Quantitative Research Approach

Quantitative research approaches increase our knowledge by gathering data that can be manipulated mathematically. This allows us to answer questions about the meanings of psychological concepts, as well as to determine their levels and variability as well as the relationships among them. Quantitative research approaches [Page 653]may be contrasted with qualitative approaches, which tend to collect data expressed in nonmathematical, symbolic representations sometimes referred to as thick descriptions, and place less focus on estimating the strength and form of relationships.

The data associated with quantitative approaches can result from simple measurement operations such as counts or categorizations, or from more complex operations that may involve the creation of measurement scales that function as psychological yardsticks. For example, quantitative research approaches have allowed industrial/organizational (I/O) psychologists to develop self-report measures of a construct called job satisfaction (JS), to determine that JS has a variety of different aspects or facets (such as satisfaction with pay, supervisor, or work setting), and to study its relationships with conditions such as organizational culture or leadership that make its general level higher or lower.

A basic tenet of any science is that scientists must collect and analyze data in a manner that can be replicated by others and is open to public inspection and criticism. Really, I/O psychologists are no different; they rely heavily on a wide range of quantitative methods to pursue two broad endeavors. The first of these is to accurately measure psychological variables of interest, such as performance, personality, intellectual capacity, work attitudes, and many more aspects of the world of work. The second endeavor consists of the systematic and theory-driven search for relationships among variables. Typically, the search for relationships involves testing theory-based hypotheses, the results of which allow for scientific inferences about the presence or absence of the relationships of interest. Next, we briefly describe quantitative approaches to measurement, the rationale for significance testing, and quantitative techniques for assessing relationships.