Chapter 2

Question 1

construct

Answer 1

A construct is a concept. It may or may not be directly observable

Question 2

indicators

Answer 2

observable measures representing the actual construct

Question 3

operatizing

Answer 3

The process of selecting an indicator(s) of a construct. Indicators should mimic a construct’s real values as closely as possible

Question 4

operational definition

Answer 4

How an indicator is defined for measurement

Question 5

multi-dimensional

Answer 5

Concepts that have several distinct subdimensions or subconstructs.

Question 6

variables

Answer 6

characteristics or measures that can be several possible values
Constructs and indicators in statistical work are variables.
Variables have definitions or descriptions

Question 7

range

Answer 7

A variable’s set of possible values

Question 8

single value variable

Answer 8

variables that have only one value
Most research involves single-valued variables
example: someone has one weight, one body temperature, and one sex

Question 9

multi-valued variable

Answer 9

variables that can have multiple values
example: Undergraduate major is multi-valued as most colleges allow multiple majors; Race

Question 10

Continuous variables

Answer 10

any value in their range
example: temperature can be 70, 72.4, 31.2 Celsius and so forth depending on how precisely it is measured

Question 11

Discrete variables

Answer 11

only be specific values in a variable’s range
example: A student’s year in secondary school can range between 1st to 12th grade but can only be an integer (a number without a decimal)

Question 12

indicator variables (aka dichotomous or dummy variables)

Answer 12

Variables that can only be one of two possible values

Question 13

Quantitative variables

Answer 13

values that can be ranked and/or their differences calculated
example: grade level and temperature

Question 14

Qualitative variables

Answer 14

values that can only be categorized. Values can be classified but there is no rank ordering or mathematical difference between the categories
examples: Race, gender, and state of residence

Question 15

latent variable

Answer 15

abstract constructs which are not directly observable or measurable

Question 16

level of measurement

Answer 16

There are four levels of measurement. In rank order from lowest amount of information to the highest they are:

1. nominal
2. ordinal
3. interval
4. ratio

Question 17

Nominal variables

Answer 17

can only be classified. They are discrete and qualitative

Question 18

ordinal variable

Answer 18

value can classified, but can also be rank ordered. the exact differences between values cannot be determined

Question 19

interval variables

Answer 19

can be classified, ranked, the difference between values can be calculated, and the differences between values are consistent but it does not have a true, absolute zero

Question 20

Ratio variables

Answer 20

interval variables (can be classified, ranked, the difference between values can be calculated) but have a true, absolute, non-arbitrary zero

Question 21

composite measure

Answer 21

indicators combined into a single measure for the construct

Question 22

Indexes

Answer 22

Composite measure that presume indicators are separate components that together form the concept. As the indicators add up to make the concept, index indicators are often simply summed to form the composite measure in an index.

Example: Dow Jones Industrial Average

Question 23

formative constructs

Answer 23

the indicators are seen as causing the construct (typical for indexes)

Question 24

Scales

Answer 24

Composite measure that presume indicators are reflections of a concept. Indicators do not combine to form a construct but rather the construct’s value is tapped by their values

Question 25

reflective constructs

Answer 25

the construct is seen as causing the construct and the indicators reflect its variation. Indicators in scales must covary

Question 26

Descriptive statistics

Answer 26

provide summary information organizing and describing the data whether a sample or population

Question 27

Inferential statistics

Answer 27

use sample data to infer the value of an attribute or relationship in a full population. testing whether some measure, feature, or relationship found in a sample can be generalized to the full population

Question 28

Univariate

Answer 28

Single variable statistic (descriptive and inferential)

Question 29

bivariate

Answer 29

two variable statistic (descriptive and inferential) Most common technique is a contingency table

Question 30

multivariate

Answer 30

3 or more variable statistics (descriptive and inferential)

Question 31

contingency table

Answer 31

a table providing a crosstabulation of the frequencies for two variables simultaneously

Question 32

independent variables

Answer 32

variables that are seen as affecting or causing others (inferential statistics)

Question 33

dependent variables

Answer 33

Variables that are being affected

Question 34

reciprocal relationships

Answer 34

Relationships where two variables affect each other