Hey I have a master degree in sociology and social research and am currently doing a PhD in data science so I hope I can help! :) methodology refers exclusively to the techniques you will be using in your analysis i.e. ethnomethodolgy, text mining etc. Case study is a type of study and not a technique :) I think you could proceed as follow 1.address the general topic like the market you are interested in 2. Define a clear research question 3. Define what techniques you will use 4. Collect data accordingly. To find a suitable technique and method you should ask yourself what the scope of your paper is, what you want to achieve, how can you treat the data structures you will create. Maybe if you give more details I can give my humble suggestion to help you find a good method. More on reddit.com

The names themselves help tell you a little about what they are ;) Experimental - you have a control that allows you to measure the change in one variable in relation to another (always remember though, that correlation is rarely the causation). That means that there would be a set independent variable (by the researchers) that is hypothesized to influence (or not in the H0 null hypothesis). Longitudinal- a study that follows the same subjects over a long period of time. Cohort- a subset of longitudinal study in which the subjects are picked because they have a share common characteristic or experience within a defined period. Cross sectional - A "snapshot" study of a population at a given time. So for example, you can have a study about a high risk population for X disease. The study will have a sample of the population that will include healthy members that don't have X, people with disease, etc. This kind of studies are observational, and therefore cannot be used to draw a relationship. They are good to study a prevalence of a condition, or as a reflection of the current conditions. Case study- a study in which the subject(s) are hand picked (no random selection) for a detailed analysis. This is useful when encountering a unique subject and intend to gain more information than a regular study. For example, HM was a man with epilepsy and had his hippocampus removed. Researchers studied his unique condition to learn more about the hippocampus and memory. Quantitative data- tangible, "hard data", objective information. Examples: height, weight, age, A1c blood glucose test. Qualitative data - subjective information (more commonly found in social sciences). Examples: pain rating, mood rating,etc. Comparative analysis studies the difference between different populations. Example: how local diet influences health in US vs Japan. More on reddit.com

There is a deep bias/assumption that qualitative research is fundamentally subjective, which goes against psychology's drive for objectivity. And many people falsely assume quantitative is always objective. More on reddit.com

Hi, OP! Search and secure readings authored by Robert Yin, Robert Stake, and Sharan Merriam—they are considered gold standards in CS.

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