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Assignment Question: Having in mind your study's main IV and DV, how would you operationalize those variables (review my post below)? The study’s research question is: Is obesity (IV) associated with
Assignment Question: Having in mind your study's main IV and DV, how would you operationalize those variables (review my post below)?
The study’s research question is: Is obesity (IV) associated with diabetes risk (DV) among Americans aged 35-45?
Dear student, let’s discuss Variables’ Operationalization and levels of measurement (Brunette, Nelson, & FOCUS Workgroup, n.d.; Gray, Williamson, Karp, & Dalphin, 2007; UTHealth, n.d.):
Types of variables: Operationalization and levels of measurement (Hammond, Malec, Nick, & Buschbacher, 2015):
Independent (IV – factor, exposure)
Dependent (DV - dependent on IV– outcome, disease)
Categorical (nonparametric)
Numerical (parametric)
A) Categorical variables: qualitative or discrete: nominal, dichotomous, and ordinal
1) Nominal (dichotomous (binary) or categorical) or 2) Ordinal – categories, levels, and rankings.
1. Nominal – variable is mutually exclusive and exhaustive; no intrinsic order between categories.
Dichotomous (binary): only two levels or categories: Examples: sex: 1. Male, 0. Female; 1. Pass, 0. Fail;
Categorical: More than two levels or categories
Examples: Marital status: 1. Single; 2. Married; 3. Divorced; 4. Widowed
Blood type: 1. A, 2. B. 3. AB, 4.O
2. Ordinal – naturally ranked; ordered differences: low, medium, and high. Likert scales.
Examples: pain level (1. mild, 2. moderate, 3. severe); educational level (1. high school, 2. undergraduate, 3. graduate).
B) Numerical variables: quantitative (discrete – countable number of values; or continuous – any value in a range of values): interval or ratio:
3. Interval – distance between intervals are constant with absolute value.
Examples: temperature F and C – has intervals of degrees; IQ scores; weight.
4. Ratio – highest degree in precision and accuracy including measurement on a scale with a meaningful zero; units of equal interval and magnitude in ranked order.
Example: BMI (ratio of weight /height); age, blood pressure; time.
References
Brunette, K., Nelson, A., & FOCUS Workgroup (n.d.). Data analysis basics: Variables and distribution
Focus on Field Epidemiology, 3(5), 1-6. Retrieved from
https://nciph.sph.unc.edu/focus/vol3/issue5/3-5DataBasics_issue.pdf
Hammond, F. M., Malec., J. F., Nick, T. G., & Buschbacher, R. M. (Eds.). (2015). Part II:
Statistics. Types of data. Handbook for clinical research: Design, statistics, and
implementation (p.79-83). New York, NY: Demons Medical Publishing, LLC.
Gray, P. S., Williamson, J. B., Karp, D. A., & Dalphin, J. R. (2007). Measurement. The research
imagination: An introduction to qualitative and quantitative methods (pp. 59-60). New York, NY:
Cambridge University Press.
UTHealth. (n.d.) Biostatistics for the clinician. Lesson 1.2.: Variables and measures.
Retrieved from https://www.uth.tmc.edu/uth_orgs/educ_dev/oser/L1_2.HTM
