The term empirical evidence refers to the attainment of knowledge through observation, measurement, or experimentation (also known as empiricism). It is accumulated through systematic observations of phenomena in natural or laboratory setting.
One of the key standards of empirical evidence in academic research is that the results can be tested and verified by others. This can increase confidence in the conclusion and demonstrate that there is substantial evidence to accept something as a natural fact.
By contrast, anecdotal evidence, which is considered unclear and open to bias, might be a form of one-off evidence that wouldn’t be accepted unless it’s replicable. Confidence in the conclusion of anecdotal evidence is minimal.
Empirical Evidence Examples
- Quantitative Data: Quantitative data is numerical data obtained through measurement, counting, or statistical analysis. An example is a person’s test scores, which are used as empirical evidence that can get you into a prestigious university. The strength of this type of data is that it tends to be objective, meaning it is less disputable than qualitative data.
- Qualitative Data: Qualitative data is non-numerical data that captures the characteristics or properties of something. Examples include interview transcripts, field notes, and descriptions of behaviors or events. Its strength is that it can lead to in-depth explanations and understandingsthat quantitative data often cannot support.
- Survey Data (Quantitative): Survey data, such as polling data in the lead up to an election, can be used as a form of empirical evidence if the study is reliable and valid. When the survey size is sufficient and population sample is sufficiently uniform, the data may be able to make population-wide predications about society and social views.
- Data from Naturalistic Observation (Qualitative): Naturalistic observations are observations that we can make in the real world, not just in a lab environment. It can pass as empirical evidence if it’s repeatable over time and renders similar results. However, if the observation is made only once and future studies do not support claims made by the original observer, it may slip into the category of anecdotal evidence.
- Case Study Data (Qualitative): Sometimes the first step to understanding a psychological disorder is to thoroughly examine how it is manifest in a single individual. This is referred to as a case study. This data is empirical and may be very sound, but its lack of population-level relevance limits is usability.
- Textual Data (Quantitative or Qualitative): Although it is not possible to read people’s minds yet, the next best thing is to ask people to write their thoughts down on paper. Those words can then be coded along a variety of dimensions to develop trends across a textual dataset (also known as textual analysis and thematic analysis).
- Experimental Lab Data (Quantitative): Randomly assigning research participants to receive different treatments is a hallmark of scientific research. By manipulating the level of one variable and observing changes in another, the researcher can draw conclusions regarding causality. Many consider this type of research as the most scientifically sound method of attaining empirical evidence.
- Longitudinal Research Data (Quantitative or Qualitative): This strategy for acquiring empirical evidence involves collecting data on a particular trait over a long period of time. Researchers will administer the same measurement tool at different points in the participants’ lifespan. This can provide valuable clues regarding the stability of personality characteristics or intellectual abilities.
- Cross-sectional Data (Quantitative or Qualitative): Cross-sectional data is data collected from different subjects at a single point in time. For example, a national census usually generates a cross-sectional dataset of a nation’s population at a specific point in time by asking everyone to complete the census on the same day.
- Historical Data (Quantitative or Qualitative): Historical data is empirical evidence collected from past records or documents that can provide valuable context and insight into past events or societal trends. Examples include analyzing economic data from past decades to understand the causes of financial crises or examining the diaries of individuals who lived through significant historical events to gain a deeper understanding of their experiences.
- Meta-Analysis Data (Generally Quantitative): Meta-analysis is a quantitative technique that combines the results of multiple studies on a similar topic to derive an overall conclusion. This type of empirical evidence can provide a more reliable and generalizable understanding of a phenomenon by aggregating the findings of individual studies, reducing the influence of individual study biases, and increasing statistical power.
- Ethnographic Data (Qualitative): Ethnographic data is a form of qualitative data that provides evidence recorded by an anthropologist or similar researcher. While this data gives extremely in-depth understandings (often called ‘think descriptions’), its inability to be replicated means it lacks the authority of many other types of empirical research provided in this list. Examples include studying the daily lives of a remote tribe or exploring the workplace culture of a specific organization.
