It is often said that a picture paints a thousand words for this reason statistical chart/graph are ways of displaying qualitative data in other to show brevity, clarity, credibility, and specificity. Statistical charts/graphs are often used to make it easier to understand large quantities of data and the relationship between different parts of the data. Charts can usually be read more quickly than the raw data that they come from. They are used in a wide variety of fields, and can be drawn by hand often on graph paper or by computer using a charting application such as Microsoft Excel spreadsheet. The bar chart also known as bar graph is one of the most common ways of displaying qualitative data. This Graph type consist of two variables, one response also called “dependent” and one predictor also called “independent”, arranged on the horizontal and vertical axis of the graph. The relationship between the dependent and the independent variables is shown by a rectangular box from one variable’s value to the other’s variable value. The bar chart’s rectangular bars of lengths is usually proportional to the magnitudes or frequencies of what they represent, this makes it very useful and easy to compare two or more values. The bars can be horizontally or vertically oriented. Sometimes a stretched graphic is used instead of a solid bar. Bar charts, like pie charts, are useful for comparing classes or groups of data. In bar charts, a class or group can have a single category of data, or they can be broken down further into multiple categories for greater depth of analysis. Bar charts are familiar to most people, and interpreting them depends largely on what information you are looking for. You might look for the tallest bar, the shortest bar, growth or shrinking of the bars over time, one bar relative to another and change in bars representing the same category in different classes.
Fig A is a bar chart and it has been used to present data from the United States National Hospital Discharge Survey for 1995-1997. Length of Hospitalization No. of Hospitals in1995 No. of Hospitals in1997
1 day or less 1,480 1,000
2-3 days 2,000 2,500
4 days or more 480 500
I will say that an appropriate graphical representation of these data will be a bar chart. The graph is vertically orientated. From figure A (the bar graph), it can be observed that the number of hospitals that hospitalized for 1 day or less in 1995 stood at about 1,500 as opposed to about 1000 hospitals in 1997 thus there was a reduction by about 500 from 1995 to 1997. Also observe that the number of hospitals that hospitalized for childbirth for 2-3days in 1995 was 2000 and in 1997 the number rose to about 2500 hospitals, showing an increase of about 500 in the number of hospitals that hospitalized patients for childbirth from 1995 to 1997. 1995 however, recorded a lesser number of hospitals that hospitalized for childbirth for 4 or more days compared with 1997 which recorded about 500 hospitals. From the bar graph it can be inferred that more patients were hospitalized for 2-3 days from 1995 to 1997 and less patients were hospitalized for 4 days or more. This trend might be because of complications arising during or after childbirth thus leading to more hospitals recommending that patients should be hospitalized for observation and further treatments.
This is definitely the best way to have presented these data in my opinion because it shows clearly what the trend of hospitalization for childbirth has been over a period of time (1995-1997) in this case, and it supports perfectly the findings in the text. The bar chart remains my best choice of presenting this type of data because of the reasons already given above i.e. for clarity, credibility and specificity. However, you must watch out for inconsistent scales. If you’re comparing two or more charts, be sure they use the same scale. If they don’t have the same scale, be aware of the differences and how they might trick your eye. Be sure that all your classes are equal. For example, don’t mix weeks and months, years and half-years, or newly-invented categories with ones that have trails of data behind them. Be sure that the interval between classes is consistent. For example, if you want to compare current data that goes month by month to older data that is only available for every six months, either use current data for every six months or show the older data with blanks for the missing months. Also take note that there is a pronounced amount of space between the individual bars in each of the graphs, this is important in that it help differentiate the Bar graph type from the Histogram graph.