data’s story by providing an explanation, showing structure, and causality, if relevant.
· Elements such as text, lines, shapes, etc., that have the same nature must look alike.	· Create multivariate displays.
· Strive for a balance between graphics and text.	· Words, numbers, images, and diagrams should be completely integrated.
· Be wary of the default options of visualization software.	· All sources, including data sources, must be cited.
· Use a grid layout to organize your visualization.	· The content of your visualization is paramount—avoid creating a “data dump.”

Fig. M1 First example of exemplary student work. This data visualization employs a grid layout and makes good use of color. While the graphs are simple, they are clean and easy to read. The narrative adds to the story and does not reiterate characteristics that can easily be read from the graphs.

Fig. M2 Second example of exemplary student work. Students used Tableau to construct this data visualization. They have employed a variety of different graphs to tell their story. While the use of clip art is generally discouraged, the addition of the clip art stork works with this data visualization. The students have used color effectively. The justified text lends a clean, crisp look to the visualization.

Fig. M3 Third example of exemplary student work. Students used Piktochart to create this data visualization. Notice the clean layout, good use of color, and playful designs created with the text.

Table

WEEK OF THE		ACTIVITY OR DELIVERABLE)
SEMESTER		(SUBMISSION)
2		Team building activities and group contract
3		Good viz, bad viz
4		Data submission on Canvas
5		Present and submit Data Viz Project Plan
6		No visualization deliverable due
7		Group check in
8		Data Viz Rough Draft submission on Canvas
9		No visualization deliverable due
10		No visualization deliverable due
11		No visualization deliverable due
12	Data Viz Presentations in class
13	Final Data Viz Project submission on Canvas
14	Data Viz Peer Review and Self-Reflection submission on Canvas

NOTE: this list of activities and deliverables only pertains to the Data Visualization Project. We will be working on other statistical content throughout the semester. Please see Appendix B for a more complete listing of topics covered.

Table

Week #	Student Learning Objectives (SLOs)		Topics
1	Differentiate between descriptive and inferential statistics and different types of studies. Use simulation to solve research questions.		What is Statistics? Simulation
2	Classify data as qualitative or quantitative. If data is quantitative, classify it as either discrete or continuous. Create and interpret graphical displays for qualitative data. Create and interpret graphical displays for quantitative data. Describe quantitative distributions using SOCS (shape, outliers, center, spread). Identify characteristics of misleading graphs. Calculate measures of center and measures of variation and determine which summary statistics are more appropriate for a set of data.		Descriptive statistics ●Data Viz Component: Team building activities and group contract
3	Identify response and explanatory variables. Differentiate between observational and experimental studies. Create experimental design diagrams. Identify lurking and confounding variables. Distinguish a sample from a population. Explain the concept of a sampling frame and bias that arises when the sampling frame does not match the intended population. Identify potential biases in selecting samples (sampling bias, undercoverage, nonresponse bias, response bias, and bias due to wording). Compare and contrast three different types of random sampling: simple random, cluster, and stratified random sampling. Explain why good sampling techniques are necessary in conducting both observational and experimental studies.		Study Design ●Data Viz Component: Good Viz, Bad Viz
Week #	Student Learning Objectives (SLOs)	Topics
4	Use rules of probability to determine likelihood of events. Read and interpret contingency tables. Find conditional probability. Determine if two events are independent.	Probability ●Data Viz Component: Data Visualization Project Data due
5	Read and create probability distributions of random variables. Calculate and interpret the mean of a random variable. Use the binomial distribution to calculate probabilities of Bernoulli trials. Calculate and interpret the mean and standard deviation of a binomial random variable. Identify the basic properties of, and sketch a normal curve. Determine areas under the standard normal curve, and other normal curves. Find probabilities for any normally distributed variable from areas under the curve. Determine z-scores corresponding to specified areas under the standard normal curve. Determine percentiles corresponding to specified normal curves.	Probability Distributions Data Viz Component: Submit and present Data Visualization Project Plan
6	Includes all SLOs from weeks 1-5	Exam 1
7	Differentiate between population, data, and sampling distributions. Compute the mean and standard deviation of the sampling distribution of the sample proportion. Compute the mean and standard deviation of the sampling distribution of the sample mean. Identify the conditions needed for the Central Limit Theorem (CLT) to hold. State and apply the Central Limit Theorem (CLT) in problem solving situations.	Sampling Distributions of the Sample Proportion and the Sample Mean Data Viz Component: Check in with Data Visualization Groups
8	Compute and interpret a large-sample confidence interval to estimate a population proportion. Compute and interpret a confidence interval (t) to estimate a population mean. Compute and interpret the margin of error for the estimate of p or μ.	Confidence Intervals and Bootstrapping to Estimate a Population Proportion and a Population Mean

