Can you graph qualitative data

Creating a Probability Model 3. Combining Probabilities 4. Probability of Independent Events 5. Conditional Probability. Sampling Distribution Intro 2. Sampling Distribution of the Sample Mean 3. Distribution of a Sample Proportion. Part 1: The Interval of Numbers 3. Part 2: The Level of Confidence C 4. Summary of Methods. Hypothesis Testing Intro 2. Four Parts of a Hypothesis 3.

Hypothesis Test — One Population Intro 2. A Hypothesis Test for a Population Proportion 3. Using Confidence Intervals to Test Hypotheses 4. Testing Claims About the Population Mean. Comparing Two Populations Intro 2.

Inference Methods for Two Population Proportions 3. Graphs of distributions created by others can be misleading, either intentionally or unintentionally. Demonstrate how distributions constructed by others may be misleading, either intentionally or unintentionally.

Unless you are constructing a graph of a distribution on your own, you need to be very careful about how you read and interpret graphs. Graphs are made in order to display data; however, some people may intentionally try to mislead the reader in order to convey certain information. In statistics, these types of graphs are called misleading graphs or distorted graphs. They misrepresent data, constituting a misuse of statistics that may result in an incorrect conclusion being derived from them.

Graphs may be misleading through being excessively complex or poorly constructed. Even when well-constructed to accurately display the characteristics of their data, graphs can be subject to different interpretation. Misleading graphs may be created intentionally to hinder the proper interpretation of data, but can also be created accidentally by users for a variety of reasons including unfamiliarity with the graphing software, the misinterpretation of the data, or because the data cannot be accurately conveyed.

Misleading graphs are often used in false advertising. Generally, the more explanation a graph needs, the less the graph itself is needed. Graphs do not always convey information better than tables. This is often called excessive usage. Pie charts can be especially misleading. Comparing pie charts of different sizes could be misleading as people cannot accurately read the comparative area of circles.

The usage of thin slices which are hard to discern may be difficult to interpret. The usage of percentages as labels on a pie chart can be misleading when the sample size is small.

A perspective 3D pie chart is used to give the chart a 3D look. Often used for aesthetic reasons, the third dimension does not improve the reading of the data; on the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. When using pictogram in bar graphs, they should not be scaled uniformly as this creates a perceptually misleading comparison.

The area of the pictogram is interpreted instead of only its height or width. This causes the scaling to make the difference appear to be squared. Improper Scaling : Note how in the improperly scaled pictogram bar graph, the image for B is actually 9 times larger than A.

A truncated graph has a y-axis that does not start at 0. These graphs can create the impression of important change where there is relatively little change. Truncated Bar Graph : Note that both of these graphs display identical data; however, in the truncated bar graph on the left, the data appear to show significant differences, whereas in the regular bar graph on the right, these differences are hardly visible. Graphs are useful in the summary and interpretation of financial data.

Graphs allow for trends in large data sets to be seen while also allowing the data to be interpreted by non-specialists. Graphs are often used in corporate annual reports as a form of impression management. Several published studies have looked at the usage of graphs in corporate reports for different corporations in different countries and have found frequent usage of improper design, selectivity, and measurement distortion within these reports.

The presence of misleading graphs in annual reports have led to requests for standards to be set. Research has found that while readers with poor levels of financial understanding have a greater chance of being misinformed by misleading graphs, even those with financial understanding, such as loan officers, may be misled. Recall the difference between quantitative and qualitative data.

Quantitative data are data about numeric values. Qualitative data are measures of types and may be represented as a name or symbol.

Statistics that describe or summarize can be produced for quantitative data and to a lesser extent for qualitative data. As quantitative data are always numeric they can be ordered, added together, and the frequency of an observation can be counted. Therefore, all descriptive statistics can be calculated using quantitative data.

As qualitative data represent individual mutually exclusive categories, the descriptive statistics that can be calculated are limited, as many of these techniques require numeric values which can be logically ordered from lowest to highest and which express a count. Mode can be calculated, as it it the most frequency observed value. Median, measures of shape, measures of spread such as the range and interquartile range, require an ordered data set with a logical low-end value and high-end value.

