research design in psychology

1. An operational definition helps the researcher maintain both internal and external validity. The researcher clearly defines terms, and shows how each variable is measured. In this way, concepts that are otherwise abstract like intelligence can be operationalized. Intelligence needs to be operationalized because the researcher needs to show what specific type of intelligence is being measured, and how.

An operational definition is “the particular method that we use to measure a variable of interest,” beyond the theoretical definitions that can be applied later (“Conducting Research in Social Psychology,” n.d., p. 1). Whereas the theoretical definitions of intelligence can be broad, like “the capacity to acquire and apply knowledge,” operational definitions are narrower in scope because they need to be easily measured (Legg & Hutter, 2006, p. 2).

For example, if mathematical intelligence is being measured, then an established mathematical skills test needs to be administered to research participants. Social skills intelligence needs to be measured using an established self-report survey or observational parameter. One of the most common operational definitions of intelligence has been performance on an Intelligence Quotient (IQ) test. The upside of operational definitions is that they are specific. Operational definitions are also standardized and can be used by more than one researcher, enhancing the external validity of intelligence research.

The downside to operational definitions is that they are limited; they are necessarily generalizable to theory. For instance, performance on an IQ test cannot be used to predict global intelligence, social skills, or reading intelligence. References “Conducting Research in Social Psychology,” (n.d.). http://open.lib.umn.edu/socialpsychology/chapter/1-3-conducting-research-in-social-psychology/ Legg, S. & Hutter, M. (2006). A collection of definitions of intelligence. http://www.vetta.org/documents/A-Collection-of-Definitions-of-Intelligence.pdf 2. External validity and generalizability are distinct concepts even if they are related.

Generalizability refers to the ability of the researcher to extrapolate theory or practice from the results of the study. Externally valid studies yield results that can be applicable to populations beyond those studied in the sample. Both external validity and generalizability are important for applied science, to meet “demands for knowledge utilization in an evidence-based practice environment,” (Ferguson, 2004, p. 16). External validity is important for research, and enabling replication or enhancements of the existing body of evidence. Generalizability is important more for practice.

Steckler & McLeroy (2008) also differentiate between different types of external validity including statistical conclusion validity, construct validity, and generalizability. Statistical conclusion validity refers to whether or not the researchers drew accurate conclusions from their statistical analyses, and whether their statistical analyses were appropriately constructed. Construct validity refers to “whether operational variables adequately represent theoretical constructs,” (Steckler & McLeroy, 2008, p. 9).

This is often what researchers mean when they consider external validity in general: does the current study coincide with or apply to existing theory, or does the study inform theory development? If so, the study can be considered externally valid. Generalizability is more applicable to whether the intervention or other independent variable can be applied to a totally different setting or population group and yield the same results. Distinguishing.