Table

Week #	Student Learning Objectives (SLOs)	Topics
	Explain the relationship between the sample size, the confidence level, and the precision of a confidence interval. Determine the sample size required for a specified confidence level and margin of error for the estimate of p or μ. Determine when to use the “plus four” interval for proportions. Assess the assumptions necessary to construct confidence intervals. Bootstrap to construct and interpret confidence intervals to estimate a population mean and/or a population proportion.	Data Viz Component: Data Visualization Rough Draft due
9	Write null and alternative hypotheses. Perform and interpret the results of a large-sample hypothesis test for a population proportion. Perform and interpret the results of a one mean t-test. Identify Type I and Type II errors and their consequences in context. Choose an appropriate significance level based on the consequences of committing Type I and Type II errors. Interpret the p-value in context of scenario. Check the assumptions necessary for the one-proportion z-test. Check the assumptions necessary for the one-mean t-test.	One-sample Hypothesis Tests for Proportions and Means
10	Perform and interpret large-sample inferences (hypothesis tests and confidence intervals) to compare two population proportions. Determine whether the samples from two populations are independent or paired. Perform and interpret an independent samples mean t-test. Construct and interpret an independent samples mean t-interval. Perform and interpret a paired t-test. Construct and interpret a paired t-interval. Check assumptions for each two-sample inference procedure.	Two-sample Inference for Proportions and Means
11	Includes all SLOs from weeks 7-10	Exam 2
Week #	Student Learning Objectives (SLOs)		Topics
12	Obtain and graph the regression equation for a set of data points, interpret the slope of the regression line, and use the regression equation to make predictions. Identify outliers and influential observations. Calculate and interpret the correlation coefficient, r, and the coefficient of determination, r^2. Compute and interpret residuals.		Simple Linear Regression ●Data Viz Component: Data Visualization Presentations in class
13	Interpret the meaning of β and β1 in the regression model. Calculate and interpret the three sums of squares (SST, SSE, and SSR) and the standard error of the estimate (se). Perform and interpret a hypothesis test (on β1) to determine if x is useful for predicting y. Obtain and interpret a confidence interval for estimating β1 Compute and interpret a prediction interval for estimating a single y at a given x. Check assumptions necessary for regression inference.		Inference for Regression
	Data Viz Component: Final Data Visualization Project due
14	Identify advantages of multiple regression over simple linear regression. Check assumptions necessary for multiple regression inference. Interpret multiple regression computer output. Determine which predictors contribute to the model. Use the F statistic and its associated p-value to make an appropriate conclusion about the model.		Multiple Regression (without Interactions) ●Data Viz Component: Peer Evaluation and Self-Reflection due
15	Includes all SLOs from weeks 1-14		Finals Week

Table

Ratings	Description
Exemplary	Submits a plan of the project on time. Plan includes a list of the variables of interest, brief, clear descriptions of the statistics and the narrative that will be included. Includes a possible title for the project. May include different data displays. Or, may list data displays the group intends to include. The project is feasible. The group is off to a good start.
Proficient		Submits a plan of the project on time. Plan includes a list of the variables of interest, a description of the statistics and narrative that will be included, a list of the different data displays and a possible title for the project. The descriptions are clear but could use some work. The project may be feasible. The group is off to a fairly good start.
Developing		Submits a rough draft of the project plan on time. Plan includes a list of the variables of interest, a description of statistics, and a list of the different data displays that will be included. Plan may or may not include a brief description of the narrative and/or a possible title. The descriptions need work and may not be well organized. The project may not be feasible. The group needs some direction.
Beginning		Submits a rough draft of the project plan late. Or, plan does not include at least three of the following: a list of the variables of interest, a description of statistics to be included, the different data displays that will be included, a description of the narrative, and/or a possible title. The descriptions are unclear. The project is not feasible. The group needs focus.
Unsatisfactory		No plan was submitted.

