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Digital Psychometrics. Estimating Behavior in the Age of Technology
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Publisher: Editura Universitară
Author: Ruxandra Victoria Paraschiv
Edition: I
Pages: 238
Publisher year: 2026
ISBN: 978-606-28-2191-3
DOI: https://doi.org/10.5682/9786062821913
Product Code:
9786062821913
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We live in an era in which human behavior is no longer simply observed, but is recorded, modeled, and predicted in real time. Digital technologies, biosensors, wearables, and AI algorithms have profoundly transformed the way we interact with the world, but especially the way our bodies and minds become continuous sources of data. Psychometrics – the science of psychological measurement – is currently at a historical inflection point.
The volume Digital Psychometrics. Estimating Behavior in the Era of Ubiquitous Technology starts from a fundamental observation: classic psychological measurement models are no longer sufficient to describe the complexity of human behavior in a technology-saturated environment. Questionnaires, point-administered tests, and retrospective assessments are today confronted with massive flows of continuous, passive, and multimodal data (Big Data), generated through the Internet of Bodies (IoB).
This book is the coherent theoretical, methodological, and applied framework for understanding and developing digital psychometrics. It is not just an extension of traditional psychometrics, but a reconceptualization of psychological measurement, adapted to a reality in which behavior, physiology, and context are inseparable. Digital psychometrics no longer measures only stable traits but psychological dynamics, not only scores but trajectories, not only differences between individuals but also intraindividual variations in real time.
The structure of the volume reflects this conceptual transition.
The first part substantiates the transition from classical to digital psychometrics, by using key notions such as continuous data, digital phenotyping, and the role of the Internet of Bodies in redefining behavioral measurement. This is followed by a detailed analysis of the technologies, data types, and statistical challenges specific to this new information ecosystem. The central parts of the book are dedicated to validity and reliability in a digital context, as well as advanced analysis models that combine psychometrics with machine learning and dynamic modeling.
A special focus is placed on the concrete applications of digital psychometrics: mental health, organizations, education and sports. These areas illustrate not only the potential of IoB technologies, but also the risks associated with their uncritical use. Therefore, the last parts of the volume address the ethical, legal and social dimensions of the collection and interpretation of psychological and biometric data, as well as the need for clear and responsible future standards.
This book is addressed to psychologists, psychometrists, cognitive scientists, data specialists, engineers, but also to policymakers interested in the impact of technology on human behavior. It can be read both as a conceptual manual and as a guide for research and application.
In essence, Digital Psychometrics is an invitation to reflection and responsibility. As the mind and body become increasingly “measurable”, the following question arises: how can we use this data to understand and support the human being, without reducing it to a simple signal generator?
The answer is not exclusively technological, but profoundly scientific, ethical, and human. This book aims to help formulate it.
The volume Digital Psychometrics. Estimating Behavior in the Era of Ubiquitous Technology starts from a fundamental observation: classic psychological measurement models are no longer sufficient to describe the complexity of human behavior in a technology-saturated environment. Questionnaires, point-administered tests, and retrospective assessments are today confronted with massive flows of continuous, passive, and multimodal data (Big Data), generated through the Internet of Bodies (IoB).
This book is the coherent theoretical, methodological, and applied framework for understanding and developing digital psychometrics. It is not just an extension of traditional psychometrics, but a reconceptualization of psychological measurement, adapted to a reality in which behavior, physiology, and context are inseparable. Digital psychometrics no longer measures only stable traits but psychological dynamics, not only scores but trajectories, not only differences between individuals but also intraindividual variations in real time.
The structure of the volume reflects this conceptual transition.
The first part substantiates the transition from classical to digital psychometrics, by using key notions such as continuous data, digital phenotyping, and the role of the Internet of Bodies in redefining behavioral measurement. This is followed by a detailed analysis of the technologies, data types, and statistical challenges specific to this new information ecosystem. The central parts of the book are dedicated to validity and reliability in a digital context, as well as advanced analysis models that combine psychometrics with machine learning and dynamic modeling.
A special focus is placed on the concrete applications of digital psychometrics: mental health, organizations, education and sports. These areas illustrate not only the potential of IoB technologies, but also the risks associated with their uncritical use. Therefore, the last parts of the volume address the ethical, legal and social dimensions of the collection and interpretation of psychological and biometric data, as well as the need for clear and responsible future standards.
