Qualitative data

Statistics is defined as “the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data” (Bluman, 2008, p. 3). There are two types of data: qualitative and quantitative. Qualitative data is categorized while quantitative data can be ranked. In order to give people further understanding, this data can be organized by using a method called frequency distribution.
This means that the data collected is shown in table form utilizing classes and frequencies (Bluman, 2008, p. 35). Once the data has been organized using frequency distribution the data is then presented in graph form. The data collected is then summarized. Measures of tendency include mean, median, and mode; however, not all of these apply to both qualitative and quantitative data. Measures of variation tell the spread of the data set. Variance and standard deviation are two measures of variation. This paper will analyze one qualitative variable and one quantitative variable of a predetermined data set and cover how to analyze this data. I have chosen to analyze the qualitative variable of gender and the quantitative variable of overall job satisfaction.
Qualitative Variable – Gender

Gender was chosen as my qualitative variable because gender is categorized in one of two categories: male or female. This qualitative variable was categorized as 1 for male and 2 for female. When setting up the frequency table, the gender column in the predetermined data set was copied into a separate Excel worksheet. Since there are two categories (1 and 2), the bin range was set at 1 and 2. Once the frequency table was created, I substituted male for the number 1 and female for the number 2. See below: This was also charted to give visual meaning. Sometimes a graph can help someone realize just how much more of one thing there is than the other. In this case, both the frequency table and chart indicate that there are substantially more females than male being evaluated. See chart below:
Measures of central tendency are numeric in nature; however, there is one method that can be used when the variable is qualitative. This method is called mode. Mode refers to the value that occurs most often in a data set (Bluman, 2008, p. 109). In this case, females occur more than males which tells me that the analysis of job satisfaction will be based mainly on a female’s opinion. A measure of variation does not apply because these are categories not numbers which can be ranked. See the chart for Excel calculations below for the mode:
Determining measures of central tendency gets a little more complicated depending on which method you use because you could come up with two possible answers. The mean is sum of all digits divided by the number of digits. In this case, the sum of all values is 123.4, and there are 25 total values. If you take 123.4 and divide it by 25, you will get 4.936. However, if you were to determine the measure of central tendency using the median method, you would get a different answer.
The median of an odd set of numbers is the middle number in that group. This means that you need to group the numbers from least to greatest then take the thirteenth number in a set of 25 numbers as in this scenario. This answer will be 5. Remember from above that mode refers to value that occurs most often. In this case, it is 4.2 because it is the only numeric value that is repeated, therefore it appears most often.
This type of data can be analyzed further by using methods of variation. Sample variance is determined as such. We take the mean and subtract it from each of data values and square each result. Next, we take the sum of the squares and divide by the number of values. In this case, the sample variance ends up being 1.7. Now the standard deviation can be determined by taking the square root of the sample variance. This means the standard deviation for this scenario is 1.3 (V1.7 = 1.3). See below for Excel data analysis computations.

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