Table

Ratings	Description
Exemplary	Submits a rough draft of the project on time. Rough draft includes a working title, at least two data displays and some narrative. Includes a brief description of any additional displays that will be added to create the final project. The project is very creative. Descriptions are coherent and clear. The group is off to a good start.
Proficient	Submits a rough draft of the project on time. Rough draft includes a working title, at least two data displays and some narrative. The project has the potential to be creative. Descriptions are clear but could use some work. The group is off to a fairly good start.
Developing	Submits a rough draft of the project on time. Rough draft may or may not include a working title. There is at least one data display and some narrative. The project lacks originality, or it is piecemealed together. The descriptions need work and may not be well organized. The group needs some direction.
Beginning	Submits a rough draft of the project late. Or, there are not any data displays. Or, no narrative is included. Rough draft may or may not include a working title. There is at least one data display and some narrative. The project is not creative and is piecemealed together. The descriptions are unclear. The group needs focus.
Unsatisfactory	No rough draft was submitted.

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Group Presentation Rubric
Exemplary
12 points	The group thoroughly discusses their process in choosing data displays and statistics to tell the story behind the data. They incorporate context in their discussion of their graphical displays and the statistics used. They also give a brief overview of where the data were obtained, the findings of the analysis, and suggestions for further analysis and/or exploration. Correct terminology is used throughout the presentation.
Proficient
9 points	The group discusses their process in choosing data displays and statistics to tell the story behind the data. They incorporate context in their discussion of their graphical displays and the statistics used. They also give a brief overview of where the data were obtained and the findings of the analysis. They may or may not offer suggestions for further analysis and/or explorations. There may be minor omissions/errors. Correct terminology is generally used throughout the presentation.
Developing
6 points	The group discusses their process in choosing data displays and statistics to tell the story behind the data. They have an inadequate connection to the context in their discussion of their graphical displays and the statistics used. They give a brief overview of where the data were obtained and/or the findings of the analysis. They may or may not offer suggestions for further analysis and/or explorations. A key omission or inaccurate conclusion may also have been made. Correct terminology is generally used throughout the presentation.
Beginning
3 points	The group attempts to discuss their process in choosing data displays and statistics to tell the story behind the data but fails to expose some key ideas. Terminology and reference to context are inadequate or missing. A presentation was attempted, but it is difficult to understand, or severely lacking.
Unsatisfactory
0 points	No presentation

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First AND Last Name of Team Member	YOURSELF
For the following section, please rate your group members on a scale of 1 to 4, where 1 = “Terrible” and 4 = “Great.” You do not need to justify your responses in this section.
Attendance at meetings held outside of class by the group
Amount of contribution outside of group meetings to the project
Quality of contribution outside of group meetings to the project
Effectiveness in working collaboratively e.g., sharing decision-making with others, soliciting others’ ideas, encouraging, energizing)
For the following section, please give specific examples to clarify and explain each of your answers.
What specifically did this group member contribute toward the project?
Relative to other team members, how would you describe this person’s contribution?
What are this person’s major strengths? What have they brought to the table for your group?
How could this person improve his/her effectiveness as a member of your team? Please note: In order to receive full credit, you must suggest improvements.
How likely would you be to recommend this person to another team? On a scale of 1 to 10, please state the option that best describes your recommendation, with 1 indicating “Do not work with this person” and 10 indicating “Work with this person!” Please justify your response.