This book is addressed to psychologists, psychometrists, cognitive scientists, data specialists, engineers, but also to policymakers interested in the impact of technology on human behavior. It can be read both as a conceptual manual and as a guide for research and application.
In essence, Digital Psychometrics is an invitation to reflection and responsibility. As the mind and body become increasingly “measurable”, the following question arises: how can we use this data to understand and support the human being, without reducing it to a simple signal generator?
The answer is not exclusively technological, but profoundly scientific, ethical, and human. This book aims to help formulate it.
The Author
PREFACE /11
PART I
FUNDAMENTALS OF PSYCHOMETRY IN THE DIGITAL ERA/13
CHAPTER 1
THE IOB REVOLUTION AND THE TRANSFORMATION OF PSYCHOMETRY/14
1.1. From classical psychometrics to digital psychometrics/14
1.2. Internet of Bodies: definitions, levels, technological generations/15
1.3. Why a new science of psychological measurement is needed/18
1.4. Discrete vs. continuous data in the study of behavior/19
1.5. Objectives and structure of the book/22
CHAPTER 2
FUNDAMENTALS OF TRADITIONAL PSYCHOMETRY/26
2.1. Key concepts: reliability, validity, measurement errors/26
2.2. Classical models: CTT, EFA, CFA/28
2.3. Modern Models: IRT, Rasch, Generalizability Theory/31
2.4. The Limits of Classical Psychometrics in the Age of Biosensors/33
2.5. Conceptual Transfer to Digital Media/37
CHAPTER 3
DIGITAL PSYCHOMETRY: DEFINITIONS, PRINCIPLES AND NEW PARADIGM/40
3.1. The concept of “digital phenotyping”/40
3.2. Active data vs. passive data/43
3.3. Multimodal data: behavior + physiology + context/46
3.4. From testing to continuous monitoring/48
3.5. From testing to continuous monitoring /51
PART II
TECHNOLOGIES AND DATA IN THE INTERNET OF BODIES/55
CHAPTER 4
TYPOLOGIES OF SENSORS AND IOB DEVICES/56
4.1. Wearables/56
4.2. Implantables/59
4.3. Ingestible devices/62
4.4. Mobile neurotechnologies (EEG, fNIRS etc.)/66
4.5. Hybrid Devices and the Future of IoB/70
CHAPTER 5
TYPES OF PSYCHOLOGICAL DATA OBTAINED THROUGH IOB/75
5.1. Psychophysiological Data: EEG, GSR, HRV, PPG/75
5.2. Behavioral Data: Movement, Voice, Facial Expressions, Keystrokes/78
5.3. Contextual Data: Location, Digital Social Interactions/81
5.4. Implicitly Generated Psychometric Data/84
5.5. Characteristics of Passive and Continuous Data/87
CHAPTER 6
STATISTICAL AND METHODOLOGICAL CHALLENGES IN DATA ANALYSIS IOB/91
6.1. Continuous vs. discontinuous sampling/91
6.2. Signal preprocessing (filters, artifacts, synchronization)/92
6.3. Multimodal data fusion/94
6.4. From latent variables to dynamic variables/95
6.5. Big data problems: volume, speed, veracity/97
PART III
VALIDITY AND RELIABILITY OF DIGITAL MEASUREMENT/100
CHAPTER 7
VALIDITY OF MEASUREMENT IN DIGITAL PSYCHOMETRY/101
7.1. Redefining the concept of “validity”/101
7.2. Ecological validity in natural contexts/102
7.3. Algorithmic validity and prediction models/104
7.4. Contextual validity (environmental and situational dependence)/106
7.5. Validation studies in IoB/107
7.6. Ethical validity and implications for users/109
CHAPTER 8
RELIABILITY OF BIOSENSOR DATA/112
8.1. Signal stability/112
8.2. Calibration and drift issues/113
8.3. Longitudinal reliability/114
8.4. Multi-device reliability/116
8.5. Measurement standardization/117
CHAPTER 9
ADVANCED ANALYSIS MODELS IN DIGITAL PSYCHOMETRY/120
