Psychometric data helps psychologists and social researchers better predict what behaviors will arise from an individual’s decisions. The most common thought that pops up with the mention of psychometrics is self-report data (S-data), or surveys with fill-in-the-bubble sheets.
Numerical and measurable quantitative data can be transformed into reports and visualizations to drive business decisions. With more open-ended qualitative data, however, the idiosyncrasies of the subjective and complex human condition can be explored with greater accuracy.
There are four distinct types of psychometric data researchers in psychology use, under different situations and experimental conditions:
- B-data (behavioral data)
- I-data (informants’ data)
- S-data (self-report data)
- L-data (life data)
Let’s take a look at the four major types of psychometric data in greater detail:
B-Data (Behavioral Data)
From blood pressure and heart rate to brain activity, B-data includes the biological umbrella of statistics directly related to correlations in behavior. Social experiments such as seeing whether one’s outer appearance (e.g. physique, attire) affects how quickly strangers offer help.
Further developments in AI can shape how B-data is processed, then transferred onto reports, and finally optimized in reality. B-data tends to be qualitative in nature, though a quantitative review can provide more workable assets.
Examples of behaviorial data (B-data):
- Body language when interrogated
- Lie detector, heart rate monitor
- Communication patterns under stress
- Eye movements, typing speed and pressure
- Facial responses to stimuli
Applications of behavioral data (B-data):
- Healthcare and psychiatry
- Biomedical innovations
- Marketing and UX design (user experience)
- AR and VR (augmented and virtual reality)
- Wellness and therapeutic institutions
I-Data (Informants’ Data)
“How would a friend describe you?”
Many interviewers ask this question to subconsciously ask the interviewee to consider themselves from a second perspective—a method to obtain I-data instead of S-data. Context and intention from a second perspective help paint a well-rounded image of an individual.
The expectancy effect occurs when a tampered (and ultimately incorrect) belief from one person gets projected onto the subject, who ultimately ends up behaving according to the informant’s initial judgements. The process occurs at a wholly subconscious level, which can be difficult to rewire.
Examples of informants’ data (I-data):
- Harder conditions to self-identify such as narcissism
- Friends’ perceptions and understandings
- Parents’ knowledge of specific behaviors in private
- Social competency and fluidity
- Speech patterns, conversational habits
Applications of informants’ data (I-data):
- Law and security protocols
- Couples counseling
- Psychopathology studies
- Communications development
- School curriculum improvements
S-Data (Self-Report Data)
Note: Also known as Q-data (questionnaire data)
Surveys, online quizzes, and long-form questionnaires all fall under the largest personality psychology data collection format: S-data, or self-report data. Typically fast, economical, and extremely easy to administer, self-reports are nevertheless prone to heavy biases from self-judgements.
Many personality assessments have built-in lie detectors in order to ensure reporting integrity and clean psychometric data. Recruitment systems in human resources can benefit from more controlled variables to ensure validity. With industrial-organizational (IO) psychologists, S-data plays a critical role in developmental workplace programs.
Examples of self-report data (S–data):
- Political leanings
- Favorite teams, bands, brands
- Values, beliefs, and attitudes
- Inner desires and thoughts
- Personality and behavioral traits
Applications of self-report data (S-data):
- Workplace reorganization and development
- Psychiatry and psychotherapy
- Schooling and pedagogy improvements
- Validity and reliability procedures in R&D
- Assessment creation and iterations
L-Data (Life Data)
Life data is a type of psychometric data includes the circumstances we find ourselves in: the life stages (as proposed by Erik Erikson), milestones, geographic locations, and more.
A life record (think of a scrapbook or rolling movie) of an individual’s experiences constitute life data—wholly qualitative in nature.
The bits and pieces of information and memories in each person’s scrapbook add up to a holistic picture of how their past, present, and future are connected. L-data can also offer more insight into S-data, as correlations and linkages lie below the surface.
Examples of life (L) data:
- Occupations and career changes
- Academic selection and affiliations
- Critical life stages: adolescence, marriage, childrearing, etc.
- Places of residence and travel
- Frequently visited restaurants and stores
Applications of life data (L-data):
- Life, relationship, career coaching and planning
- Community building and development
- Forensics and law enforcement
- Behavioral market research
- Personas and branding procedures
In Summary: Types of Psychometric Data
- Behavioral (B)
- Informants’ (I)
- Self-Report (S)
- Life (L)
Psychometric data and psychographics have many powerful applications in both therapy and business—the personal and professional. Entire team reorganizations with I-O psychologists can greatly benefit from looking at different types of psychometric data.
The four major types of psychometric data: behavioral (B), informants’ (I), self-report (S), and life (L) have different purposes to support a variety of research projects and developments.
In the future, new derivations of psychometric data will emerge—as our understanding of human behavior becomes more comprehensive.
Bagner, Daniel & Berkovits, Michelle & Eyberg, Sheila. (2006). Psychometric Considerations. https://doi.org/10.1016/B978-012343014-4/50004-6.
Borsboom, Denny & Molenaar, Dylan. (2015). Psychometrics. International Encyclopedia of the Social & Behavioral Sciences. https://doi.org/10.1016/B978-0-08-097086-8.43079-5.
Buchanan, Roderick & Finch, Susan. (2005). History of Psychometrics. https://doi.org/10.1002/0470013192.bsa282.
Kyriazos, Theodoros. (2018). Applied Psychometrics: Writing-Up a Factor Analysis Construct Validation Study with Examples. Psychology. 09. 2503-2530. https://doi.org/10.4236/psych.2018.911144.
Nartgün, Zekeriya & Sahin K., Merve. (2015). Psychometric Properties of Data Gathering Tools Used in Thesis. Procedia – Social and Behavioral Sciences. 174. 2849-2855. https://doi.org/10.1016/j.sbspro.2015.01.978.
Olino, T. M., & Klein, D. N. (2015). Psychometric Comparison of Self- and Informant-Reports of Personality. Assessment, 22(6), 655–664. https://doi.org/10.1177/1073191114567942.
Sijtsma, Klaas. (2011). Future of Psychometrics: Ask What Psychometrics Can Do for Psychology. Psychometrika. 77. 4-20. https://doi.org/10.1007/s11336-011-9242-4.
Sijtsma K. (2006). Psychometrics in Psychological Research: Role Model or Partner in Science? Psychometrika, 71(3), 451–455. https://doi.org/10.1007/s11336-006-1497-9.
Wijsen, Lisa & Borsboom, Denny & Alexandrova, Anna. (2021). Values in Psychometrics. Perspectives on Psychological Science. 17. 174569162110141. https://doi.org/10.1177/17456916211014183.