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Title
Ratings	Description		Points
Exemplary	The project includes a descriptive, informative, and intriguing title that grabs the reader’s attention helps the reader better understand the data.		4
Proficient	The project includes a descriptive title that is informative OR intriguing, and it helps the reader better understand the data.		3
Developing	The project includes a descriptive title that does not help the reader better understand the data.		2
Beginning	The project includes a title.		1
Unsatisfactory	No title.		0
Context
Ratings	Description		Points
Exemplary	Provides enough pertinent background information about the data so that the reader clearly understands any abbreviations used as well as the context from which the writer gathered the data.		8
Proficient	Provides enough pertinent background information about the data so that the reader clearly understands any abbreviations used but communicates only limited information concerning the context from which the writer gathered the data.		6
Developing	May not provide enough pertinent background information about the data so that the reader clearly understands any abbreviations used, and communicates very little, or no information concerning the context from which the writer gathered the data.		4
Beginning	Provides limited pertinent background information about the data so that the reader does not clearly understand any abbreviations used, and communicates no information concerning the context from which the data were gathered.		2
Unsatisfactory	Provides no pertinent background information about the data so that the reader does not clearly understand any abbreviations used, and communicates no information concerning the context from which the data were gathered.		0
Balance between Text and Graphics
Ratings	Description		Points
Exemplary	There is a good balance between text and graphics. The graphics are the focus of the visualization and are not overwhelmed by lots of text. All graphics stand alone and can be understood without having to read the text. Additionally, the text is concise and does not explain characteristics of the data that are visible in the graphs.		20
Proficient		There is a fairly good balance between text and graphics. The text does not overwhelm the graphics, for the most part. Most of the graphics can be understood without reading the text. Also, the text may explain some of the characteristics of the data that are visible in the graphs.	15
Developing		An attempt has been made to balance text and graphics. Most of the graphics cannot be understood without reading the text, or, the text explains characteristics of the data that are visible in the graphs.	10
Beginning		There is a poor balance between text and graphics. Either the data visualization is too text heavy or has a sparse amount of text. None of the graphics can be understood without reading the text. The text may explain many characteristics of the data that are visible in the graphs.	5
Unsatisfactory		The data visualization does not include both text and graphics. It is either all text or all graphics without any text.	0
Accuracy of Graphical Displays
Ratings		Description	Points
Exemplary		All graphs are created correctly. Graphs are titled and well labeled. The graphs make use of direct labeling instead of using legends.	8
Proficient		Generally, each graph is created correctly. There may be minor omissions or errors. Graphs may be titled or labeled, but not necessarily both. Some graphs use legends instead of direct labels.	6
Developing		Some of the graphs are created correctly. There are, however, significant errors in one or more graphs or a plethora of minor mistakes. Graphs may be titled or labeled. Most graphs use legends instead of direct labels.	4
Beginning		An attempt has been made to create graphs correctly, but the effort is flawed in some major way. Graphs may be titled or labeled. All graphs use legends instead of direct labels.	2
Unsatisfactory		No graphs are present.	0
Appropriateness of Graphical Displays
Ratings		Description	Points
Exemplary		All graphical displays are appropriate for displaying the data and highlight key features of the data. Variables are plotted on the same graph when comparisons between them are desired.	8
Proficient		The majority of the graphical displays are appropriate for displaying the data and highlight key features of the data. When comparisons are desired between two or more variables, most are made on the same graph.	6

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Developing	At least half of the graphical displays are appropriate for displaying the data. Graphs may or may not highlight key features of the data. When comparisons are desired between two or more variables, some are made on the same graph.	4
Beginning	Fewer than half of the graphical displays are appropriate for displaying the data. Graphs may or may not highlight key features of the data. No multivariate comparisons are made.	2
Unsatisfactory	None of the graphical displays are appropriate for displaying the data. Graphs do not highlight key features of the data.	0
Variety of Graphical Displays
Ratings	Description	Points
Exemplary	Uses a variety of different displays to present unique perspectives and reveal new insights about the data. Where appropriate, multivariate graphs are used for comparisons between variables. The visualization answers an interesting question and tells a compelling story.	12
Proficient	Uses a variety of different displays to present unique perspectives and reveal new insights about the data. Where appropriate, multivariate graphs are used for comparisons between variables. The visualization may answer an interesting question but doesn’t tell the whole story.	9
Developing	Uses at least two different displays to reveal new insights about the data. No multivariate graphs are used for comparisons between variables. An incomplete attempt has been made to answer an interesting question and tell a compelling story.	6
Beginning	Uses two different displays to reveal characteristics of the data. A flawed attempt has been made to answer an interesting question and to tell a compelling story.	3
Unsatisfactory	Only includes one type of data display. May or may not answer an interesting question or tell a compelling story.	0
Appropriateness and Accuracy of Statistics
Ratings	Description	Points
Exemplary	All statistical terminology has been used correctly and appropriately All reported statistics are used correctly and help to tell the story; they are not just tacked on somewhere. The rounding rule has been consistently applied.	12
Proficient	Statistical terminology is generally used correctly and appropriately. Reported statistics are generally used correctly and appropriately. There may be minor omissions or errors. The rounding rule may not be applied consistently.	9

