A bar graph is the most common method of statistical representation and it is used to create visual presentations of quantifiable data variables. This tool helps researchers to effectively manage large sets of data by categorizing them based on their numerical values.

Specifically, it breaks categorical data sets into groups based on numerical differences, and it is made up of response and predictor variables. It is important for researchers to know how to plot a bar graph as well as the different types of bar graphs that are available.

**What is a Bar Chart or Graph? **

A bar chart or bar graph is a diagrammatic representation of data in quantities. It is a common statistical tool used for data categorization and it often highlights the differences in the numerical values of specific groups of data.

Typically, the data in a bar graph is represented using vertical or horizontal bars that are plotted in accordance with the statistical value of each data category. This means that the length or height of each bar is proportionally equivalent to the data that it represents.

In some instances, the bar chart can be plotted in clusters to show more than one measured group of data. Typically, one axis of the graph details specific data categories while the other axis highlights the measured value for comparison.

**Features of a Bar Chart**

- A bar chart represents data categories using vertical or rectangular bars that are proportional to numerical values.
- It highlights the relationship between data groups and statistical values.
- A bar graph details changes in data groups over time.
- A bar chart shows the frequency of each data category.

**Types of Bar Chart **

A bar chart can be categorized into two broad types, namely; horizontal and vertical bar charts. These groups are further subdivided into the various categories such as vertical stacked bar chart, horizontal grouped bar chart, and the like.

**Horizontal Bar Chart**

A horizontal bar chart is a type of bar graph that represents data variables using proportional horizontal bars. Here, the data categories are placed on the vertical axis of the graph while the numerical value is placed on the horizontal axis of the graph.

Horizontal bar charts are often used to represent comparisons between nominal variables. With this tool, you can display long data labels using horizontal rectangles and still have enough room for textual information.

**Types of Horizontal Bar Chart **

- Horizontal Stacked Bar Chart

A horizontal stacked bar chart is a graphical variation that is used for data segmentation. Typically, each horizontal bar in the graph represents a data category which is divided into subcategories using different colors within the same bar.

This type of bar graph is extremely useful for viewing the different segments that make up a data variable. It helps you to know which subcategory contributes the most to a data variable.

- Horizontal Grouped Bar Chart

A horizontal grouped bar chart is a variant of a bar graph in which multiple data categories are compared and a particular color is used to denote a definite series across all data categories represented. It is also known as a clustered bar graph or a multi-set bar chart.

In simple terms, a horizontal grouped bar chart uses horizontal bars to represent and compare different data categories of 2 or more groups. The data categories are placed side by side so that it is easy to identify and analyze the differences in the same category across data groups.

A horizontal grouped bar chart is used for market performance evaluation and financial data comparison.

- Segmented Horizontal Bar Chart

A segmented horizontal bar chart is a type of stacked bar chart. It is also called a 100% stacked bar graph because each horizon bar represents 100% of the discrete data value and all the bars are of the same length while numerical variations are indicated in percentages.

Other types of horizontal bars chart include a reverse horizontal bar chart and a basic horizontal bar chart.

**Vertical Bar Chart**

A vertical bar graph is the most common type of bar chart and it is also referred to as a column graph. It represents the numerical value of research variables using vertical bars whose lengths are proportional to the quantities that they represent.

**Types of Vertical Bar Chart **

- Vertical Stacked Bar Chart

A vertical stacked bar chart is a type of bar graph that uses vertical bars to compare individual data variables. This statistical tool stacks data categories so that each bar shows the total number of subcategories that make up a data set.

- Grouped Vertical Bar Chart

A grouped vertical bar chart is also known as a cluster chart and shows information about different subcategories of a data set. It can be used to show several sub-groups of each category however if the chart contains too much information, it can become complicated and difficult to read and interpret.

- Segmented Vertical Bar Chart

Just as a segmented horizontal bar graph, this method of data representation uses vertical bars to show total discrete variables in percentages.

**Question Samples** + Excel Use Case

- The image below shows a data set comprising the total revenue generated by different departments of an organization over a 3-year period. Plot this information on a bar graph.

- The image below shows the total number of price change announcements by different alcoholic beverage companies in the same country over a 3-month period. Plot out this information on a bar graph.

- The image below shows the total sales of individual properties for a real estate company over a 3-month period. Represent this information on a bar graph.

**How to Construct a Bar Chart in Excel**

**How to Create a Stacked Bar Chart in Excel **

Here is a step-by-step guide on how to represent data categories in a stacked bar graph using a spreadsheet

- Step 1: Input the data categories into your spreadsheet. As seen in the question samples above, data categories and their numerical values are outlined in a tabular form.

- Step 2: Select the Data. Highlight all data categories to be included in your stacked bar chart. Ensure that you highlight all headings including row and column headings.

- Step 3: Click on the "insert" tab in the toolbar at the top of the excel sheet. If you are creating a horizontal stacked bar chart, choose the "stacked bar" option. On the other hand, if you are creating a vertical stacked bar graph, then choose the "stacked column" option.

