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The Vocabulary of Science Part II

1. Operationalization
2. Level of measurement

Operationalization

• Operationalization is the process of strictly defining

variables into measurable factors

• Operationalization also sets down exact definitions of

each variable, increasing the quality of the results, and improving the robustness of the design

Example

• “Children grow quicker if they eat vegetables”
• How many fuzzy concepts are here?

Fuzzy concepts

• What does the statement mean by ‘children’? Are they

from America or Africa. What age are they? Are the children boys or girls? There are billions of children in the world, so how do you define the sample groups?

• How is ‘growth’ defined? Is it weight, height, mental

growth or strength? The statement does not strictly define the measurable, dependent variable.

Fuzzy concepts

 What does the term ‘quicker’ mean? What units, and what timescale, will be used to measure this? A short-term experiment, lasting one month, may give wildly different results than a longer-term study.

 What are ‘vegetables’? There are hundreds of different types of vegetable, each containing different levels of vitamins and minerals. Are the children fed raw vegetables, or are they cooked?

How to measure fuzzy concepts?

• “Children grow quicker if they eat vegetables”
• The above hypothesis is not a bad statement, but it

needs clarifying and strengthening, a process called operationalization

• Or we come up with operational definition for each

of our variables

Step 1: Operational Definition

• The researcher could narrow down the range of

children, by specifying age and sex

• Age (from birth to 15)
• Sex (both boys and girls)

Step 1: Operationalizing

• Gender

Female Male

• Age

1 and younger 2- 6- 11 and older

Operationalizing “Occupation”

Professional

Manager or owner of business

Skilled laborer

Unskilled laborer

Not employed

Other

Operationalizing “Income”

$5,000 or less

$5, 001-15,

$15,001-25,

$25,001-35,

$35,001-50,

$50,001 or more

Operationalizing “ Marital status”

Never married

Married

Divorced

Separated

Widowed

 Other

Operationalization

• “Love”

Sternberg (1988) The Psychology of Love

• Emotional Intimacy dimension focuses on friendship, trust and feelings of emotional closeness that result from being able to share one's innermost thoughts and feelings with a partner
• The passion dimension focuses on those intense feelings of arousal that arise from physical attraction and sexual attraction
• The commitment dimension of love is often viewed as the decision to stay with one's partner for life. Commitments may range from simple verbal agreements (agreements not to become emotionally and/or sexually involved with other people) to publically formalized legal contracts (marriage)

“Love”

• Desiring to promote the welfare of the loved one;
• Experiencing happiness with the loved one;
• Having high regard for the loved on;
• Being able to count on the loved one in times of need;
• Mutual understanding with the loved one;
• Sharing one's self and one's possessions with the loved one;
• Receiving emotional support from the loved one;
• Giving emotional support to the loved one;
• Having intimate communication with the loved one;

Response categories: “Always” “Often” “Occasionally” “Rarely” “Never”

Exercise

• GPA
• Exercising

Why is Level of Measurement Important?

• First, knowing the level of measurement helps

you decide how to interpret the data from

that variable

• Second, knowing the level of measurement

helps you decide what statistical analysis is

appropriate on the values that were assigned

• If a measure is nominal, then you know that

you would never average the data values or

do a t-test on the data.

Four levels of measurement

• Nominal
• Ordinal
• Interval
• Ratio

Nominal Measurement

• At the nominal level of measurement, numbers or other symbols are assigned to a set of categories for the purpose of naming, labeling, or classifying the observations
• Gender is an example of a nominal level variable.
• Using the numbers 1 and 2, for instance, we can classify our observations into the categories "females" and "males,"
• When numbers are used to represent the different categories, we do not imply anything about the magnitude or quantitative difference between the categories.

Nominal Variables

Ordinal variables

• In ordinal measurement the attributes can be rank- ordered.
• For example, on a survey you might code Educational Attainment as
• 0= less than H.S.;
• 1= H.S. degree;
• 2= college degree;
• 3= post college
• In this measure, higher numbers mean more education
• But is distance from 0 to 1 same as 2 to 3? Of course not. The interval between values is not interpretable in an ordinal measure

Ordinal variable

Overall, how satisfied or dissatisfied are you

with the quality of education that you are

getting at WSU?

• 1=Very satisfied
• 2=Somewhat satisfied
• 3=Neither
• 4=Somewhat dissatisfied
• 5=Very dissatisfied

Interval variables

• In interval measurement the distance

between attributes does have meaning

• For example, when we measure temperature

(in Fahrenheit), the distance from 30-40 is

same as distance from 70-80

Interval variable

• The interval between values is interpretable
• We can compute an average of an interval

variable

• There is no absolute zero
• But note that in interval measurement ratios

don't make any sense - 80 degrees is not twice

as hot as 40 degrees (although the attribute

value is twice as large)

Ratio-level variables

• In ratio measurement there is always an absolute zero that is meaningful
• This means that you can construct a meaningful fraction (or ratio) with a ratio variable
• Weight is a ratio variable
• In applied social research most "count" variables are ratio, for example, the number of clients in past six months.
• Why? Because you can have zero clients and because it is meaningful to say that "...we had twice as many clients in the past six months as we did in the previous six months."

Hierarchy of levels

• There is a hierarchy implied in the level of measurement idea.
• At lower levels of measurement, assumptions tend to be less restrictive and data analyses tend to be less sensitive
• At each level up the hierarchy, the current level includes all of

the qualities of the one below it and adds something new

• In general, it is desirable to have a higher level of

measurement (e.g., interval or ratio) rather than a lower one (nominal or ordinal).