File Name: conceptualization and operationalization in research .zip
Published on June 25, by Pritha Bhandari. Operationalization means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.
Table of contents Why operationalization matters How to operationalize concepts Strengths of operationalization Limitations of operationalization Frequently asked questions about operationalization. Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods.
Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioral avoidance of crowded places.
This means that your results are context-specific, and may not generalize to different real-life settings. Generally, abstract concepts can be operationalized in many different ways. If you test a hypothesis using multiple operationalizations of a concept, you can check whether your results depend on the type of measure that you use.
Based on your research interests and goals, define your topic and come up with an initial research question. Your main concepts may each have many variables , or properties, that you can measure.
Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure. You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.
You can evaluate how your choice of operationalization may have affected your results or interpretations in the discussion section. See an example.
Operationalization makes it possible to consistently measure variables across different contexts. Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics. A standardized approach for collecting data leaves little room for subjective or biased personal interpretations of observations. A good operationalization can be used consistently by other researchers.
If other people measure the same thing using your operational definition, they should all get the same results. For example, poverty is a worldwide phenomenon, but the exact income-level that determines poverty can differ significantly across countries.
Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers. For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.
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.
Receive feedback on language, structure and layout Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Grammar Style consistency See an example. What is operationalization? What is data collection? Is this article helpful? Pritha Bhandari Pritha has an academic background in English, psychology and cognitive neuroscience.
As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics. Other students also liked. An introduction to quantitative research Quantitative research means collecting and analyzing numerical data to describe characteristics, find correlations, or test hypotheses.
Reliability vs validity: what's the difference? Reliability is about a method's consistency, and validity is about its accuracy. You can assess both using various types of evidence. Understanding types of variables Variables can be defined by the type of data quantitative or categorical and by the part of the experiment independent or dependent.
What is your plagiarism score? Scribbr Plagiarism Checker. The difference between how well people think they did on a test and how well they actually did overestimation. The difference between where people rank themselves compared to others and where they actually rank overplacement. The number of uses for an object e. Average ratings of the originality of uses of an object that participants come up with in 3 minutes.
Physiological responses of higher sweat gland activity and increased heart rate when presented with threatening images. Customer ratings on a questionnaire assessing satisfaction and intention to purchase again.
Records of products purchased by repeat customers in a three-month period. Average number of hours of sleep per night. Most frequently used social media platform. Amount of time spent using social media before sleep.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Moscou Published Psychology, Medicine Nursing inquiry. Racial and ethnic variables are routinely used in health services research. Content analyses extracted manifest and latent meanings to construct categories depicting respondents' understandings of race and ethnicity in research. Race and ethnicity held several meanings but the subtext was often not clear because these terms were not operationalized.
KEYWORDS: Conceptualization, Operationalization, Research Process, Empirical Research,. Levels of measurement, Reliability and Validity.
Saturation has attained widespread acceptance as a methodological principle in qualitative research. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing.
Metrics details. Measurement of quality of life demands thoroughly developed and validated instruments. The development steps from theory to concepts and from empirical data to items are sparsely described in the literature of questionnaire development.
Published on June 25, by Pritha Bhandari. Operationalization means turning abstract concepts into measurable observations.