Chapter 8 Principles of Measurement

Question 1

Construct validity

Answer 1

1. Extent to which an instrument or test measures an intended hypothetical concept or construct.
2. The most valuable - yet the most difficult (takes years) - way to assess an instrument’s validity.
3. Example: The Safe Sex Behavior Questionnaire was determined to have construct validity based on significant correlations with measures of risk-taking and self-expression.

Question 2

Content validity

Answer 2

1. Extent to which an instrument or test measures an intended content area. The most common type of validity.
2. Determined by a panel of experts. Example: Experts in nutrition were chosen to assess the items in the Barriers to Healthy Eating Scale.
3. Usually this type of validity is used in the development of a questionnaire, interview schedule, interview guide, or instrument.

Question 3

Continuous variable

Answer 3

1. Variable that takes on an infinite number of different values presented on a continuum.
2. Examples: Age in years; education in years.

Question 4

Criterion-related validity

Answer 4

1. Extent to which an instrument or test measures a particular concept compared with a criterion.
2. Measured by a validity coefficient; a higher coefficient indicates high criterion-related validity.
3. Example: A new instrument is compared with an older, more reputable instrument.
4. Two types: Concurrent and predictive.

Question 5

Cronbach’s alpha (coefficient alpha)

Answer 5

1. Widely used index of the extent to which a measuring instrument is internally stable.
2. A measurement of the extent to which all the items in an instrument measure the same concept.
3. An acceptable level of reliability for any measurement instrument is an alpha coefficient of 0.70.
4. Can be used with instruments composed of items that can be scored with three or more possible values, such as a Likert-type scale.

Question 6

Dichotomous variable

Answer 6

A nominal variable that consists of two categories.

Question 7

Instrument

Answer 7

1. A device, piece of equipment, or paper-and-pencil test that measures a concept or variable of interest.
2. Examples from nursing research: Questionnaires, surveys, and rating scales.

Question 8

Interval level of measurement

Answer 8

1. Level of measurement characterized by a constant unit of measurement or equal distances between points on a scale.
2. Possesses all characteristics of a nominal and ordinal scale in addition to having an equal interval size based on an actual unit of measurement, but lacks a true zero point.
3. May be referred to as “interval data” or “interval variables.”

Question 9

Measurement

Answer 9

1. The assignment of numerical values to concepts, according to well-defined rules.
2. The numerical values reflect properties of the concepts under study.

Question 10

Nominal level of measurement

Answer 10

1. The lowest level of measurement, which consists of assigning numbers as labels for categories. These numbers have no numerical interpretation (i.e., they are not stating that one category has “more” and one category has “less”).
2. Data simply show the frequency of subjects or objects in each category.
3. May be referred to as “nominal scale,” “nominal data,” “nominal measurement,” or “categorical data.”

Question 11

Operational definition

Answer 11

1. A definition that assigns meaning to a variable and the terms or procedures by which the variable is to be measured.
2. Especially important for quantitative research. Usually found in the methods section.
3. Concepts such as “spiritual well-being” must be translated to measurable definitions that are valid reflections of the concepts.

Question 12

Ordinal level of measurement

Answer 12

1. Level of measurement that yields rank-ordered data (i.e., highest to lowest, most to least).
2. Specifies the order of items being measured, without specifying how far apart they are.
3. May be referred to as “ordinal scale,” “ordinal data,” “ordinal variables,” or “ordinal measurement.”

Question 13

Psychometric evaluation

Answer 13

Evaluating properties of reliability and validity in relation to instruments being used to measure a particular concept or construct.

Question 14

Predictive validity

Answer 14

1. Ability to predict future events, behaviors, or outcomes.
2. Example: A university admissions committee uses applicants’ GRE scores to decide whether to admit them to a graduate program, based on the idea that a high GRE score is predictive of success in their program.

Question 15

Ratio level of measurement

Answer 15

1. The highest level of measurement, characterized by equal distances between scores having an absolute zero point.
2. The zero point indicates absolutely none of the property.

