Chapter 14 Quantitative data analysis

Question 1

univariate analysis

Answer 1

methods for analysing a single variable at a time

Question 2

bivariate analysis

Answer 2

methods for analysing relationships between variables

Question 3

multivariate analysis

Answer 3

the analysis of relationships between three variables

Question 4

Interval /ratio variables

Answer 4

These are variables where the distances between the categories are identical across the range of categories. E.g. temperatures in celsius (interval), Kelvin (ratio). (A ratio variable, has all the properties of an interval variable, but also has a clear definition of 0.0.)

Question 5

Ordinal variables

Answer 5

These are variables whose categories can be rank ordered (as in the case of interval/ratio variables) but the distances between the categories are not equal across the range. E.g. how much do you sleep per night? 0-5h, 6-7h or 8+h

Question 6

Dichotomous variables

Answer 6

These variables contain data that have only two categories (for example, gender).

Question 7

frequency table

Answer 7

provides the number of people and the percentage belonging to each of the categories for the variable in question. (part of univariate analysis)

Question 8

Arithmetic mean

Answer 8

all the values in a distribution and then divide by the number of values (part of univariate analysis)

Question 9

Mode

Answer 9

This is the value that occurs most frequently in a distribution. (part of univariate analysis)

Question 10

Which are the 3 types of: Measures of central tendency

Answer 10

Arithmetic mean, Median and Mode. (part of univariate analysis)

Question 11

Pearson’s r

Answer 11

a method for examining relationships between interval/ratio variables. it results in a computed statistic that varies between 0 and + or −1. (part of bivariate analysis)

Question 12

Phi (φ) and Cramér’s V

Answer 12

two closely related statistics. The phi coefficient is used for the analysis of the relationship between two dichotomous variables. it results in a computed statistic that varies between 0 and + or −1. (part of bivariate analysis)

Question 13

Nominal variables

Answer 13

Variables that have two or more categories, but which do not have an intrinsic order. E.g., a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows.

Question 14

intervening variable (apparently the same as mediating variable)

Answer 14

An intervening variable allows us to answer questions about the bivariate relationship between variables.
e.g. income is an intervening variable that helps explain the relationship between level of education (independent variable) and spending (dependent variable).

Question 15

a moderating variable

Answer 15

a variable that can strengthen, diminish, negate, or otherwise alter the association between independent and dependent variables. Moderating variables can also change the direction of this relationship.

For example, let’s say you are conducting a study to examine the relationship between exercise and weight loss. You collect data on participants’ exercise habits and their weight loss over a 12-week period. However, you also want to consider the potential moderating effect of age.

In this case, age is the moderating variable. You might find that the relationship between exercise and weight loss is stronger for younger participants compared to older participants. This means that age is affecting the strength of the relationship between exercise and weight loss.

Question 16

null hypothesis

Answer 16

stipulates that two variables are not related in the population