Exam 2 - Handout 5 Flashcards

1
Q

What is QUALITATIVE data? Example?

A

Meaningful information collected in words

Not typically used for healthcare research w/ large populations

Ex. Written observation in medical records

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2
Q

What is QUANTITATIVE data? Examples?

A

Numerical/countable information

Ex. Age, weight, BP

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3
Q

What are discrete variables? Examples?

A
  • Categorical
  • Have a few possible values
  • Often defined as “counts”

Ex. Sex, number of hospitalizations, yes/no

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4
Q

What are continuous variables?

A

Exist on a defined scale

Ex. Age, body temp, weight

(Think number lines)

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5
Q

What are the levels of measurement?

A

Nominal data
Ordinal data
Interval data
Ratio data

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5
Q

nominal data

A

Discrete categories with no particular order (e.g., sex)

(NOminal = NO order)

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5
Q

ordinal data

A

Discrete categories that can be ranked

e.g., Likert-type questions, pain scales

(likert-type questions are ones where you answer with “agree” “strongly disagree”)

(ORDinal = in ORDer)

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6
Q

interval data

A

continuous data with:
- a defined scale
- constant intervals

DOES NOT HAVE A TRUE ZERO POINT (this is what makes it different from ratio data)

ex. temperature (because even if the temp is 0, there is still a temp)

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7
Q

ratio data

A

continuous data with:
- defined scale
- constant intervals
- true zero point

ex. age, weight, income

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8
Q

Independent variable

A

The variable hypothesized to explain an observed clinical phenomenon

Think of it as the cause

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8
Q

Dependent variable

A

Variable that is predicted/explained by the independent variable

Think of it as the effect

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9
Q

Control variables

A

Other explanatory variables included to hold external conditions constant and isolate the effect of the independent variable

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10
Q

Measures of central tendency

A

Mean
Median
Mode

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11
Q

Mean

A

Arithmetic average of a set of values

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12
Q

Median

A

The middle value when data is arranged in order

Preferred when data has outliers that skew the mean

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13
Q

Mode

A

The value that appears most often

Useful for non-numerical, categorical values

14
Q

Measures of dispersion

A

Range
Interquartile range
Variance of standard deviation
Skewness

15
Q

Interquartile range

A

The difference between the 75th and 25th percentiles

Represents the middle 50% of the data

15
Q

Range

A

The difference between the highest and lowest values

16
Q

Variance

A

Represented by σ²

The average squared distance of values from the mean

(SD/mean)

17
Q

Standard deviation

A

Represented by σ

The square root of the variance

17
Q

Skewness

A

Indicates if data are evenly distributed around the mean

17
Q

Coefficient of variation (CV)

A

Standardized measure of dispersion

σ/μ

18
Q

Positive skew

A

More data concentrated to the LEFT of the mean

19
Negative skew
More data concentrated to the right of the mean
20
Box plots
Visually display the range, IQR, and median of a variable Useful for comparing distributions across groups
20
Frequency tables
Organize discrete or continuous data by counting the frequency of each value Should include clear titles, column names, and formatting for easy interpretation
21
Bar charts
Discrete, categorical data
22
Pie charts
Represent proportions or relative quantities of values Should be limited to a small # of clearly labeled categories
23
Histograms
Continuous data divided into discrete categories
23
Proportions
The number of observations with a given characteristic divided by the total number of observations Often reported as percentages (proportion x 100%)
24
Rates
Computed over a specific time period and use a multiplier Ex. Per 1000 Examples include mortality and incidence rate
25
Sensitivity
The ability of a test to correctly identify individuals w/ the disease Proportion of true positives out of all individuals w/ the disease
26
Negative predictive value
Probability an individual does NOT have the disease given a negative test result
26
Specificity
The ability of a test to correctly ID individuals WITHOUT the disease Proportion of true negatives out of all individuals without the disease
27
Positive predictive value
Probability an individual HAS the diseases given a positive test
28
Receiver operating characteristic (ROC) curves
Illustrate the tradeoff between sensitivity and false positive rate at different decision thresholds The area under the ROC curve indicates the overall discriminating power of the test