13 - Quantitative Analysis

Question 1

types of variables

Answer 1

nominal, ordinal, interval/ration

Question 2

nominal variables

Answer 2

• aka categorical, composed of categories with no relationship except that they are different
• order of categories is arbitrary

Question 3

ordinal variables

Answer 3

• categories that can be ranked
• can be described as
• likert scale is common
• difference between categories is not necessarily equal
• no unit to measure

Question 4

interval/ration variables

Answer 4

• can be measured by unit
• difference between categories is equal
• can have 0 value
• can be ranked

Question 5

frequency tables

Answer 5

provides number and percent of subjects belonging to each category of variable

Question 6

measures of central tendency

Answer 6

mean, median, mode; provides typical score in one number

Question 7

mode

Answer 7

value that occurs most frequently, applicable to all types of variables, especially nominal data

Question 8

median

Answer 8

mid point of scores, if there is an even number of scores the median is the mean of the middle 2 values. applicable to interval/ration and ordinal variables

Question 9

mean

Answer 9

average, vulnerable to outliers

Question 10

range

Answer 10

difference between the highest and lowest value, vulnerable to outliers

Question 11

standard deviation

Answer 11

variation around the mean, vulnerable to outliers

work out the general mean, subtract the mean from every value, square every value, then find the mean of those values

Question 12

bivariate analysis

Answer 12

examines relationship between 2 variables, esp through use of contingency tables

Question 13

pearson’s r

Answer 13

statistic used to examine relationship between 2 interval/ratio variables
the relationship must be broadly linear

Question 14

statistical significance

Answer 14

indication of risk of genralizing sample statistic to population
set up null hypothesis, establish acceptable level of statistical significance, determine statistical significance, decide whether or not to reject the null

Question 15

two types of error

Answer 15

type I - true null was rejected

type II - false null wasn’t rejected

Question 16

chi-sqaure test

Answer 16

applied to contingency tables

• measure of likelihood that relationship between variables in sample will also be found in population
• large chi-square to reject null hypothesis, larger n makes this easier

Question 17

spurious relationship

Answer 17

when relationship appears to exist but isn’t real

Question 18

intervening variable

Answer 18

suggests relationship between 2 variables is not direct

Question 19

3 Questions to ask during bivariate anlaysis

Answer 19

• does the association exist?
• how strong is the association?
• in what direction does the association exist?

Question 20

calculating association with bivariate table

Answer 20

“percentage down, compare across”

• an association exists if column percentages change
• the greater the change, the stronger the relationship
• to measure maximum difference, find biggest difference in column percentage for any row of the table

Question 21

p

Answer 21

probability that results are not due to chance is 95%

Question 22

null hypothesis

Answer 22

there is no relationship between 2 variables