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Flashcards in 13 - Quantitative Analysis Deck (22):
1

types of variables

nominal, ordinal, interval/ration

2

nominal variables

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

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ordinal variables

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

4

interval/ration variables

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

5

frequency tables

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

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measures of central tendency

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

7

mode

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

8

median

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

9

mean

average, vulnerable to outliers

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range

difference between the highest and lowest value, vulnerable to outliers

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standard deviation

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

12

bivariate analysis

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

13

pearson's r

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

14

statistical significance

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

15

two types of error

type I - true null was rejected
type II - false null wasn't rejected

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chi-sqaure test

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

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spurious relationship

when relationship appears to exist but isn't real

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intervening variable

suggests relationship between 2 variables is not direct

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3 Questions to ask during bivariate anlaysis

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

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calculating association with bivariate table

"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

21

p

probability that results are not due to chance is 95%

22

null hypothesis

there is no relationship between 2 variables