Variance and standard deviation require the mean to be calculated, which is not appropriate for categorical variables as they have no numerical value. There are a number of ways in which qualitative data can be displayed. A good way to demonstrate the different types of graphs is by looking at the following example:. Was the iMac just attracting previous Macintosh owners?

Or was it purchased by newcomers to the computer market, and by previous Windows users who were switching over? To find out, iMac customers were interviewed. Each customer was categorized as a previous Macintosh owners, a previous Windows owner, or a new computer purchaser. The qualitative data results were displayed in a frequency table. Frequency Table for Mac Data : The frequency table shows how many people in the study were previous Mac owners, previous Windows owners, or neither.

The key point about the qualitative data is that they do not come with a pre-established ordering the way numbers are ordered. For example, there is no natural sense in which the category of previous Windows users comes before or after the category of previous iMac users.

People of one weight are naturally ordered with respect to people of a different weight. One way in which we can graphically represent this qualitative data is in a pie chart. In a pie chart, each category is represented by a slice of the pie.

The area of the slice is proportional to the percentage of responses in the category. This is simply the relative frequency multiplied by Pie Chart for Mac Data : The pie chart shows how many people in the study were previous Mac owners, previous Windows owners, or neither. Pie charts are effective for displaying the relative frequencies of a small number of categories.

They are not recommended, however, when you have a large number of categories. Pie charts can also be confusing when they are used to compare the outcomes of two different surveys or experiments. Here is another important point about pie charts. If they are based on a small number of observations, it can be misleading to label the pie slices with percentages. With so few people interviewed, such a large percentage of Windows users might easily have accord since chance can cause large errors with small samples.

In this case, it is better to alert the user of the pie chart to the actual numbers involved. Pie charts are effective for displaying the relative frequencies of a small number of categories. They are not recommended, however, when you have a large number of categories. Pie charts can also be confusing when they are used to compare the outcomes of two different surveys or experiments.

Here is another important point about pie charts. If they are based on a small number of observations, it can be misleading to label the pie slices with percentages.

With so few people interviewed, such a large percentage of Windows users might easily have accord since chance can cause large errors with small samples. In this case, it is better to alert the user of the pie chart to the actual numbers involved. The slices should therefore be labeled with the actual frequencies observed e. Bar charts can also be used to represent frequencies of different categories. Frequencies are shown on the Y axis and the type of computer previously owned is shown on the X axis.

Typically the Y-axis shows the number of observations rather than the percentage of observations in each category as is typical in pie charts. The bar chart shows how many people in the study were previous Mac owners, previous Windows owners, or neither. Boundless Statistics. Frequency Distributions. Frequency Distributions for Qualitative Data. The bars can be either horizontal or vertical. Bar graphs with vertical bars are sometimes called vertical bar graphs.

A graph is a picture designed to express words, particularly the connection between two or more quantities. You can see a graph on the right.

A simple graph usually shows the relationship between two numbers or measurements in the form of a grid. A graph is a kind of chart or diagram. The 4 main types of graphs are a bar graph or bar chart , line graph , pie chart , and diagram.

Bar graphs are used to show relationships between different data series that are independent of each other. In this case, the height or length of the bar indicates the measured value or frequency. Stem-and-Leaf Plots A stem-and-leaf plot is a graph of quantitative data that is similar to a histogram in the way that it visually displays the distribution.

A stem-and-leaf plot retains the original data. The leaves are usually the last digit in each data value and the stems are the remaining digits. A line graph , also known as a line chart , is a type of chart used to visualize the value of something over time. For example, a finance department may plot the change in the amount of cash the company has on hand over time.

The line graph consists of a horizontal x-axis and a vertical y-axis. It is a disk divided into wedge-shaped pieces proportional to the relative frequencies of the qualitative data. It is useful because it shows the useful because it shows the values of the observations and how often they occur graphically they occur. The main point to remember while presenting qualitative interview data is that the reader should not be bored with the minute details — mention the key points and themes as they relate to the research question, rather than reporting everything that the interviewees said; use charts or tables to help the reader.

In statistics, qualitative data —sometimes referred to as categorical data —is data that can be arranged into categories based on physical traits, gender, colors or anything that does not have a number associated with it.

Oftentimes, quantitative data is used to analyze qualitative data sets. There are two types of data that we can collect: Qualitative data describes a subject, and cannot be expressed as a number.