As you begin your investigation into your research project it's important to think about what data you think you will need and the sources where you might find this data. An advantage of being a university student is that you have a wealth of information at your fingertips. You may want to find a research article where the authors have done a similar study and examine the data sources they used in the process of writing the article. Reading such research articles can also provide you an idea if your research topic is adequately focused or if the topic might be to broad to treat in one paper.
As you consider which data sources you need you may have to be flexible in how you approach finding data. For example, you may want to employee satisfaction at a specific company after moving to a four-day work week. Data from that company might not be available, however, research might have been done about four-day work weeks, flexible schedules, or non-traditional work weeks. You may have to consider these outside sources as you approach your research question:
Quantitative Data - generally refers to observations that are represented in numerical form. Examples include program funding level (in dollars), clients’ ages, number of hours of services received, and children's standardized test scores. All of these can be expressed as numbers, as amounts, or as degrees; that is, as quantitative data. Quantitative data can be analyzed with statistics, both descriptive and inferential. (Chua & Mark, 2005)
Qualitative Data - Qualitative data is the general term given to evidence that is text based rather than numeric in representation. These kinds of data result from interviews (group, individual, focus, and so on), observations (more typically unstructured but also structured), and documents (both formal, such as mission statements, as well as informal, such as letters) that may be analyzed from a variety of perspectives. The distinction between qualitative and quantitative data is somewhat arbitrary because all evidence has dimensions of both. (Chua & Mark, 2005)
Longitudinal Data - Present information about what happened to a set of research units (such as people, business firms, nations, cars, etc.) during a series of time points. The participants in a typical longitudinal study are asked to provide information about their behavior and attitudes regarding the issues of interest at a number of separate occasions in time (also called the ‘phases’ or ‘waves’ of the study). (Taris, 2000)
Cross-sectional Data - Refer to the situation at one particular point in time. (Taris, 2000)
See the SAGE Research Methods Database for in-depth explanations of data types and collection methods.
Chua, P. & Mark, M.M. (2005). Quantitative Data. In S. Mathison (Ed.) Encyclopedia of Evaluation. : SAGE Publications Ltd doi: 10.4135/9781412950558.n461
Taris, T. W. (2000). Longitudinal data and longitudinal designs. In Taris, T. W. (Ed.) A primer in longitudinal data analysis (pp. 1-16). : SAGE Publications Ltd doi: 10.4135/9781849208512
The electronic book below is a great primer for understanding data needs. Includes case studies and examples showing how companies use data for improved decision making (see also: Research a Topic tab on this guide):