Question 16

Reliability

Answer 16

1. Value that refers to the consistency with which an instrument or test measures a particular concept. Different ways of assessing reliability include test-retest, internal consistency, and interrater.
2. An instrument with high reliability should yield essentially the same scores each time the test is administered.
3. Asks, “Is it consistently generating the same measurements?”.

Question 17

Test-retest reliability

Answer 17

1. An approach to reliability examining the extent to which scores are consistent over time (their stability).
2. A researcher measures a group of individuals twice with the same measuring instrument or test, with the two testings separated by a particular period of time.

Question 18

Validity

Answer 18

1. Value that refers to the accuracy with which an instrument or test measures what it is supposed to measure. Different types of validity include content, criterion, and construct.
2. Asks, “Are the measurements meaningful?”.

Question 19

Relationship between reliability and validity

Answer 19

1. Both are essential to the meaning and accuracy of the results produced by an instrument.
2. They are the two most important issues to consider when examining the worth of any instrument used to measure variables in a study.
3. Reliability refers to whether an instrument generates consistent measurements; validity refers to whether an instrument measures what it is supposed to measure.
4. An instrument’s data must be reliable if they are to be valid; an instrument can be reliable without being valid.
5. They are context/population-specific - an instrument designed for premenopausal women is not reliable and valid for homeless youth, unless tested and proven.

Question 20

In order to make sense out of data collected, each variable must be _

Answer 20

Defined operationally using a variety of measurement techniques.

Question 21

The selection of a measurement technique depends upon _

Answer 21

The particular research question and the availability of instruments.

Question 22

The four levels of measurement

Answer 22

• In order of increasing sophistication*:
  1. Nominal (lowest level - numbers are merely labels for categories).
  2. Ordinal (more precise than nominal, but does not lend itself well to statistical analysis).
  3. Interval (equal interval sizes and units allow addition, subtraction, and computation of averages, but there is no true zero point).
  4. Ratio (highest level - has an absolute zero point; data can be manipulated using any arithmetic operation).

Question 23

Examples of nominal data

Answer 23

The numbers are labels arranged in random order, and do not mean that one category is “more” or “less” than another:

1. Gender (1 for males, 2 for females).
2. Religion (1 for Catholic, 2 for Protestant, 3 for Jewish).
3. Blood type (1 for type AB, 2 for type A, 3 for type B, 4 for type O).

Question 24

If there are only two categories associated with nominal data (such as gender), then the variable of interest is said to be _ in nature.

Answer 24

Dichotomous.

Question 25

Many instruments and scales used to measure _ variables yield ordinal data.

Answer 25

Psychosocial. (For this reason, ordinal data are common in nursing research.)

Question 26

Examples of ordinal data

Answer 26

Only the order of categories is important, as intervals between ranks are not equal:

1. Satisfaction (1 for “very satisfied;” 2 for “somewhat satisfied;” 3 for “not satisfied”).
2. Education (1 for 6 years or less; 2 for 8 years; 3 for 11-12 years; 4 for 13-16 years).
3. Household income (1 for $0-$4,999; 2 for $5,000-$9,999; 3 for $10,000-$19,999; 4 for $20,000-$29,999; 5 for $30,000-$49,999).

Question 27

Many instruments and scales used in nursing research report “scores” and are usually referred to as _

Answer 27

Interval data or interval variables. (Many highly reliable psychosocial instruments or scales yield interval data.)

Question 28

Example of interval data

Answer 28

Numerical values with equal interval sizes and an actual unit of measurement, but no meaningful zero point:
Temperature (Fahrenheit or Celsius: 0° does not indicate an “absence” of temperature; 90°F is not “twice as hot” as 45°F).

Question 29

Examples of ratio data

Answer 29

Categories are different, ordered, separated by a constant unit of measurement, and have an absolute zero point:

1. Age (e.g., number of years).
2. Time (e.g., number of hours).
3. Weight (e.g., number of kilograms).
4. Length (e.g., number of inches).

Question 30

From a statistical standpoint, _ and _ measurements are treated as the same.

Answer 30

Interval and ratio. (The mean and standard deviation can be calculated for both of these.)