9.1. Dynamic Latent Variable Models/120
9.2. Real-Time Behavior Modeling/121
9.3. Network Psychology Modeling/122
9.4. Bayesian Analysis for Continuous Data/124
9.5. Hybrid Models: Psychometrics + Machine Learning/125
PART IV
MACHINE LEARNING AND BIOSENSOR INTERPRETATION/128
CHAPTER 10
MACHINE LEARNING IN DIGITAL PSYCHOMETRY/129
10.1. Explainable ML vs. Opaque ML/129
10.2. Predictive Models for Behavior/130
10.3. Physiological Pattern Detection/131
10.4. Supervised Learning vs. unsupervised/133
10.5. Sequential models (RNN, LSTM, Transformers)/134
CHAPTER 11
INTERPRETATION OF PSYCHOPHYSIOLOGICAL SIGNALS WITH ML/ 136
11.1. EEG/GSR/PPG signal preprocessing/136
11.2. Feature extraction and feature engineering/137
11.3. Models for stress, emotions, attention/139
11.4. Classification, regression, clustering/141
11.5. Interpretability: SHAP, LIME, occlusion analysis/143
CHAPTER 12
SIMULS, DIGITAL TWINS AND PERSONALIZED MODELS/145
12.1. What are digital twins/145
12.2. Personalized psychometric models/146
12.3. Simulating individual behavior/147
12.4. Real-time adaptive predictions/149
12.5. Digital twins in mental health/151
PART V
APPLICATIONS IN PSYCHOLOGY, HEALTH, ORGANIZATIONS AND SPORTS/153
CHAPTER 13
APPLICATIONS IN MENTAL HEALTH/154
13.1. Stress and anxiety detection/154
13.2. Digital depression monitoring/155
13.3. Sensor-assisted psychotherapy/157
13.4. Personalized interventions/158
13.5. Early predictors of psychological crisis/160
CHAPTER 14
OBI IN THE ORGANIZATIONAL ENVIRONMENT/162
14.1. Performance monitoring/162
14.2. Identifying burnout/163
14.3. Data-driven HR decisions/164
14.4. Ethics of employee supervision/166
14.5. Predictive models for productivity/167
14.6. Conclusions /168
CHAPTER 15
APPLICATIONS IN EDUCATION AND AUGMENTED LEARNING/170
15.1. Attention and Concentration in the Classroom/170
15.2. Individualized Feedback/171
15.3. Adaptive Educational Platforms/172
15.4. Real-Time Competency Assessment/173
15.5. Psychological Risks in Sensor-Based Education/175
15.6. Conclusions/176
CHAPTER 16
SPORT AND HUMAN PERFORMANCE/178
16.1. Effort and Recovery Analysis/178
16.2. Cognitive Performance Optimization/179
16.3. Physiological Feedback Training/180
16.4. Injury Risk Assessment/181
16.5. Predictive Models in Performance Sports/183
16.6. Conclusions /184
PART VI
ETHICS, REGULATIONS AND FUTURE STANDARDS/186
CHAPTER 17
ETHICS OF BIOMETRIC AND PSYCHOLOGICAL DATA COLLECTION /187
17.1. Bodily Autonomy and Digital Integrity/187
17.2. Sensitive Data and Psychological Risks/188
17.3. Surveillance, Manipulation, Digital Nudging/189
17.4. Ethical Issues in the Use of Biosensors in Children/190
17.5. Social Acceptability of IoB/191
17.6. Conclusions /193
CHAPTER 18
REGULATIONS AND PUBLIC POLICIES/194
18.1. GDPR, HIPAA, AI Act and Emerging Legislation/194
18.2. Biometric Data Classification/195
18.3. IoB User Rights/196
18.4. International Standards for IoB/197
18.5. The Future of Digital Psychometrics Regulation/199
18.6. Conclusions/200
CHAPTER 19
FUTURE STANDARDS IN PSYCHOLOGICAL MEASUREMENT/202
19.1. Testing vs. Continuous Monitoring/202
19.2. Standardization of ML Algorithms in Psychology/203
19.3. Universal Data Structures (Open Psychometrics)/204
19.4. Sensor Interoperability Protocols/205
19.5. The Evolution of the Digital Psychometrician Profession/207
19.6. Conclusions /208
PART VII
SYNTHESIS, FUTURE DIRECTIONS AND CONCLUSIONS/209
