Ch11 Quantitative analysis

Question 1

Codebook

Answer 1

a document that describes the procedure for coding variables and their location in a format for computers

Question 2

4 ways to get quantitative raw data into computer

Answer 2

1. code sheet - paper with printed grid - record info so it can be easily entered
2. Direct entry - a method of entering data into a computer by typing data without code or optical scan sheets
3. optical scan - gather the information then enter it into optical scan sheets by filling the correct dots
4. bar code - gather the information, then convert it into different widths of bars that are associated with specific

Question 3

possible code cleaning

Answer 3

cleaning data using a computer in which the researcher looks for responses or answer categories that cannot have cases. also called wild code checking

Question 4

contingency cleaning

Answer 4

cleaning data using a computer in which the researcher looks at the combination of categories for two variables for logically impossible cases

Question 5

descriptive statistics

Answer 5

describe numerical data

Question 6

univariate statistics

Answer 6

one variable - easiest way to describe numnerical data of one variable is with a frequency distribution = used with any type of data

Question 7

bimodal

Answer 7

a distribution with two modes

Question 8

multimodal

Answer 8

distribution with more than one mode

Question 9

skewed distribution

Answer 9

distribution of cases among the categories of a variable that is not normal

Question 10

z scores

Answer 10

compare two or more distributions or group - standardized score

number of standard deviations it is above or below the mean

Question 11

bivariate statistics

Answer 11

only involve two variables

Question 12

correlation

Answer 12

means that things go together are associated

Question 13

independence

Answer 13

opposite of correlation - no association between two variables

Question 14

scattergram/ scatterplot

Answer 14

A diagram to display the statistical relationship between two variables based on plotting each case’s values for both of the variables

Question 15

Precision

Answer 15

Amount of spread in points on the graph

Question 16

Cross-tabulation

Answer 16

placing data for two variables in a contingency table to show the number or percentage of cases at the intersection of categories of the two variables

Question 17

contingency table

Answer 17

a table that shows the cross-tabulation of two or more variables.

it ususally shows bivariate quantitative data for variables in the formof percentages across rows or down columns for the categories of one variable.

Question 18

Three ways to percentage a table

Answer 18

by row

by column

the total - total columns or marginals

Question 19

measure of association

Answer 19

a single number that expresses the strength, and often the direction of a relationship.

it condenses information about a bivariate relationship into a single number.

Question 20

5 measures of association - gamma

Answer 20

used for ordinal level data

based on comparing pairs of variable categories and seeing whether a case has the same rank on each -1 to 1 and 0 is no association

Question 21

5 measures of association -lambda

Answer 21

nomial level data

it is based on a reduction in errors based on the mode and ranges between 0 - nothing and 1 - strongest possible relationship

Question 22

5 measures of association -tau

Answer 22

ordinal level data

Takes care of problems that occur with gamma

several statistics named tau and one is kendalls tau -1 to 1 0 = nothing

Question 23

5 measures of association -rho

Answer 23

Pearson’s product moment correlation coefficient

when they use the term correlation

can only be used for interval and ratio

Used for the mean and SD

Question 24

5 measures of association -chi squared

Answer 24

two different uses

Can be used as a measure of association in descriptive statistics

or can be used in inferential statistics

Question 25

Assumptions of linerarity

Answer 25

Pearsons - two variables assumed linear

many relationships that are not linear

Question 26

control variables

Answer 26

a third variable that shows whether a bivariate relationship holds up to alternative explanations

it can occur before or between other variables

Question 27

trivariate tables

Answer 27

meet conditions for causality - control for - get rid of an alternative explanation for a causal relationship

trivariate tables - consist of multiple bivariate tables - has a bivariate table of the independent and dependent variable for each category of the control variables - new tables called partials - tables that show the association between the independent and dependent variables for each category of a control variable

Question 28

trivariate tables have three limitations

Answer 28

1. difficult to interpret if a control variable has numerous categories
2. control variables can be at any level of measurement, but interval or ratio control variables must be grouped and how cases are grouped can affect the interpretation of effects
3. total number of cases is a limiting factor because the cases are divided among cells in partials

Question 29

Linear Regression

Answer 29

interval/ ratio level data

controls for many alternative explanations and variables simultaneously

widely used in social science

Tells reader 2 things:
1. the results have a measure called R^2 - which tells how well a set of variables explains a dependent variable - independent variable accounts for a large percentage of variation in a dependent variable

2. the regression results measure the direction and size of the effect of each variable on a dependent variable

Question 30

Univariate

Answer 30

frequency distribution

measure of central tendency

standard deviation

z-score

purpose is to describe one variable

Question 31

bivariate

Answer 31

correlation
percentage table
chi-square

Purpose - describe a relationship or the association between two variables

Question 32

multivariate

Answer 32

see how several independent variables have an effect on a dependent variable 7

Question 33

statistical significance

Answer 33

results not likely due to chance

a way to discuss the likelihood of finding a statistical relationship in a sample is due to random factors rather than due to the existence of an actual relationship in the entire population

Question 34

Type 1 error

Answer 34

relationship exists when in fact none exists

Question 35

type 2 error

Answer 35

when a researcher says a relationship does not exist but in reality it does - falsely accepting null hypothesis