- Computer Simulation Data (Quantitative): Computer simulations can be used to model complex systems or processes, providing empirical evidence that may not be easily obtained through direct observation or experimentation. Examples include modeling the spread of infectious diseases to inform public health interventions, or simulating the effects of climate change on ecosystems.
- Physiological Measurement Data (Quantitative): Physiological measurements are the empirical data that result from the recording of physical or biological signals from the body. This can provide evidence about a person’s physical state and whether it fits within physiological norms required for healthy living. Examples include measuring heart rate or skin conductance to assess stress levels.
- Cultural Artifacts (Quantitative or Qualitative): Cultural artifacts and provide powerful empirical evidence about past cultures. For example, the etchings of Aboriginal rock art in Australia has been valuable in providing clear evidence about the historical longevity of the world’s oldest continuous culture.
Case Studies of Empirical Evidence
1. Evidence on Who Shares their Passwords
Evidence Collected by: Surveys
We all know that sharing passwords is risky. Experts would like to know who is most likely to engage in this risky behavior. As a group of these professionals sit around the table debating the issue, they quickly realize that everyone can provide good arguments to support their opinion.
So, how can this question be answered objectively?
The answer: through the scientific method.
To this end, Whitty et al. (2015) measured several personality characteristics of 630 internet users in the UK. Participants were administered questionnaires that assessed Impulsivity, Self-monitoring, and Locus of Control.
Age and knowledge of cyber security issues were also measured.
The results were sometimes surprising:
- Younger people were more likely to share passwords than older people.
- Those who scored high on a lack of perseverance (i.e., impulsivity) were more likely to share passwords.
- Knowledge about cybersecurity did not distinguish between those who share passwords and those who do not share passwords.
The researchers concluded that:
“psychology plays an important role in providing answers to why individuals engage in cyber security practices” (p. 6).
It should be noted that several of the researchers’ hypotheses were not supported by the data. This points to a key reason empirical evidence is so valuable.
2. Linguistic Inquiry and Word Count (LIWC)
Evidence Collected by: Text Analysis
Language is the most common way that people communicate their internal thoughts and emotions. Language is key to business, relationships, scientific innovation and nearly every facet of human existence.
Studying people through language is the way that cognitive, clinical, and social psychologist try to understand human behavior.
Today, communication via texting has never been easier, offering researchers an opportunity like never before. In the old day, text analysis was conducted by hand. Weintraub (1981, 1989) pioneered this approach by hand, analyzing political speeches and medical interviews.
Tausczik and Pennebaker’s (2010) pay respects to Weintraub’s work:
“He noticed that first-person singular pronouns (e.g., I, me, my) were reliably linked to people’s levels of depression. Although his methods were straightforward and his findings consistently related to important outcome measures, his work was largely ignored” (p. 26).
Fortunately, the volume of texts today can be handled with the use of linguistic technology.
The LIWC is unique in that it has the capability of analyzing text that can
“…provide important psychological cues to their thought processes, emotional states, intentions, and motivations…that reflect language correlates of attentional focus, emotional state, social relationships, thinking styles, and individual differences” (p. 37).
Human coding is subject to bias, but empirical evidence via computer technology is much more objective.
3. Measuring Neural Activity as Empirical Evidence
Evidence collected by: Physiological instruments
One of the most common methods of measuring the brain’s neural activity in psychological research is the EEG (electroencephalogram). A few electrodes attached to the scalp can measure this activity in real time.
This kind of data collection technique has allowed researchers to study a wide range of psychological phenomena, such as memory, attention span, and emotions.
In one interesting application, Wong et al. (2007) examined if music-related experience could enhance the processing of a tonal language such as Mandarin.
After all, music involves a lot of tones, as does Mandarin. Therefore, it would seem logical that musicians would be better at processing the sounds of Mandarin than non-musicians.