- Step 4: Rearrange Rows and Columns. If you want to adjust the row/column arrangement, click on the "design" tab and choose the "switch row/column" option.

- Step 5: Data Summation. You can also sum up the value of your data categories by adding total labels to your stacked bar chart. To do this, simply right-click on the totality series then select the "add data labels" option in the context menu. This will add up the data categories in each bar or column.

**How to Create a Grouped Bar Chart in Excel**

Here is a step-by-step guide on how to create a grouped bar chart graph in Excel:

**Vertical Grouped Bar Chart**

- Step 1: Enter your research variables in the spreadsheet. You would most likely make use of multivariate data categories.

- Step 2: Select the data ranges you wish to represent in your grouped bar chart.

- Step 3: Choose the "Insert" tab in the toolbar at the top of the screen then click on the "Column Chart" tab.
- Step 4: Choose the "clustered column" option in the 2-D column section. The vertical grouped bar chart would appear in your spreadsheet.

**Horizontal Grouped Bar Chart**

- Step 1: Feed in your research variables in the spreadsheet in a tabular form.
- Step 2: Choose the data categories that you wish to represent in your grouped bar chart. The selection should include the row and column headings.
- Step 3: Click on the "Insert" tab in the toolbar at the top of the screen then select the "Bar Chart" tab.
- Step 4: Choose the "clustered bar" option in the 2-D Bar section. The horizontal grouped bar chart would appear in your spreadsheet.
- You can interchange rows and columns by clicking on the "Switch Row/column" option in the Design tab. You can also tweak the visual appearance of your graph by selecting any of the bar chart elements including the axis titles, data label, data table, error bars, gridlines, and trendlines.

**How to Create a Segmented Bar Chart in Excel**

**Horizontal Segmented Bar Chart **

- Step 1: Enter your data variables into the spreadsheet.
- Step 2: Highlight the data categories then click on the "insert" tab in the toolbar.
- Step 3: Click on the "bars" tab then select the "100% stacked bar" icon.

**Vertical Segmented Bar Chart**

- Step 1: Enter your data variables into the spreadsheet.
- Step 2: Highlight the data then click on the "insert" tab in the toolbar.
- Step 3: Click on "columns" then select the "100% stacked column" icon under the 2-D option.

## How t**o Collect Online Data and Export as CSV or Google Sheets **

You can use Formplus to create and share online surveys and questionnaires for qualitative and quantitative observation. With our easy-to-use form builder, all you need to do is drag and drop your preferred form fields into your online survey form to add them.

Formplus allows you to create powerful online surveys and questionnaires in minutes. It also offers multiple sharing options for you and respondents can fill and submit form responses even when they do not have access to the internet.

Apart from the reports summary tool in the form builder which helps you to generate custom visual reports in minutes, Formplus is fully integrated with Google sheets. Google sheets integration means that form responses are automatically sent to your spreadsheet without the need to export or import data.

Formplus also allows you to export form responses as a CSV file. This makes it easy for you to generate different types of bar graphs and charts for form data analysis.

**Advantages of a Bar Graph**

- Simplified Data Analysis

A bar graph simplifies the data analysis process by helping you to manage large volumes of data easily. Plotting your research data in a bar chart would allow you to easily compare, visualize and comprehend different data variables at a glance.

- Track Data Changes

Another advantage of using a bar graph for statistical analysis is that it helps you to map out and track data changes over a period of time. Unlike other statistical tools like a pie-chart that can only represent one data set, a bar graph can be used to represent different data sets over a period of time.

- Track Organizational Growth Patterns

A bar chart is extremely useful for tracking customer base growth or revenue generation patterns.

- Data Comparison

In addition, a bar chart aids comparison across different sets of data because it clearly shows the relationship between each research variable and the fixed numerical value.

**Disadvantages of a Bar Graph**

- It cannot be used for Qualitative Analysis

Bar graphs cannot be used to represent research data gathered through qualitative observation methods. This is because a bar chart deals specifically with quantifiable data variables, that is, data sets that can be measured via numerical values.

- Complexity

Bar graphs can become extremely complex to understand; especially when dealing with large sets of data.

- It does not Account for Causative Factors

A bar chart does not account for any causative factors, key assumptions or patterns responsible for the statistical variations in a research data sample.

**Conclusion**

It is almost impossible to avoid plotting out quantitative data on a bar graph or chart because this statistical tool aids data categorization for analysis. A researcher must understand the different types of bar graphs, the characteristics of each type and more importantly, how to plot out a bar graph on a data spreadsheet.

In this article, we have highlighted step-by-step guides for plotting different types of bar graphs including horizontal bar graphs, segmented bar graphs, and cluster vertical graphs. Although bar graphs can become extremely complex to understand, they help a researcher to easily process large amounts of data and arrive at research outcomes.