ArticleIndian journal of psychological medicine2021
A Student's Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 2.
Article in Indian journal of psychological medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Reply to Comments on "The digital health dilemma: Exploring cyberchondria, well-being, and smartphone addiction in medical and non-medical undergraduates".Indian journal of psychiatry · 2024Article
- Primary outcomes, secondary outcomes, and their relevance to how many papers are published from a study: A primer for authors, reviewers, and editors.Indian journal of psychiatry · 2024Article
- Depression, coping, and attempted suicide: Suggestions on issues related to data analysis.Indian journal of psychiatry · 2024Article
- The Young Researcher's Guide to Starting a Cohort Study.Indian journal of psychological medicine · 2022Article
- Comments on "Caregiver Burden and Disability in Somatoform Disorder: An ExploratoryStudy".Indian journal of psychological medicine · 2022Article
- A Student's Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 2.Indian journal of psychological medicine · 2021Article
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Abstract
Students without prior research experience may not know how to conceptualize and design a study. This is the second of a two-part article that explains how an understanding of the classification and operationalization of variables is the key to the process. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. They may be operationalized as categorical or continuous variables. Categorical variables are expressed as category frequencies in the sample as a whole, while continuous variables are expressed as absolute numbers for each subject in the sample. Continuous variables should not be converted into categorical variables; there are many reasons for this, the most important being that precision and statistical power are lost. However, in certain circumstances, such as when variables cannot be accurately measured, when there is an administrative or public health need, or when the data are not normally distributed, it may be justifiable to do so. Confounding variables are those that increase (or decrease) the apparent effect of an independent variable on the dependent variable, thereby producing spurious (or suppressing true) relationships. These and other concepts are explained with the help of clinically relevant examples.
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