CHAPTER 20
DIGITAL PSYCHOMETRICS IN THE FUTURE SOCIETY/210
20.1. Convergence between IoB, AI and Neuroscience/210
20.2. Digitization of Mind and Body/211
20.3. Integrated Models of Psychological Functioning/212
20.4. Social Benefits and Risks/213
20.5. General Conclusions/214
20.6. Conclusions /215
EPILOGUE /217
BIBLIOGRAPHY /219
PART I
FUNDAMENTALS OF PSYCHOMETRY IN THE DIGITAL ERA/13
CHAPTER 1
THE IOB REVOLUTION AND THE TRANSFORMATION OF PSYCHOMETRY/14
1.1. From classical psychometrics to digital psychometrics/14
1.2. Internet of Bodies: definitions, levels, technological generations/15
1.3. Why a new science of psychological measurement is needed/18
1.4. Discrete vs. continuous data in the study of behavior/19
1.5. Objectives and structure of the book/22
CHAPTER 2
FUNDAMENTALS OF TRADITIONAL PSYCHOMETRY/26
2.1. Key concepts: reliability, validity, measurement errors/26
2.2. Classical models: CTT, EFA, CFA/28
2.3. Modern Models: IRT, Rasch, Generalizability Theory/31
2.4. The Limits of Classical Psychometrics in the Age of Biosensors/33
2.5. Conceptual Transfer to Digital Media/37
CHAPTER 3
DIGITAL PSYCHOMETRY: DEFINITIONS, PRINCIPLES AND NEW PARADIGM/40
3.1. The concept of “digital phenotyping”/40
3.2. Active data vs. passive data/43
3.3. Multimodal data: behavior + physiology + context/46
3.4. From testing to continuous monitoring/48
3.5. From testing to continuous monitoring /51
PART II
TECHNOLOGIES AND DATA IN THE INTERNET OF BODIES/55
CHAPTER 4
TYPOLOGIES OF SENSORS AND IOB DEVICES/56
4.1. Wearables/56
4.2. Implantables/59
4.3. Ingestible devices/62
4.4. Mobile neurotechnologies (EEG, fNIRS etc.)/66
4.5. Hybrid Devices and the Future of IoB/70
CHAPTER 5
TYPES OF PSYCHOLOGICAL DATA OBTAINED THROUGH IOB/75
5.1. Psychophysiological Data: EEG, GSR, HRV, PPG/75
5.2. Behavioral Data: Movement, Voice, Facial Expressions, Keystrokes/78
5.3. Contextual Data: Location, Digital Social Interactions/81
5.4. Implicitly Generated Psychometric Data/84
5.5. Characteristics of Passive and Continuous Data/87
CHAPTER 6
STATISTICAL AND METHODOLOGICAL CHALLENGES IN DATA ANALYSIS IOB/91
6.1. Continuous vs. discontinuous sampling/91
6.2. Signal preprocessing (filters, artifacts, synchronization)/92
6.3. Multimodal data fusion/94
6.4. From latent variables to dynamic variables/95
6.5. Big data problems: volume, speed, veracity/97
PART III
VALIDITY AND RELIABILITY OF DIGITAL MEASUREMENT/100
CHAPTER 7
VALIDITY OF MEASUREMENT IN DIGITAL PSYCHOMETRY/101
7.1. Redefining the concept of “validity”/101
7.2. Ecological validity in natural contexts/102
7.3. Algorithmic validity and prediction models/104
7.4. Contextual validity (environmental and situational dependence)/106
7.5. Validation studies in IoB/107
7.6. Ethical validity and implications for users/109
CHAPTER 8
RELIABILITY OF BIOSENSOR DATA/112
8.1. Signal stability/112
8.2. Calibration and drift issues/113
8.3. Longitudinal reliability/114
8.4. Multi-device reliability/116
8.5. Measurement standardization/117
CHAPTER 9
ADVANCED ANALYSIS MODELS IN DIGITAL PSYCHOMETRY/120
9.1. Dynamic Latent Variable Models/120
9.2. Real-Time Behavior Modeling/121
9.3. Network Psychology Modeling/122
9.4. Bayesian Analysis for Continuous Data/124
9.5. Hybrid Models: Psychometrics + Machine Learning/125
PART IV
MACHINE LEARNING AND BIOSENSOR INTERPRETATION/128
CHAPTER 10
MACHINE LEARNING IN DIGITAL PSYCHOMETRY/129
10.1. Explainable ML vs. Opaque ML/129
