PSICOMETRÍA FÁCIL | 1. TEORÍA CLÁSICA DE LOS TEST😎 (MODELO, SUPUESTOS, CRÍTICAS) CON EJEMPLOS FÁCIL

Introduction to Psychometric Testing

Overview of the Course

• Javier introduces a new chapter on classical test theory, emphasizing a DIY approach to psychometrics.

• The course aims to teach step-by-step creation and validation of psychometric tests, targeting those with basic statistical knowledge.

• Recommendations for supplementary videos will be provided throughout the course to enhance learning.

Importance of Psychometrics

• The course will cover models in psychometry, merging psychology with statistics, which may seem daunting but is rooted in scientific data.

• Psychometrics measures psychological attributes and assigns numerical values, allowing for operationalization and generalization across populations.

Understanding Psychological Attributes

Definition and Examples

• Psychological attributes include skills, personality traits, and knowledge; examples like language development in children illustrate their importance.

• These attributes are assessed by comparing individuals or samples against expected population scores based on shared characteristics.

Historical Context of Psychometric Testing

Origins of Measurement Systems

• The practice of measuring abilities dates back to ancient China during the Tang dynasty (618–907 AD), where it was used for selecting officials.

• Academic testing emerged in Europe during the 13th century, evolving into more objective assessments as paper became widely available.

Key Figures in Test Development

• Notable figures like Galton, Cattell, and Binet focused on cognitive ability measurement; Binet created early tests for identifying cognitive disabilities in children.

• The Stanford-Binet test remains influential today; key practices from its development include item review and clear administration guidelines.

Cultural Influences on Testing

Impact of Cultural Context

• Psychometric tests were also utilized for military recruitment during wars; this led to awareness about cultural biases affecting test outcomes.

• An example illustrates how cultural differences can affect responses—understanding local context is crucial when designing tests.

Methodologies in Test Construction

Evolution of Testing Methodologies

• Historical challenges have led to advanced methods such as factor analysis and validity assessment that are still relevant today.

Understanding Measurement in Attention Tests

Factors Influencing Test Results

• The response of participants in attention tests is influenced by various factors, including the time of day, participant motivation, and even their physical state (e.g., hangover).

• The relationship between the evaluator and the evaluated can also impact test scores, highlighting the complexity of measuring attention accurately.

Classical Test Theory Model

• Gran's model proposes that an individual's empirical score on a test consists of two components: true score and error. This can be expressed as: Empirical Score = True Score + Error.

• For example, if a person's IQ is 120 points, this value includes both their true score and an unquantifiable error.

Assumptions of the Model

• The first assumption states that if a test were administered infinitely to the same person, the average of all empirical scores would equal their true score.

• The second assumption posits no correlation between individuals' true scores and measurement errors; high true scores can coexist with small errors or vice versa.

• When using two tests measuring the same construct, there should also be no correlation between measurement errors across different occasions.

Limitations and Critiques

• These assumptions are not empirically verifiable but help understand that any evaluation test will never yield a pure score due to inherent measurement errors.

• A significant critique is that measurements may vary based on the instrument used; for instance, comparing IQ scores from different tests may not be valid due to differing scales.

Item Response Theory

• Another critique highlights that classical theory lacks invariance regarding test properties across different populations; psychometric properties like item difficulty depend on sample characteristics.

• Improvements have been made through Item Response Theory (IRT), which addresses these critiques by providing better analysis methods for understanding test responses.

Conclusion and Further Learning

• Understanding these critiques leads to more robust testing methodologies. Future discussions will delve into IRT in greater detail.