The quantitative data analysis process typically follows the following four steps:
Validate Your Data
Edit Your Data
Code Your Data
Analyze
Step 1: Validate the data
Data validation consists of ensuring that all the data that has been collected for your program evaluation has been cleaned, is complete, and is labeled and stored properly. When using tables, labels are often the top row and may be called fields, columns, or variables. At this point, the evaluator and/or members of the program team will review all the quantitative data sets to remove any duplicates and unwanted data points. This is also where all identifiable information about participants that is not relevant for the evaluation should be removed. Data of this nature usually consists of names, addresses, phone numbers, and personal or protected health information. Once complete, the result is a strong quantitative data pool that is accurate, relevant, and usable.
Step 2: Edit the data
The purpose of data editing is to ensure that the data is clear and understandable by viewers and those who may analyze the data. A common situation involves shortening data so that tables don’t include unnecessarily long entries that break visual flow. This usually involves reading through the data to identify raw data output that can be converted to formats that are easier for a computer to read and analyze. For example:
This editing involves using reason to decipher the meaning or fill in missing information, where appropriate. While editing, the goal is to make data unambiguous and clearer to a viewer. It is very important to remain objective and avoid biased editing. Biased editing can occur when the editor:
This deep dive into your quantitative data can be both time consuming and tedious, so it is important to allocate enough time for this effort. It is also helpful to consider not waiting to edit all the data at the end of the data collection program, and instead move to edit segments of validated data throughout the data collection process.
Step 3: Code the data
Data coding refers to the process of grouping and assigning value to the quantitative responses. By coding data, you can take large sets of information and break them down into simplified brackets or categories. Below is an example of how to code quantitative data received from a survey.
Step 4. Analyze the data
The most used quantitative data analysis method is descriptive statistics. Descriptive statistics refers to analyzing data in a way that helps to describe or summarize the relationships and patterns that are present. Essentially, it takes large amounts of data and breaks it down into several categories of useful information to examine "what happened."
Table (4.9). Here are some common examples of descriptive analysis.
Mean | a numerical average |
---|---|
Median | the midpoint of a data set when in chronological order |
Mode | the most common value |
Percentage | the ratio or number that represents a fraction of 100 |
Frequency | the number of occurences |
Range | the largest number minus the smallest number in the data set |
Note: Descriptive analysis can help reveal outliers, which are data points likely to be incorrect or highly abnormal, such as when someone enters their age as 7,591. These outliers are often removed so as not to skew critical data points, such as "average age." Excluding outliers should be done with care, as some results may be true but abnormal. Start with data that is undeniably incorrect, such as “our clinic is open 28 hours a day.” Statistical methods for identifying outliers can be found in the Quantitative Analysis resources section below.
Inferential statistics goes a step beyond descriptive statistics by using the same quantitative data to draw conclusions (or inferences) and make predictions about the larger population. Common examples of inferential analysis are correlation (describing the relationship between two variables) and regression (showing the strength of the relationship between two variables). Inferential analysis is more complex than descriptive analysis and typically requires a more advanced understanding of statistics to appropriately apply it to your program evaluation.
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HELPFUL TOOLS
For descriptive statistics, Microsoft Excel and Google sheets are commonly used.
For inferential statistics, tools such as SPSS, SAS or STATA are commonly used.
Here are some resources to learn more about inferential analysis:
When engaging in your quantitative analysis process, it is helpful to keep the following in mind:
Here are some resources on conducting a quantitative analysis: