Data: Finding, Interpreting, and Managing Data: Analyze Data & Statistics

What is the difference between data and statistics?  Sometimes the two words are used interchangeably, but they are two different things.

Data is the raw information from which statistics are created.  

Statistics provide an interpretation and summary of the data.  

For help with any of the software, please check out one of our many books!

Before you begin your data analysis, you must identify the level of measurement associated with your data. The four levels of measurement are:

1. Nominal: categorical data where names and numbers are used as identifies rather than having a logical order (Example: jersey numbers, gender)
2. Ordinal: ordered series of relationships within the data where names are sometimes used as identifiers (Example: competition results)
3. Interval: similar to interval, but there is no meaningful 0 (Example: degrees in Fahrenheit) 
4. Ratio: a scale that represents quantity and has a meaningful 0 (Example: weight)

Nominal and ordinal variables will often be coded into numerical data if names are used. For example, if gender is used as a variable, it may be coded as 0 for Male and 1 for Female.

Once the measurement of the variables in your data are determined, you can begin running statistical analysis on the variables to determine relationships and significance.

Quantitative statistical software options include:

Are you unsure which software is best for you? Use the resource below to see a table of the differences and similarities between the software. For whichever software you decide on, we have books to help!

Qualitative data analysis is a more continuous process that can occur during the data collection part. These steps are listed as a suggestion, but you should decide for yourself what process works best for you.  

1. Record data immediately after collection. Most of the time this process will be transcribing interviews.
2. Begin to analyze data as it is being collected. Begin to look for patterns in the data as they emerge.
3. Once the data has been collected and transcribed, code the data (see link below).  
4. Identify emerging themes and patterns.
5. Conduct a thematic analysis, where you discover in what ways these themes help answer your research question.

Qualitative data analysis software options include:

Note: Not all of these software have free versions.