How is research designed measured and operationalized

Subsequent investigation found that the engineers at the sub-contractor, Lockheed, had used imperial units instead of metric units of force. A failure in operationalization meant that the units used during the construction and simulations were not standardized.

The US engineers used pound force, the other engineers and software designers, correctly, used metric Newtons. This led to a huge error in the thrust calculations, and the spacecraft ended up in a lower orbit around Mars, burning up from atmospheric friction.

This failure in operationalization cost hundreds of millions of dollars, and years of planning and construction were wasted. Martyn Shuttleworth Jan 17, Retrieved Nov 14, from Explorable. The text in this article is licensed under the Creative Commons-License Attribution 4. That is it. You can use it freely with some kind of link , and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations with clear attribution.

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Get PDF. Menu Search. Menu Search Login Sign Up. You must have JavaScript enabled to use this form. Sign up Forgot password. Leave this field blank :. Search over articles on psychology, science, and experiments. Search form Search :. Reasoning Philosophy Ethics History. The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socio-economic status.

In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender male versus female. Such technique allows for greater generalizability but also requires substantially larger samples.

In statistical control , extraneous variables are measured and used as covariates during the statistical testing process. Finally, the randomization technique is aimed at canceling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random non-systematic nature. Two types of randomization are: 1 random selection , where a sample is selected randomly from a population, and 2 random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomization also assures external validity, allowing inferences drawn from the sample to be generalized to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalizability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for few of those dimensions. As noted earlier, research designs can be classified into two categories — positivist and interpretive — depending how their goal in scientific research.

Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalized patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research while examples of interpretive designs include case research, phenomenology, and ethnography.

Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research.

Following are brief descriptions of some of these designs. Additional details are provided in Chapters For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups treatment and control groups , administer the drug to subjects in the treatment group, but only give a placebo e.

More complex designs may include multiple treatment groups, such as low versus high dosage of the drug, multiple treatments, such as combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned between each group. If random assignment is not followed, then the design becomes quasi-experimental. Experiments can be conducted in an artificial or laboratory setting such as at a university laboratory experiments or in field settings such as in an organization where the phenomenon of interest is actually occurring field experiments.

Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments.

Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity.

Experimental data is analyzed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalizability since real life is often more complex i.

Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations. This entry focuses on operationalization and how this procedure relates to concepts and conceptualizations in the social scientific process. It also provides examples of various approaches commonly used to move from concepts to operationalizations.

Throughout the entry, potential issues and challenges likely to be encountered by researchers are discussed. To more clearly articulate the role of operationalization, this entry begins with an introduction to concepts and conceptualizations prior to discussing operationalization. Context-specific operationalizations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly. For example, corruption can be operationalized in a wide range of ways e.

Operationalization means turning abstract conceptual ideas into measurable observations. In scientific research, concepts are the abstract ideas or phenomena that are being studied e.

Variables are properties or characteristics of the concept e. The process of turning abstract concepts into measurable variables and indicators is called operationalization.

Reliability and validity are both about how well a method measures something:. If you are doing experimental research, you also have to consider the internal and external validity of your experiment. Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! APA Citation Generator. Home Knowledge Base Methodology A guide to operationalization. A guide to operationalization Published on June 25, by Pritha Bhandari. For example: self-rating scores on a social anxiety scale number of recent behavioral incidents of avoidance of crowded places intensity of physical anxiety symptoms in social situations Table of contents Why operationalization matters How to operationalize concepts Strengths of operationalization Limitations of operationalization Frequently asked questions about operationalization.

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