Quantitative data will be numerical.  With the advent of statistical packages for personal computers, most quantitative research data can be entered into files stored in the computer, and later analyzed with this software.

DePoy lists seven steps used in preparing data for statistical analysis:
1. Check the data collection instrument for accuracy and completeness of information.  The data collection instrument can range from a sheet of paper for pencil in paper questionnaire responses, up to computer files generated by a sophisticated electronic measurement device.  In either case there may be erroneous data entries, or missing data points.  Try to identify errors or missing data as soon as possible.
2. Label each variable on the instrument.  This starts to organize your data in the computer file so doesn't become a jumble of numbers.
3. Assign variable labels to computer locations.  The personal identification number is usually listed in one of the first file locations.  The use of identification numbers is one way to provide anonymity to subjects.
4. Develop a codebook and control file.  This is your record to keep track of what data is in what column.
5. Enter data using double verification (entering the data twice) or other quality control procedures.  Part of this process is checking to see if there are data entries out of the expected range for that variable.
6. Clean the raw data files.  This does not imply that the researcher can arbitrarily change or fix up the data.  There are rules for this process, and all steps taken to deal with missing data and data errors must be disclosed by the researcher.
7. Develop summative scores.  If you are looking at 1000 different numbers that represent measurements of patient satisfaction, you may not be able to say much about what that data represents.  However, if you have the mean of the data, and it is 4.8 out of a possible 5, that summary statistic helped to see the bigger picture.

Now the data should be ready for statistical analysis.  There are two course notes files that you should read about analyzing quantitative data.  One summarizes descriptive statistics, in the other file is an introduction to inferential statistics.
 
 

Qualitative Research

Your approach to data management and data analysis is different from that employed in quantitative research.  the first difference is that you are interested in obtaining a qualitative understanding of the phenomena studied. Data includes narratives (primarily), artifacts, or videos of nonverbal behaviors. Second, the process of data analysis is ongoing throughout the study rather than waiting until the study is completed as is found in quantitative research.  The interaction of data collection or gathering and data analysis is critical to qualitative research and is referred to as the interactive process.  Third, the data is managed as the fieldwork is conducted.  Strategies that are employed in data analysis are designed to transform volumes of interview transcripts, audiotapes, videotapes, fieldnotes, and other written materials into meaningful categories also called themes.  These themes explain the underlying meaning and patterns of the phenomenon of interest.  Other strategies are designed to explicate the essence of personal experiences as told to the researcher.  The information tends to be verbal and nonverbal rather numerical or quantitative. Volumes of information in audiotapes are transcribed into written form.   Much detail goes into the instructions for the person who is responsible for transcribing audiotapes, including how to deal with pauses, laughter, repetitive phrases, etc. It is a very lengthy process, often requiring as much as 6-10 hours of typing for a 1 hour interview.   Following this, the narrative must be checked agianst the audiotape multiple times for accuracy. As categories of information are identified, some organizational system must be developed so that information can be easily retrieved.  Information may be coded for easier retrieval.  the researcher may keep personal notes in the margins of the narrative to identify insights gained or to direct  him/her to the passage for later review.  Data review results in an ongoing process of summarizing information and "memoing".  This leaves a train that is referred to as the audit trail.  This trail demonstrates that the researcher has uncovered and revealed new understandings or meanings of the phenomenon of interest.  Videotapes are often viewed multiple times with frames marked that contain behaviors of interest.  This is also a lengthy process.
Data analysis is an important research task.  It is an important source of error for both types of research.