So, musicians and non-musicians watched a video that contained audio recordings of Mandarin while EEG data were collected.
The results showed that the auditory brainstem regions of musicians
“…showed more faithful representation of the stimulus…Musicians showed significantly better identification and discrimination” (p. 421).
More simply put, the brains of musicians encoded the tones of Mandarin more accurately than the brains of non-musicians.
When empirical evidence is gathered using high-tech equipment, it lends a lot of credibility to the findings.
4. Reaction Time and Semantic Memory
Evidence collected by: Computational data
A frequently used measure of cognitive processing is called “reaction time.” This is a precise measurement of how long it takes for a person to process a specific piece of information.
For example, a research participant is presented with two words that are either related or unrelated.
If the two words are related, then they press one key. If the words are unrelated, then they press a different key.
The computer records how long it takes between the presentation of the words and the key press.
In one of the most influential studies in cognitive psychology, Collins and Loftus (1975) built a foundation of evidence suggesting that semantic information is stored in memory based on strengths of associations.
The reaction time of processing words that were related was much faster than words unrelated. This is because related words are more strongly connected in the memory network:
“The more properties two concepts have in common, the more links there are between the two nodes via these properties and the more closely related are the concepts…When a concept is processed (or stimulated), activation spreads out along the paths of the network in a decreasing gradient” (p. 411).
Empirical evidence in the form of reaction time is not subject to bias and the precision of measurement is quite impressive.
5. Classroom Décor and Learning
Evidence collected by: Naturalistic observation
Most teachers enjoy decorating the classroom environment with educational posters, student artwork, and theme-based materials. But if you were to tell them that those decorations actually impair learning, it might be a tough sell.
However, Fisher et al. (2014) have empirical evidence suggesting this is a real possibility.
Visual stimuli can be distracting, especially to young children. They already have short attention spans.
To put the hypothesis to the test, 24 kindergarten students participated in six lessons over a two-week period; the classroom was either fully decorate or sparsely decorated.
The lessons were recorded and coded for on-task and off-task behaviors. In addition, students took a test immediately after each lesson.
“…spent significantly more instructional time off task in the decorated-classroom condition than in the sparse-classroom condition…learning scores were higher in the sparse-classroom condition than in the decorated-classroom condition” (p. 6).
Empirical evidence isn’t perfect, and every study has limitations, but it is far more objective than opinions based on subjective judgements.
Empirical evidence is attained objectively through methods that adhere to rigorous scientific standards.
The precision of empirical evidence is quite wide. On one end of the continuum, data collected from surveys simply involves participants circling a number that is supposed to reflect their attitude.
On the other end, computer programs can be designed that track how long it takes a person to process a stimulus down to milliseconds.
But perhaps the greatest value of empirical evidence is that it can resolve debates. Smart people can generate convincing arguments to support their opinions on either side of an issue.
However, evidence that comes from scientific methods can settle those debates. Even if it takes several studies to arrive at a firm conclusion, the end result helps move science forward.
Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8(2), 240-247.
Collins, W. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407-428.
Fisher, A. V., Godwin, K. E., & Seltman, H. (2014). Visual environment, attention allocation, and learning in young children: When too much of a good thing may be bad. Psychological Science, 25(7), 1362-1370.
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Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24-54.
Weintraub, W. (1981). Verbal behavior: Adaptation and psychopathology. New York: Springer.
Weintraub, W. (1989). Verbal behavior in everyday life. New York: Springer
Whitty, M., Doodson, J., Creese, S., & Hodges, D. (2015). Individual differences in cyber security behaviors: an examination of who is sharing passwords. Cyberpsychology, Behavior, and Social Networking, 18(1), 3-7.
Wong, P. C., Skoe, E., Russo, N. M., Dees, T., & Kraus, N. (2007). Musical experience shapes human brainstem encoding of linguistic pitch patterns. Nature Neuroscience, 10(4), 420–422. https://doi.org/10.1038/nn1872