The Vocabulary of Science

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The Vocabulary of Science
1. Concepts 2. Operationalization 3. Direction
of the relationship 4. Level of measurement
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Concepts

• Concept is an abstraction/representation of an
  object or a behavioral phenomenon
• Each discipline develops its unique set of
  concepts
• Political science power, social status,
  relative deprivation
• Psychology depression, perception,
  learning
• Sociology social status, role

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Why do we need concepts?

• Concepts provide a common language, which enables
  scientists to communicate with one another within
  an area
• Power can mean different thing to different
  people
• Science cannot progress with ambiguous and
  imprecise language
• Vocabulary of science

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Vocabulary of science

• Conceptual Definitions (definitions that describe
  concepts by using other concepts)
• Power has been conceptually defined as the
  ability of an actor (group/the state) to get
  another actor to do something that the latter
  would not otherwise do
• Concepts ability, actor, group, state can be
  defined by other concepts, and so on.

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Vocabulary of science

• At a certain point in this process, scientists
  encounter concepts that cannot be defined by
  other concepts
• These are called primitive terms
• For example, a group is two or more individuals
• Use of primitive terms is less efficient than use
  of more complex concepts it is easier to say
  the word group than constantly repeat the
  primitive terms that compose the definition of
  group.

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Concepts Variables

• A variable is any entity that can take on
  different values.
• Anything that can vary can be considered a
  variable
• For instance, age can be considered a variable
  because age can take different values for
  different people or for the same person at
  different times
• Similarly, country can be considered a variable
  because a person's country can be assigned a value

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Variables

• Variables aren't always 'quantitative' or
  numerical
• The variable 'gender' consists of two text
  values 'male' and 'female'.
• We can, if it is useful, assign quantitative
  values instead of (or in place of) the text
  values, but we don't have to assign numbers in
  order for something to be a variable

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Attribute

• An attribute is a specific value on a variable
• The variable sex or gender has two attributes
• 1 male
• 2 female

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Attribute

• The variable agreement might be defined as having
  five attributes
• 1 strongly disagree
• 2 disagree
• 3 neutral
• 4 agree
• 5 strongly agree

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Variable should be exhaustive

• Each variable should be exhaustive, it should
  include all possible answerable
  responses/attributes
• Variable Religion"
• 1. "Protestant",
• 2. "Jewish
• 3. "Muslim"
• The list does not exhaust all possibilities.
• The way to deal with this is to explicitly list
  the most common attributes and then use a general
  category like "Other" to account for all
  remaining ones.

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Attributes should be mutually exclusive

• No respondent should be able to have two
  attributes simultaneously
• Variable "Employment Status
• 1) "employed
• 2)"unemployed"
• But these attributes are not necessarily mutually
  exclusive -- a person who is looking for a second
  job while employed would be able to check both
  attributes!
• we can ask the respondent to "check all that that
  apply" and then list a series of categories

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Mutually Exclusive Attributes

• With whom do you currently live? (Choose all that
  apply)
• Alone
• Roommate(s)
• Housemate(s
• Spouse
• Partner
• Parent(s)
• Other relative(s)
• Other________________

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Types of Variables

• Independent Variable (Causal variable, variable
  you change
• Dependent variable (Effect, variables you are
  trying to predict)

Independent Variable
Dependent Variable
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Types of variables
Independent Variable
Dependent Variable
Male
lt 5,000
gt 5,000
Attributes
Attributes
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Hypotheses

• After we indentify the variables of interest, we
  posit a relationship between themHYPOTHESIS
• Hypotheses can be either true or false
• We create them in order to test whether the
  posited relationship between the variables are
  true or false

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Example

• H1 Gender affects occupation
• H2 Differences in age are related to difference
  in income

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Relationship between Variables

• Positive
• An increase/decrease in the independent variable
  yields an increase/decrease in the dependent
  variable
• Independent variable/ dependent variable
• Independent variable/ dependent
  variable

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Example of positive relationship

• H1 People with higher education are more likely
  to earn more money
• Dependent variable
• Independent variable

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Relationship between Variables

• Negative
• An increase/decrease in the independent variable
  yields a decrease/increase in the dependent
  variable
• Independent variable/ dependent variable
• Independent variable/ dependent variable

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Example of negative relationship

• H1 Increased exercise causes decreased weight
• H2 The higher your social class the less likely
  you are arrested for committing a crime
• Dependent variable
• Independent variable

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Undetermined

• H1 Males are more likely to earn more money than
  females are
• Independent variable / dependent variable

Male
Low Income
Female
High income
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Practice

• Let say you want to test the relationship between
  exercise and weight
• Formulate the hypothesis which posits a positive
  relationship between these two variables

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Operational Definition

• After we select variables and formulate the
  hypothesis, we must create operational definition
  for each of our variables
• Operational definition transforming a variable
  into something we can observe
• Listing attributes

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Operationalizing

• Gender
• Female
• Male

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Operationalizing

• Occupation
• Professional
• Manager or owner of business
• Skilled laborer
• Unskilled laborer
• Not employed
• Other

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Operationalizing

• Income
• 5,000 or less
• 5, 001-15,000
• 15,001-25,000
• 25,001-35,000
• 35,001-50,000
• 50,001 or more

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Practice in Operationalizing

• Marital status
• Never married
• Married
• Divorced
• Separated
• Widowed
• Other

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Operationalization

• Love

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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)

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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
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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.

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Four levels of measurement

• Nominal
• Ordinal
• Interval
• Ratio

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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.

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Nominal Variables
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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

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Ordinal variable

• Overall, how satisfied or dissatisfied are you
  with the quality of education that you are
  getting at WSU?
• 1Very satisfied
• 2Somewhat satisfied
• 3Neither
• 4Somewhat dissatisfied
• 5Very dissatisfied

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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

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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)

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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."

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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).

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Exercise

• Political Affiliation is measured as
• 0 Republican
• 1 Democrat
• 2 Independent
• 3 Reform Party
• 4 Green Party
• 5 Socialist
• 6 Other
• This measure is a(n) _____ scale