Biostatistic

Question 1

Nominal

Answer 1

data that is arranged in categories and cannot be arranged in a particular order
Ex: gender, eye color

Question 2

Ordinal

Answer 2

data arranged in some order, but differences between data cannot be determined or are meaningless
Ex: Stages of PU ( Stage 1- IV)

Question 3

Interval

Answer 3

data can be arranged in some order and meaningful amount of differences can be determined. There is no true zero.
Ex: Temperature on Fahrenheit

Question 4

Ratio

Answer 4

data can be arranged in some order and meaningful amount of differences can be determined with a inherent zero starting point.
Ex. Age, monthly income of physical therapists

Question 5

Symmetric distribution

Answer 5

A distribution where left-hand side is a mirror image of right hand side

Question 6

positively skewed

Answer 6

A distribution where the scores pile up on the left side and taper off to the right.

Question 7

negatively skewed

Answer 7

A distribution where the scores pile up on the right side and taper off to the left.

Question 8

Variability

Answer 8

provides a quantitative measure of the degree to which scores in a distribution are spread out or clustered together

Question 9

Range

Answer 9

The distance/difference between the largest score and the smallest score

Question 10

Interquartile range

Answer 10

Interquartile range (IQR) is the distance between Q1 and Q3.
Semi-Interquartile range is one half of the IQR.

Question 11

Variance

Answer 11

is the square of the average distance from the mean.

Question 12

The SD

Answer 12

approximates the average distance from the mean.

SD = √ variance

Question 13

Parametric

Answer 13

Used for ratio or interval data

Data must be normally distributed

Data is linearly related

Question 14

Nonparametric

Answer 14

Used for nominal or ordinal data

Does not rely on normal distribution – used for skewed interval and ratio data

Interval or ratio data – small sample size (n)

Question 15

Tests relationship

Answer 15

Coefficient correlation is 0-1

1 = perfect association

0 = no association

Question 16

Parametric Statistics- Test relationship

Answer 16

Dependent variable = outcome (y)
Independent variable = predictors/ factors (x1, x2.. xn)

Pearson product Moment correlation (Pearson’s r)
Intraclass Coefficient (ICC)
Multiple correlation
Linear Regression
Multiple Linear Regression

***Correlation describes a relationship between two variables. It does not tell “causality”

Question 17

Pearson’s r

Answer 17

Reliability of two variables

Association between two variables

Ex: relationship between height (in meters) and weight (in pounds) in adults

Question 18

ICC

Answer 18

Association between three or more variables

Reliability of repeated measures

Ex: Interrater reliability for strength testing using dynamometer

Question 19

Multiple correlation

Answer 19

Association between three or more variables

Question 20

Linear Regression

Answer 20

Studies about prognostic factors

Mathematically a straight line can be described by an equation:
y = b0+ b1x + e1

b0 and b1 have constant values.
b0 (y-intercept of the line): the value of y when x = 0
b1 (slope): the amount of change in y for each 1-unit increase in x
e1 is standard error estimate
Can an outcome “y” be predicted based on factor (s)?

Question 21

Linear Regression

Answer 21

Can outcome “y” be predicted by one factor “x”? Ex: Can systolic BP be predicted by age

Question 22

Multiple Linear Regression

Answer 22

Can outcome “y” be predicted by two or more factor “x1, x2..etc.”? Ex: Can systolic BP be predicted by age, weight, LDL levels,…?

Question 23

Independent

t-test

Answer 23

Between 2 groups

Compare SBP post exercise in 2 groups of elderly: 65-74 & 75-84 yrs

Question 24

Paired t test

Answer 24

within 2 group test

Compare SBP at baseline and 6 wk post exercise in elderly 65-74 yrs

Question 25

Analysis of Variance (ANOVA)

Answer 25

Between 2 or more group test

Compare SBP post intervention in 3 groups of elderly: 65-74, 75-84 & >85