10.2. Predictive Models for Behavior/130
10.3. Physiological Pattern Detection/131
10.4. Supervised Learning vs. unsupervised/133
10.5. Sequential models (RNN, LSTM, Transformers)/134
CHAPTER 11
INTERPRETATION OF PSYCHOPHYSIOLOGICAL SIGNALS WITH ML/ 136
11.1. EEG/GSR/PPG signal preprocessing/136
11.2. Feature extraction and feature engineering/137
11.3. Models for stress, emotions, attention/139
11.4. Classification, regression, clustering/141
11.5. Interpretability: SHAP, LIME, occlusion analysis/143
CHAPTER 12
SIMULS, DIGITAL TWINS AND PERSONALIZED MODELS/145
12.1. What are digital twins/145
12.2. Personalized psychometric models/146
12.3. Simulating individual behavior/147
12.4. Real-time adaptive predictions/149
12.5. Digital twins in mental health/151
PART V
APPLICATIONS IN PSYCHOLOGY, HEALTH, ORGANIZATIONS AND SPORTS/153
CHAPTER 13
APPLICATIONS IN MENTAL HEALTH/154
13.1. Stress and anxiety detection/154
13.2. Digital depression monitoring/155
13.3. Sensor-assisted psychotherapy/157
13.4. Personalized interventions/158
13.5. Early predictors of psychological crisis/160
CHAPTER 14
OBI IN THE ORGANIZATIONAL ENVIRONMENT/162
14.1. Performance monitoring/162
14.2. Identifying burnout/163
14.3. Data-driven HR decisions/164
14.4. Ethics of employee supervision/166
14.5. Predictive models for productivity/167
14.6. Conclusions /168
CHAPTER 15
APPLICATIONS IN EDUCATION AND AUGMENTED LEARNING/170
15.1. Attention and Concentration in the Classroom/170
15.2. Individualized Feedback/171
15.3. Adaptive Educational Platforms/172
15.4. Real-Time Competency Assessment/173
15.5. Psychological Risks in Sensor-Based Education/175
15.6. Conclusions/176
CHAPTER 16
SPORT AND HUMAN PERFORMANCE/178
16.1. Effort and Recovery Analysis/178
16.2. Cognitive Performance Optimization/179
16.3. Physiological Feedback Training/180
16.4. Injury Risk Assessment/181
16.5. Predictive Models in Performance Sports/183
16.6. Conclusions /184
PART VI
ETHICS, REGULATIONS AND FUTURE STANDARDS/186
CHAPTER 17
ETHICS OF BIOMETRIC AND PSYCHOLOGICAL DATA COLLECTION /187
17.1. Bodily Autonomy and Digital Integrity/187
17.2. Sensitive Data and Psychological Risks/188
17.3. Surveillance, Manipulation, Digital Nudging/189
17.4. Ethical Issues in the Use of Biosensors in Children/190
17.5. Social Acceptability of IoB/191
17.6. Conclusions /193
CHAPTER 18
REGULATIONS AND PUBLIC POLICIES/194
18.1. GDPR, HIPAA, AI Act and Emerging Legislation/194
18.2. Biometric Data Classification/195
18.3. IoB User Rights/196
18.4. International Standards for IoB/197
18.5. The Future of Digital Psychometrics Regulation/199
18.6. Conclusions/200
CHAPTER 19
FUTURE STANDARDS IN PSYCHOLOGICAL MEASUREMENT/202
19.1. Testing vs. Continuous Monitoring/202
19.2. Standardization of ML Algorithms in Psychology/203
19.3. Universal Data Structures (Open Psychometrics)/204
19.4. Sensor Interoperability Protocols/205
19.5. The Evolution of the Digital Psychometrician Profession/207
19.6. Conclusions /208
PART VII
SYNTHESIS, FUTURE DIRECTIONS AND CONCLUSIONS/209
CHAPTER 20
DIGITAL PSYCHOMETRICS IN THE FUTURE SOCIETY/210
20.1. Convergence between IoB, AI and Neuroscience/210
20.2. Digitization of Mind and Body/211
20.3. Integrated Models of Psychological Functioning/212
20.4. Social Benefits and Risks/213
20.5. General Conclusions/214
20.6. Conclusions /215
EPILOGUE /217
BIBLIOGRAPHY /219
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