Empirical evidence is information that researchers generate to help uncover answers to questions. They can have significant implications for our society. Claims and arguments that rely on empirical evidence are often referred to as a posteriori (next experience). This is unlike a priori (which precedes it). A priori knowledge or justification is independent of experience, while knowledge or justification a posteriori depends on experience or empirical evidence. The standard positivist view of empirically acquired information has been that observation, experience, and experiment serve as neutral arbiters between rival theories.
What is Empirical Evidence?
Empirical evidence is information that verifies the truth (which corresponds exactly to reality) or the falsity (inaccuracy) of a claim. From the empirical point of view, one can claim to have knowledge only when it is based on empirical evidence.
Empirical evidence is information acquired by observation or experimentation, in the form of recorded data, which can be the subject of analysis. This is the main source of empirical evidence. Secondary sources describe, discuss, interpret, comment, analyze, evaluate, summarize, and process primary sources. Materials from secondary sources can be articles in popular newspapers or magazines, book reviews, or movies. These can also be articles found in academic journals that discuss or evaluate someone else’s original research.
Empirical evidence can be synonymous with the outcome of an experiment. In this sense, an empirical result is a unified confirmation. In this context, the term semi-empirical is used to qualify theoretical methods that use, in part, basic axioms or postulated scientific laws and experimental results. Such methods are opposed to theoretical ab initio methods, which are purely deductive and based on the first principles.
Theory versus empirical evidence
Researchers may have theories about how something will play out. However, what is observed or experienced may be different from what the theory might predict. In this way, to know the effectiveness of something, you have to try it. Typically, researchers collect data through direct or indirect observation and analyze this data to answer empirical questions. That is, questions that can be answered by observation. In this regard, social scientists produce empirical evidence in a variety of ways to test theories and measure the ability of A to produce an expected result that would be B.
Let’s look at an example: Engineers and scientists equipped cars with various safety devices in various configurations. They then smashed them into walls, poles and other cars and recorded what happened. Over time, they were able to figure out which types of security devices worked and which didn’t. They didn’t do everything right right immediately. For example, the first seat belts were not retractable. Some airbags fired pieces of metal at passengers. But, auto safety improved and although people drive more and more miles, fewer and fewer die on the road.
Types of empirical evidence
The two main types of empirical evidence are qualitative evidence and quantitative evidence.
Qualitative evidence is the type of data that describes non-measurable information. Qualitative data is used in several disciplines, especially in social sciences, as well as in market research and finance. In such fields, research usually investigates human behavior and its patterns. The non-measurable nature of qualitative data, as well as their subjective analysis, makes them prone to potential bias.
Quantitative evidence refers to numerical data that can be further analyzed using mathematical and / or statistical methods. Quantitative data is used in almost all disciplines of science. Unlike qualitative data, evidence obtained using quantitative data is generally considered impartial. The validity of the data can be easily verified by calculations or mathematical or statistical analysis. Quantitative data is used in almost every discipline of science.
Unlike qualitative data, evidence obtained using quantitative data is generally considered unbiased. The validity of the data can be easily verified by mathematical or statistical calculations or analyses.
Empirical Evidence and Analysis of Social Networks
Studying social networks provides a concise and comprehensive introduction to the process of empirical network research. Social Network Analysis is the most in-depth and most important online research method today. It is the most used by both academic and extra-academic researchers around the world, so it is necessary to understand the use of empirical evidence in this case.
In that sense, when doing a brief investigation about it, highlights the name of Helen Hall Jennings. His contribution was the development, before anyone else, of quantitative research methods that gave rise to Sociometrics, a quantitative method for measuring social relations. This pioneering work is considered the birth of Social Network Analysis.
Jennings was a social psychologist, specializing in empirical research designs. Working in the Psychology lab of psychologist Gardner Murphy, he met Jacob Moreno. This was an eminent Psychosociologist, founder of Psychodrama and Group Psychotherapy, which would be essential to his research. Through his expertise in quantitative and statistical methods, Jennings worked with Moreno to develop an empirical approach to Social Media research. Researcher Christina Pell points out that together they studied how social relationships affect the psychological well-being of individuals. They also used quantitative methods to study group structure and the positions of individuals within groups.
The Jennings and Moreno Investigations
Jennings’ work is incomplete without the effective duo he built with Jacob Moreno. They developed an approximation method that included all the characteristics of Social Network Analysis. It was based on structural intuitions, involved the systematic collection of empirical data, and an explicit mathematical model was used. More importantly, that structural perspective was uniformly applied to a wide range of phenomena. Thus, Moreno and Jennings’ group used the four characteristics that define Social Network Analysis.
Jennings’ input was crucial, as Moreno’s approach was more instinctive. He suggested ideas intuitively, Jennings placed them within a quantitative and numerical framework.
Jennings and Moreno’s investigations at Sing Sing Maximum Security Prison and the Hudson School for Young Ladies were an important step forward. This systematic analysis and data collection led to two famous works: “Applications of the Group Method for classification” and “A new approach to the problem of human interrelations”. Both are considered to be the genesis of Sociometria.
In 1943, Jennings’ doctoral thesis “Leadership and Isolation: A Study of Personality in Interpersonal Relationships” examines how elected and isolated leaders emerge in a given population. It was a continuation of the analysis of the data collected at the Hudson School for Young Ladies. Respondents were asked who they would like to work with and who they would like to live with. Eight months later, their views on who they had chosen as leaders and who as outcasts or isolates had not changed. Morris Janowitz, founder of military sociology, notes that this work by Jennings is an “ingenious empirical study that helped turn sociometrics into a research tool.” Many consider it one of the most in-depth analyses of leadership and isolation in the field of Social Psychology.
Collect empirical evidence in social sciences
Educational research is not the same as automotive research. However, education can be improved by trying new things. By collecting data on those efforts, the data is rigorously analyzed. All the available empirical evidence is then weighed to see if those new things achieve the expected results.
Unfortunately, rigorous analysis is often difficult in the social sciences. In automotive engineering labs, one design bit can be changed at a time so that each test isolates the individual factor. In social sciences trying to isolate variables is challenging, but it is possible if researchers can make comparisons using the randomized control trial (RCT).
The randomised control trial (RCT) is a trial in which subjects are randomly assigned to one of two groups. One (the experimental group) that receives the intervention being tested and the other (the comparison or control group) that receives an alternative (conventional). The two groups are then tracked to see if there is any difference between them in the outcome. The results and subsequent analysis of the trial are used to assess the effectiveness of the intervention. This is the degree to which a treatment, procedure, or service makes patients more beneficial than harmful. RCTs are the strictest way to determine whether there is a cause-and-effect relationship between intervention and outcome.
The goal of science is that all empirical data that has been collected through observation, experience, and experimentation is unbiased. The strength of any scientific research depends on the ability to collect and analyze empirical data. In this regard, it must be carried out in the most impartial and controlled manner possible.
Because scientists are human and prone to error, scientists typically collect empirical data that replicates experiments independently. This also protects against scientists who unconsciously or in rare cases consciously, deviate from prescribed research parameters. This, of course, could skew the results.
The recording of empirical data is also crucial to the scientific method. Science can only advance if data is shared and analyzed.
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