## Visual Displays of Data Paper-Walden University

MODULE 5 VISUAL DISPLAYS FOR CONTINUOUS VARIABLES Page navigation • previous: Visual Displays of Categorical Variables: Summary • next: Identifying Continuous Variables • Go to page 20 Current Module | Pages 20 – 23 Visual Displays for Continuous Variables Introduction Learning Objectives • Evaluate visual displays of data for continuous variables. A researcher conducted a study in which she observed students’ scores on an examination. One of the first steps in analyzing a sample of data is to examine the distribution of values for variables in the data set. The distribution of the data tells her about the frequency with which various values are observed. Distributions can be examined in visual displays such as tables and graphs. A good graph or table is informative and allows researchers to identify and communicate important characteristics of the data. Different approaches are taken for visually displaying categorical and continuous variables. Visual Displays of Categorical Variables Introduction Learning Objectives • Evaluate visual displays of categorical variables. A researcher asks students how they perceived their body weight. They might respond with overweight, underweight, or just about right, in which case each student is a unit of analysis, the answer options represent categories of responses, each answer option is a value, and all of the students’ responses comprises a data set. One of the first steps in analyzing a sample of data such as this one is to examine what is referred to as the distribution of values for the data set’s variables. Visual displays of data help researchers communicate the distribution and other key information (the story they are telling with their data) both effectively and efficiently, including for their own exploration. Put another way, visual displays of data allow researchers to quickly identify interesting aspects of their data (for example, are the study’s participants predominately satisfied with their body weight?), and to do so more efficiently than merely using words. Researchers take different approaches for visually displaying categorical and continuous variables. This skill builder focuses on visual displays for the former. Identifying Categorical Variables Categorical variables are those that have a small number of possible values. Usually, categorical variables involve nominal or ordinal levels of measurement. For example, political party affiliation is an example of a nominal, categorical variable. This variable places individuals into one of just a few categories (e.g. Democrat, Republican, or Independent). An example of an ordinal, categorical variable is highest grade completed, with categories of less than high school, high school diploma, and more than high school. Again, this variable has just a small number of possible values. You will typically use categorical methods of displaying data, such as a bar chart or a pie chart, when the number of categories is less than 10 or 12. If there are too many categories, the displays become messy and difficult to read. Also keep in mind that pie charts and bar charts are not typically used for non-categorical variables. An example of a non-categorical variable would be students’ percentile ranking on a standardized math test; this variable has a large range of values and students aren’t simply placed into one of a limited number of categories. Learn by Doing Hints, displayed below Which of the following variables would (YES) or would not (NO) be considered a categorical variable amenable to a categorical visual display? Table of multiple choice questions Yes No Weight perception with values of underweight, about right, and overweight An individual’s weight measured in whole pounds Hair color coded as black, brown, blonde, red, or other Getting back to the study of body image, presume that the researcher actually has a random sample of 1,200 U.S. college students who were asked the question of how they perceive their body weight as part of a larger survey. The following table shows part of the responses collected: Body Image Student Body Image student 25 overweight student 26 about right student 27 underweight student 28 about right student 29 about right Here is some information that would be interesting to get from these data: • What percentage of the sampled students fall into each category? • How are students divided across the three body image categories? Are they equally divided? If not, do the percentages follow some other kind of pattern? There is no way to answer these questions by looking at the raw data, which are in the form of a long list of 1,200 responses, and thus not very manageable. However, both of these questions can be easily answered once the researcher summarizes how often each of the categories occurs and looks at the frequency distribution of the different values for the variable Body Image. Creating a table that presents the different values (categories) for the variable Body Image is the first step to take to summarize the distribution of a categorical variable. For example, the table below shows how many times the value “About right” occurs (count), and, more importantly, how often this value occurs (relative frequency) as a percentage. To convert the counts to percentages, divide the frequency (855) by the total number of observations (1200) to obtain the relative frequency, and multiply by 100 to convert to a percentage. Body Image Distribution Category Count Percent About right 855 (855/1200) * 100 = 71.3% Overweight 235 (235/1200) * 100 = 19.6% Underweight 110 (110/1200) * 100 = 9.2% Total n = 1200 100% Did I Get This What are the correct percentages for each of the two remaining values (“Overweight” and “Underweight”) for the Body Image variable displayed in the table below? Drag each percentage to its correct location. Accessible mode Screen reader users: use the accessible mode button above and use alt+down arrow to open the combo boxes. Category Count Percent About right 855 (855/1200) ∗ 100=71.3% Overweight 235 Underweight 110 Total n=1200 100% (235/1200) ∗ 100=19.6% ( 110/1200) ∗ 100=9.2% Page navigation • previous: Unit 2: Visual Displays for Categorical and Continuous Variables • next: Visual Displays of Categorical Variables: Graphical Displays • Go to page 16 © 2020 Acrobatiq

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