The Scientific Method at Work

Research objectives and assumptions

Research in the field of work is defined as a systematic inquiry whose primary purpose is the discovery of new knowledge to validate theories and predictions about human behavior.

In organizational psychology, research is the central ingredient of the scientific method, providing the key to understanding whether hypotheses about a company's performance are accurate or require adjustment.

For research to be considered high quality under scientific rigor, it must comply with non-negotiable principles: objectivity, to prevent personal biases from altering the data; control of variables, to ensure that results are not due to chance; and precision in definitions and measurements.

The final objective is to generate replicable findings, allowing other researchers to repeat the study in different contexts to verify whether the same results hold.

Systematic data collection and analysis

The scientific process in organizations begins with the systematic collection of information to answer questions of interest.

Researchers must first decide what type of data they need: qualitative, which focuses on patterns and meanings through words, or quantitative, which uses numbers to perform statistical analyses and test relationships between variables.

Once collected, the data must be analyzed objectively to transform the raw information into conclusions understandable to non-psychological experts.

The scientific method acts as a filter that minimizes errors in corporate decision making, ensuring that proposed solutions to everyday problems, such as employee turnover or low productivity, have verifiable empirical support and are not based on mere assumptions.

Summary

Labor research is a systematic inquiry that seeks to discover knowledge to validate theories about human behavior. It is based on objectivity, control of variables, and precision.

The process collects qualitative or quantitative data to answer questions of organizational interest. This method acts as a filter that minimizes errors in strategic corporate decision making.

By transforming raw data into understandable conclusions, it ensures that solutions are empirically supported. The ultimate goal is to generate replicable findings that lend credibility to interventions.