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Developing	Some of the statistical terminology has been used correctly and appropriately. Some of the reported statistics are correct and appropriate. There are, however, significant errors, or a plethora of minor mistakes. The rounding rule may not be applied.	6
Beginning	Some attempt has been made to use statistical terminology and/or statistics correctly and appropriately, but the effort is flawed in some major way, or statistical terminology and/or the statistics are just thrown in without thought. The rounding rule may not be applied.	3
Unsatisfactory	No attempt to report any summary statistics.	0
Organization, Transition, and Appearance
Ratings	Description	Points
Exemplary	The project shows evidence of careful organization, flows naturally from graphical displays and statistical summaries to narrative, and is neat in appearance. Judicious use of space and color make the visualization “pop.” The project is a cohesive whole, not just individual displays piecemealed together. The visualization fits on a single page and utilizes the grid layout. The project makes use of visual hierarchy to guide the audience through the story of the data. No distracting fonts or unnecessary clip art used.	20
Proficient	There are minor flaws in one of the areas: organization, transition, or appearance. Judicious use of space and color make the visualization esthetically pleasing, overall. The visualization fits on a single page and utilizes the grid layout.	15
Developing	There are major flaws in one of the areas: organization, transition, or appearance. May or may not have chosen good use of space and color. The visualization fits on a single page and may or may not utilize the grid layout.	10
Beginning	Completely inadequate in two of the areas: organization, transition, and appearance.	5
Unsatisfactory	Inadequate in all three areas.	0
English Mechanics
Ratings	Description	Points
Exemplary	The narrative is grammatically correct, punctuated properly, and flows logically from one point to the next. No spelling mistakes.	4
Proficient	The narrative has a minor flaw in one of the following areas: grammar, punctuation, logical flow, or spelling.	3
Developing	The narrative has significant errors in one of the following areas: grammar, punctuation, logical flow, or spelling, OR minor flaws in two areas.	2
Beginning	The narrative has major flaws in at least two of the following areas: grammar, punctuation, logical flow, or spelling.	1

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Unsatisfactory	The narrative has major flaws in more than two of the following areas: grammar, punctuation, logical flow, or spelling.	0
Utilized Feedback
Ratings	Description	Points
Exemplary	It is evident that students have utilized feedback from the rough draft and presentation extensively and incorporated it into the final project. Or, rough draft and presentation were exemplary, and no revisions were necessary.	8
Proficient	Utilized feedback from rough draft and presentation and made major changes to the final project. A few minor revisions are still needed.	6
Developing	Utilized some feedback from rough draft and presentation and attempted to make major changes to the final project.	4
Beginning	Utilized some feedback from rough draft and presentation and made minor changes to the final project.	2
Unsatisfactory	Did not utilize feedback from rough draft and presentation. Did not make any changes between the rough draft, presentation and the final project.	0
Source Documentation
Ratings	Description	Points
Exemplary	Direct URLs for data sources are provided for ALL graphs and tables. URLs for all data sources are included below the corresponding graph or table.	4
Proficient	Direct URLs for data sources are provided for MOST graphs and tables. URLs are included below the corresponding graph or table.	3
Developing	Direct URLs for data sources are provided for SOME graphs and tables OR the URLs do not link directly to the data source. URLs are included below the corresponding graph or table.	2
Beginning	Direct URLs for data sources are missing for almost all graphs and tables AND the URLs do not link directly to the data source.	1
Unsatisfactory	No data sources are referenced.	0

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Reflection Questions (5 total)
Answers the Self Reflection question thoroughly. Provides a well thought-out response that includes details and examples.	2
Answers the Self Reflection question. Provides a response that includes minimal details and/or examples.	1
Does not answer the Self Reflection question.