Ch 8. Quantitative Data Analysis

Question 1

The biggest mistake in quantitative research is to think that data analysis decisions can…

Answer 1

…wait until after the data have been collected.

Question 2

How do we handle it when a respondent does not complete an answer? Is it missing because…(3)

Answer 2

1. they accidentally skip it?
2. they do not want to answer it?
3. it does not apply to them?

Question 3

Three types of variables (levels of measurement)

Answer 3

1. Nominal
2. Ordinal
3. Interval/Ratio

Question 4

Nominal Variable

Answer 4

The only difference that exists between participants is being in one category or another. Categories cannot be ordered by rank. Examples: Male/Female, Employed/Unemployed, Homeless/Housed

Question 5

Ordinal Variable

Answer 5

The categories of the variable can be rank ordered (ex. high enthusiasm, moderate enthusiasm, low enthusiasm). Distance between categories may not be equal.

Question 6

Interval/Ratio

Answer 6

Distance or amount of difference between categories is uniform (e.g., 0 siblings, 1 sibling, 2 siblings, etc.) Ratio variables have a real “0” start position. Can do arithmetic and mathematical operations with the categories (e.g., 1 sibling + 3 siblings = 4 siblings)

Question 7

Univariate Analysis

Answer 7

Analysis of one variable at a time. Often the first step is to create frequency tables.

Question 8

score that shows up the most in a particular category

Answer 8

Mode

Question 9

middle score when all scores have been arrayed in order

Answer 9

Median

Question 10

sum of all scores, divided by the number of scores

Answer 10

Mean

Question 11

Highest score minus lowest score

Answer 11

Range

Question 12

Measures the amount of variation around the mean

Answer 12

Standard Deviation

Question 13

Determines whether there is a relationship between two variables

Answer 13

Bivariate Analysis

Question 14

• Allow simultaneous analysis of two variables
• Identify patterns of association
• Can be used for any variable type
• Normally used for nominal or ordinal data

Answer 14

Contingency tables (cross-tabulations)

Question 15

Has values of 0 (no relationship) +1 (perfect positive relationship) -1 (perfect negative relationship)

Answer 15

Pearson’s r

Question 15

To be able to label a finding as “significant” means you need to:

Answer 15

• set up a null hypothesis
• establish an acceptable level of significance (p-value)
• if the null is correct there is no relationship
• if the null is rejected and the statistical significance (p) of the findings is at less than 0.05 level.

Question 16

show the number of times a particular variable shows up in the distribution, expressed as an actual number and as a percentage of the whole

Answer 16

Frequency Table

Question 17

Diagrams can be used to illustrate frequency distributions. What can be used for displaying nominal, ordinal, or interval/ratio variables?

Answer 17

bar charts/pie charts: nominal or ordinal variables
histograms: interval/ratio variable

Question 18

nominal-nominal, nominal-ordinal, and nominal-interval/ratio bivariate analysis uses:

Answer 18

contingency table + chi-squre (x2) + cramer’s V

Question 19

ordinal-ordinal and ordinal-interval/ratio bivariate analysis can be measured by:

Answer 19

kendall’s tau-b

Question 20

interval/ratio - interval/ratio bivariate analysis can be measured by

Answer 20

Pearson’s R

Question 21

Shows correlation between pairs of ordinal variables (ex., 2 Likert Scale responses) or with one ordinal and one interval/ratio variable (ex., frequency of activity/BP)
Like Pearson’s r, values range from 0 to +1

Answer 21

Kendall’s tau-b

Question 22

Shows correlation between pairs of ordinal variables
Like Pearson’s r, values range from 0 to +1
Will predict a rank position from one variable to another

Answer 22

Spearman’s Rho

Question 23

Shows the strength of the relationship between two nominal variables (ex., gender/homelessness)
Values range from 0 to 1
(Nominal categories cannot be rank ordered)
Usually reported with a contingency table and a chi-square test

Answer 23

Cramer’s V

Question 24

tests the significance of the bivariate association

Answer 24

null hypothesis

Question 25

Two types of errors in statistical significance

Answer 25

1. rejecting a true null hypothesis (the results are a chance association)
2. not rejecting a false hypothesis

Question 26

Correlation and statistical significance must be weighed together. The smaller the sample…

Answer 26

…the higher the correlation required to achieve statistical significance

Question 27

Examines the relationship between three or more variables

Answer 27

elaboration

Question 28

exists if two variables are correlated but only through a third variable

Answer 28

spuriousness

Question 29

An interaction exists if the effect of one independent variable varies at different levels to that of a…

Answer 29

…second independent variable.(ex. if coffee consumption and excercise BOTH contributed to better sleep)

Question 30

can determine how much of the variation in the dependent variable is explained/predicted by the independent variables

Answer 30

multiple linear regression