While all students deal with data analysis to some extent, social science research relies on it the most. Social sciences students can’t possibly survive even the first semester of their programs without learning about different types of data, data analysis methods, and at least the basics of statistics. Luckily, data analysis at the level required from students is not as complicated as most seem to believe.

Types of data in social science research

Qualitative data refers to non-numerical data that can’t be quantified, for instance, people’s beliefs and personal characteristics. Qualitative data are quite common in social science research. While they lack the precise nature of their quantitative counterpart, qualitative data are helpful for an in-depth analysis of various population groups and phenomena. An example of research using qualitative data is a study exploring the reasons for American nurses’ intention to leave the profession.

In turn, as the name suggests, quantitative data are numerical and can be quantified. First-year students often struggle with quantitative research. They are scared of all the numbers and formulas and struggle trying to figure out the answer to the question of what is p hat in statistics. If that is the case for you, consider getting professional assistance from reliable writing services; they have writers who specialize in social science research and can help.

Data analysis methods for qualitative research

The most common methods of data analysis in qualitative research include:

• Content analysis

Content analysis is all about identifying meaningful patterns in data. For example, participants’ responses to interview questions can be studied to determine what the keywords are and how frequently they are used. In a study of the reasons for American nurses’ intention to leave the profession, such keywords would probably include “burnout,” “fatigue,” and the likes.

• Narrative analysis

Narrative analysis is focused on participants’ stories. It is especially common in ethnographic and case study research. For example, a researcher interested in exploring Mexican migrants’ experiences at the Mexico-U.S. border can use narrative analysis when processing participants’ first-hand accounts.

• Discourse analysis

Discourse analysis is also about narratives, yet its focus is on the social context of the language that participants use and the conversations they have. The same researcher (interested in studying migrants’ experiences at the Mexico-U.S. border) could use discourse analysis to analyze how American border patrol agents talk to migrants and vice versa.

Data analysis methods for quantitative research

The two key methods of data analysis in quantitative research are:

• Descriptive statistics

Descriptive statistics are basically “preparatory” statistics in that they refer to simpler operations. Some of the common tests that belong to descriptive statistics are mean, median, and standard deviation, among others. 

• Inferential statistics

Inferential statistics are a step up from descriptive statistics. They include more complex operations, such as sampling distributions, linear modeling, and factorial designs. Even though the question of what is p hat in statistics is not particularly advanced, it also belongs to inferential statistics.