# NURS 2031H Chapter Notes - Chapter 7: Observational Error, Central Tendency, Likert Scale

by OC1266326

This

**preview**shows pages 1-2. to view the full**8 pages of the document.**Chapter 7

MEASUREMENT

1. THE PROCESS OF MEASUREMENT

Simplest level of measurement is giving a numerical value to a phenomenon

Hypothetical constructs: measure attributes that are not directly observable,

consists of behaviors, attitudes, process and other attributes that tend to occur

together

oCannot observe depression, but can observe manifestations of depression

oThis is the most common measurement technique in health

oProblems arise with this because “it all depends” is very present

Many varying conceptualizations may exist for a phenomenon and this creates many

existing scales to measure the same construct

oUsers of scales must be clear (because of stated above) about their own

definitions of constructs and selection of an instrument must be congruent

with their definition

Definitions of constructs: how it is conceptualized, what they believe

should/should not be included, how construct can be

operationalized)

Instrument: what you use to measure with

2. VARIABLES

Types of variables: used to consider differences in variables

Categorical variables: must satisfy two conditions

oMutually exclusive – person cannot be placed into two or more categories

oCollectively exhaustive – all possible options must be covered

Discrete variables: the answer is constrained to be a whole number and cannot be

any value in between

Obsessive variables – continuous variables

A categorical variable is exclusive and exhaustive; numerical values assigned have

no quantitative (mathematical) meaning; values assigned as labels only

(nominal/ordinal levels of measurement)

A discrete variable consists of separate, indivisible categories – no values can exist

between two neighboring categories (can’t take on any values between whole

numbers)

Note: Many textbooks do not differentiate between categorical and discrete

variables, but the distinction here is important in that discrete variables

A continuous variable is divisible into an infinite number of fractional parts – there

are an infinite number of possible values that fall between any two observed values

Levels of variables: different ways of describing variables

Nomial, ordinal, interval and ratio – differentiated by 3 attributes:

oRules for assigning numbers to different values of the variable

find more resources at oneclass.com

find more resources at oneclass.com

Only pages 1-2 are available for preview. Some parts have been intentionally blurred.

oMathematical properties of the resulting scales

oTypes of statistics that can be used with them

Nomial (similar to categorical variables)

oMust be mutually exclusive and collectively exhausted

oNumbers can be assigned but they are simply numbers

We can change the numerical assignment without gaining or losing

any information

Assign a hospital with #1 and the other #2 but the actual #s don’t

mean anything.

oAre limited

All that can be done is determine which category has the most

members

Some cases can have two categories that are just about equal

(bimodal distributions)

If sample size is small model value can change as new people are

added to the sample

Ordinal

oMust be mutually exclusive and collectively exhaustive

oCategories must be in rank order

oDistances between successive values are not consistent

oMust use median to describe central tendency, and range or interquartile

range for dispersion

oStatistics that can be used are limited to non-parametric ones based on

ranks

oNot all ordinal scales are constructed equally

Interval and ratio scales

oInterval restrictions: be mutually exclusive, collectively exhaustive, and

values have to be equally spaced

oRatio scale restrictions: be mutually exclusive, collectively exhaustive, values

have to be equally spaced, and needs to have a meaningful zero

oDifference between interval and ratio scale:

Interval: Celsius and Fahrenheit use different temperatures to define

zero degrees

It is an arbitrary value and not a meaningful value

Ratio scale: Kelvin scale begins at absolute zero

oDifference lies the mathematical operations that can be done for each

oFor both differences are meaningful:

Intervals are equal, adding and subtracting values are meaningful

oRatios between values are only meaningful for ratio scales

oMeasure of central tendency is the mean and measure of dispersion is the

standard deviation

3. CONSTRUCTING SCALES

Psychometric theory

Scale development is based on classical psychometric theory which assumes that

the score obtained on a scale consists of two parts: the unobserved true score plus

some degree of error.

find more resources at oneclass.com

find more resources at oneclass.com

###### You're Reading a Preview

Unlock to view full version

Only pages 1-2 are available for preview. Some parts have been intentionally blurred.

So in other words, we never see the true score because there is always a degree of

error associated with it.

Assumptions:

oThe error is unrelated to the true score (the amount of error is the same

throughout the entire range of the scale’s possible scores)

oExpected value of the error for each of the items on the scale is zero (the

errors will cancel each other out)

oNB: The longer the scales, the more the errors will cancel each other out –

longer scales are better than shorter ones.

Classical test theory (CTT): assumes that the score that’s obtained on a scale actually

consists of two parts –

oThe true (unobserved) score

oSome error

CTT is so prevalent due to underlying assumptions are considered “weak” = apply in

most situations

To understand assumptions need to understand basic premise of scale construction

oThe score we have observes (Xo) is made of two parts

The true score (Xt)

Some error (E)

Xo = Xt + E

oMeans we never see true score this is due to there always being some error

associated with it

Assumed the error has a mean of zero

Sometimes add to true score, sometimes makes it smaller

If added up errors for large numbers of items the average value

would be zero

oTrue score = score person would obtain if scale has infinite number of

items/if respondent completed it an infinite number of times

So error terms of individual item/administration cancel each other

out

oA true score does not mean an honest score – person could consistently lie

about something

True is just referring to the score being free from error

Assumptions of CTT

oError is unrelated to the true score (amount of error is same throughout

entire range of scales possible scores)

oExpected value of error for each item is zero and the errors will cancel each

other out as all items are added up to get the total score

The above suggests longer scales are freer from random error than

shorter scales because there is more chance for errors to cancel out

Devising the items:

Most of what is measured in health are hypothetical constructs

oHas direct implication for what items do/do not appear on scale

What the scale should encompass arises from one’s theory or

conceptualization of the construct

find more resources at oneclass.com

find more resources at oneclass.com

###### You're Reading a Preview

Unlock to view full version