John Rust is the founder of The Psychometrics Centre at the University of Cambridge, UK. He is a Senior Member of Darwin College, UK, and an Associate Fellow of the Leverhulme Centre for the Future of Intelligence, University of Cambridge, UK.

Michal Kosinski is an associate professor of organizational behavior at Stanford Graduate School of Business, USA.

David Stillwell is the academic director of the Psychometrics Centre at the University of Cambridge, UK. He is also a reader in computational social science at the Cambridge Judge Business School, UK.

1. The history and evolution of psychometric testing

Introduction

What is psychometrics?

Psychometrics in the 21st century

History of assessment

Chinese origins

The ability to learn

The nineteenth century

Beginnings of psychometrics as a science

Intelligence testing

Eugenics and the dark decades

Psychometric testing of ability

The dark ages come to an end

An abundance of abilities

Tests of other psychological constructs

Personality

Integrity

Interests

Motivation

Values

Temperament

Attitude

Belief

Summary

2. Constructing your own psychometric questionnaire

The purpose of the questionnaire

Making a blueprint

Writing items

Alternate-choice items

Multiple-choice items

Rating-scale items

All questionnaires

Knowledge-based questionnaires

Person-based questionnaires

Designing the questionnaire

Piloting the questionnaire

Item analysis

Facility

Discrimination

Distractors

Obtaining the reliability

Cronbach's alpha

Split-half reliability

Assessing validity

Face validity

Content validity

Standardization

3. The Psychometric principles

Reliability

Test-retest reliability

Parallel-forms reliability

Split-half reliability

Interrater reliability

Internal consistency

The standard error of measurement (SEM)

Comparing test reliabilities

Restriction of range

Validity

Face validity

Content validity

Predictive validity

Concurrent validity

Construct validity

Differential validity

Standardization

Norm referencing

Criterion referencing

Equivalence

Differential item functioning

Measurement invariance

Adverse impact

Summary

4. Psychometric measurement

True-score theory

Identification of latent traits with factor analysis

Spearman's two-factor theory

Vector algebra and factor rotation

Moving into more dimensions

Multidimensional scaling

Application of factor analysis to test construction

Eigenvalues

Identifying the number of factors to extract using the Kaiser criterion

Identifying the number of factors to extract using the Cattell scree test

Other techniques for identifying the number of factors to extract

Factor rotation

Rotation to simple structure

Orthogonal rotation

Oblique rotation

Limitations of the classical factor-analystic approach

Criticisms of psychometric measurement theory

The Platonic true score

Psychological vs. physical true scores

Functional assessment and competency testing

Machine learning and the black box

Summary

5. Item response theory and computer adaptive testing

Introduction

Item banks

The Rasch model

Assessment of educational standards

The Birnbaum model

The evolution of modern psychometrics

Computer adaptive testing

Item equating

Polytomous IRT

An intuitive graphical description of item tesponse theory

Limitations of classical test theory

A graphical Introduction to item response theory

The logistic curve

3PL-model: difficulty parameter

3PL model: discrimination parameter

3PL model: guessing parameter

The Fisher information function

The test information function and its relationship to the standard error of measurement

How to score an IRT test

Principles of computer adaptive testing

Summary of item response theory

Confirmatory factor analysis

6. Personality theory

Theories of personality

Psychoanalytic theory

Humanistic theory

Social learning theory

Behavioral genetics

Type and trait theories

Different approaches to personality assessment

Self-report techniques and personality profiles

Reports by others

Online digital footprints

Situational assessments

Projective measures

Observations of behavior

Task performance methods

Polygraph methods

Repertory grids

Sources and management of bias

Self-report techniques and personality profiles

Reports by others

Online digital footprints

Situational assessments

Projective measures

Observations of behavior

Task performance methods

Polygraph methods

Repertory grids

Informal methods of personality assessment

State versus trait measures

Ipsative scaling

Spurious validity and the Barnum Effect

Summary

7. Personality assessment in the workplace

Prediction of successful employment outcomes

Validation of personality questionnaires previously used in employment

Historical antecedents to the five-factor model

Stability of the five-factor model

Cross-cultural aspects of the five-factor model

Scale independence and the role of facets

Challenges to scale construction for the five-factor model

Impression management

Acquiescence

Response bias and factor structure

Development of the five OBPI personality scales

Assessing counterproductive behavior at work

The impact of behaviorism

Prepsychological theories of integrity

Modern integrity testing

Psychiatry and the medical model

The dysfunctional tendencies

The dark triad

Assessing integrity at work

The OBPI integrity scales

Conclusion

8. Employing digital footprints in psychometrics

Introduction

Types of digital footprint

Usage logs

Language data

Mobile sensors

Images and audiovisual data

Typical applications of digital footprints in psychometrics

Replacing and complimenting traditional measures

New contexts and new constructs

Predicting future behavior

Studying human behavior

Supporting the development of traditional measures

Advantages and challenges of employing digital footprints in psychometrics

High ecological validity

Greater detail and longitude

Less control over the assessment environment

Greater speed and unobtrusiveness

Less privacy and control

No anonymity

Bias

Enrichment of existing constructs

Developing digital-footprint-based psychometric measures
Collecting digital footprints

How much data is needed?

Preparing digital footprints for analysis

Respondent-footprint matrix

Data sparsity

Reducing the dimensionality of the respondent-footprint matrix

Singular value decomposition

Latent Dirichlet allocation

Building prediction models

9. Psychometrics in the era of the intelligent machine

History of computerization in psychometrics

Computerized statistics

Computerized item banks

Computerized item generation

Automated advice and report systems

The evolution of AI in psychometrics

Expert systems

Neural networks (machine learning)

Parallel processing

Predicting with statistics and machine learning

Explainability

Psychometrics in cyberspace

What and where is cyberspace?

The medium is the message

Moral development in AI

Kohlberg's theory of moral development

Do machines have morals?

The laws of robotics

Artificial general intelligence

Conclusion