Quantitative Methods

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Quantitative Methods

• Survey Research

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The Measurement Process

• Measurement- refers to the process of describing
  abstract concepts in terms of specific
  indicators. You take a concept, idea or
  construct and develop a measure by which one can
  observe the idea empirically.
• Indicator- is an observation that is assumed to
  be evidence of the attributes or properties of
  some phenomenon.
• Item- a single indicator of a variable.
• Index or Scale- multiple items measuring a
  concept form an index or a scale.

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Conceptualization

• Refers to the process of taking a construct and
  refining it by giving it a conceptual or
  theoretical definition.
• A conceptual definition is a definition in
  abstract, theoretical terms. It refers to other
  ideas or constructs.
• To do this one must think about the meanings of a
  construct.

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Operationalization

• Operationalization links a conceptual definition
  to a specific set of measurement techniques or
  procedures.
• The constructs operational definition is a
  definition in terms of the specific operations of
  actions a researcher carries out. It could be a
  survey questionnaire, a method of observing
  events in a field setting, a way to measure
  symbolic content in the mass media, or any
  process carried out by the researcher that
  reflects, documents, or represents the abstract
  construct as it is expressed in the conceptual
  definition.

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Five Suggestions for Coming Up With a Measure

• 1) Remember the Conceptual Definition- the
  measure should match your conceptual definition
  of your construct.
• 2) Keep an Open Mind- be creative and always look
  for new measures.
• 3) Borrow from Others- borrow and cite others
  measures.
• 4) Anticipate Difficulties- logical and practical
  problems often arise when trying to measure
  variables of interest Try to anticipate and
  mitigate.
• 5) Do not forget your units of analysis- you need
  to be able to generalize your findings to the
  area of interest.

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Ways of Measuring

• Verbal Reports
• Observation
• Archival Records

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Types of Variables

• Independent variable- the variable that acts on
  something else. It is unaffected by changes in
  the dependent variable.
• Dependent variable- the variable that is the
  effect or the result or outcome of another
  variable.
• Intervening variables- is a variable that comes
  between the independent and dependent variable
  as part of a relationship. It is the link or
  mechanism between them.

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Levels of Measurement

• Nominal Variables- a nominal variable has no
  numeric value. Ie. Race, gender, political party,
  religion
• Ordinal Variables- are rank ordered, but there is
  no definable numeric distance between them. Ie.
  Highest degree earned, socioeconomic status.
• Interval variables- is numerically relevant.
  Equal spacing between categories Ie . IQ. The
  zero point, however, is arbitrary. Someone with
  a 140 IQ is not twice as intelligent as a person
  with a 70 IQ.
• Ratio variables- is numerically relevant. Equal
  spacing between categories but the zero point is
  set. Ie. Income, (0 is not arbitrary) Kelvin
  scale of temperature

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Discrete versus Continuous Variables

• Discrete Variables- possess a finite number of
  distinct and separate values or categories. Ie.
  Sex, race, household size, number of days absent
• Continuous Variables- could theoretically be
  divided into an infinite number of categories.
  Ie. Height- could use smaller and smaller units

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Reliability and Validity

• All quantitative studies can be evaluated in
  terms of reliability and validity.

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Reliablity

• Refers to a measures ability to yield consistent
  results.
• Dependability
• A measure is reliable if the measurement does not
  change when the concept being measured remains
  constant in value.

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Two Principles of Reliability

• Stability- idea that a reliable measure should
  not change from one application to the next.
• Equivalence- idea that all items that make up a
  measuring instrument should be consistent with
  one another.

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Three Types of Reliability

• Stability Reliability
• Representative Reliability
• Equivalence Reliability

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Stability Reliability

• is reliability across time. Does the measure
  deliver the same answer when applied in different
  time periods?
• Test-Retest Method
• Multiple Forms

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Representative Reliability

• is reliability across sub-populations or groups
  of people. Does the indicator deliver the same
  types of answers when applied to different
  groups?

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Steps to Increase Representative Reliability

• Steps to increase Representative Reliability
• 1) Care must be taken to assess whether
  measurements might lead to a distorted view of
  minorities.
• 2) Researchers can immerse themselves in the
  culture of the group to be studied, experiencing
  the daily activities of life and the cultural
  products as the natives do.
• 3) Researchers should use key informants-people
  who participate routinely in the culture of the
  group to be studied- to help assess the
  measurement instrument.
• 4) When translating an instrument from English
  into another language, researchers should use the
  most effective translation methods, usually
  double translation
• 5) After developing or translating measuring
  instruments for use with minority populations,
  the instruments should be tested for validity and
  reliability on that population.

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Equivalence Reliability

• applies when researchers use multiple indicators
  in the operationalization of a concept.
• split half

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Improving Reliability

• Clearly conceptualize constructs
• Rreliability increases when a single construct or
  subdimension of a construct is measured.
• Use a precise level of measurement
• Use multiple indicators
• Use pilot tests
• Interview subjects about measuring devices
• Use the highest level of measurement available
• Conduct an item by item assessment of multiple
  item measures.

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Validity

• refers to the accuracy of a measure does it
  accurately measure the variable that it is
  intended to measure.
• Are you measuring what you think you are
  measuring.

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Three Types of Validity

• Face Validity
• Content Validity
• Criterion Validity
• Concurrent validity
• Predictive Validity

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Face Validity

• Involves assessing whether a logical relationship
  exists between the variable and the proposed
  measure. Commonsense comparison of what
  comprises the measure and the theoretical
  definition of the variable.
• This is accomplished by simply looking over the
  measure and determining if it is valid on the
  face of it.
• Does the definition and method of measurement
  seem to fit?
• Is there consensus on this fact?
• The researcher should include relative literature
  or other studies which have assessed similar
  concepts.

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Content Validity

• A special type of face validity. It addresses
  the question, is the full content of a
  definition, represented in a measure?
• A conceptual definition holds ideas it is a
  space containing ideas and concepts.
• First, specify the content in a constructs
  definition.
• Sample from all areas of the definition
• Develop an indicator that taps all of the parts
  of the definition

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Criterion Validity

• Refers to establishing validity by showing a
  correlation between a measuring device and some
  other criterion or standard that we know or
  believe accurately measures the variable under
  consideration.
• Is established by comparing scores on the test
  with pre-established measures. These terms refer
  to its ability to predict future events, and its
  ability to match other measures given at the same
  time.

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Concurrent Validity

• Type of criterion validity in which the
  instrument being evaluated is compared to some
  already existing criterion such as the results of
  another measuring device.

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Predictive Validity

• Second form of criterion validity. An instrument
  is used to predict some future state of affairs.

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Relationships between Reliability and Validity

• Reliability is necessary for validity and it is
  easier to achieve. It does not, however,
  guarantee validity. You can measure something
  consistently and still not be measuring what you
  think you are, or you can obtain an invalid
  measure reliably.

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Other Types of Reliability and Validity

• Inter-coder reliability- if there are several
  coders, observers, raters etc. you may be
  concerned with inter-coder reliability. Is
  everyone coding the same?
• Internal Validity- Within the data. Does the
  difference really exist? Can it be explained as
  a measurement artifact? There are no errors
  internal to the design of the research project.
• External Validity- To whom does the data apply.
  To what populations can this data be applied.
  The ability to generalize findings from a
  specific setting and small groups to a broad
  range of settings and people.
• Statistical Validity -The correct statistical
  procedure is chosen and its assumptions are
  fully met.