Biostats Modules 1 & 2

Question 1

Categorical Data

Answer 1

• aka count, discrete or attribute data
• counted and not measured on a scale
• whole numbers
• two types: nominal level and ordinal level

Question 2

Continuous Data

Answer 2

• aka variable or measurement data
• different values on a continuous scale
• as many deciimal places as a measurement can read
• two types: interval and ratio

Question 3

Nominal level data

Answer 3

• qualitative data (not numerical)
• variable that can be counted
• i.e. gender, race, tumor type, occupation, smokers v nonsmokers

Question 4

Rules of Nominal level data

Answer 4

• order does not matter
• i.e. it doesnt make sense to say children >adults or M>F
• can only have a set number of discrete possible values
• numbers do not mean anything

Question 5

Ordinal level data

Answer 5

• objects represent the rank order (1st, 2nd, 3rd, etc)
• cannot measure the distance between the two
• order matters
• example: mild, moderate, severe pain or pressure ulcer staging

Question 6

Interval level data

Answer 6

• all of the features of ordinal measurements except its all numerical
• equal differences between measurements
• no natural zero
• examples: year date in calenders and temperature in C and F
• order matters

Question 7

Ratio level data

Answer 7

• highest level of measurement
• order matters
• differences are measureable
• example: height, weight, and length
• addition, subtraction, multiplication and division friendly

Question 8

Ratio level data rules

Answer 8

• distance between the intervals on the scales are numerically equal
• the variables have an absolute zero

Question 9

statistical analysis: interval/ratio level

Answer 9

• mean
• standard deviation
• range

Question 10

statistical analysis: nominal level

Answer 10

• mode
• frequency
• percentage

Question 11

statistical analysis: ordinal level

Answer 11

• median
• range
• often frequencies and percentage

Question 12

Power

Answer 12

capacity of the study to detect differences or relationships that actually exist in the population

Question 13

power analysis

Answer 13

determining the sample size needed to obtain sufficient power for a study

Question 14

4 elements of power analysis

Answer 14

• significance level or alpha
• effect size
• power
• sample size

Question 15

Normal Distrubution

Answer 15

• statistical term that does not imply that the results are “normal”
• refers to a particular shape–a bell-shaped curve

Question 16

Normal distribution assumptions

Answer 16

• sample mean equals population mean
• sample SD equals the population SD
• infinite # of value

Question 17

Variability

Answer 17

attempt to describe or quantify the spread or range of data

Question 18

Range

Answer 18

simplest measure of variability, considers difference between largest and smallest values (subtract smallest from largest)

Question 19

Central Tendency

Answer 19

• middle of a distribution
• indicate locality or centrality of data
• mean, median, mode

Question 20

Mode

Answer 20

• most often

- only acceptable measure of central tendency for analyzing nominal data (non numeric)

Question 21

Median

Answer 21

• middle number when lined up from greatest to least

- most precise central tendency for ordinal data and non normal distributed/skewed interval or ratio data

Question 22

Mean

Answer 22

• the average
• sum of values in a sample divided by total # of values
• most accurate central tendency for interval/ratio data

Question 23

Variance

Answer 23

• amount of dispersion or spread that exists among the values of a data set w respect to the mean
• large variance = more disparate scores

Question 24

Standard Deviation

Answer 24

• most common measure used to establish how data are distributed
• measure of dispersion/spread of data around the mean
• can show by how much the data deviates from the mean
• Square root of variance
• flat/spread out curve= large standard deviation
• steep rise and fall curve=small standard deviation (all scores similar)

Question 25

Standard Deviation Curve

Answer 25

• in a normal curve: 68% will be within one SD above or below the mean
• 34% above the mean and 34% below

Question 26

Skewness

Answer 26

• measure of symmetry of distributions

- (mean-median)/SD

Question 27

Skewness (+/-)

Answer 27

-positive: mode

Question 28

Kurtosis

Answer 28

• measure of the shape of the curve

- normal, flat, peaked

Question 29

Leptokurtic

Answer 29

• peaks sharply with fat tails

- less variability K>0

Question 30

Mesokurtic

Answer 30

• normal distribution

- K=0

Question 31

Platykurtic

Answer 31

• flattened
• highly dispersed
• K<0

Question 32

Nonparametric methods

Answer 32

• used to summarize categorical data (nominal and ordinal)

- used in place of commonly used parametric methods for continuous level data

Question 33

Advantages of Non parametrics

Answer 33

• not susceptible to outliers (like parametrics)
• ranked
• easier to calculate
• not as powerful

Question 34

Chi-Square

Answer 34

• Chi-Square (X2) is used to examine differences among groups with variables measured at the nominal level
• X2 compares frequencies observed with frequencies expected
• easier to calculate/understand
• parametric statistic

Question 35

Fisher’s exact test

Answer 35

• used to examine differences among groups with variable measure at the nominal level
• more accurate and more useful with small samples