Question 31

Measurement error

Answer 31

1. How well or how poorly a particular instrument performs in a given population.
2. Can be random (scores vary in a random way) or systematic (scores are incorrect, but they are incorrect in the same direction).

Question 32

_ measures are by far the most commonly used data collection measures in nursing research.

Answer 32

Psychosocial.

Question 33

Reliability coefficient (r)

Answer 33

A numerical expression of reliability between 0.00 and 1.00; a higher value indicates higher reliability (r = 0.00 is a completely unreliable test, and r = 1.00 is a completely reliable test).

Question 34

3 aspects considered by reliability testing and 3 corresponding measures of reliability

Answer 34

1. Stability (the consistency of repeated measurements): Test-retest reliability.
2. Homogeneity: Internal consistency.
3. Equivalence: Interrater reliability.

Question 35

Test-retest reliability and time intervals

Answer 35

1. Reliability is generally higher when the time lapse between testing is short, usually no longer than 4 weeks.
2. Caution is advised when reading research reports regarding how stable the measurement is when researchers do not indicate the length of time between the two testings.

Question 36

Internal consistency

Answer 36

1. Consistency across items of an instrument, with individual items being individual questions. To the extent that certain items “hang together” and measure the same thing, an instrument is said to possess high internal consistency reliability.
2. Can be assessed by calculating Cronbach’s alpha, which should be 0.70 or higher.

Question 37

Kuder-Richardson 20 (KR-20)

Answer 37

A statistical procedure used to assess internal consistency when the items of an instrument are scored dichotomously (e.g., “yes” or “no”, “true” or “false”).

Question 38

Interrater reliability

Answer 38

1. An approach to reliability examining the extent to which raters are in agreement on something.
2. A researcher has raters evaluate a particular situation, then assesses the consistency among the raters.

Question 39

Cohen’s kappa

Answer 39

A statistical procedure used to assess interrater reliability for situations involving nominal data in which raters classify the items being rated according to discrete categories.

Question 40

Determining interrater reliability is very common in nursing research and is used in many _ studies.

Answer 40

Observational. (Interrater reliability values should be reported in any study in which observational data are collected or when judgments are made by two or more data collectors).

Question 41

Concurrent validity

Answer 41

1. When a new instrument is administered at about the same time that data are collected on the criterion.
2. Example: An instrument designed to assess well-being and an instrument designed to assess self-esteem are administered in the same sitting.

Question 42

“Stability” is associated with _

Answer 42

Test-retest reliability.

Question 43

“Cronbach’s alpha” is associated with _

Answer 43

Internal consistency.

Question 44

“Kappa statistic” is associated with _

Answer 44

Interrater reliability.

Question 45

Pilot testing

Answer 45

Means of evaluating a newly developed or adapted instrument, especially when there is no relevant previous research; identifies problems in an instrument before it is administered in its final form.

Question 46

As a nurse, you rate a patient’s pain on a scale from severe to low. This results in which type of scale?

Answer 46

Ordinal.

Question 47

Which of the following is not an assumption underlying testing and measurement? (Various approaches to measuring aspects of the same thing can be useful; Error is rarely present in the measurement process; Present day behavior predicts future behavior; Testing and assessment benefit society.)

Answer 47

Error is rarely present in the measurement process.

Question 48

The _ the level of measurement, the greater flexibility in choosing statistical procedures.

Answer 48

Higher. (The highest level of measurement is going to provide the maximum amount of information.)

Question 49

Nominal data can be either _

Answer 49

Dichotomous (e.g., gender) or categorical (e.g., marital status).

Question 50

Measurement error can be decreased by increasing the _

Answer 50

Intervention fidelity - using the same data collection methods with the same type of subjects at the same point in time.

Question 51

Type I error

Answer 51

A false positive (detecting something that was not actually there) - the researcher rejected a null hypothesis that was true. Considered more serious.

Question 52

Type II error

Answer 52

1. A false negative (failing to detect something that was present) - the researcher accepted a null hypothesis that was false.
2. Occurs when a sample size is not large enough (“underpowered”) - can minimize through the use of power analysis.

Question 53

Reliability is concerned with _ error and validity is concerned with _ error.

Answer 53

Random; systematic.