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The type of variable "age in years" is best classified as: A) Nominal B) Ordinal C) Interval D) Ratio
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Question 70: The type of variable "age in years" is best classified as:
Age in years has a true zero point (0 years means no age) and equal intervals between values. This makes it a ratio variable.
- D) Ratio
Question 71: In a study of maternal mortality and distance to health facility, the confounding variable could be:
A confounding variable is one that is associated with both the independent variable (distance to health facility) and the dependent variable (maternal mortality), and can distort the observed relationship. Health insurance coverage can influence both a person's access to health facilities (potentially mitigating the effect of distance) and the quality of care received, thus affecting maternal mortality.
- A) Health insurance coverage
Question 72: Which scale of measurement lacks a true zero?
An interval scale has ordered data with equal intervals between values, but it does not have a true zero point (e.g., temperature in Celsius or Fahrenheit, where 0 does not mean the absence of temperature).
- C) Interval
Question 73: The presence or absence of a policy intervention is typically what kind of variable?
A policy intervention is something that is introduced or manipulated by researchers or policymakers to observe its effect on an outcome. Therefore, it is an independent variable.
- C) Independent
Question 74: Which of the following is a categorical variable?
Categorical variables represent types or categories. Marital status (e.g., single, married, divorced) falls into distinct categories.
- B) Marital status
Question 75: A well-operationalized variable:
Operationalization is the process of defining a concept in terms of how it will be measured. A well-operationalized variable is therefore clearly defined and measurable.
- C) Is clearly defined and measurable
Question 76: Which of the following variables is best controlled in analysis using stratification?
Stratification is a method used in statistical analysis to control for confounding variables by dividing the data into subgroups (strata) based on the levels of the confounder and then analyzing the association within each stratum.
- C) Confounder
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