PHLT 301 GRIFFITH EXAM QUESTIONS
WITH 100% CORRECT ANSWERS!!
How do we know something is a public health issue?
number of deaths, majority of people are impacted, population issue, affects the community
inductive reasoning
1. observation
2. pattern
3. hypothesis
4. theory
deductive reasoning
1. Theory
2. Hypothesis
3. observation
4. confirmation
2 major categories of data
qualitative and quantitative
qualitative data
descriptive, observable characteristics
quantitative
numerical, measures or counts
examples of quantitative data
gender, race/ethnicity, education level
examples of qualitative data
age, income, date of birth
, types of numeric data
discrete and continuous
discrete data
specific value, count, or tally, whole numbers
examples of discrete data
number of students, count of attendees
continuous data
value within a range, does not have to be a whole number
examples of continuous data
grades, height, weight
ordinal data
categories that DO have an implicit rank or order
examples of ordinal data
educational level, level of agreement
nominal data
categories that do NOT have an implicit rank/order
example of nominal data
eye color, city name
accuracy
how close measurements are to the true value
precision
how close measurements are to each other
reliability is:
WITH 100% CORRECT ANSWERS!!
How do we know something is a public health issue?
number of deaths, majority of people are impacted, population issue, affects the community
inductive reasoning
1. observation
2. pattern
3. hypothesis
4. theory
deductive reasoning
1. Theory
2. Hypothesis
3. observation
4. confirmation
2 major categories of data
qualitative and quantitative
qualitative data
descriptive, observable characteristics
quantitative
numerical, measures or counts
examples of quantitative data
gender, race/ethnicity, education level
examples of qualitative data
age, income, date of birth
, types of numeric data
discrete and continuous
discrete data
specific value, count, or tally, whole numbers
examples of discrete data
number of students, count of attendees
continuous data
value within a range, does not have to be a whole number
examples of continuous data
grades, height, weight
ordinal data
categories that DO have an implicit rank or order
examples of ordinal data
educational level, level of agreement
nominal data
categories that do NOT have an implicit rank/order
example of nominal data
eye color, city name
accuracy
how close measurements are to the true value
precision
how close measurements are to each other
reliability is:
