Quantitative vs. Qualitative Research, Big Data, and Cultivation Theory
Quantitative vs. Qualitative Research
Quantitative research involves recognizing and analyzing quantitative data on variables. Qualitative research, on the other hand, avoids quantification. Qualitative researchers create narrative records of phenomena through techniques such as participant observation and unstructured interviews.
The fundamental difference between these methodologies is that quantitative research studies the association or relationship between quantified variables, while qualitative research focuses on structural and situational contexts.
Qualitative research seeks to identify the deep nature of realities, their system of relationships, and their dynamic structure. Quantitative research aims to determine the strength of association or correlation between variables, generalizing and objectifying the results through a sample. After studying the association or correlation, quantitative research aims to explain why things happen or do not happen in a certain way.
Big Data
Big data is an evolving term that describes a large volume of structured, semi-structured, and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications.
Characteristics of Big Data
- Volume: Volume refers to the amount of data generated that must be understood to make data-based decisions. A text file is a few kilobytes, a sound file is a few megabytes, while a full-length movie is a few gigabytes. Example: Amazon handles 15 million customer clickstream user data per day to recommend products. An extremely large volume of data is a major characteristic of big data.
- Velocity: Velocity measures how fast data is produced and modified and the speed with which it needs to be processed. An increased number of data sources, both machine and human-generated, drive velocity. Example: 72 hours of video are uploaded to YouTube every minute. This is velocity. An extremely high velocity of data is another major characteristic of big data.
- Variety: Variety defines data coming from new sources—both inside and outside of an enterprise.
Thick Data
Thick Data bases its research on purely qualitative data, which is why important sociologists and anthropologists are among its greatest proponents. The anthropologist Tricia Wang coined the term “thick data.” Another fundamental characteristic of Thick Data is that the information usually comes from a small sample. Knowledge comes from the depth of the stories of the small surveys. Generally, in mass sampling, the nuances that reflect the emotions of human behavior are not appreciated, and there is usually the key to something that can be very important.
Cultivation Theory
Cultivation theory examines the long-term effects of television. The primary proposition of cultivation theory states that the more time people spend ‘living’ in the television world, the more likely they are to believe social reality aligns with reality portrayed on television. The images and ideological messages transmitted through popular television media heavily influence perceptions of the real world.
Cultivation theory was founded by George Gerbner and is positivistic, meaning it assumes the existence of objective reality and value-neutral research. A study conducted by Jennings Bryant and Dorina Miron in 2004, which surveyed almost 2,000 articles published in three top mass-communication journals since 1956, found that Cultivation Theory was the third-most frequently utilized theory, showing that it continues to be one of the most popular theories in mass-communication research.